MA

ST

ER

THESIS

Master´s Programme in Mechanical Engineering, 60 credits

Surface Topographical Analysis Of CuttingInserts

Zoel-fikar El-ghoul , Shobin John

Master Thesis 15 credits

Halmstad 2016-10-10

Preface

i

Preface

This study is a result of master’s thesis in mechanical engineering at Halmstad University in

collaboration with Sandvik Coromant during spring term 2016.

The main contribution of the present work focus on the development of a significant approach

to identify best possible surfaces finish strategy in terms of topographical study. The aim of

the thesis was to analyze, compare differently pre- and post-treated cutting tool inserts, and

correlate surface properties with the different treatment methods and to work out a method for

such analysis to be used by the company in the future.

We would like to emphasize our thanks Professor Bengt-Göran Rosén for his support

guidance, opportunely posed questions that raised new lines of thought and motive to get

good work on the thesis.

We would like to emphasis sincere thanks and gratitude to Isabel Källman to guide

throughout the thesis and support during urgent need.

We are grateful to other dissertation committee members Dr. Z. Dimkovski and Dr. Sabina Rebeggiani for enlightening and inspiring discussion and their advice provided us guidelines

in difficult times.

We would like as a final word of appreciation to thank the people of functional surfaces

research group at Halmstad University for their thoughtful comments and suggestion, which

continually improve the quality of the dissertation.

Zoel-fikar El-ghoul Shobin John

Abstract

ii

Abstract

The following report conducted with collaboration of the University of Halmstad and AB

Sandvik Coromant.

The focus of the project is characterizing the surface topography of different surface treatment

variants before and after chemical vapor deposition (CVD).

As a part of improving the knowledge about the surface area characterization and accomplish

a better knowledge and understanding about surfaces and its relation to wear of uncoated

WC/Co cutting tools The project initiated in February 2016 and end date was set to May

2016.

The methodology used in this thesis based on the statistical analysis of surface topographical

measurements obtained from interferometer and SEM by using Digital-Surf-MountainsMap

software.

The finding from this thesis showed that Mean and Standard deviation method, Spearman’s

correlation analysis and Standard deviation error bar followed by ANOVA and T-test are

effective and useful when comparing between different variants.

The thesis resulted in a measurement approach for characterizing different surface

topographies using interferometer and SEM together with statistical analysis.

Keywords: 3D-Surfaces Texture, CVD coating inserts, Interferometer, Spearman’s correlation and

ANOVA & T-test.

Tables of Contents

iii

Tables of Contents

Preface ............................................................................................................................... i

Abstract ............................................................................................................................. ii

Tables of Contents ........................................................................................................... iii

Symbols and Abbreviations .............................................................................................. v

1. INTRODUCTION ................................................................................................... 1

1.1 Background .......................................................................................................... 1

1.1.1. Presentation of the client ............................................................................... 3

1.2 Aim of the study ................................................................................................... 4

1.3 Problem definition ................................................................................................ 4

1.4 Limitations ........................................................................................................... 4

1.5 Individual responsibility and efforts during the project ....................................... 4

1.6 Study environment ............................................................................................... 5

2. METHOD ................................................................................................................ 6

2.1 Alternative methods ............................................................................................. 6

2.1.1 Average and Standard Deviation Method .................................................... 6

2.1.2 Spearman’s rank order correlation method .................................................. 7

2.1.3 Standard deviation error bar followed by Anova and T-test ........................ 8

2.2. Chosen methodology for this project ................................................................... 11

2.3. Preparations and data collection ........................................................................... 11

3. THEORY ............................................................................................................... 12

3.1. Summary of the literature study and state-of-the-art ........................................... 12

3.1.1 Function ...................................................................................................... 13

3.1.2 Manufacturing ............................................................................................. 15

3.1.3 Characterization .......................................................................................... 15

4. RESULTS .............................................................................................................. 20

4.1 Presentation of experimental results of work package 1 ....................................... 20

4.1.1 Parameters Selection Methods .................................................................... 20

4.1.2 Average and Standard Deviation method ................................................... 20

4.1.3 Spearman’s rank correlation method .......................................................... 23

4.1.4 Standard deviation Error Bar (EB) followed by Anova &T-test method .. 23

4.3. Presentation of experimental results of work package 2 ...................................... 25

4.3 Methods for selecting the parameters ................................................................ 25

iv

5. CONCLUSIONS AND FUTURE WORK ............................................................ 27

5.1 Conclusions ........................................................................................................ 27

5.1.1 Work Package 1 .......................................................................................... 27

5.1.2 Work Package 2 .......................................................................................... 32

5.1.3 Recommendation to future activities .......................................................... 37

6. CRITICAL REVIEW ............................................................................................ 38

6.1 What factors affect the work been done differently ........................................... 38

6.2 Environmental and sustainable development ..................................................... 38

6.3 Health and Safety ............................................................................................... 38

6.4 Economy ............................................................................................................. 39

6.5 Ethical aspects .................................................................................................... 39

REFERENCES ............................................................................................................... 40

TABLE OF CONTENT FOR APPENDICES ................................................................ 43

Symbols and Abbreviations

v

Symbols and Abbreviations

WP 1: Work Package1

WP 2: Work Package 2

MSG: Name to represent different variants

CNMG120408-MM: Cutting inserts Specification

SEM: Scanning Electron Microscope

3D: Three Dimension

316L: Sanmac 316/316L is a molybdenum-alloyed austenitic chromium-nickel steel with

improved machinability

Ti(C, N): Titanium Carbon nitride

Al2O3: Aluminum Oxide

TiN : Titanium Nitride

Co : Cobalt

ANOVA: Analysis of Variance named for Fisher

WC: Tungsten carbide

SE: Standard Error

S.D: Standard Deviation

E.B: Error Bar

V: Number of Variants

NEBNO: Number of error Bar Not Overlapping

Si: Significant Values in ANOVA test

TRUES: Parameter is disjunct for variants with 95 percentage confident interval

CVD: Chemical Vapor Depositio

INTRODUCTION

1

1. INTRODUCTION

Surface integrity is defined as the inherent or enhanced condition of a surface produced by

machining or other generating operation. It contains not only the geometry consideration,

including surface roughness and accuracy, but also another surface/subsurface microstructure.

The success of the transformation is dependent on a number of variables such as surface

texture, wetting properties of the solid surface by the liquid and coating viscosity. Coatings

and painting applied to the surface; the purpose of such operations may be to improve their

chemical and mechanical properties. The existence of the correct functional groups in an

accessible position is an important factor to be identified and controlled. Thus, surfaces are

produced with a texture resembling a landscape, the determination and control the surface

area and surface composition are essential for the study of catalysts, even small variation of

properties may lead to unwanted results in production and can cause the rejection of the batch.

It is useful to modify the surface performance when it does not possess the specified

requisites; it is possible to change mechanical or visual properties of surfaces improvement

in sliding, thermal properties, corrosion, adhesion, wear, yield and appearance.

The wide variety of parameters that used in the characterization of surface finishing is a piece

of evidence of its magnitude. The characterization of surface finishing is usually

accomplished defining numerical 3D surface texture parameters (ISO-25178). Today

selections of appropriate parameters for analyzing the surfaces are widely investigated. The

detailed study about the surface (relation between manufacturing processes, directionality

etc.) by using the selected parameters is also highlighted of this study.

1.1 Background

The precise characterization of surface roughness is of paramount importance because of its

considerable influence on the functionality of manufactured products [1]. Modern technologies

depend for the Satisfactory functioning of their processes on special properties of some solids,

mainly the bulk properties, as an important group of these properties [2]. The behavior of

material depends on the surface of the material, surface contact area and environment under

which the material operates, to make a better understanding for the surface properties and their

influence on the performance of the various components, machines and units, surface science has

been developed. Surface science defined as a branch if science dealing with any type and any

level of surface and interactions between two or more entities, these interactions could be

chemical, physical mechanical, thermal and metallurgical [3]. Our important concern area is the

surface engineering which provides on the of most important means of engineering product

differentiations in terms of quality, lifecycle cost and performance, it is the definition of the

design of the surface and substrate together as a functionally graded system as a functionally

graded system to give a cost effective enhancement. The various manufacturing processes

applied in industry produce the desired shapes in the components within the prescribed

dimensional tolerances and surface quality requirements. Surface topography and texture is a

foremost characteristic among the surface integrity magnitudes and properties imparted by the

tools used in the processes, machining mostly, and especially their finishing versions. Surface

INTRODUCTION

2

quality and integrity can be divided in three main fields: surface roughness, microstructure

transformations and residual stress.

Surface integrity describes not only the topological (geometric) features of surfaces and their

physical and chemical properties, but also their mechanical and metallurgical properties and

characteristics [4]. Surface integrity is an important consideration in manufacturing

operations, because it influences such properties as fatigue strength, resistance to corrosion,

and service life. Most manufacturing process will have some impact on surface integrity,

when these processes performed using poor techniques, this can be responsible for inadequate

surface integrity and can lead to significant changes and defects, and these defects usually

caused by a combination of factors, such as:

Improper control of the process parameter, (which can result surface deformation,

excessive stress, excessive heat, cold or speed or work can also lead to significant

changes).

Defects in the original material.

The method by which the surface produced, and manufactured.

More invasive procedures usually have some permanent effect on surface integrity. Almost

any chemical treatment, as well as excessive heat, can alter the material at its molecular level,

bringing about irreversible changes to its very structure. These changes can be positive or

negative. Positive changes are those that give the material the desired finish or appearance

also include those that improve properties like strength and hardness, while negative change

could mean that the material no longer be used as intended.

The surface topography and material characteristics can affect how two bearing part slide

together, how fluids interact with the part and how it looks and feel, the need to control and

hence measure surface become increasingly important [5]. The various manufacturing

processes applied in industry produce the desired shapes in the components within the

prescribed dimensional tolerances and surface quality requirements for the last five decades

the complex relationship between surface texture and adhesion has interested scientists and

engineers. Authors identify that types and degrees of surface texture appear to have

beneficial effects on adhesion. Surface profile parameters may potentially be restrictive and

misleading, In Particular cases of tribology the surface roughness influences adhesion,

brightness, wear, friction in wet and dry environment [6]. Very few adhesion researchers

have considered areal surface texture parameters to characterize surface texture over the

last ten years, a period of time within which equipment, data processing software and

published texts have provided access to the use of areal parameters. Whilst an example of the

use of the Arithmetic mean surface texture (Sa) parameter can be cited in the context of

adhesion little attempt has been made to consider the breadth of parameters (and consequently

surface disruption) available.

Surface topography greatly influences not only the mechanical and physical properties of

contacting parts, but also the optical and coating properties of some non-contacting

components. The characteristics of surfaces topography in amplitude, spatial distribution and

pattern of surface feature dominate the functional application, surface in contact, residual

stresses in the surface layer and oxides on the metal surface [7] as shown in Figure 1.

INTRODUCTION

3

Figure1.1: Metallic outer surface layers displaying the complex structure machined surface superimposed on

the base metal [8].

The areal characterization of surface texture plays an increasing important role in control the

quality of the surfaces of a work piece. Surface texture parameter, which is the profile

parameter, which developed to monitor the production process, as assessment we do not

usually see field parameter values but pattern of features such as hills and valleys. The

relationship between them and by detecting and the relationships between them, it can

characterize the pattern in surface texture, parameter that characterize surface features and

their relationships are termed feature parameter [9].

1.1.1. Presentation of the client

Sandvik Coromant headquartered in, Sweden. A Swedish company supplies cutting tools and

services to the metal cutting industry. It is part of the business area of Sandvik Machining

Solutions, which is within the global industry group Sandvik. In 2012 Sandvik was #58 on

Forbes list of the world's most innovative companies. Sandvik Coromant is a global company

with production facilities connected worldwide to three distribution centers in the US, Europe

and Asia. Sandvik Coromant is represented in more than 130 countries with some 8,000

employees worldwide; with extensive investments in research and development, they create

unique innovations and set new productivity standards together with their customers. These

include the world's major automotive, aerospace and energy industries. Their metal working

operations of Coromant mainly focus on milling, turning, boring and drilling.

Figure1.2: Sandvik product

Sandvik Coromant its large investment in research and development, as much as twice the R&D

spending every year of the average company in its industry.

INTRODUCTION

4

1.2 Aim of the study

The main objective of this study is the characterization of cutting insert (CNMG120408-MM)

surface topography. The geometry of the inserts is CNMG120408-MM; the characterization

divided into work packages one and two, which presented below:

Work package 1: Surface characterization of uncoated WC-Co inserts surfaces

Which parameters describing the topography of the variants are important to

look at when comparing the different variants?

How well does the study of surface topography of variants correlate to the

manufacturing process?

Is there any predominant direction of the topography of the different variants?

Work package 2: Analysis of CVD coated surface treatment variants.

Which parameters are important for comparing the different variants to each

other?

Can a connection found between the treatment prior to coating and the outcome

of the treatment after coating?

Is there any different measurement approach needed to evaluate the surface

roughness on variants in Work Package 2 compared to Work Package 1?

1.3 Problem definition

In the first meeting with Sandvik Coromant, the tasks were assigned and the authors started to

investigate about the surface topography of the variants by finding the appropriate method in

order to select the parameters when comparing between different variants.

In work package one, before the chemical vapor deposition; they manufactured three variants

MSG 157, MSG158 and MSG160. Variants MSG 157 and MSG158 had treated with two

different processes in order to find the effects of adhesion of the CVD coating. While the

variant MSG 160 treated by polishing in order to investigate if any predominant direction of

the topography.

In work package two, it is required to investigate the surface texture between five different

variants with different kinds of treatment.

1.4 Limitations

Due to the time limitation, the variants were measured by using Interferometer only, the

methods were found in order to compare surfaces of different variants after the coating. The

limitations consist of:

Only discussed methodology and quantitative study of the surface integrity of the

variants

Machining test needs more investigation.

1.5 Individual responsibility and efforts during the project

Both authors have put the same amount of the effort in this thesis. The amount of time spent

for measurements, analyzing the measurements and gathering information regarding the

INTRODUCTION

5

project, also the presentation with Sandvik Coromant including research and writing the

report.

1.6 Study environment

Both of the authors have worked on this thesis at different locations, practical and theoretical

framework of the thesis including writing the report at the Halmstad University.

METHOD

6

2. METHOD

This study (Quantitative and qualitative) is based on the topographic analysis of the Work

Package One (WP1) and Work Package 2 (WP2) of cutting inserts supplied by Sandvik

Coromant and surface topographical analysis occurring at Halmstad University. The impact of

surface topography on the performance in machining not fully understood and this is an

attempt to investigate and gain knowledge on the effect in a specific segment, turning in 316L

with CNMG120408-MM inserts. This work will mainly focus on characterizing the different

surface treatment variants before and after coating deposition. Variants MSG157, MSG158

and MSG 160 are the cutting inserts before coating and MSG186, MSG18, MSG189 and

MSG190 is the cutting inserts after the coating process.

The analysis of reading from the interferometer has different kind of methods. The methods

are:

Average and Standard Deviation method

Spearman’s rank correlation coefficient method

Error bar followed by ANOVA and t-test method

The 3D surface texture parameters used in this thesis computed by MountainsMap 7software

from Digital Surf. 3D Roughness parameters defined by the following standards: ISO 25178-

2 define 30 parameters, the selected parameter. This section of results considered to single out

the surface topographical analysis of coated and uncoated cutting inserts. 3D surface texture

parameter and image analysis obtained from the equipment’s interferometer (readings with

10X and 50X magnifications) and SEM.

2.1 Alternative methods

2.1.1 Average and Standard Deviation Method

The average and standard deviation method analyses the variation of each parameter based on

the standard deviation and confidence intervals [10]. This method explained by using the

readings from the interferometer. The method summarized in the following steps:

For each parameter s'i = ( s'i . . . s1ni of class G and s′′

i =(s′′i …s′′n

i ) of

class B, the average B, the average µ and the standard deviation σ is

calculated

𝜇′𝑖 =1

𝑛∑ 𝑠′𝑘

𝑖

𝑛

𝑘=1 (1)

𝜇′′𝑖 =1

𝑛∑ 𝑠′′𝑘

𝑖

𝑛

𝑘=1

(

(2)

𝜎′𝑖 = √𝑣𝑎𝑟(𝑠′𝑖) (

(3)

𝜎′′𝑖 = √𝑣𝑎𝑟(𝑠′′𝑖). (

(4)

METHOD

7

For each parameter, an interval for good parts and for bad parts is calculated

with the coverage factor K,

𝐼′𝑖 = 𝜇′𝑖 ∓ 𝑘𝜎′𝑖

((5)

𝐼′′𝑖 = 𝜇′′𝑖 ∓ 𝑘𝜎′′𝑖

(6)

If the intervals 𝐼′ and 𝐼′′ for a parameter Si are disjunctive, this parameter can

be used for thresholding and the significance Si of this parameter can be

computed

The parameter with the highest significance value is that which can be used for classification.

To find the most significant surface texture parameter, the significance values must be

comparable. This could achieve by normalizing them with the average values. The

significance S; is computed on the basis of the intervals and the means

𝑆 =𝑑(𝐼′𝑖, 𝐼′′𝑖)

12 (𝜇′𝑖 + 𝜇′′𝑖)

((7)

Check the ‘+’ significant value (disjunct entry-level) parameter. These non-

overlapping intervals of the parameters indicate highly significant for the

study. Select the parameters highly significant, analysis the parameter with

surface characteristics.

2.1.2 Spearman’s rank order correlation method

Spearman’s correlation coefficient is a statistical measure of the strength of a monotonic

relationship between paired data see figure 2.1, is denoted by

𝑟𝑠 − 1 ≤ 𝑟 ≤ 1 A monotonic function is one that either never increases or never decreases as its independent

variable increases. The following graphs illustrate monotonic functions: [13]-[14]

𝑃 = 𝑟𝑠 = 1 −6 ∑ 𝑑𝑖

2

𝑁3 − ∑ 𝑑𝑖2 𝑁

(8)

Where: P= Spearman rank correlation, di= the difference between the ranks of corresponding

values Xi and Yi, n= number of value in each data set

The formula to use when there are tied ranks is

P=∑ (𝑋𝑖𝑖 −𝑋)̅̅̅̅ (𝑌𝑖−𝑌)̅̅ ̅

√∑ (𝑋𝑖𝑖 −𝑋)̅̅̅̅ 2(𝑌𝑖−𝑌)̅̅ ̅2

((9)

Where i = paired score.

METHOD

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Fig 2.1 monotonically increasing monotonically decreasing not monotonic

If the correlation coefficient, 𝑟𝑠 , is positive, then an increase in X would result in an increase

in Y, however if r was negative, an increase in X would result in a decrease in Y. Larger

correlation coefficients, such as 0.8 would suggest a stronger relationship between the

variables, whilst figures like 0.3 would suggest weaker ones.

Correlation is an effect size and so we can verbally describe the strength of the correlation

using the following guide for the absolute value of 𝑟𝑠

00 -0,19 Very weak

0, 20-0,39 Weak

0, 40 -0, 69 Moderate

0, 70-0,89 strong

0.90 1, 0 very strong

However, the correlation coefficient does not imply can satisfy that is it may show that two

variables which strongly correlated; however, it does not mean that they are responsible for

each other see figure 2.2.

Significance of Spearman's Rank Correlation Coefficient

Figure 2.2: The significance f the spearmen’s rank correlation coefficients and degree of freedom

http://geographyfieldwork.com/SpearmansRankSignificance.htm

2.1.3 Standard deviation error bar followed by Anova and T-test

Standard Deviation (SD) is the measure of spread of the numbers in a set of data from its

mean value. It has also called as SD and represented using the symbol σ (sigma). This can

METHOD

9

also be as a measure of variability or volatility in the given set of data (n). A low standard

deviation indicates that the data points tend to be very close to the mean, whereas high

standard deviation indicates that the data which spread out over a large range of values.

𝜎 = √∑ (𝑋 − 𝜇)2𝑛

𝑖=1

𝑁

((10)

Error bars used on graphs to indicate the error, or uncertainty in a reported measurement.

Error bars often indicate one standard deviation of uncertainty, but may also indicate the

standard error. These quantities are not the same and so the measure selected should state

explicitly in the graph or supporting text. Error bars used to compare visually two quantities if

various other conditions hold. This can determine whether differences are statistically

significant. Error bars can also show how good a statistical fit the data has to a given function.

Standard error of the mean: The standard error of the mean (SE of the mean) estimates the

variability between Sample means that you would obtain if you took multiple Samples from

the same population [48]. The standard error of the mean estimates the variability between

Samples whereas the standard deviation measures the variability within a single Sample

σ𝑀 =𝜎

√𝑁 (

(11)

Where σ is the standard deviation of the original distribution and N is the Sample size. The

formula shows that the larger the Sample size, the smaller the standard error of the mean.

Confidence interval error bars: Error bars that show the 95% confidence interval (CI) is

wider than SE error bars. It does not help to observe that two 95% CI error bars overlap, as

the difference between the two means may or may not be statistically significant. Useful rule

of thumb: If two 95% CI error bars do not overlap, and the Sample sizes are nearly equal, the

difference is statistically significant with a P value much less than 0.05 [48].

Posttest following one-way ANOVA (Analysis of variance) it accounts for multiple

comparisons, so the yield higher P values than t -tests comparing just two groups. Therefore,

the same rules apply. If two SE error bars overlap, you can be sure that a posttest comparing

those two groups will find no statistical significance. However, if two SE error bars do not

overlap, you cannot tell whether a post-test will, or will not, find a statistically significant

difference

The T-test: T-test used to determine whether the mean of a population significantly differs

from a specific value (called the hypothesized mean) or from the mean of another population.

This analysis is appropriate whenever you want to compare the means of two groups, and

especially appropriate as the analysis for the posttest-only two-group randomized

experimental design. The formula for the t-test is a ratio. The top part of the ratio is just the

difference between the two means or averages. The bottom part is a measure of the variability

or dispersion of the scores [46]

t − value: Signal

𝑁𝑜𝑖𝑠𝑒 =

𝑑𝑖𝑓𝑓𝑒𝑟𝑒𝑛𝑐𝑒 𝑏𝑒𝑡𝑤𝑒𝑒𝑛 𝑔𝑟𝑜𝑢𝑝 𝑚𝑒𝑎𝑛𝑠

𝑣𝑎𝑟𝑎𝑏𝑖𝑙𝑖𝑡𝑦 𝑜𝑓 𝑡ℎ𝑒 𝑔𝑟𝑜𝑢𝑝=

𝑋𝑇̅̅ ̅̅ −𝑋𝑐̅̅̅̅

𝑆𝐸(𝑋𝑇̅̅ ̅̅ −𝑋𝑐̅̅̅̅ ) ((12)

On the other hand, alternate formula for paired sample t-test is:

t =∑ 𝑑

√𝑛(∑ 𝑑2) − (∑ 𝑑) 2 𝑛 − 1

((13)

METHOD

10

Figure.2.3: Flow chart, which explained the Error Bar, followed by ANOVA and t-test applied on WP 1 and WP 2 (Readings:

obtained from interferometer (50 X magnification) and MountainsMap software).

• V: Number of Variants

• NEBNO: Number of error Bar Not Overlapping

• Si: Significant Values in ANOVA test

• TRUE: Parameters are disjunctive for variants with 95% confident interval

METHOD

11

The procedure followed for this study explained in the above flow chart in Fig.2.3.

First, find all the mean and standard deviation of each variant by using the readings from the

interferometer. Draw the mean graph for each variants and apply the custom Error Bars

(Analysis on Microsoft excel 2010). For WP 1 check the condition NEBNO=V, then reject

the parameter otherwise select. WP2 shows all the error bars are overlapping, and then go to

the ANOVA test followed by t-distribution test.

Analysis of variance:

Find the sum of parameters for each variant

Find the mean(average) for each variant

Find the difference between the observation and the mean (X-mean)

Find the variance (X-mean)2

Sum of the square

Find the total sum of the observation of the variants

Find the total sum of the square between group and the sum within the group

Find the degree of freedom between the group as well as with the group

Divide the sum of squares between groups by the degree of freedom between groups

MSw, divide the sum of squares within groups by degree of freedom within groups

MSB

Find F statistic ratio equal = MSw/ MSB

F > (F Critical) and P value less than 0.05 (p < 0.05) with (95% confidence), and

degree of freedom between group <F < degree of freedom within group, means

variants interval are “disjunct” for particular parameter (TRUE).

2.2. Chosen methodology for this project

The different methods within the area evaluated accordance to the requirements and the goals

of the project. For analyzing work package one (WP 1), by using the method mean and

standard deviation method, Error Bar analysis and Spearman’s rank Correlations method are

used for select the relevant parameters. Error Bar followed by ANOVA and T-test,

Spearman’s correlation method used for analyzing the work package two (WP 2).

2.3. Preparations and data collection

Appropriate literature study, articles, international journal and other study of similar

study.

Collect the cutting insert (CNMG120408-MM) of work package 1 and work package

from Sandvik Coromant.

Clean (Ultrasonic sterilizations) the surfaces of cutting inserts and take the

measurement by using interferometer and scanning electron microscope (SEM). Then

import the measurement to digital surf mountain software and analyze these readings

by different statistical method (ANOVA, T-test, Spearman’s rank correlation, F-test

etc. and software’s (IBM SPSS, MATLAB etc.).

Plan for weekly meeting with Sandvik Coromant and data collected from experts from

Sandvik Coromant as well as Halmstad University.

THEORY

12

3. THEORY

The authors started with a literature research regarding the task topography and how

simulated surface topography being measured, the authors make a deep investigation relates

to the surface integrity. Surface texture and 3D surface texture parameter. Select the

appropriate parameters to analyses the surfaces and the literature research including books,

and other relevant documentation regarding measuring of surface structure and their analysis

Surface Texture characterization and evaluation related to machining.

3.1. Summary of the literature study and state-of-the-art

Surface integrity is an important consideration in manufacturing operations, because it

influences such properties as fatigue strength, resistance to corrosion, and services life,

which- strongly influenced, by the nature of the surface produced. Surface integrity achieved

by the selection and control of manufacturing processes, estimating their effects on the

significant engineering properties of work materials, such as fatigue performance.

Surface integrity is a measure of the quality of a machined surface that describes the actual

structure of both surface and subsurface. Severe failures produced by fatigue, creep and stress

corrosion cracking start at the surface of components. Therefore, in machining any

component, it is necessary to satisfy the surface integrity requirements. Micro hardness, micro

crack, surface roughness, and metallurgical structure are features that used to determine the

surface integrity as shown in Figure3.

.

Schematic section through a machined surface [15]

Therefore, in machining any component, it is necessary to satisfy the surface integrity

requirements. This study based on the idea of Surface integrity loop (figure 3.2) where

focusing on the post coated and pre coated surfaces. The loop introduced to highlight the

connection between function, manufacturing, and characterization of the surfaces. Function

gives an idea about impression of products, tribological properties [16]. Manufacturing

methodology influence the surface layer of inserts which have influence on practical

properties [17]. Characterization of the surface integrity stands for types of measurement

takes and analysis occurred.

THEORY

13

Figure.3.2: “The surface integrity loop explained the relationship between function, manufacturing, measurement

and characterization of surface” [18]

The surface control loop can explain the complexity of surface design, the three facets

manufacturing, Characteristics and Functions. The characterization and measurement of

surface is very complex because the character of a machined surface involves three dimension

of space, any numerical assessment of a surface finish will be influenced by the direction in

which measurements are taken in relation to the lay and arbitrary distinguish between

roughness and waviness.

The engineering surface achieves, after the relevant process, new properties and

characteristics compared to the initial one that constitute what we call surface integrity.

Surface integrity can be express by Surface character, which the integrity can be judged by

four main elements [8]

1. Topography and texture, which describes the geometric characteristics

2. Chemical properties such as reactivity at the surface

3. Metallography such as structure, orientation and grain size

4. Mechanics, describing states of stress at the surface

The quantitative 3D surface description and analysis gives an effective understanding of

phenomena. The detailed analysis of loop leads to the solution of WP 1 & WP2. The

directional properties affect the tribological function of the surface (frictional behavior, wear,

lubricant retention, etc.) also the state of anisotropy can change during function. The surface

integrity loop consists of three sections (Functions, Manufacturing and Characterization) is

explained below.

3.1.1 Function

Surface Integrity Issues on Coated Cemented Carbides

Successful functionality of a hard coating system depends not only on composition,

microstructure and architecture of the layer itself [19-20], but also on the surface integrity of

the supporting substrate as well as on the interface nature and strength. On the other hand,

only a few investigations address the influence of surface topography or subsurface integrity

resulting from changes induced at different manufacturing stages, particularly regarding those

implemented prior to coating deposition, i.e., grinding, lapping, polishing, blasting and

peening [21]-[22].

A cutting insert must have the following properties in order to produce economical and good

quality parts:

Function

Manufacturing Characterization

THEORY

14

Hardness – The strength and hardness of inserts must maintain at elevated temperature

(hot hardness).

Toughness– to resistance chip, fracture and crack during the manufacturing and

cutting operations.

Wear resistance – to attain acceptable tool life.

Corrosion resistance – to withstand from chemical reactions.

Heat treatment capacity – to maintain the dimension stability while applying the heat

treatment.

T series (Tungsten type) cutting inserts are one of the commonly used in cutting inserts.

Titanium nitride is deposited on the tool does not affect the hardness (heat treatment) of the

tool being coated but it can extend the life or to allow the higher speed operations. The

hardness, tool life and high-speed operations of cemented tungsten carbide are greater than

other tool materials. In order to get better strength cobalt (Co) added as a binding agent to

Tungsten carbide (WC). The most commonly used coating materials are:

Titanium Carbo- Nitride Ti(C,N)

Ceramic coating

Titanium Nitride

Titanium carbo-nitride black color coating, Titanium carbo nitride is commonly used

intermediate layer of multilayered coating. The duty of Ti (C, N) maintains the strong bond

between the other coating layer and cutting inserts. The Ceramic coating (Aluminum oxide)

is the one of the mainly used ceramic coating because of its higher hardness and brittleness,

less chances for producing scaly cut and hard spot in the work piece. Because of outstanding

resistance to abrasive wear, heat and chemical reaction of ceramic coating provide higher

cutting speed. The main disadvantage of ceramic coating is it subjected to failure by chipping.

The main advantages of Titanium nitride coating are resistance to cratering, abrasive wear

resistance, and high heat resistance at high cutting speed (cutting interface with less friction-

produce a smooth surface of the coating).

The condition of cutting inserts determined by the following factors [23]

Microstructure – to maintain uniform crystal or grain structure, it is normally

recommended but is any variation in microstructure affects the machinability.

Grain size- – Small and undistorted grains are more ductile and gummy. Hardness

of the material generally correlated with grain size. Large grain size is generally

associated with low strength, low ductility, and low hardness.

Heat treatment – a material may be treated with cooling and heating leads to

reduce brittleness, remove stress, obtain ductility and toughness, to increase the

strength and to obtain definite microstructure.

Lay means for any predominant directionality of the surface texture of the cutting insert

surfaces. Usually the production method and geometry are determining the directionality

(lay). Surfaces produced having no characteristic directions are peening and grit blasting

(sometimes it has non-directional or protuberant lay). A smooth surface looks like more rough

THEORY

15

if it has strong lay and the rough surface looks like the more uniform weather it has no lay

[24].

3.1.2 Manufacturing

Abrasive slurry blasting is the type of wet abrasive slurry blasting of cutting insert coating

process. Fracture strength, hardness, the presence of impurities, density, type, and shape

(depends on the erosion and lubrication Properties-Void parameters) and size of abrasive

media has key roles in material selection of blasting process. The major problem related to

shot blasting related to method of process, defect of original materials and improper control of

parameters (stress temperature and surface deformations). The coating surfaces also depend

on the selection and matching of abrasive, nozzle, air pressure and abrasive/air mixing ratio

[25]-[26]. More Detail about the treatment, tool geometry and wear see appendix.7.

Chemical vapor deposition (CVD) is the generally used coating process in which coating

material introduced in the environmentally controlled chamber as a chemical vapor. Another

commonly used coating process is the Physical vapor deposition (PVD). The normal

thickness of CVD coating is 2µm to 15µm. Because of the high temperature 1000 ℃ using in

the CVD operations have high bonding between the tungsten carbide cutting inserts and

coating materials. The highest bonding leads to increase in toughness results in minimal

chipping and good surface finish [27].

The experienced polishers prepare coating by high-speed hand held rotary tools, abrasive

brushes and self-prepared carriers used for producing the smooth coated surfaces. Robot

assisted multi axis equipment’s are the ongoing development to achieve the effective surface

finish. Even though using different types of finishing process, the fine grain process is the

mandatory for producing smooth surfaces. This is the kind surface flow treatment in which

little hard rough particles are leads to small grooves and pits leads to the one directional

scratch. Now a days polishing treated as wear process in which abrasion, erosion, adhesion

and surface fatigue are normally occurred defects [28]. The grooves occurring on the surface

is mainly depends on the abrasive grain shapes of polishing. The angular shaped abrasive has

a higher wear rate with narrower and sharper grooves than the round edge shaped. Abrasive

rolling behavior (high load with low abrasive density) also effect on the groove formations

[29].

3.1.3 Characterization

The characterization of this study explained by following areas:

a. Region of interest:

All treatments had done on the rake face of the inserts; a worn edge of an insert as shown in

fig 3.3 and figure 3.4 below.

THEORY

16

’ Figure.3.3” The region of interest in rake face”.

Figure.3.4: “LOM image of worn edge of insert in region of interest”

b. Measurement Instrument:

In this thesis, there are two types of instruments used: optical interferometer and Scanning

Electron microscope (SEM).

Interferometer:

The MICROXAM 100 HR with objective of 10X and 50X magnification her were used

giving a measuring area of 0.8*0.6mm and 162*123μm. Interferometer is an instrument

taking the pictures with good accuracy and resolution. This is an optical technique providing

quantitative 3D data up to nanometer level. Interferometer meant dimensional metrology

rather than surface metrology. 5 X magnifications are overlapped the surfaces on rake face

[1]-[37]. The optical profilometer is an instrument that uses the interference patterns of light

to scan through a range of heights and create a three-dimensional profile of a desired surface

without physically touching it.

Scanning Electron Microscope (SEM)

A SEM of type JEOL JSM-6490LV used for taking images where produced by the secondary

electron detector and electron magnets with maximum of 5nm lateral resolution. Higher

resolution and large depth of field are the advantages of SEM [30]. SEM is intensively used

characterize surface topography and cross-sectional structure, as well as fractography of the

(coated) hard metals. SEM permits the observation of a variety of materials from micrometer

to nanometer scale. SEM capabilities variants extend from high resolution topographic

imaging to both qualitative and quantitative chemical analysis, the types of signals collected

from the interaction of the electron beam and the Sample surface include secondary electrons,

backscattered electrons, characteristic x-rays, and other photons of various energies, coming

from specific emission Sample volume [31].

THEORY

17

Figure 3.5 A SEM instrument of type JEOLJSM-6490LV

The table below explained about the summary of used instruments to measure the surfaces in

which mentioned about the magnifications, merit & demerits and comments of the equipment

Instrumentation Magnification Merits/Demerits Comments

Profilometric

3-D

measurement

Optical no contact

instrument:

Scanning

differential

interferometry

50 X and 10 X

magnification;

resolution in

micrometer

Measure small

area, easy to tune

the fringes

5 X

magnification

overlap the

edges

Scanning Electron

Microscope(SEM)

1KX,5KX & 10KX

magnification;

resolution in

micrometer

Better results; take

time for scanning

and operating

No need of

any

optimization

technique to

analysis

Table 3.1: Summary of used instruments for measurements [32]

c. Software used:

The software used for 3 D Surface texture parameters, profile and image analysis of SEM

pictures was the Digital surf MountainsMap 7 surface imaging and metrology [33] For

selecting the appropriate parameters of the surface having usage of several methods including

IBM SPSS, MATLAB and Microsoft excel. MountainsMap software is surface imaging and

metrology software published by the company Digital Surf. Its main application is micro-

topography, the science of studying surface texture and form in 3D at the microscopic scale.

THEORY

18

The software used mainly with stylus-based or optical Profilometer, optical microscopes and

scanning probe microscopes (SEM’s) and Raman and FT-IR spectrometers. These new

solutions added to an enhanced range of existing imaging and metrology software solutions

for areal 3D optical microscopes, scanning probe microscopes, 3D and 2D surface

Profilometer, and form measuring systems.

In this thesis used MountainsMap software Version 7 which introduces new imaging and

metrology solutions for scanning electron microscopes. All functions organized in groups and

sub-groups that clearly labeled. Groups and sub-groups associate related studies, operators

and editing tools.

d. Measuring Procedure and Analytical techniques

All the measurement (Reading) was precondition according to the software installation as

following:

First step the inserts carried out by ultrasonic sterilization and then dried by using hair

dryer.

The insets placed at the interferometer table and then take reading of 10 X and 50X

magnification see appendix 6, 20 readings taken for each inserts.

The analysis computed by Mountains Map 7software.

In MountainsMap7 load the reading

Fill the non-measured points.

Further, a form removal for 3D profiles by fitting a 2nd

degree polynomial to measured

data carried out.

Filtering using cutoff wavelengths of 80 micrometers and the robust Gaussian filter

see appendix 2. The measurement located on the rake face of the cutting inserts

toward both co-linear direction of nose radius from the nose [34].

e. Featured characterization:

Surface texture parameter, which is the profile parameter and the real field parameters, use a

statistical basis to characterize the cloud of measurement points.

Profile parameter in particular were developed primarily to monitor the production process, as

assessment we do not usually see field parameter values but pattern of features such as hills

and valleys, and the relationship between them. By detecting and the relationships between

them, it can characterize the pattern in surface texture, parameter that characterizes surface

features and their relationships are termed feature parameters [35].

ISO 25178: Geometric Product Specifications (GPS) – Surface texture: areal is an

International Organization for Standardization collection of international standards relating to

the analysis of 3D areal surface texture [8]. Particularly in the academic field, there is a

growing number of works, which advocate the usage of three-dimensional measuring

elements. The search of a higher precision and resolution in measures, reduction in costs of

processing and storing systems and continuous progress in microscopy techniques are the

reasons of the emergence of these works.

THEORY

19

3D roughness parameters are defined by the following Standards: ISO 25178 define 30

parameters (appendix 1), EUR 15178N also define 30 parameters but some are identical to

those of ISO 25178. Only 16 parameters are the latest ones, however Sz (maximum height of

surface roughness) and Std (texture direction) are calculated differently in both standards [36]

RESULTS

20

4. RESULTS

Measurements with 10 X respectively 50 X magnification used, 20 different measurements

performed with each magnification on every sample. The data was collected and analysis

performed by MountainsMap to evaluate the surfaces more closely. The results had a few

unmeasured points, which easily solved in the software. The Same filter and operations later

performed for the other Samples this can followed in appendix 3. The analysis of reading

from the interferometer has different kind of methods.

The methods used in this thesis, Average and Standard Deviation method, Error bar followed

by ANOVA and t-test method, Spearman’s correlation matrix method. The standard ISO

25178 used for selecting the parameters from MountainsMap Software. This section of results

considered to single out the surface topographical analysis of coated and uncoated cutting

inserts. 3D surface texture parameter and image analysis obtained from the equipment’s

interferometer and SEM.

4.1 Presentation of experimental results of work package 1

4.1.1 Parameters Selection Methods

The parameter selected by using the methods, which explained in the methodology. The

methods are used for the optimizing the parameters of variants MSG157, MSG158 and MSG

160.

4.1.2 Average and Standard Deviation method

Parameters - According

To ISO 25178

Comparison between MSG157 and MSG 158

MSG157 MSG158

Mean SD Imax Imin Mean SD' I´max I´min

Smc (p = 10 %) 0,39 0,01 0,42 0,36 0,52 0,04 0,59 0,44

Vv (p = 10 %) 0,40 0,02 0,43 0,37 0,54 0,04 0,62 0,46

Vmc (p = 10 %, q = 80

%) 0,27 0,01 0,29 0,24 0,34 0,02 0,38 0,31

Vvc (p = 10 %, q = 80

%) 0,35 0,01 0,38 0,32 0,47 0,03 0,53 0,41

SD&SD': Standard deviation of MSG157 and MSG158 respectively Table 4.1: shows the mean, standard deviation and I value for MSG157 and MSG158

A zoom in the comparison in table 4.1, highlights on the selected parameter . The variation of

each parameter based on the standard deviation, mean and confidence intervals. Where the

interval 𝐼′ and 𝐼′′ for the factor Si are disjunctive.

The mean or average calculated from the equation (1) and (2), as well as the variance from the

equations (3) and (4). The interval for good parts and for bad parts calculated from the

equations (5) and (6) with the coverage factor K (k=2). Then the significant factor computed

in equation (7).

RESULTS

21

Si between MSG157 and MSG158

Parameters - According

to ISO 25178

Description of

Selected Parameter

Significant

Factor

Significant factor is '+'

and disjunct interval

Smc (p = 10%) Inverse areal material

ratio

0,054 Accepted

Vv (p = 10%) Void volume 0,049 Accepted

Vmc (p = 10%, q=80%) Core material volume 0,065 Accepted

Vvc (p = 10%, q =80%) Core void volume 0,092 Accepted

Table 4.2: shows the significant factor and accepted conditions for selected parameters

Table 4.2 showing the significance factor Si; is computed on the basis of the intervals and the

mean, the Select parameter have ´+´ve (disjunct) significant factor (Accepted).

Parameters - According to

ISO 25178(157and 160)

Comparison between MSG157 and MSG 160

MSG157 MSG160

Mean SD Imax Imin Mean2 SD2 I´´max I´´min

Sa 0,25 0,01 0,28 0,23 0,19 0,01 0,22 0,16

Smc (p = 10%) 0,39 0,01 0,42 0,36 0,29 0,02 0,32 0,25

Sxp (p = 50%, q =96.5%) 0,71 0,04 0,79 0,63 0,52 0,04 0,61 0,44

Vv (p = 10%) 0,40 0,02 0,43 0,37 0,30 0,02 0,34 0,26

Vmc (p = 10%, q = 80%) 0,27 0,01 0,29 0,24 0,20 0,01 0,22 0,17

Vvc (p = 10%, q = 80%) 0,35 0,01 0,38 0,32 0,26 0,01 0,29 0,23

Table 4.3: Shows the mean, standard deviation and I value for MSG157 and MSG160

Table 4.3 shows the comparison between MSG 157 and MSG 160 on the selected parameter.

The variation of each parameter based on the standard deviation, mean and confidence

intervals. Where the interval 𝐼′ and 𝐼′′ for the factor Si are disjunctive. The mean or average

calculated from the equation (1) and (2), as well as the variance from the equations (3) and

(4). The interval for good parts and for bad parts calculated from the equations (5) and (6)

with the coverage factor K (k=2). Then the significant factor computed in equation (7)

Comparison between MSG157 and MSG 160

Parameters According to ISO

25178-2

Description Of Selected

Parameters

Significant

Factor

Accepted/

Rejected

Sa Arithmetic Mean height 0,05 Accepted

Smc (p = 10 %) Inverse areal material ratio 0,1 Accepted

Sxp (p = 50 %, q = 97.5%) Extremepeak height 0,04 Accepted

Vv (p = 10 %) Void Volume 0,1 Accepted

Vmc (p = 10 %, q = 80 %) Core material volume 0,11 Accepted

Vvc (p = 10 %, q = 80 %) Core void volume 0,12 Accepted

Table 4.4 showing the Accepted parameter has ´+´ve (disjunct) significant factor

RESULTS

22

The above table (4.4) shows the selected parameters of the variants MSG157 and MSG 160

from equation (7), the results from equation (7) has ´+´ve (disjunct) significant factor, that

mean select the parameter or accept the parameters which has ´+´ve (disjunct) significant

factor. Table 4.5 and table 4.6 shows the comparison between MSG 158 and MSG 160, the

selected parameters calculated from the equation (1) and (2), as well as the variance from the

equations (3) and (4). The interval for good parts and for bad parts calculated from the

equations (5) and (6) with the coverage factor K (k=2). Then the significant factor computed

in equation (7).

Parameters -

According To ISO

25178

Comparison Between MSG158 and MSG160

MSG158 MSG160

Mean SD I´max I´min Mean2 SD2 I´´max I´´min

Sa 0,33 0,03 0,40 0,27 0,19 0,01 0,22 0,16

Smc (p = 10%) 0,52 0,04 0,59 0,44 0,29 0,02 0,32 0,25

Sxp (p= 50%,q =96.5%) 0,88 0,09 1,07 0,70 0,52 0,04 0,61 0,44

Vv (p = 10%) 0,54 0,04 0,62 0,46 0,30 0,02 0,34 0,26

Vmc(p=10%,q=80%) 0,34 0,02 0,38 0,31 0,20 0,01 0,22 0,17

Vvc(p=10%,q= 80%) 0,47 0,03 0,53 0,41 0,26 0,01 0,29 0,23 Table 4.5: Shows the mean, standard deviation& I value for MSG158 and MSG15

Parameters - According

to ISO

25178(MSG157and

MSG160)

Description of selected

parameters

Comparison Between MSG158

and MSG160

Significant

Factor Accepted/Rejected

Sa Arithemetic Mean Height 0,20 Accepted

Smc (p = 10%) Inverse areal material ratio 0,29 Accepted

Sxp (p = 50%,q =97.5%) Extreme Peak height 0,13 Accepted

Vv (p = 10%) void volume 0,29 Accepted

Vmc (p = 10%, q =80%), Core material volume 0,32 Accepted

Vvc (p = 10%, q = 80%) Core void volume 0,35 Accepted Table 4.6: Shows the Significant factor and accepted conditions for selected parameter

RESULTS

23

Parameters - According

to ISO 25178

Significant factor

between MSG157 and

MSG158

Significant Factor

between MSG158

and MSG160

Significant Factor

between MSG157 and

MSG160

Sa (Arithemetic Mean

Height)

Si Factor ´-´ve Rejected 0,2 0,05

Smc (p = 10%) (Inverse

Areal Material Ratio 0,05 0,29 0,11

Sxp (p=50%,q=96.5%)

Extreme Peak Height

Si Factor ´-´ve Rejected 0,13 0,04

Vv (p = 10%)(Void

Volume) 0,05 0,29 0,1

Vmc (p = 10%, q = 80%

Core Material Volume 0,07 0,32 0,11

Vvc (p = 10%, q = 80%)

Core Void Volume 0,09 0,35 0,12

Table4.7: shows the significant values for selected parameters

The parameters selected from the above table according to significant value with disjunct

interval (‘+’ve value). Sa and Sxp shows ´-´ve Si factor in this case reject the parameters,

while comparing between MSG 157 and MSG158.The selected parameters gives idea about

topographical difference between three variants.

4.1.3 Spearman’s rank correlation method

Spearman’s rank correlation method to select the parameters explained in method section

2.1.2. The selected Parameters as shown in table 4.8, which has highest correlation factor

calculated from the equation (8).

Selected parameters correlations Smc Sq Vm Vv Vmc Sdq

Sxp 0,96

Sa 0,96

Vmp 1

Vmc 0,96

Vvc 0,99 0,99

Sdr 0,99 Table 4.8 the correlation for selected parameters in work package 1

The Parameters Sxp and Smc have very strong correlation (0, 96) means that these parameters

are significant for comparison between the variants. The parameters Sa and Sq shows highly

correlation in which select the Sa because both readings represent the Same sense. Vmp Vm,

Sdr and Sdq show strong correlations. Again, the parameters Vmc and Vv, Vvc and Vv, Vmc

are also showing strong correlation, more details explained in appendix 5.

4.1.4 Standard deviation Error Bar (EB) followed by Anova &T-test method

The error bar method can use as primary analyzing method to optimize the parameters. The

EB method involves calculating the mean, standard deviation (SD) from equation (10) for

each parameter, and 20 readings from interferometer.

RESULTS

24

Table 4.9: Error-Bar method for selecting 3D parameters (Mean and SD).

Tables 4.9 highlight the selected parameters, by using Excel to plot the mean graph for each

parameter then plot the custom error of each variant by using excel sheet as shown down in

Figure 4.1, or by using equation (10), (11) and (12) explained in Method.

Figure 4.1: Custom Error Bars on the different Variants of mean graph for selected parameters

In the above graphs Error Bar (Dark caped lines) with mean graphs of parameters having

disjunctive (Non-overlapped Error bar) can be selected. Standard deviation used to measure

the dispersion of the mean value. The low SD value indicates data are close to the mean,

while large values of SD indicate data has spread out over a wide range. Error bars give an

idea about statically significant parameters in which experimental data are falling far outside

of the range of standard deviation are considered as significant (Example Software Version:

Microsoft ® Excel 2010 in Windows® 7). The parameters Sa, Smc, Sxp, Vv, Vmc and Vvc

SaSmc (p =

10%)

Sxp (p =

50%, q =

97.5%)

Vv (p =

10%)

Vmc (p =

10%, q =

80%)

Vvc (p =

10%, q =

80%)

MSG157 0,25 0,39 0,71 0,40 0,27 0,35

MSG158 0,33 0,52 0,88 0,54 0,34 0,47

MSG160 0,19 0,29 0,52 0,30 0,20 0,26

0,00

0,20

0,40

0,60

0,80

1,00

1,20

Mea

n

Standard Deviation Error Bar chart for WP 1

Parameters -

According to

ISO 25178-2

DescriptionF

or Selected

parameter

Units

Error Bar Method

Mean Standard Deviation

MSG

157

MSG

158

MSG

160

MSG

157

MSG

158

MSG

160

Sa Arithmetic

mean height µm 0,25 0,33 0,19 0,01 0,18 0,01

Smc(p=10%) Inverse areal

material ratio µm 0,39 0,52 0,29 0,01 0,04 0,02

Sxp(p=50%,

q = 96.5%)

Extreme peak

height µm 0,71 0,88 0,52 0,04 0,09 0,04

Vv(p= 10%) Void Volume µ3/µ

2 0,4 0,54 0,3 0,02 0,04 0,02

Vmc(= 10%,

q = 80%)

Core material

volume µ

3/µ

2 0,27 0,34 0,2 0,01 0,02 0,01

Vvc(p=10%,

q = 80 %)

Core void

volume µ

3/µ

2 0,35 0,47 0,26 0,01 0,03 0,01

RESULTS

25

are the chosen parameters which have disjoint Error Bar; remaining parameters are explained

in the appendix 1.

.

4.3. Presentation of experimental results of work package 2

4.3 Methods for selecting the parameters

While applying Custom error bar on variants of work package two show that most of the error

bars are overlapping. Then we shift to our study to one-way analysis of variance followed to

t-test. Procedures are:

Check the Error Bars of different variants are overlapped

Find the variance and analysis of variance for single factor

Check the condition that F value >> F critical value; F between the degree of freedom

and p<0, 05, if parameter show this condition means that variants are significantly

varied between each other.

All these values calculated from excel sheet. F=Mean square of the model/mean

square of the error (large value indicates that not over lapping), P value indicates the

likelihood of observing a value of the F condition statistics as or more extreme.

Then make the table which showing below in which find the probability value for t-

test in which TRUE means P (T=t) two tail < (0, 05 /5) (condition from t test) which

indicates comparison between the variants are highly significant (95% confident entry-

level). FALSE indicates comparisons between the variants are not significant.

Selected parameters have highest number of trues (greater than variant number, 5)

The important comparison between the variants also can find out by using this method

(show in the green highlight) see table 4.10.

PA

RA

ME

TE

RS

MS

G186 a

nd

187

MS

G186an

d 1

89

MS

G186an

d 1

90

MS

G186an

d 1

91

MS

G187an

d 1

89

MS

G187an

d 1

90

MS

G187an

d 1

91

MS

G189an

d 1

90

MS

G189an

d 1

91

MS

G190an

d 1

91

Sq F T F F F F F T T F

Ssk T F T F F T T T T F

Sku F F T T F T T T T F

Sp F T F F F F F T T F

Sv F T F F T F F T T F

Sz F T F F T F F T T F

Sa F T T T F T T T T F

Smr T T F F T T F T T F

Smc T T T T F T T T T F

Sxp F T F T F F F T T F

Sal T F T F T T T F F F

Str F T T F F F F T T F

Std F F F F F F F F F F

RESULTS

26

Sdq F T F F T F F T T F

Sdr F T F F T F F T T F

Vm F T F F F F F T T F

Vv T T T T F T T T T F

Vmp F T F F F F F T T F

Vmc T T T T F T T T T F

Vvc T T T T F T T T T F

Vvv F T F F T F F T T F

Spd F F T T F T T T T F

Spc F T T T T F F T T F

F: FALSE T: TRUE

Table 4.10: show the result from ANOVA &t-test (Selected parameters and important comparisons are in green color)

TRUE P(T<=t) two-tail

<(0,05)

Parameter is disjunct for variants with

95%confident interval

FALSE P(T<=t) two-tail

>(0,05)

Parameter is non-disjunct for variants with 95%

confident interval Table 4.11: show physical meaning of TRUE and FALSE values in Table 11

PARAMETERS (ISO25178,WP2) NumberTRUES

(Row)>6

Accept/ Reject

Sa(Arithemefic Mean Height) 7 Accept

Smc (InverseAreal Material Ratio) 8 Accept

Vv(Void Volume) 8 Accept

Vmc (Core Material Volume) 8 Accept

Vvc(Core Void Volume) 8 Accept Table4.12: Selected Parameters in which number of TRUES (row)>6

ComparisnBetweenDifferentVariants(WP2) Number of

TRUES(Coulumn)

>15

SignificantI

Not Significant

Comparison between NESG186& 189 18 Significant

Comparison between MSG189& 190 22 Significant

Comparison between MSG189& 191 22 Significant Table 4.13: Significant comparison in which number of TRUES >15

Table 4.11 and table 4.12 explained the results obtained from the ANOVA followed by the T-

test in which plotted the number of TRUES and FALSE of each parameters with different

types of comparison. Table 4.13 explained about how pick the important parameters to

compare between different variants in which number of trues greater than 6 are selected

(Statistically significant different to compare between different parameters). Here chose

number six is arbitrary, once need more parameters change the limits and pick the more

parameters for comparison. The significant comparison between the variants also find out by

using the Same method that explained in table 4.13 The comparison between the variants

having number of trues greater than 15 selected.

CONCLUSIONS AND FUTURE WORK

27

5. CONCLUSIONS AND FUTURE WORK

5.1 Conclusions

5.1.1 Work Package 1

Which parameters describing the topography of the variants are important to look at

when comparing the different variants?

The parameters which are important to look at when comparing the different

variants to each other are arithmetic mean height(Sa), extreme peak height(Smc),

void volume(Vv), Core material volume(Vmc), Core void volume(Vvc) and Area

height difference(Sxp).

The methods used for selecting the appropriate parameters are Mean and standard

deviation method, Error bar method and Spearman’s correlation method

Table 5.1 described the effect of selected parameters on different variants in work package

one. The comparison of different variants with selected parameters also explained below. The

colour code of the table is based on the visual estimations [47].

Table 5.1: Comparison between different variants with selected parameters (comparison based on the visual estimation,

B: blasting, FGB: fine grain blasting, P: polishing) [47]

SURFACE

TEXTURE ANALYSIS

Comparison only for WP 1

variants

Description for highest

values

Paramete Selected IS025178)

Sa

Arithemetic

Mean

Height

Sxp

(p = 50%),

(q=97.5%)

Smc

(P=10%)

Vv

(p =10%)

Vmc

(p=10%)

(q=80%)

Vvc

(p=10%,

q= 80%)

Units µm µm µm µm³/µm² µm³/µm² µm³/µm²

Smooth <0,20 <0,60 <0,30 < 0,30 <0,02 <0,30

Medium 0,20-0,30 0,6-0,80 0,30-0,40 0,30-0,50 0,20-0,30 0,30-0,40

Rough >0,30 >0.80 >0.50 >0,50 >0,30 >0,40

MSG157

( B)

Higher bearing

of the material

frompeak, More

Texture.

0,25 0,71 0,39 0,40 0,27 0,35

MSG158

(B-FGB)

Higher overall

texture, Higher

Bearing area.

Higher amount

fluid retention.

0,33 0,88 0,52 0,54 0,34 0,47

MSG160

(B.P)

Widespace

texture,

Comparatively

smooth

0,19 0,52 0,29 0,30 0,20 0,26

CONCLUSIONS AND FUTURE WORK

28

Arithmetic Mean Height, (Sa)

The arithmetic mean height or Mean surface roughness defined as the arithmetic mean of

the absolute value of the height within Sampling area and which show measure of overall

texture. In the observation MSG158 and MSG157 shows more overall texture (Sa).

MSG160 show more surface finish (less value of Sa) as shown below in figure 5.1

Figure 5.1 Sa parameter with the values for work package 1

Peak Extreme Height, (Sxp)

Peak extreme height is defined the peak characterized difference between two material

ratio between 2.5% and 50% (ISO25178-3 2011). The peak height characterized upper

part of the surface without taking account of small percentage of peak height. The peak

extreme height is high for MSG157 and MSG158 and low for MSG160.

Inverse Areal Material Ratio, (Smc)

Inverse material ratio is the just opposite of the material ratio in which evaluates the

height value c corresponding to the material ratio p.

Void Volume (Vv)

The parameter stands for the surface texture of component, which contact with other

surface. For MSG158, Vv=0,5 µ3/µ2 which means 0,5µm thick film over the measured

area would provide the Same volume fluid needed to fill to the lowest valley

corresponding to the material ratio.

Core Material Volume (Vmc)

MSG 157,Sa=0,31µm MSG158,Sa=0,34 µm MSG 160,Sa=0,23µm

CONCLUSIONS AND FUTURE WORK

29

This parameter gives an idea about part of the material, which does not interact with other

surface in contact and not significant for lubrication. Core Material Volume can be

defined as the difference between material volume at mr2=80% and mr1=10%. This

parameter stands for amount of material removed from the peaks of the surface (Figure

4.2). Variants MSG157 and MSG158 have value Vmc=0,3 µ3/µ2 means these variants

have high material is available for load support once the top levels of a surface are worn

away.

Core Void Volume (Vvc)

The core void volume is the difference in void volume between the mr1=10% (Void

volume corresponding to the peak at 10% of material ratio) and mr2=80% (Void volume

corresponding to the material ratio 80%). For MSG158, Vvc= 0, 5 µ3/µ2 means high

amount of material available for seal engagement (more fluid entrapment). The variants

MSG157 and MSG160 are Same Vvc value (Figure 5.2).

Figure 5.2: Core Parameters for MSG157, MSG158 and MSG160’

In the above figure 5.2, Vmc curve stands for the bearing curve (material beard from the

peaks during the operations) provide the idea about the wearing occurring on the variants

surfaces. MSG158 variants show higher curve values (figure 5.2) indicates higher wear

occurred on that surface.

How well does the study of surface topography of variants correlate to the

manufacturing process?

MSG158 (Blasted followed by fine grain blasting) show more texture, MSG160

(blasting followed by the polishing) shows smoother Surface and MSG157

(Blasted) surface characteristics in between MSG158 and MSG160. Materials are

brittle so hardness test does not work for comparing the variants. Machining test

preferred to get exact result see table 5.2

Variants Manufacturing Process pre

treatment

Comments obtained from the

parameter

MSG157 Blasting Higher bearing of the material from peak,

More Texture.

MSG158 Blasting followed by fine grain

blasting

Higher overall texture, Higher Bearing

area. Higher amount fluid retention.

MSG160 Blasting followed by polishing Wide space texture, Comparatively

smooth Table 5.2 show variants and comments obtained from the parameter

Is there any predominant direction of the topography of the different variants?

0 20 40 60 80 100 %

µm

0

1

2

3

4

5

6

7

Vmp

Vmc

Vvc

Vvv

10.0 %

80.0 %

0 20 40 60 80 100 %

µm

0

1

2

3

4

5

6

7

Vmp

Vmc Vvc

Vvv

10.0 %

80.0 %

0 20 40 60 80 100 %

µm

0

1

2

3

4

5

6

7

8

9

Vmp

Vmc Vvc

Vvv

10.0 %

80.0 %

CONCLUSIONS AND FUTURE WORK

30

Spatial Parameters (Directionality) [9], [28], [2]

Variants (WP 1) Description for

parameters

Spatial Parameters(IS025178)

Sal(S=0.2) (Auto

correlation length

Str (S=0.2)

(Texture

aspect ratio)

Std

(Reference

angle = 0')

UNIT um Degree

MSG157(Blasting)

Texture as suggesting

highly isotropic

texture, without any

lay. Uniform surfaces

texture in all direction

3,5 0,7 93

MSG158

(Blasting-Fine

Grain Blasting)

Surface has a medium

anisotropic texture,

indicates or the

presence of a

dominating pattern in

certain directions.

3,6 0,4 94

MSG160

(Blasting-

Polishing)

Surface shows a high

amount of

directionality,

Antistrophic

which again points to a

high amount of wear

on the surface

4,3 0,3 33

Table 5.3: Spatial Parameters of variants MSG157, MSG158 and MSG160 (50X magnification)

SEM image Analysis from interferometer.

Figure 5.3: Shows grooves occurred on MSG160 readings (50 X magnifications)

MSG160 extracted area (SEM image analyzed from interferometer by Mountain Map software.)

The spatial parameters Std, Sal and Str are of variants in work package 1 explained in Table

5.1. The descriptions of parameters mentioned below. Figure 5.3, shows the highest grooves

occurring on MSG160 (Extracted area). The SEM image shows that there is no predominant

lay in direction of the three variants but in MSG 160 shows some scratches over the surfaces.

CONCLUSIONS AND FUTURE WORK

31

Autocorrelation Length, Sal

The Sal parameter is a quantitative measure of the distance along the surface in which a

texture that is statically different from the original location. MSG 160 shows higher

value, MSG157 and MSG158 are almost same value. It is the horizontal distance of the

Auto Correlation Function (ACF) (tx, ty) which has fastest decay to specified values

“S”. ACF (tx, ty) is the autocorrelation function which is used for studying periodicity

and check the isotropy of a surface. The specified value for smooth surface is taken as

(0,2) (ISO25178-2) for a practical application. Sal is perpendicular to the surface lay for

anisotropic surface.

Texture Aspects Ratio, Str

Texture aspects ratio, Str is defined as the ratio between rmin and rmax where rmin and rmax

are the minimum and maximum radius on the central lobe of the ACF respectively. The

Str value lies between 0 and 1(0% and 100%). Str is used for evaluating surface texture

isotropy. Str varies in between 0 and 1, with values closer to 1 suggest isotropic features

without any lay and values close to 0 suggest directionality of the surface texture [41].

Experts agree that a Str > 0.5 means a surface has an isotropic texture whereas a value

below 0.3 shows a high amount of directionality. MSG 157 surface has an isotropic

texture while MSG 160 shows a high amount of directionality see figure 5.4a, figure

5.4b and figure 5.5 for more details.

Figure 5.4a show the texture isotropy direction of variants in WP 1 (readings from interferometer)

Figure 5.4b SEM images (Source Sandvik Coromant) for WP1 showing texture directions

0.200

Parameters Value Unit

Isotropy 90.3 %

Periodicity ***** %

Period ***** µm

Direction of period ***** °

0.200

Parameters Value Unit

Isotropy 59.1 %

Periodicity ***** %

Period ***** µm

Direction of period ***** °

0.200

Parameters Value Unit

Isotropy 84.5 %

Periodicity ***** %

Period ***** µm

Direction of period ***** °

MSG 160 MSG 157 MSG 158

CONCLUSIONS AND FUTURE WORK

32

Figure 5.5 MSG 157 shows isotropy (Str=0,7) MSG 158 showsanisotropy(Str=0,4)

MSG160 shows high amount of directionality (Str=0,3)

Texture Direction, Std

The texture direction is the angle between 0degree and 180degree of the spectrum,

which derived from the Fourier spectrum. Std parameters showing scratches and

oriented texture direction, which gives idea about the directionality of the variants.

Three variants MSG157, MSG158 and MSG160 show almost same Texture direction

(Std almost equal to 90 degree). Appendix “3”explain Fourier polar spectral graph of

directionality.

For MSG157, MSG158 show Same Surface texture direction.

MSG 157 shows larger ratio values i.e. Str 0.5, indicate isotropy or uniform

surface texture in all directions.

MSG 158 indicates anisotropy or the presence of a dominating pattern in certain

directions.

MSG 160 Str= 0,3 value shows small value; indicate anisotropy or the presence of

a dominating pattern in certain directions. It shows high amount of directionality.

See appendix 4. The surface shows high amount of directionality.

5.1.2 Work Package 2

Which parameters are important for comparing the different variants to each other?

Parameters Sa, Smc, Vv, Vmc and Vvc are selected by using the Error bar

followed by ANOVA and t-test.

The parameters which are important to look at when comparing the different

variants to each other are arithmetic mean height (Sa) see figure 5.6 for more

explanation extreme peak height (Smc), void volume (Vv), Core material

volume (Vmc) and Core void volume (Vvc) , more about core parameter see

figure 5.7 and figure 5.8.

0 50 100 150 200 µm

µm

0

50

100

µm

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 50 100 150 200 µm

µm

0

50

100

µm

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

CONCLUSIONS AND FUTURE WORK

33

Figure 5.6 Sa parameters for work package 2

µm

3.865

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

Roughness (Gaussian filter, 80 µm)

µm

7.706

0

1

2

3

4

5

6

7

Roughness (Gaussian filter, 80 µm)

µm

11.736

0

1

2

3

4

5

6

7

8

9

10

11

Roughness (Gaussian filter, 80 µm)

µm

5.378

0

1

2

3

4

5

Roughness (Gaussian filter, 80 µm)

µm

5.005

0

1

2

3

4

Roughness (Gaussian filter, 80 µm)

MSG186, Sa=0,20 µm medium texture properties MSG187, Sa =0,32 µm higher texture properties

MSG189, Sa =0,37 µm higher texture properties MSG190, Sa =0,17 µm medium texture properties

MSG191, Sa =0,19 µm medium texture properties as MSG 190

CONCLUSIONS AND FUTURE WORK

34

Figure 5.7: Core Parameters for MSG 186, MSG 187and MSG 189

Figure 5.8: Core Parameters for MSG 190 and MSG 191

Error bar followed by the ANOVA and T-test and Spearman’s correlation

method can use for selecting the parameters.

Table 5.4 describes the effect of selected parameters on different variants in work package

two. The comparison of different variants with selected parameters also explained below.

Color in the table based on visual estimation

PARAMETERS Selected From ISO 25718-2

Sa Smc (p =

10%)

Vv (p = 10%) Vmc (p

= 10%,

q =

80%)

Vvc (p =

10%, q =

80%) SURFACE TEXTURE ANALYSIS

Comparison only for WP2 variants

Description for highest values

Units µm µm µm³/µm² µm³/µm² µm³/µm²

Smooth <0,2 <0,25 <0,25 <0,20 <0,20 Medium 0,2-0,35 0,25-0,45 0,25 -0,50 0,2-0,30 0,20-0,35

Rough >0,35 >0,45 >0,50 >0,30 >0,35 Variant Surface

MG186 B-0-B

High bearing of

materials from peak 0,20 0,30 0,32 0,19 0,26

0 20 40 60 80 100 %

µm

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

Vmp

Vmc Vvc

Vvv

10.0 %

80.0 %

0 20 40 60 80 100 %

µm

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

Vmp

Vmc Vvc

Vvv

10.0 %

80.0 %

0 20 40 60 80 100 %

µm

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

Vmp

Vmc Vvc

Vvv

10.0 %

80.0 %

0 20 40 60 80 100 %

µm

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

Vmp

Vmc Vvc

Vvv

10.0 %

80.0 %

0 20 40 60 80 100 %

µm

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

Vmp

Vmc Vvc

Vvv

10.0 %

80.0 %

CONCLUSIONS AND FUTURE WORK

35

MSG187 B-FGB-B

High fluid retention

and scrap entrapment,

Much material beard

away during process,

high bearing area

0,32 0,46 0,47 0,28 0,39

MSG189 B-P-B

High overall texture,

high bearing of

material from peaks,

more fluid retention,

more wetted surface

0,37 0,49 0,52 0,26 0,40

MSG190 B-P-B, P

Surface in good

condition, smooth flat

surfaces

0,17 0,26 0,24 0,15 0,19

MSG191 B-0-B,P

Surface in good

condition, smooth

and flat surfaces 0,19 0,22 0,21 0,15 0,17

B: Blasting; FGB: Fine Grain Blasting; P: Polishing Table 5.4: Comparison between different variants with selected parameters (The comparison based

On visual estimation) [47]

Highlights at the selected parameters of work package two in table 5.4. Compare between the

different variants, the parameter arithmetic mean height (Sa) means the overall texture of the

surface. Sa is insensitive in differentiating peaks, valleys and the spacing of the various

texture features. The remaining volume parameter (Vv, Vmc and Vvc) indicates the material

beard from the highest peak and entrapped in the valley, fluid retention, wetted surface etc.

The comparison is in the decimal place are not that much stable but we can say the

comparison is important because most of the parameters shows the same result. Out of the

five variants MSG190 and MSG 191 shows more smooth and flat surfaces. The variants

MSG190, MSG189 and MSG186 show area, which has more bearing from the peaks and

more fluid retention.

If any connection found between the treatment prior to coating and the outcome of the

treatment after coating?

The table 5.5 below shows variants MSG 186, MSG 187, MSG 189, MSG 190 and MSG

191and the manufacturing process, also the comments obtained from the selected parameter

from work package two.

CONCLUSIONS AND FUTURE WORK

36

Variants ER

Method

Pre coating

treatment

Post coating

treatment

Comments obtained from the

parameter

MSG 186 Blasting Blasting High bearing of materials from peak

MSG 187

Blasting

Fine grain

blasting

Blasting

High fluid retention and scrap

entrapment, Much material beard away

during process, high bearing area

MSG 189

Blasting

Polishing

Blasting

High overall texture, high bearing of

material from peaks, more fluid

retention, more wetted surface

MSG 190 Blasting Polishing

Blasting,

Polishing

Surface in good condition, smooth flat

surfaces

MSG 191 Blasting

Blasting,

Polishing

Surface in good condition, smooth and

flat surfaces

Table 5.5 shows the comments obtained from the parameter

MSG 186 shows medium texture as shown in figure 5.5

MSG189 and MSG187 show higher texture properties out of five variants see

Figure 5.5

MSG190 and MSG191 show same surface property see figure 5.5

The comparison between the MSG186M-MSG189, MSG189-SG190 and

MSG189-MSG191 are the highly significant comparison

Is there any different measurement approach needed to evaluate the surface roughness

on variants in Work Package 2 compared to Work Package 1?

Spearman’s correlation methods followed by ANOVA and T-test and

Spearman’s correlation method are effective to compare roughness between

different variants between the work packages

PHASE 1 PHASE 2 PHASE 3

cc

Flow chart representing a process for variants surface treatment

𝑆𝑎 = 0,31um

MSG 157

Blasting,

MSG 158

Blasting-Fine Grain

Blasting

Sa=0,34um

𝑆𝑎 = 0,23um

MSG 160

Blasting-Polishing

𝑆𝑎 = 0,2um

MSG 186

Blasting-0-Blasting

MSG 187

Blasting-Fine Grain

Blasting-Blasting

Sa=0,32um

MSG 189

Blasting-Polishing-

Blasting

𝑆𝑎 =0,37um 𝑆𝑎 = 0,19um

MSG 190

Blasting-Polishing-

Blasting, Polishing

𝑆𝑎 = 0,17um

MSG 191

Blasting-0-Blasting,

Polishing

CONCLUSIONS AND FUTURE WORK

37

In the above chart, in phase 1 MSG 160 shows less texture (Sa=0,23um) value compare to

MSG 157 (Sa=0,31um) and MSG 158 (Sa=0,34um).

In phase 2, MSG189 (modification of MSG 160) shows higher texture (Sa=0,37um) while

MSG 186 (modification of MSG 157) shows lower texture (Sa=0,2um). MSG

187(modification of MSG 158) show almost the same surface texture value (Sa=0,32um).

In Phase 3 MSG 191(modification of MSG 186) shows almost the same surface texture

(Sa=0,19um) as MSG 190 (modification of MSG 189), maybe because of both surfaces were

polished. For a proper conclusion before and after machining test, analysis is required.

5.1.3 Recommendation to future activities

Factors need consideration, to specify the same point during measuring by either the

white interferometer or SEM, it is important to have enough constraints to avoid error.

Identify the changes in displacement characteristics due to tool wear condition for

worn tool by online tool condition monitoring.

Further detail study about the manufacturing process, we recommended for fix the

measurement approaches to measure the surface roughness on variants in WP 1 and

WP2.

Investigate the Same texture properties propagated from WP 1 to WP2 by regression

method

Without pre-coating polishing process lead to get good result.

MSG187 showing better surface finish than MSG186 (Post treatment of MSG187

may get good surface finish than MSG191.

CRITICAL REVIEW

38

6. CRITICAL REVIEW

This critical review of thesis based on the self-emphasize, explained following:

6.1 What factors affect the work been done differently

The environmental aspect has not taken into consideration during the study. The choose

equipment’s are used for a long time may be affect the measurement accuracy and drying of

specimen after the cooling using normal procedure. The equipment table should be more

stable because some equipment’s are sensitive to vibrations. Mostly journals and Scientific

articles have been reviewed and a few books. The subject is good new research area; thus, it is

still in the experimental future. Other critical point of view the software is which used for the

study. The interferometer readings and SEM image analysis are obtained from the

interferometer software started with no knowledge within the area has emerged during the

studies. SPSS software, which helps to analyzing the parameters readings and work with

different analytical methods, more reasonable idea about the software helps, is necessary to

maintain reliable data.

6.2 Environmental and sustainable development

Understand the effect of the cutting parameters on surface finish, material removal rate and

energy consumption. The surface roughness influenced by cutting environment and the kind

of tool, in many studies; it was found that the tool type, feed and cutting velocity, influences

the material removal rate. In order to obtain this result our purpose was to investigate the

surface roughness and to evaluate the manufacturing process of the cutting inserts, which

cutting inserts had the better surface finish that will affect the cutting inserts tool life as well

as the lubrication of the process. The lubrication has its role both for the electrical power

consumption of the machining process than for the treatment of the scraps at the end of the

machining Process. By achieving the desired surface quality is of great importance for the

functional behavior of a part, that will lead to a significant design specification, which

influence on the properties such as wear resistance, coefficient of friction, wear rate, etc. The

quality of surface finish is a factor of importance in the evaluation of machine tool

productivity

6.3 Health and Safety

This part is very important and being sensitive due to the responsibility of human life. It is

very important to indicate this part, it is a multidisciplinary field concerned with the health,

Safety of people at work. Workplace hazards also present risks to the health and Safety of

people at work. Machining leads to environmental pollution mainly because of use of cutting

fluids [42, 43]. Fluids often contain chlorine (Cl), sulfur (S), or other extreme-pressure

additives to improve the lubricating performance. These chemicals present health hazards.

Furthermore, the cost of treating the waste liquid is high and the treatment itself is a source of

air pollution.

Skin exposure to cutting fluid can cause various skin diseases [44]. Inhalation of mists or

aerosols, airborne inhalation diseases have been occurring with cutting fluid aerosols exposed

workers for many years. Bennett and Bennett [45] stated that during machining operations,

39

workers could be exposed to cutting fluids by skin contact and inhalation, in response to these

health effects through skin contact or inhalation, Diseases include lipid pneumonia, asthma,

acute airways irritation, chronic bronchitis, hypersensitivity pneumonitis and impaired lung

function [44].

6.4 Economy

The cost of preparing these materials into cutting inserts is relatively high and continuing to

increase, as well as the cost of carbide and other tool material. It is very important to choose

tool inserts wisely. Surface roughness is a widely used index of product quality, performance

and surface life of any machined component is influencing by surface integrity of that

component.

Tool life improvement is essential to reduce the cost of production as much as possible.

6.5 Ethical aspects

The ethical value is one of the most important factors in human being life not only in the field

of science, as a member of this profession; the authors exhibit the highest standards of

honesty and integrity, the authors handled the equipment’s and the data collected carefully.

This considered from a critical point of view since the knowledge of the software and the

equipment is limited.

REFERENCES

40

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Cambridge UniÍersity Engineering Department, Trumpington Street, Cambridge, CB2

1PZ, UK

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and three-body abrasive wear: influence of test conditions in the microscale abrasive

wear test, Volumes 225–229, Part 1, Pages 205–214.

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areal- part 2: Teams, Definitions and surface texture parameter, international

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Low Carbon Steel by Cryogenically Precooking the Workpiece”, Trans. of the ASME,

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TABLE OF CONTENT FOR APPENDICES

43

[44] Thornburg, J., Leith, D. (2000). “Mist Generation During Metal Machining”,

[45] Bennett E.O., Bennett D.L. (1987). “Minimizing Human Exposure to Chemicals in

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Polished Tool Steels

[48] http://www.ncbi.nlm.nih.gov/pmc/articles/PMC524673/

TABLE OF CONTENT FOR APPENDICES

APPENDIX 1

Surface Parameter -ISO25178

APPENDIX 2

Template used in MountainsMap 7 software

APPENDIX 3

Fourier Polar Spectrum of Work Package

APPENDIX 4

ANOVA & T-test for Work Package 2

APPENDIX 5

Spearman’s rank correlation for WP 1 and WP2

APPENDIX 6

Interferometer Readings

APPENDIX 7

Insert Geometry and wear

Appendix: 1 Surface Parameter -ISO25178

44

Appendix: 1 Surface Parameter -ISO25178 Surface texture Parameters according ISO25178)

Function Parameters unit Name of parameter

Height Parameter ( amplitude) Uits

Sq μm Root mean square height

Ssk _ Skewness

Sku _ Kurtosis

Sp μm Maximum peak height

Sv μm Maximum pit height

Sz μm Maximum height

Sa μm Arithmetical mean height

Functional Parameter (stratified Surfaces)

Smr (c = 1 µm under the highest peak) μm Inverse areal material ratio

Smc (p = 10%) μm Extreme peak height

Sxp (p = 50%, q = 97.5%) μm Areal height difference

Spatial parameters

Sal (s = 0.2) μm Auto-correlation length

Str (s = 0.2) Texture-aspect ratio

Std (Reference angle = 0°) ° Texture direction

Hybrid parameters

Sdq _ Root mean square gradient

Sdr ° Sdr % Developed interfacial area ratio

Spatial parameters

Sal (s = 0.2) μm Auto-correlation length

Str (s = 0.2) Texture-aspect ratio

Std (Reference angle = 0°) ° Texture direction

Function Parameter (Volume)

Vm (p = 10%) μm³/ μm² Material volume

Vv (p = 10%) μm³/ μm² Void volume

Vmp (p = 10%) μm³/ μm² Peak material volume

Vmc (p = 10%, q = 80%) μm³/ μm² Core material volume

Vvc (p = 10%, q = 80%) μm³/ μm² Core void volume

Vvv (p = 80%) μm³/ μm² Pit void volume

Feature Parameter

Spd (pruning = 5%) 1/ μm ² Density of peaks

Spc (pruning = 5%) 1/ μm Arithmetic mean peak curvature

S10z (pruning = 5%) μm Ten point height

S5p (pruning = 5%) μm Five point peak height

S5v (pruning = 5%) μm Five point pit height

Sda (pruning = 5%) μm ² Mean dale area

Sha (pruning = 5%) μm ² Mean hill area

Sdv (pruning = 5%) μm ² Mean dale volume

Shv (pruning = 5%) Mean hill volume

Table 1.2: 3D roughness parameters calculated and analyzed in this study

Appendix: 1 Surface Parameter -ISO25178

45

3D roughness parameters defined by the following Standards: ISO 25178 define 30

parameters, EUR 15178N also define 30 parameters but some are identical to those of ISO

25178. Only 16 parameters are the latest ones, however Sz (maximum height of surface

roughness) and Std (texture direction) are calculated differently in both standards.

The 3D roughness parameters (see Table 1) can be classified into the following groups:

a. Height Parameter(Amplitude)

Sq Root

mean square height

Standard deviation of the height distribution or RMS surface roughness

Computes the standard deviation for the amplitudes of the surface (RM

Ssk Skewness Skewness of the height distribution. Third statistical moment, qualifying the

symmetry of the height distribution. A negative Ssk indicates that the surface

is composed with principally one plateau and deep and fine valleys. In this

case, the distribution is sloping to the top. A positive Ssk indicates a surface

with lots of peaks on a plane. The distribution is sloping to the bottom. Due to

the big exponent used; this parameter is very sensitive to the Sampling and to

the noise of the measurement.

Sku Kurtosis Kurtosis of the height distribution. Fourth statistical moment, qualifying the

flatness of the height distribution. Due to the big exponent used, this

parameters very sensitive to the Sampling and to the noise of the measurement

Sp Maxiumu peak

height

Height between the highest peak and the mean plane.

Sv Maximum pit height Depth between the mean plane and the deepest valley.

Sz Maximum height

Height between the highest peak and the deepest valley.The definition of the

(ISO 25178) Sz parameter is different from the definition of the (EUR

15178N) Sz parameter. The value of the (EUR 15178N) Sz parameter is

always smaller than the value of the (ISO 25178) Sz parameter. The (ISO

25178) Sz parameter replaces the (EUR 15178N) St parameter.

Sa Arithmetical mean

height

Mean surface roughness. is parameter is

deprecated and shall be replaced by Sq in the

future

Appendix: 1 Surface Parameter -ISO25178

46

b. Spatial parameter Parameters (ISO 25178) (Surface)

Spatial parameters describe topographic characteristics based upon spectral analysis.

They quantify the lateral information present on the X- and Y-axes of the surface.

Sal Auto-

correlation

length

Horizontal distance of the autocorrelation function (tx, ty) which

has the fastest decay to a specified value s, with 0 < s < 1. The

default value for s in the software is 0.2.This parameter

expresses the content in wavelength of the surface. A high value

indicates that the surface has mainly high wavelengths (low

frequencies).

Str Texture-

aspect

ratio

This is the ratio of the shortest decrease length at 0.2 from the

autocorrelation; on the greatest length. This parameter has a

result between 0 and 1. If the value is near 1, we can Say that the

surface is isotropic, i.e. has the Same characteristics in all

directions. If the value is near 0, the surface is anisotropic, i.e.

has an oriented and/or periodical structure.

Appendix: 1 Surface Parameter -ISO25178

47

Std Texture

direction

This parameter calculates the main angle for the texture of the

surface, given by the maximum of the polar spectrum. This

parameter has a meaning if Str is lower than 0.5.

If the surface has a circular texture (turning, Sawing), this

parameter will give a wrong direction near to the tangential of

the circle. In case the surface has two or more main directions,

the Std parameter will give the angle of the main direction.

The angle is given between 0° and 360° counterclockwise, from

a reference angle. The reference angle may be set to another

value than 0°.

Note: The (ISO 25178) Std parameter and the (EUR 15178N)

Std parameter are calculated the Same way, but the angle is

given differently.

Calculation of the Str and Sal Parameters 1. Auto-correlation function of the surface.

b) Thresholding of the Auto-correlation at a

height s (the black spots are above the

threshold).

2.

c) Threshold boundary of the central

threshold portion.

d) Polar coordinates leading to the auto-

correlation lengths in different directions.

c. Functional Parameters (ISO 25178) (Surface)

Functional parameters are calculated from the Abbott-Firestone curve obtained by the

integration of height distribution overall surface.

Hybrid Parameters (ISO 25178) (Surface)

Appendix: 1 Surface Parameter -ISO25178

48

Hybrid parameters are a class of surface finish parameters that quantify the information

present on the X-, Y- and Z-axes of the surface, i.e. those criteria that depend both on the

amplitude and on the spacing, such as slopes, curvatures, etc...

Functional Volume Parameter ISO 25178) (Surface)

Functional volume parameters are typically used in tribological studies. They are calculated

by using the Abbott-Firestone curve (areal material ratio curve) calculated on the surface

Sdq Root mean

square

gradient

Root-Mean-Square slope of the surface.

Sdr Developed

interfacial

area ratio

Ratio of the increment of the interfacial area of the scale limited surface

within the definition area over the definition area.

The developed surface indicates the complexity of the surface thanks to the

comparison of the curvilinear surface and the support surface. A completely

flat surface will have a Sdr near 0%. A complex surface will have a Sdr of

some percent’s

Appendix: 1 Surface Parameter -ISO25178

49

Vm(p) Material volume Volume of the material at a material ratio p (in %).

Vv(p) Void volume Volume of the voids at a material ratio p (in %)

Vmp Peak material

volume of the

scale limited

surface

Volume of material in the peaks, between 0% material ratio

and a material ratio p (in %), calculated in the zone above c1.

Vmp = Vm(p)

Vmc Core material

volume of the

scale limited

surface

Volume of material in the core or kernel, between two material

ratios p and q (in %), calculated in the zone between c1 and c2.

Vmc = Vm(q) - Vm(p)

Vvc Core void volume

of the scale

limited surface

Volume of void in the core or kernel, between two material

ratios p and q (in %), calculated in the zone between c1 and c2.

Vvc = Vv(p) - Vv(q)

Vvv Pit void volume

of the scale

limited surface

Volume of void in the valleys, between a material ratio p (in

%) and 100% material ratio, calculated in the zone below

c2.Vvv = Vv(p)

Graphical study of Volume Parameters (Surface)

d. Functional Stratified Surface Parameters (ISO 25178) (Surface)

Functional Parameters (also called bearing ratio parameters) are a class of surface finish

parameters characterizing the functional aspect of a surface, particularly lubrication and

grinding. They are specifically dedicated to the automotive industry.

Appendix: 1 Surface Parameter -ISO25178

50

Sk Kernel roughness depth

(roughness depth of the core)

Extrapolation of 2D parameter Rk (ISO

13565-2)

Spk Reduced peak height

(roughness depth of the peaks)

Extrapolation of 2D parameter Rpk (ISO

13565-2)

Svk Reduced valley depth

(roughness depth of the valleys)

Extrapolation of 2D parameter Rvk (ISO

13565-2)

Smr1 Upper material ratio Extrapolation of 2D parameter MR1 (ISO

13565-2)

Smr2 Lower material ratio Extrapolation of 2D parameter MR2 (ISO

13565-2)

e. Feature parameter

The feature parameters are a new family of parameters that is integrated in the ISO 25178

standard. Feature parameters are derived from the segmentation of a surface into motifs (hills

and dales). Segmentation is carried out in accordance with the watersheds algorithm for the

moment, all feature parameters are calculated after discrimination by segmentation using a

Wolf pruning of 5% of the value of the Sz parameter (Maximum height).

Appendix: 1 Surface Parameter -ISO25178

51

Std Density

of Peaks

Number of peaks per unit area. The (ISO 25178) Spd parameter

replaces the (EUR 15178N) Sds parameter, since software

version 5.0. The peaks taken into account for the (EUR 15178N)

Sds parameter are detected by local neighborhood (with respect

to 8 neighboring points) without discrimination between local

and significant peaks. The (ISO 25178) Spd parameter is

calculated the Same way, but takes into account only those

significant peaks that remain after discrimination by

segmentation (Wolf pruning of 5% of Sz). Therefore the value

of the (ISO 25178) Spd parameter is smaller than the value of

the (EUR 15178N) Sds parameter.

Spc Arithmetic

mean peak

curvature

Arithmetic mean of the principle curvatures of peaks within a

definition area.This parameter enables to know the mean form

of the peaks: either pointed, either rounded, according to the

mean value of the curvature of the surface at these points.The

(ISO 25178) Spc parameter replaces the (EUR 15178N) Ssc

parameter.he peaks taken into account for the (EUR 15178N)

Ssc parameter are detected by local neighborhood (with respect

to 8 neighboring points) without discrimination between local

and significant peaks. The (ISO 25178) Spc parameter is

calculated the Same way, but takes into account only those

significant peaks that remain after discrimination by

segmentation (Wolf pruning of 5% of Sz). Therefore the value

of the (ISO 25178) Spc parameter is more accurate and

significant than the value of the (EUR 15178N) Ssc parameter.

S10z Tenpoint

height

Average value of the heights of the five peaks with the largest

global peak height added to the average value of the heights of

the five pits with the largest global pit height, within the

definition area.S10z = S5p + S5v

S5p point peak

height

Average value of the heights of the five peaks with the largest

global peak height, within the definition area.

S5v Five point

pit height

Average value of the heights of the five pits with the largest

global pit height, within the definition area.

Sda Closed

dale area

Average area of dales connected to the edge.

Sha Closed hill

area

Average area of hills connected to the edge.

Sdv Closed

dale

volume

Average volume of dales connected to the edge.

Shv Closed hill

volume

Average volume of hills connected to the edge.

f. References of the Standards

ISO 25178-2 Geometrical product specifications (GPS) —Surface texture:

Areal — Part 2: Terms, definitions and surface texture

parameters

Appendix.2: Template used in MountainsMap 7 software

52

Appendix.2: Template used in MountainsMap 7 software

Appendix.3: Fourier Polar Spectrum of Work Package 1

53

Appendix.3: Fourier Polar Spectrum of Work Package 1

The above diagrams show the texture direction of variants in WP 1 (readings from SEM)

0°

10°

20°

30°

40°

50°

60°

70°80°90°100°

110°

120°

130°

140°

150°

160°

170°

180°

Parameters Value Unit

Isotropy 56.1 %

First Direction 0.250 °

Second Direction 146 °

Third Direction 33.8 °

0°

10°

20°

30°

40°

50°

60°

70°80°90°100°

110°

120°

130°

140°

150°

160°

170°

180°

Parameters Value Unit

Isotropy 52.7 %

First Direction 0.309 °

Second Direction 146 °

Third Direction 33.8 °

0°

10°

20°

30°

40°

50°

60°

70°80°90°100°

110°

120°

130°

140°

150°

160°

170°

180°

Parameters Value Unit

Isotropy 48.6 %

First Direction 146 °

Second Direction 138 °

Third Direction 162 °

MSG157

MSG158

MSG160

MSG 157 MSG 158 MSG 160

Appendix: 4 ANOVA ad T-test for Work Package 2

54

Source of Variation SS df MS F P-value F crit

Between Groups 0,60871 4 0,152177 19,00998 0,0000000000164 2,467494

Within Groups 0,760488 95 0,008005

Total 1,369197 99

Appendix: 4 ANOVA ad T-test for Work Package 2

This method explained for parameters, which considered for sudy.

a. Arithmetic Mean Height (Sa)

One way-ANOVA test table

T-test results

t-Test: Two-Sample Assuming Equal Variances t-Test: Two-Sample Assuming Equal Variances

MSG186 MSG187 MSG186 MSG189

Mean 0,207321 0,310146 Mean 0,207321 0,361352

Variance 0,00043 0,0303 Variance 0,00043 0,008113

Observations 20 20 Observations 20 20

Pooled Variance 0,015365 Pooled Variance 0,004272

Hypothesized Mean Difference 0 Hypothesized Mean Difference 0

df 38 df 38

t Stat -2,62317 t Stat -7,45268

P(T<=t) one-tail 0,006235 P(T<=t) one-tail 3,03E-09

t Critical one-tail 1,685954 t Critical one-tail 1,685954

P(T<=t) two-tail 0,01247 0,01 FALSE P(T<=t) two-tail 6,05E-09 0,01 TRUE

t Critical two-tail 2,024394 t Critical two-tail 2,024394

t-Test: Two-Sample Assuming Equal Variances t-Test: Two-Sample Assuming Equal Variances

MSG187 MSG189 MSG187 MSG190

Mean 0,310146 0,361352 Mean 0,310146 0,174009

Variance 0,0303 0,008113 Variance 0,0303 0,000849

Observations 20 20 Observations 20 20

Pooled Variance 0,019207 Pooled Variance 0,015575

Hypothesized Mean Difference 0 Hypothesized Mean Difference 0

df 38 df 38

t Stat -1,16841 t Stat 3,449602

P(T<=t) one-tail 0,124959 P(T<=t) one-tail 0,000695

t Critical one-tail 1,685954 t Critical one-tail 1,685954

P(T<=t) two-tail 0,249918 0,01 FALSE P(T<=t) two-tail 0,001389 0,01 TRUE

t Critical two-tail 2,024394 t Critical two-tail 2,024394

Appendix: 4 ANOVA ad T-test for Work Package 2

55

b. Extreme Peak Height (Smc)

c. One way-ANOVA test table

T-test results

Source of Variation SS df MS F P-value F crit

Between Groups 1,181241 4 0,29531 35,54228 3,94E-18 2,467494

Within Groups 0,789327 95 0,008309

Total 1,970569 99

t-Test: Two-Sample Assuming Equal Variances

MSG190 MSG191

Mean 0,174009 0,166251

Variance 0,000849 0,000333

Observations 20 20

Pooled Variance 0,000591

Hypothesized Mean Difference 0

df 38

t Stat 1,008912

P(T<=t) one-tail 0,159699

t Critical one-tail 1,685954

P(T<=t) two-tail 0,319398 0,01 FALSE

t Critical two-tail 2,024394

t-Test: Two-Sample Assuming Equal Variances

MSG187 MSG191

Mean 0,310146 0,166251

Variance 0,0303 0,000333

Observations 20 20

Pooled Variance 0,015317

Hypothesized Mean Difference 0

df 38

t Stat 3,676711

P(T<=t) one-tail 0,000364

t Critical one-tail 1,685954

P(T<=t) two-tail 0,000727 0,01 TRUE

t Critical two-tail 2,024394

t-Test: Two-Sample Assuming Equal Variances t-Test: Two-Sample Assuming Equal Variances

MSG189 MSG190 MSG189 MSG191

Mean 0,361352 0,174009 Mean 0,361352 0,166251

Variance 0,008113 0,000849 Variance 0,008113 0,000333

Observations 20 20 Observations 20 20

Pooled Variance 0,004481 Pooled Variance 0,004223

Hypothesized Mean Difference 0 Hypothesized Mean Difference 0

df 38 df 38

t Stat 8,850272 t Stat 9,493789

P(T<=t) one-tail 4,54E-11 P(T<=t) one-tail 7,09E-12

t Critical one-tail 1,685954 t Critical one-tail 1,685954

P(T<=t) two-tail 9,08E-11 0,01 TRUE P(T<=t) two-tail 1,42E-11 0,01 TRUE

t Critical two-tail 2,024394 t Critical two-tail 2,024394

t-Test: Two-Sample Assuming Equal Variances t-Test: Two-Sample Assuming Equal Variances

MSG186 MSG190 MSG186 MSG191

Mean 0,207321 0,174009 Mean 0,207321 0,166251

Variance 0,00043 0,000849 Variance 0,00043 0,000333

Observations 20 20 Observations 20 20

Pooled Variance 0,00064 Pooled Variance 0,000382

Hypothesized Mean Difference 0 Hypothesized Mean Difference 0

df 38 df 38

t Stat 4,165468 t Stat 6,646139

P(T<=t) one-tail 8,62E-05 P(T<=t) one-tail 3,72E-08

t Critical one-tail 1,685954 t Critical one-tail 1,685954

P(T<=t) two-tail 0,000172 0,01 TRUE P(T<=t) two-tail 7,44E-08 0,01 TRUE

t Critical two-tail 2,024394 t Critical two-tail 2,024394

Appendix: 4 ANOVA ad T-test for Work Package 2

56

t-Test: Two-Sample Assuming Equal Variances t-Test: Two-Sample Assuming Equal Variances

MSG187 MSG189 MSG187 MSG190

Mean 0,444182 0,485249 Mean 0,444182 0,235662

Variance 0,025916 0,012337 Variance 0,025916 0,002202

Observations 20 20 Observations 20 20

Pooled Variance 0,019126 Pooled Variance 0,014059

Hypothesized Mean Difference 0 Hypothesized Mean Difference 0

df 38 df 38

t Stat -0,93902 t Stat 5,561244

P(T<=t) one-tail 0,176825 P(T<=t) one-tail 1,14E-06

t Critical one-tail 1,685954 t Critical one-tail 1,685954

P(T<=t) two-tail 0,353649 0,01 FALSE P(T<=t) two-tail 2,28E-06 0,01 TRUE

t Critical two-tail 2,024394 t Critical two-tail 2,024394

t-Test: Two-Sample Assuming Equal Variances t-Test: Two-Sample Assuming Equal Variances

MSG189 MSG190 MSG189 MSG191

Mean 0,485249 0,235662 Mean 0,485249 0,217224

Variance 0,012337 0,002202 Variance 0,012337 0,000571

Observations 20 20 Observations 20 20

Pooled Variance 0,00727 Pooled Variance 0,006454

Hypothesized Mean Difference 0 Hypothesized Mean Difference 0

df 38 df 38

t Stat 9,256973 t Stat 10,55019

P(T<=t) one-tail 1,4E-11 P(T<=t) one-tail 3,77E-13

t Critical one-tail 1,685954 t Critical one-tail 1,685954

P(T<=t) two-tail 2,79E-11 0,01 TRUE P(T<=t) two-tail 7,55E-13 0,01 TRUE

t Critical two-tail 2,024394 t Critical two-tail 2,024394

t-Test: Two-Sample Assuming Equal Variances t-Test: Two-Sample Assuming Equal Variances

MSG189 MSG190 MSG189 MSG191

Mean 0,485249 0,235662 Mean 0,485249 0,217224

Variance 0,012337 0,002202 Variance 0,012337 0,000571

Observations 20 20 Observations 20 20

Pooled Variance 0,00727 Pooled Variance 0,006454

Hypothesized Mean Difference 0 Hypothesized Mean Difference 0

df 38 df 38

t Stat 9,256973 t Stat 10,55019

P(T<=t) one-tail 1,4E-11 P(T<=t) one-tail 3,77E-13

t Critical one-tail 1,685954 t Critical one-tail 1,685954

P(T<=t) two-tail 2,79E-11 0,01 TRUE P(T<=t) two-tail 7,55E-13 0,01 TRUE

t Critical two-tail 2,024394 t Critical two-tail 2,024394

t-Test: Two-Sample Assuming Equal Variances t-Test: Two-Sample Assuming Equal Variances

MSG186 MSG187 MSG186 MSG189

Mean 0,305656 0,444182 Mean 0,305656 0,485249

Variance 0,000518 0,025916 Variance 0,000518 0,012337

Observations 20 20 Observations 20 20

Pooled Variance 0,013217 Pooled Variance 0,006427

Hypothesized Mean Difference 0 Hypothesized Mean Difference 0

df 38 df 38

t Stat -3,81042 t Stat -7,084

P(T<=t) one-tail 0,000247 P(T<=t) one-tail 9,47E-09

t Critical one-tail 1,685954 t Critical one-tail 1,685954

P(T<=t) two-tail 0,000493 0,01 TRUE P(T<=t) two-tail 1,89E-08 0,01 TRUE

t Critical two-tail 2,024394 t Critical two-tail 2,024394

Appendix: 4 ANOVA ad T-test for Work Package 2

57

t-Test: Two-Sample Assuming Equal Variances t-Test: Two-Sample Assuming Equal Variances

MSG186 MSG187 MSG186 MSG189

Mean 0,316324 0,472679 Mean 0,316324 0,52424

Variance 0,00064 0,048254 Variance 0,00064 0,016009

Observations 20 20 Observations 20 20

Pooled Variance 0,024447 Pooled Variance 0,008325

Hypothesized Mean Difference 0 Hypothesized Mean Difference 0

df 38 df 38

t Stat -3,16227 t Stat -7,20609

P(T<=t) one-tail 0,001537 P(T<=t) one-tail 6,48E-09

t Critical one-tail 1,685954 t Critical one-tail 1,685954

P(T<=t) two-tail 0,003073 0,01 TRUE P(T<=t) two-tail 1,3E-08 0,01 TRUE

t Critical two-tail 2,024394 t Critical two-tail 2,024394

d. Void volume(Vv)

One way-ANOVA test table

T-test results

t-Test: Two-Sample Assuming Equal Variances

MSG187 MSG191

Mean 0,444182 0,217224

Variance 0,025916 0,000571

Observations 20 20

Pooled Variance 0,013243

Hypothesized Mean Difference 0

df 38

t Stat 6,236567

P(T<=t) one-tail 1,35E-07

t Critical one-tail 1,685954

P(T<=t) two-tail 2,7E-07 0,01 TRUE

t Critical two-tail 2,024394

t-Test: Two-Sample Assuming Equal Variances t-Test: Two-Sample Assuming Equal Variances

MSG187 MSG189 MSG187 MSG190

Mean 0,472679 0,52424 Mean 0,472679 0,243822

Variance 0,048254 0,016009 Variance 0,048254 0,002426

Observations 20 20 Observations 20 20

Pooled Variance 0,032132 Pooled Variance 0,02534

Hypothesized Mean Difference 0 Hypothesized Mean Difference 0

df 38 df 38

t Stat -0,9096 t Stat 4,546355

P(T<=t) one-tail 0,184383 P(T<=t) one-tail 2,71E-05

t Critical one-tail 1,685954 t Critical one-tail 1,685954

P(T<=t) two-tail 0,368767 0,01 FALSE P(T<=t) two-tail 5,42E-05 0,01 TRUE

t Critical two-tail 2,024394 t Critical two-tail 2,024394

Source of Variation SS df MS F P-value F crit

Between Groups 1,465749 4 0,366437 26,95874 5,91E-15 2,467494

Within Groups 1,291289 95 0,013593

Total 2,757038 99

t-Test: Two-Sample Assuming Equal Variances

MSG190 MSG191

Mean 0,235662 0,217224

Variance 0,002202 0,000571

Observations 20 20

Pooled Variance 0,001387

Hypothesized Mean Difference 0

df 38

t Stat 1,565684

P(T<=t) one-tail 0,062857

t Critical one-tail 1,685954

P(T<=t) two-tail 0,125713 0,01 FALSE

t Critical two-tail 2,024394

Appendix: 4 ANOVA ad T-test for Work Package 2

58

e. Core material volume(Vmc)

One way-ANOVA test table

T-test results

t-Test: Two-Sample Assuming Equal Variances t-Test: Two-Sample Assuming Equal Variances

MSG189 MSG190 MSG189 MSG191

Mean 0,52424 0,243822 Mean 0,52424 0,224808

Variance 0,016009 0,002426 Variance 0,016009 0,000633

Observations 20 20 Observations 20 20

Pooled Variance 0,009218 Pooled Variance 0,008321

Hypothesized Mean Difference 0 Hypothesized Mean Difference 0

df 38 df 38

t Stat 9,236297 t Stat 10,38006

P(T<=t) one-tail 1,48E-11 P(T<=t) one-tail 5,99E-13

t Critical one-tail 1,685954 t Critical one-tail 1,685954

P(T<=t) two-tail 2,96E-11 0,01 TRUE P(T<=t) two-tail 1,2E-12 0,01 TRUE

t Critical two-tail 2,024394 t Critical two-tail 2,024394

t-Test: Two-Sample Assuming Equal Variances t-Test: Two-Sample Assuming Equal Variances

MSG186 MSG190 MSG186 MSG191

Mean 0,316324 0,243822 Mean 0,316324 0,224808

Variance 0,00064 0,002426 Variance 0,00064 0,000633

Observations 20 20 Observations 20 20

Pooled Variance 0,001533 Pooled Variance 0,000637

Hypothesized Mean Difference 0 Hypothesized Mean Difference 0

df 38 df 38

t Stat 5,855828 t Stat 11,46811

P(T<=t) one-tail 4,49E-07 P(T<=t) one-tail 3,32E-14

t Critical one-tail 1,685954 t Critical one-tail 1,685954

P(T<=t) two-tail 8,98E-07 0,01 TRUE P(T<=t) two-tail 6,64E-14 0,01 TRUE

t Critical two-tail 2,024394 t Critical two-tail 2,024394

Source of Variation SS df MS F P-value F crit

Between Groups 0,30329 4 0,075822 39,89053 1,43E-19 2,467494

Within Groups 0,180572 95 0,001901

Total 0,483862 99

t-Test: Two-Sample Assuming Equal Variances t-Test: Two-Sample Assuming Equal Variances

MSG187 MSG189 MSG187 MSG190

Mean 0,282943 0,261187 Mean 0,282943 0,152628

Variance 0,006395 0,001935 Variance 0,006395 0,00081

Observations 20 20 Observations 20 20

Pooled Variance 0,004165 Pooled Variance 0,003603

Hypothesized Mean Difference 0 Hypothesized Mean Difference 0

df 38 df 38

t Stat 1,066017 t Stat 6,865794

P(T<=t) one-tail 0,146571 P(T<=t) one-tail 1,87E-08

t Critical one-tail 1,685954 t Critical one-tail 1,685954

P(T<=t) two-tail 0,293142 0,01 FALSE P(T<=t) two-tail 3,74E-08 0,01 TRUE

t Critical two-tail 2,024394 t Critical two-tail 2,024394

Appendix: 4 ANOVA ad T-test for Work Package 2

59

t-Test: Two-Sample Assuming Equal Variances t-Test: Two-Sample Assuming Equal Variances

MSG186 MSG187 MSG186 MSG189

Mean 0,195624 0,282943 Mean 0,195624 0,261187

Variance 0,000147 0,006395 Variance 0,000147 0,001935

Observations 20 20 Observations 20 20

Pooled Variance 0,003271 Pooled Variance 0,001041

Hypothesized Mean Difference 0 Hypothesized Mean Difference 0

df 38 df 38

t Stat -4,82799 t Stat -6,42575

P(T<=t) one-tail 1,13E-05 P(T<=t) one-tail 7,43E-08

t Critical one-tail 1,685954 t Critical one-tail 1,685954

P(T<=t) two-tail 2,27E-05 0,01 TRUE P(T<=t) two-tail 1,49E-07 0,01 TRUE

t Critical two-tail 2,024394 t Critical two-tail 2,024394

t-Test: Two-Sample Assuming Equal Variances t-Test: Two-Sample Assuming Equal Variances

MSG186 MSG190 MSG186 MSG191

Mean 0,195624 0,152628 Mean 0,195624 0,148837

Variance 0,000147 0,00081 Variance 0,000147 0,000217

Observations 20 20 Observations 20 20

Pooled Variance 0,000478 Pooled Variance 0,000182

Hypothesized Mean Difference 0 Hypothesized Mean Difference 0

df 38 df 38

t Stat 6,217406 t Stat 10,97701

P(T<=t) one-tail 1,43E-07 P(T<=t) one-tail 1,2E-13

t Critical one-tail 1,685954 t Critical one-tail 1,685954

P(T<=t) two-tail 2,87E-07 0,01 TRUE P(T<=t) two-tail 2,4E-13 0,01 TRUE

t Critical two-tail 2,024394 t Critical two-tail 2,024394

t-Test: Two-Sample Assuming Equal Variances t-Test: Two-Sample Assuming Equal Variances

MSG189 MSG190 MSG189 MSG191

Mean 0,261187 0,152628 Mean 0,261187 0,148837

Variance 0,001935 0,00081 Variance 0,001935 0,000217

Observations 20 20 Observations 20 20

Pooled Variance 0,001373 Pooled Variance 0,001076

Hypothesized Mean Difference 0 Hypothesized Mean Difference 0

df 38 df 38

t Stat 9,266135 t Stat 10,83092

P(T<=t) one-tail 1,36E-11 P(T<=t) one-tail 1,77E-13

t Critical one-tail 1,685954 t Critical one-tail 1,685954

P(T<=t) two-tail 2,72E-11 0,01 TRUE P(T<=t) two-tail 3,55E-13 0,01 TRUE

t Critical two-tail 2,024394 t Critical two-tail 2,024394

t-Test: Two-Sample Assuming Equal Variances

MSG187 MSG191

Mean 0,282943 0,148837

Variance 0,006395 0,000217

Observations 20 20

Pooled Variance 0,003306

Hypothesized Mean Difference 0

df 38

t Stat 7,375596

P(T<=t) one-tail 3,84E-09

t Critical one-tail 1,685954

P(T<=t) two-tail 7,68E-09 0,01 TRUE

t Critical two-tail 2,024394

t-Test: Two-Sample Assuming Equal Variances

MSG190 MSG191

Mean 0,152628 0,148837

Variance 0,00081 0,000217

Observations 20 20

Pooled Variance 0,000513

Hypothesized Mean Difference 0

df 38

t Stat 0,529056

P(T<=t) one-tail 0,299922

t Critical one-tail 1,685954

P(T<=t) two-tail 0,599843 0,01 FALSE

t Critical two-tail 2,024394

Appendix: 4 ANOVA ad T-test for Work Package 2

60

f. Core void volume(Vvc)

One way-ANOVA test table

T-test results

Source of Variation SS df MS F P-value F crit

Between Groups 0,941693 4 0,235423 31,96389 7,27E-17 2,467494

Within Groups 0,699702 95 0,007365

Total 1,641396 99

t-Test: Two-Sample Assuming Equal Variances t-Test: Two-Sample Assuming Equal Variances

MSG186 MSG187 MSG186 MSG189

Mean 0,259125 0,391528 Mean 0,259125 0,396416

Variance 0,000307 0,025037 Variance 0,000307 0,009238

Observations 20 20 Observations 20 20

Pooled Variance 0,012672 Pooled Variance 0,004772

Hypothesized Mean Difference 0 Hypothesized Mean Difference 0

df 38 df 38

t Stat -3,71945 t Stat -6,28463

P(T<=t) one-tail 0,000321 P(T<=t) one-tail 1,16E-07

t Critical one-tail 1,685954 t Critical one-tail 1,685954

P(T<=t) two-tail 0,000643 0,01 TRUE P(T<=t) two-tail 2,32E-07 0,01 TRUE

t Critical two-tail 2,024394 t Critical two-tail 2,024394

t-Test: Two-Sample Assuming Equal Variances t-Test: Two-Sample Assuming Equal Variances

MSG187 MSG189 MSG187 MSG190

Mean 0,391528 0,396416 Mean 0,391528 0,186742

Variance 0,025037 0,009238 Variance 0,025037 0,001852

Observations 20 20 Observations 20 20

Pooled Variance 0,017137 Pooled Variance 0,013445

Hypothesized Mean Difference 0 Hypothesized Mean Difference 0

df 38 df 38

t Stat -0,11807 t Stat 5,585042

P(T<=t) one-tail 0,453315 P(T<=t) one-tail 1,06E-06

t Critical one-tail 1,685954 t Critical one-tail 1,685954

P(T<=t) two-tail 0,906631 0,01 FALSE P(T<=t) two-tail 2,11E-06 0,01 TRUE

t Critical two-tail 2,024394 t Critical two-tail 2,024394

t-Test: Two-Sample Assuming Equal Variances

MSG190 MSG191

Mean 0,152628 0,148837

Variance 0,00081 0,000217

Observations 20 20

Pooled Variance 0,000513

Hypothesized Mean Difference 0

df 38

t Stat 0,529056

P(T<=t) one-tail 0,299922

t Critical one-tail 1,685954

P(T<=t) two-tail 0,599843 0,01 FALSE

t Critical two-tail 2,024394

t-Test: Two-Sample Assuming Equal Variances

MSG187 MSG191

Mean 0,282943 0,148837

Variance 0,006395 0,000217

Observations 20 20

Pooled Variance 0,003306

Hypothesized Mean Difference 0

df 38

t Stat 7,375596

P(T<=t) one-tail 3,84E-09

t Critical one-tail 1,685954

P(T<=t) two-tail 7,68E-09 0,01 TRUE

t Critical two-tail 2,024394

Appendix: 4 ANOVA ad T-test for Work Package 2

61

t-Test: Two-Sample Assuming Equal Variances t-Test: Two-Sample Assuming Equal Variances

MSG189 MSG190 MSG189 MSG191

Mean 0,396416 0,186742 Mean 0,396416 0,170617

Variance 0,009238 0,001852 Variance 0,009238 0,000393

Observations 20 20 Observations 20 20

Pooled Variance 0,005545 Pooled Variance 0,004815

Hypothesized Mean Difference 0 Hypothesized Mean Difference 0

df 38 df 38

t Stat 8,904143 t Stat 10,28993

P(T<=t) one-tail 3,88E-11 P(T<=t) one-tail 7,67E-13

t Critical one-tail 1,685954 t Critical one-tail 1,685954

P(T<=t) two-tail 7,75E-11 0,01 TRUE P(T<=t) two-tail 1,53E-12 0,01 TRUE

t Critical two-tail 2,024394 t Critical two-tail 2,024394

t-Test: Two-Sample Assuming Equal Variances t-Test: Two-Sample Assuming Equal Variances

MSG186 MSG190 MSG186 MSG191

Mean 0,259125 0,186742 Mean 0,259125 0,170617

Variance 0,000307 0,001852 Variance 0,000307 0,000393

Observations 20 20 Observations 20 20

Pooled Variance 0,00108 Pooled Variance 0,00035

Hypothesized Mean Difference 0 Hypothesized Mean Difference 0

df 38 df 38

t Stat 6,966392 t Stat 14,96504

P(T<=t) one-tail 1,37E-08 P(T<=t) one-tail 8,13E-18

t Critical one-tail 1,685954 t Critical one-tail 1,685954

P(T<=t) two-tail 2,73E-08 0,01 TRUE P(T<=t) two-tail 1,63E-17 0,01 TRUE

t Critical two-tail 2,024394 t Critical two-tail 2,024394

t-Test: Two-Sample Assuming Equal Variances

MSG190 MSG191

Mean 0,186742 0,170617

Variance 0,001852 0,000393

Observations 20 20

Pooled Variance 0,001123

Hypothesized Mean Difference 0

df 38

t Stat 1,521949

P(T<=t) one-tail 0,06815

t Critical one-tail 1,685954

P(T<=t) two-tail 0,136301 0,01 FALSE

t Critical two-tail 2,024394

t-Test: Two-Sample Assuming Equal Variances

MSG187 MSG191

Mean 0,391528 0,170617

Variance 0,025037 0,000393

Observations 20 20

Pooled Variance 0,012715

Hypothesized Mean Difference 0

df 38

t Stat 6,195305

P(T<=t) one-tail 1,54E-07

t Critical one-tail 1,685954

P(T<=t) two-tail 3,07E-07 0,01 TRUE

t Critical two-tail 2,024394

Appendix.5 Spearman’s rank correlation for WP 1 and WP2

62

Appendix.5 Spearman’s rank correlation for WP 1 and WP2

1. Work Package One

Figure 1.5 Spearman’s Correlation matrix for 3- dimensional profile parameters evaluated

from the data reading taken from the Profilometer for the three variants MSG 157, MSG 158

and MSG 160. Sixty reading in total from three different variants [Work Package one]

l

Figure 1.5 Spearman’s Correlation matrix for WP

WP

1 P

ara

mete

r

Sq

Ssk

Sku

Sp

Sv

Sz

Sa

Sal (s = 0.2)

Str (s = 0.2)

Std (Reference angle =

0°)

Sdq

Sdr

Smr (c = 1 µm under

the highest peak)

Smc (p = 10%)

Sxp (p = 50%, q =

97.5%)

Vm (p = 10%)

Vv (p = 10%)

Vmp (p = 10%)

Vmc (p = 10%, q =

80%)

Vvc (p = 10%, q =

80%)

Vvv (p = 80%)

Spd (pruning = 5%)

Spc (pruning = 5%)

S10z (pruning = 5%)

S5p (pruning = 5%)

S5v (pruning = 5%)

Sda (pruning = 5%)

Sha (pruning = 5%)

Sdv (pruning = 5%)

Shv (pruning = 5%)

Sq

100%

Ssk

-54%

100%

Sku

44%

-78%

100%

Sp

20%

25%

27%

100%

Sv

77%

-84%

74%

7%

100%

Sz

71%

-50%

73%

64%

81%

100%

Sa

83%

-9%

-9%

11%

39%

36%

100%

Sal (s =

0.2

)-7

3%

66%

-71%

-29%

-74%

-74%

-37%

100%

Str (s =

0.2

)-7

1%

86%

-74%

-3%

-90%

-71%

-34%

77%

100%

Std

(Reference angle =

0°)

8%

10%

-6%

7%

-4%

1%

14%

11%

9%

100%

Sdq

96%

-67%

61%

24%

86%

80%

68%

-85%

-82%

0%

100%

Sdr

93%

-29%

16%

22%

57%

56%

93%

-65%

-53%

6%

86%

100%

Sm

r (c = 1

µm und

er the highest peak

)-3

9%

-9%

-9%

-66%

-26%

-59%

-44%

41%

28%

-5%

-39%

-50%

100%

Sm

c (p =

10%

)76%

-3%

-16%

6%

33%

29%

99%

-31%

-28%

14%

62%

90%

-42%

100%

Sxp

(p =

50%

, q =

97.5

%)

76%

-5%

-17%

7%

34%

31%

98%

-28%

-30%

15%

61%

88%

-47%

97%

100%

Vm

(p =

10%

)54%

21%

-10%

44%

13%

36%

64%

-24%

-2%

20%

41%

60%

-47%

56%

58%

100%

Vv (p

= 1

0%

)77%

-2%

-16%

9%

32%

30%

99%

-32%

-27%

15%

62%

91%

-43%

100%

97%

60%

100%

Vm

p (p

= 1

0%

)54%

21%

-10%

44%

13%

36%

64%

-24%

-2%

20%

41%

60%

-47%

56%

58%

100%

60%

100%

Vm

c (p =

10%

, q =

80%

)66%

10%

-30%

1%

18%

15%

96%

-17%

-15%

15%

48%

82%

-38%

98%

95%

56%

98%

56%

100%

Vvc (p

= 1

0%

, q =

80%

)68%

10%

-27%

8%

21%

21%

97%

-22%

-16%

15%

52%

85%

-42%

99%

95%

60%

99%

60%

99%

100%

Vvv (p

= 8

0%

)98%

-58%

40%

12%

78%

67%

82%

-69%

-74%

6%

95%

91%

-38%

77%

79%

43%

77%

43%

66%

67%

100%

Spd (p

runing = 5

%)

2%

41%

-59%

-21%

-46%

-48%

41%

14%

40%

1%

-12%

27%

-3%

48%

43%

16%

47%

16%

56%

54%

1%

100%

Spc (p

runing = 5

%)

7%

6%

43%

82%

16%

61%

-13%

-24%

-9%

5%

18%

2%

-42%

-15%

-15%

15%

-14%

15%

-23%

-16%

1%

-29%

100%

S10z (p

runing = 5

%)

83%

-58%

69%

48%

84%

93%

51%

-82%

-78%

5%

91%

72%

-53%

44%

45%

38%

44%

38%

29%

34%

81%

-29%

46%

100%

S5p (p

runing = 5

%)

28%

15%

32%

94%

16%

67%

18%

-37%

-10%

15%

33%

30%

-66%

13%

15%

48%

16%

48%

7%

14%

20%

-16%

84%

58%

100%

S5v (p

runing = 5

%)

86%

-75%

68%

16%

92%

80%

52%

-81%

-88%

-1%

94%

72%

-33%

46%

47%

24%

46%

24%

32%

34%

87%

-27%

18%

93%

24%

100%

Sda (p

runing = 5

%)

-15%

-16%

63%

48%

21%

44%

-46%

-12%

-16%

-3%

0%

-33%

-1%

-48%

-51%

-23%

-48%

-23%

-55%

-51%

-19%

-62%

72%

23%

44%

8%

100%

Sha (p

runing = 5

%)

1%

-54%

46%

-26%

43%

17%

-29%

-12%

-45%

-14%

9%

-25%

19%

-35%

-30%

-22%

-35%

-22%

-37%

-41%

3%

-63%

-23%

8%

-33%

24%

31%

100%

Sdv (p

runing = 5

%)

8%

-14%

62%

59%

30%

58%

-19%

-27%

-23%

-2%

20%

-5%

-16%

-21%

-26%

-7%

-20%

-7%

-29%

-24%

3%

-49%

77%

40%

56%

23%

94%

13%

100%

Shv (p

runing = 5

%)

21%

-68%

66%

-6%

63%

45%

-19%

-37%

-62%

-13%

32%

-7%

2%

-27%

-21%

-13%

-27%

-13%

-35%

-36%

23%

-68%

0%

38%

-8%

49%

39%

92%

27%

100%

Appendix.5 Spearman’s rank correlation for WP 1 and WP2

63

Functional Parameter (stratified Surfaces)

Highlighting in the correlation table of the functional parameter (stratified Surfaces, Table

1.5). Areal material ratio Smc that is the ratio of the material at specified height c to the

elevation area expressed as percentage, the table 1.5 shows very strong correlation between

the areal material ratio Smc and peak extreme height Sxp, Sxp parameter aimed at

characterizing the upper part of the surface. Therefore, we can choose both Smc and Sxp for

our considerations.

Functional Parameters (Stratified surfaces)

Sm

r (c

= 1

µm

un

der

the

hig

hes

t

pea

k)

Sm

c (p

=

10%

)

Sxp

(p

=

50%

,

q

= 9

6.5

%)

Smr (c = 1 µm under the highest peak) 100%

Smc (p = 10%) -42% 100%

Sxp (p = 50%, q = 96.5%) -47% 96% 100%

Table 1.5 Functional Parameter correlations

Height parameters

Height parameter describes amplitude properties of a surface. It consists of three

subgroups that of average height parameters (i.e. Sa and Sq). That is of extreme parameters

(i.e. Sp, Sz and Sv), and that of Sku/Ssk parameter (i.e. shape of a probability distribution,

where Ssk represents the degree of symmetry of the surface heights about the mean plane, and

Sku is a measure of the sharpness of the height distribution). Sa and Sq show very strong

(0,96) correlation, the extreme parameters, Sz also show strong linear correlation with Sv, and

strong linear correlation with sq and kurtosis Sku, Kurtosis Sku appears strong correlation

with skewness (Ssk) a comparatively large positive Ssk, Say Ssk>1, may indicate the

presence of a few spikes on the surface. Out of height parameters Arithmetic mean height (Sa)

choose for the comparison because of its strong correlations. See table 2.5

Height Parameter

Sq

Ssk

Sk

u

Sp

Sv

Sz

Sa

Sq 100%

Ssk -54% 100%

Sku 44% -78% 100%

Sp 20% 25% 27% 100%

Sv 77% -84% 74% 7% 100%

Sz 71% -50% 73% 64% 81% 100%

Sa 83% -9% -9% 11% 39% 36% 100% Table 2.5 Height parameters

Appendix.5 Spearman’s rank correlation for WP 1 and WP2

64

Function Parameter (Volume)

Another zoom on table 3.5 , visualizaing and summarizng the correlation between Vmp , Vvc,

Vm, Vvc and Vv. the four volume parameters Vmp, Vmc, Vvc and Vvv calculated from two

bearing ratio levels mr1 (material volume and void volume) from the material ratio curve. The

parameter Vvc show hoigh correelation between Vv and Vmc. So we can select that highest

correlated parameters for the study.

Function Parameter

(Volume)

Vm

(p

= 1

0%

)

Vv (

p =

10%

)

Vm

p (

p =

10%

)

Vm

c (p

= 1

0%

,

q =

80%

)

Vvc

(p =

10%

,

q =

80%

)

Vvv (

p =

80%

)

Vm (p = 10%) 100%

Vv (p = 10%) 60% 100%

Vmp (p = 10%) 100% 60% 100%

Vmc (p = 10%, q = 80%) 56% 98% 56% 100%

Vvc (p = 10%, q = 80%) 60% 99% 60% 99% 100%

Vvv (p = 80%) 43% 77% 43% 66% 67% 100% Table 3.5 Functional parameter (volume) correlation

Hybrid parameters

A zoom in the correlation table 4.5, highlighting Parameters Sdq (root mean square gradient)

and Sdr (developed interfacial area ratio) describe the combinantion of height and specing

property. The table results show a very strong corelation (0,99) between Sdr and sdq.

Table 4.5 Hybrid Parameters Correlation

Feature Parameter

A zoom in the correlation table 5.5, highlighting Parameters Sdv (Average volume of dales

connected to the edge.) and S5v (Average value of the heights of the five pits with the largest

global pit height, within the definition area), it shows very strong correlation(0,94 and 0.93)

Hybrid parameters Sdq Sdr

Sdq 100%

Sdr 99% 100%

Appendix.5 Spearman’s rank correlation for WP 1 and WP2

65

Sp

d (

pru

nin

g =

5%

)

Sp

c (p

run

ing =

5%

)

S10z

(pru

nin

g =

5%

)

S5p

(p

run

ing =

5%

)

S5v (

pru

nin

g =

5%

)

Sd

a (

pru

nin

g =

5%

)

Sh

a (

pru

nin

g =

5%

)

Sd

v (

pru

nin

g =

5%

)

Sh

v (

pru

nin

g =

5%

)

Spd (pruning = 5%) 100%

Spc (pruning = 5%) -29% 100%

S10z (pruning = 5%) -29% 46% 100%

S5p (pruning = 5%) -16% 84% 58% 100%

S5v (pruning = 5%) -27% 18% 93% 24% 100%

Sda (pruning = 5%) -62% 72% 23% 44% 8% 100%

Sha (pruning = 5%) -63% -23% 8% -33% 24% 31% 100%

Sdv (pruning = 5%) -49% 77% 40% 56% 23% 94% 13% 100%

Shv (pruning = 5%) 68% 0% 38% -8% 49% 39% 92% 27% 100%

Table 5.5 Feature Parameters Correlation

A zoom in the correlation table 5.5, highlighting Parameters Sdv (Average volume of dales

connected to the edge.) and S5v (Average value of the heights of the five pits with the largest

global pit height, within the definition area), it shows very strong correlation(0,94 and 0.93)

2. Work Package 2

Figure 2.5 Spearman’s Correlation matrix for 3- dimensional profile parameters evaluated

from the data reading taken from the Profilometer for the five variants MSG (186,187, 189,

190 and 191)

Appendix.5 Spearman’s rank correlation for WP 1 and WP2

66

Figure 2.5 Spearman’s Correlation matrix for WP2

WP

2 P

ara

mete

r

Sq

Ssk

Sku

Sp

Sv

Sz

Sa

Sal (s = 0.2)

Str (s = 0.2)

Std (Reference angle =

0°)

Sdq

Sdr

Smr (c = 1 µm under

the highest peak)

Smc (p = 10%)

Sxp (p = 50%, q =

97.5%)

Vm (p = 10%)

Vv (p = 10%)

Vmp (p = 10%)

Vmc (p = 10%, q =

80%)

Vvc (p = 10%, q =

80%)

Vvv (p = 80%)

Spd (pruning = 5%)

Spc (pruning = 5%)

S10z (pruning = 5%)

S5p (pruning = 5%)

S5v (pruning = 5%)

Sda (pruning = 5%)

Sha (pruning = 5%)

Sdv (pruning = 5%)

Shv (pruning = 5%)

Sq

1

Ssk

41%

100%

Sku

-38%

-90%

100%

Sp

69%

47%

-23%

100%

Sv

82%

1%

1%

49%

100%

Sz

87%

27%

-12%

85%

87%

100%

Sa

96%

55%

-52%

63%

74%

79%

100%

Sal (s =

0.2

)-4

9%

-32%

22%

-62%

-54%

-67%

-47%

100%

Str (s =

0.2

)89%

59%

-60%

55%

69%

73%

96%

-49%

100%

Std

(Reference angle =

0°)

92%

42%

-44%

60%

66%

73%

89%

-38%

82%

100%

Sdq

15%

73%

-70%

9%

-14%

-3%

37%

-6%

45%

17%

100%

Sdr

72%

62%

-49%

69%

37%

61%

77%

-31%

72%

72%

42%

100%

Sm

r (c = 1

µm und

er the highest peak

)-5

%-5

%11%

8%

-4%

2%

-8%

-3%

-14%

-14%

-13%

-8%

100%

Sm

c (p =

10%

)98%

38%

-37%

68%

82%

87%

93%

-51%

86%

92%

7%

70%

-4%

100%

Sxp

(p =

50%

, q =

97.5

%)

97%

44%

-44%

66%

76%

83%

93%

-48%

87%

95%

13%

73%

-7%

99%

100%

Vm

(p =

10%

)89%

49%

-40%

63%

62%

73%

92%

-34%

80%

78%

34%

75%

4%

83%

83%

100%

Vv (p

= 1

0%

)92%

59%

-58%

58%

70%

75%

99%

-48%

99%

84%

45%

75%

-11%

88%

89%

86%

100%

Vm

p (p

= 1

0%

)89%

49%

-40%

63%

62%

73%

92%

-34%

80%

78%

34%

75%

4%

83%

83%

100%

86%

100%

Vm

c (p =

10%

, q =

80%

)79%

65%

-64%

48%

60%

63%

91%

-46%

97%

69%

58%

69%

-13%

74%

74%

77%

96%

77%

100%

Vvc (p

= 1

0%

, q =

80%

)85%

63%

-62%

53%

64%

68%

95%

-47%

99%

76%

53%

72%

-12%

81%

81%

82%

99%

82%

99%

100%

Vvv (p

= 8

0%

)99%

38%

-38%

66%

79%

84%

95%

-45%

87%

96%

13%

71%

-7%

98%

98%

87%

89%

87%

75%

81%

100%

Spd (p

runing = 5

%)

47%

71%

-72%

29%

8%

21%

56%

-11%

58%

52%

54%

50%

0%

45%

52%

53%

58%

53%

58%

60%

46%

100%

Spc (p

runing = 5

%)

72%

31%

-14%

84%

55%

80%

63%

-44%

53%

67%

-1%

64%

0%

71%

68%

61%

56%

61%

41%

49%

72%

23%

100%

S10z (p

runing = 5

%)

89%

49%

-41%

75%

74%

87%

85%

-56%

82%

78%

13%

65%

-3%

88%

86%

74%

83%

74%

74%

79%

85%

44%

71%

100%

S5p (p

runing = 5

%)

83%

52%

-36%

89%

57%

84%

78%

-49%

70%

78%

14%

78%

0%

82%

81%

76%

73%

76%

61%

67%

81%

46%

88%

87%

100%

S5v (p

runing = 5

%)

90%

24%

-20%

59%

93%

88%

84%

-58%

81%

76%

-1%

52%

1%

91%

86%

72%

82%

72%

72%

76%

87%

28%

61%

87%

69%

100%

Sda (p

runing = 5

%)

-40%

-56%

72%

1%

-26%

-15%

-49%

15%

-58%

-40%

-41%

-34%

17%

-41%

-44%

-31%

-55%

-31%

-61%

-58%

-39%

-46%

0%

-44%

-21%

-41%

100%

Sha (p

runing = 5

%)

-11%

0%

-4%

-16%

-7%

-13%

-7%

9%

-3%

-10%

-4%

-10%

-3%

-11%

-11%

-10%

-5%

-10%

0%

-2%

-11%

-12%

-21%

-4%

-13%

-10%

-13%

100%

Sdv (p

runing = 5

%)

-18%

-47%

63%

15%

-6%

5%

-29%

-4%

-39%

-21%

-42%

-21%

14%

-20%

-23%

-15%

-35%

-15%

-43%

-40%

-18%

-37%

15%

-24%

-5%

-20%

90%

-17%

100%

Shv (p

runing = 5

%)

-2%

2%

-6%

-9%

6%

-2%

2%

-7%

8%

-5%

-2%

-6%

-10%

-3%

-4%

-5%

6%

-5%

11%

9%

-4%

-13%

-15%

5%

-8%

3%

-18%

86%

6%

100%

Appendix.5 Spearman’s rank correlation for WP 1 and WP2

67

Correlations matrix for work Package two (WP2) profile parameter evaluated using the

parameter reading for work package one, the Table based on parameters evaluated from the

MountainsMap 7 according to the ISO25718 five variants MSG (186,187, 189, 190 and 191)

Correlation for parameters within the same group

Explanations of the Spearman’s correlation factor among parameters. Five groups

detailed as the following

Height parameters

Height parameter describes amplitude properties of a surface. It consists of three subgroups,

that of average height parameters (i.e. Sa and Sq), that of extreme parameters (i.e. Sp, Sz and

Sv), and that of Sku/Ssk parameter (i.e. shape of a probability distribution, where Ssk

represents the degree of symmetry of the surface heights about the mean plane, and Sku is a

measure of the sharpness of the height distribution). Sa and Sq shows very strong (0,96)

correlation, the extreme parameters, Sz also shows strong linear correlation with Sv, and

strong linear correlation with Sq and kurtosis Sku, Kurtosis Sku appears strong correlation

with skewness Ssk a comparatively large positive Ssk, Say Ssk>1, may indicate the presence

of a few spikes on the surface. Out of height parameters Arithmetic mean height (Sa) choose

for the comparison because of its strong correlations.

Height Parameter Sq Ssk Sku Sp Sv Sz Sa

Sq 100%

Ssk 41% 100%

Sku -38% -90% 100%

Sp 69% 47% -23% 100%

Sv 82% 1% 1% 49% 100%

Sz 87% 27% -12% 85% 87% 100%

Sa 96% 55% -52% 63% 74% 79% 100%

Table 6.5 Correlation rank with height parameter

Hybrid parameters

Hybrid parameters Sdq Sdr

Sdq 100%

Sdr 99% 100% Table 7.5 Correlations matrix for Hybrid parameter

A zoom in the correlation table 7.5, highlighting Parameters Sdq (root mean square gradient)

and Sdr (developed interfacial area ratio) describe the combinantion of height and specing

property. The table results show a very strong correlation between Sdr and sdq

Functions and related parameters

Appendix.5 Spearman’s rank correlation for WP 1 and WP2

68

Functional Parameter (stratified Surfaces)

Highlighting in the correlation table 8.5 of the functional parameter (stratified Surfaces). The

areal material ratio Smr, which is the ratio of the material at specified height c to the elevation

area expressed as percentage, the table show a very strong correlation between the areal

material ratio Smr and peak extreme height Sxp, Sxp parameter is aimed at characterizing the

upper part of the surface.

Table 8.5 Correlation matrix in the Functional Parameter

Function Parameter (Volume)

Another zoom on table 9.5, visualizaing and summarizng the correlation between Vmp , Vvc,

Vm, Vvc and Vv. the four volume parameters Vmp, Vmc, Vvc and Vvv calculated from two

bearing ratio levels mr1 (material volume and void volume).

from the material ratio curve.

Function Parameter

(Volume)

Vm

(p

= 1

0%

)

Vv (

p =

10%

)

Vm

p (

p =

10%

)

Vm

c (p

= 1

0%

, q

= 8

0%

)

Vvc

(p =

10%

, q

= 8

0%

)

Vvv (

p =

80%

)

Vm (p = 10%) 100%

Vv (p = 10%) 86% 100%

Vmp (p = 10%) 100% 86% 100%

Vmc (p = 10%, q = 80%) 77% 96% 77% 100%

Vvc (p = 10%, q = 80%) 82% 99% 82% 99% 100%

Vvv (p = 80%) 87% 89% 87% 75% 81% 100% Table 9.5 Correlations matrix with the volume function parameter

Spatial Parameters

Functional Parameter (stratified Surfaces)

Sm

r (c

= 1

µm

un

der

th

e

hig

hes

t p

eak

)

Sm

c (p

= 1

0%

)

Sxp

(p

= 5

0%

, q

= 9

7.5

%)

Smr (c = 1 µm under the

highest peak)

100%

Smc (p = 10%) -49% 100%

Sxp (p = 50%, q = 97.5%) -38% 82% 100%

Appendix.5 Spearman’s rank correlation for WP 1 and WP2

69

Highlighting in the correlation table 10.5 of the Spatial parameters Sal (auto-correlation

length), is defined as the horizontal distance of the ACF (tx, ty) which has the fastest decay to

a specified value s,. The Sal parameter is a quantitative measure of the distance along the

surface by which a texture that is statistically different from that at the original location would

be found. The texture aspect ratio parameter, Str is one of the most important parameters

when characterizing a surface in an aerial manner as it characterizes the isotropy of the

surface. The correlation of two parameters is very weak.

Spatial parameters

Sal

(s =

0.2

)

Str

(s

= 0

.2)

Std

(R

efer

ence

an

gle

= 0

°)

Sal (s = 0.2) 100%

Str (s = 0.2) 42% 100%

Std (Reference angle = 0°) -13% -8% 100%

Table 10.5 Correlation matrix in the Spatial Parameter family

Feature Parameter

A zoom in the correlation table 11.5, highlighting Parameters Sdv (Average volume of

dales connected to the edge.) it shows very strong correlation(0.9

Feature Parameter

Sp

d (

pru

nin

g =

5%

)

Sp

c (p

run

ing =

5%

)

S10z

(pru

nin

g =

5%

)

S5p

(p

run

ing =

5%

)

S5v (

pru

nin

g =

5%

)

Sd

a (

pru

nin

g =

5%

)

Sh

a (

pru

nin

g =

5%

)

Sd

v (

pru

nin

g =

5%

)

Sh

v (

pru

nin

g =

5%

)

Spd (pruning = 5%) 100%

Spc (pruning = 5%) 23% 100%

S10z (pruning = 5%) 44% 71% 100%

S5p (pruning = 5%) 46% 88% 87% 100%

S5v (pruning = 5%) 28% 61% 87% 69% 100%

Sda (pruning = 5%) -46% 0% -44% -21% -41% 100%

Sha (pruning = 5%) -12% -21% -4% -13% -10% -13% 100%

Sdv (pruning = 5%) -37% 15% -24% -5% -20% 90% -17% 100%

Shv (pruning = 5%) -13% -15% 5% -8% 3% -18% 86% 6% 100% Table 11.5 Correlation matrix with the Feature Parameter

Appendix 6: Interferometer Readings

70

Appendix 6: Interferometer Readings Three-dimensional profile parameters measurement with their values 50 X magnification, 20

reading for each variant, profiles filtered by 2nd

degree polynomial cut off.

Work Package 1 Reading

12

34

56

78

910

1112

1314

1516

1718

1920

Sqµm

0,3370,461

0,4290,306

0,4010,402

0,3830,327

0,4010,369

0,3950,348

0,4050,332

0,3590,386

0,3250,369

0,3880,342

Ssk<no unit>

-0,136-4,716

0,453-1,057

-3,242-3,335

-3,855-1,593

-4,119-2,628

-3,666-0,776

-3,309-1,504

-2,738-3,861

-1,197-1,308

-3,982-2,079

Sku<no unit>

7,34954,074

14,7657,265

26,88527,224

41,07114,534

40,19121,379

36,4857,135

29,99013,850

23,88939,490

8,3609,625

40,14816,334

Spµm

2,7663,383

4,3331,210

1,0954,194

1,5593,395

1,1841,990

2,5822,738

1,7021,155

1,6041,224

1,5951,334

1,3461,261

Svµm

3,2238,294

4,5292,522

5,8596,028

7,7454,917

6,1124,951

6,0694,087

8,3205,351

5,7356,348

3,4184,541

6,6834,383

Szµm

5,98911,676

8,8623,732

6,95410,222

9,3048,312

7,2956,941

8,6516,824

10,0226,506

7,3387,572

5,0135,875

8,0295,644

Saµm

0,2520,262

0,2790,229

0,2650,259

0,2520,237

0,2550,253

0,2570,261

0,2660,246

0,2460,253

0,2410,271

0,2500,240

Smr (c = 1 µm

under the highest peak)%

0,1300,070

0,03023,898

43,1450,002

2,5430,004

30,2550,032

0,0180,040

1,11432,176

1,43425,235

1,72116,001

12,92819,329

Smc (p = 10%

)µm

0,3860,389

0,3870,357

0,3980,392

0,3840,366

0,3870,387

0,3940,404

0,4080,388

0,3810,392

0,3760,421

0,3880,375

Sxp (p = 50%, q = 97.5%

)µm

0,6800,686

0,7610,665

0,8010,746

0,7120,670

0,7280,729

0,7180,742

0,7450,673

0,7000,691

0,6700,762

0,6680,674

Sal (s = 0.2)µm

4,2621,647

3,5663,816

6,6112,626

3,3043,881

1,9443,525

2,4154,058

3,5114,596

3,1053,102

3,0545,377

2,1523,051

Str (s = 0.2)<no unit>

0,5130,020

0,5300,546

0,2540,032

0,0400,391

0,0240,113

0,0300,507

0,0700,482

0,0870,038

0,4600,431

0,0260,269

Std (Reference angle = 0°)°

85,24085,248

125,24622,746

117,004105,746

126,9933,747

58,997135,993

100,001145,499

3,508172,745

54,50184,756

46,745177,494

92,263113,738

Sdq<no unit>

0,7111,342

0,9480,633

0,9241,034

0,9600,749

1,1040,916

1,0560,764

1,0590,704

0,8790,959

0,7660,766

1,0410,818

Sdr%

15,63524,334

20,37412,873

16,20818,882

16,62814,665

20,15418,135

19,48717,094

19,30314,055

16,71517,414

16,03215,550

18,84916,174

Vm

(p = 10%)

µm³/µm

²0,017

0,0170,034

0,0120,012

0,0120,012

0,0130,012

0,0120,013

0,0150,013

0,0120,012

0,0120,012

0,0150,012

0,012

Vv (p = 10%

)µm

³/µm²

0,4020,406

0,4210,368

0,4090,404

0,3960,379

0,3990,400

0,4070,418

0,4210,400

0,3920,403

0,3880,435

0,4000,388

Vm

p (p = 10%)

µm³/µm

²0,017

0,0170,034

0,0120,012

0,0120,012

0,0130,012

0,0120,013

0,0150,013

0,0120,012

0,0120,012

0,0150,012

0,012

Vm

c (p = 10%, q = 80%

)µm

³/µm²

0,2750,255

0,2710,249

0,2720,262

0,2630,253

0,2610,264

0,2660,284

0,2740,267

0,2580,265

0,2610,291

0,2600,254

Vvc (p = 10%

, q = 80%)

µm³/µm

²0,359

0,3410,367

0,3240,347

0,3420,341

0,3320,340

0,3440,349

0,3700,361

0,3550,339

0,3490,342

0,3820,346

0,338

Vvv (p = 80%

)µm

³/µm²

0,0440,064

0,0550,044

0,0630,062

0,0550,047

0,0590,056

0,0570,049

0,0600,046

0,0530,054

0,0460,053

0,0540,050

Spd (pruning = 5%)

1/µm²

0,0290,004

0,0100,051

0,0140,004

0,0070,010

0,0150,017

0,0100,022

0,0050,022

0,0160,015

0,0410,027

0,0120,030

Spc (pruning = 5%)

1/µm3,202

5,3436,878

2,1352,698

3,0293,695

4,1842,991

3,1833,851

3,4214,149

2,5102,465

2,6292,781

2,6442,961

2,635

S10z (pruning = 5%)

µm4,344

7,9565,773

2,8855,325

6,3006,293

4,1375,442

5,8306,817

4,3686,732

4,1215,812

5,5524,023

4,1835,927

4,380

S5p (pruning = 5%)

µm1,407

1,4992,564

0,6780,701

1,5480,945

1,4410,775

1,7731,388

1,9511,183

0,7690,997

0,7801,090

0,9930,854

0,784

S5v (pruning = 5%)

µm2,937

6,4583,209

2,2074,624

4,7525,348

2,6964,667

4,0575,429

2,4175,549

3,3524,815

4,7722,933

3,1905,072

3,596

Sda (pruning = 5%)

µm²

21,71743,246

32,89414,963

37,00960,275

55,00640,132

30,68624,895

39,91423,862

44,51130,990

36,30135,986

19,80626,169

38,12523,031

Sha (pruning = 5%)

µm²

33,001186,185

91,84319,618

69,894240,231

161,248100,967

70,43457,070

95,80743,302

182,66743,947

62,33062,772

24,48935,868

81,37933,839

Sdv (pruning = 5%)

µm³

0,7211,698

1,2610,400

1,4272,323

1,7951,508

1,0950,870

1,4430,751

1,6341,012

1,3531,372

0,6120,835

1,4380,743

Shv (pruning = 5%)

µm³

1,2129,217

3,7770,549

2,39011,800

7,3544,227

2,7602,193

4,2641,736

8,4821,552

2,6102,415

0,8381,185

3,0631,146

Parameters

MSG

157

12

34

56

78

910

1112

1314

1516

1718

1920

Sqµm

0,4200,507

0,4260,451

0,6240,606

0,4860,463

0,4710,565

0,9120,418

0,4910,482

0,4240,417

0,3980,435

0,4620,421

0,4940,117

24%

Ssk<no unit>

-0,544-3,641

-0,359-1,687

-2,297-3,987

-0,5330,297

-1,151-2,403

-6,377-0,576

-2,135-3,195

-1,628-1,974

-0,903-1,502

-2,390-1,965

-1,9481,527

-78%

Sku<no unit>

7,35838,425

4,16814,470

29,36238,283

7,28410,074

16,77227,138

71,3475,795

22,62131,595

13,76616,751

8,34914,431

21,08117,467

20,82715,751

76%

Spµm

2,5333,566

1,9532,305

4,6192,600

3,5974,133

3,4164,878

7,8751,742

4,6492,489

1,5471,584

1,7971,588

1,7981,620

3,0141,614

54%

Svµm

4,5609,124

3,1896,657

10,31410,076

4,7373,995

7,7059,096

13,6474,135

6,9877,504

5,9374,668

4,1637,176

6,3866,195

6,8132,638

39%

Szµm

7,09212,691

5,1438,962

14,93312,677

8,3338,128

11,12213,974

21,5225,877

11,6359,993

7,4846,252

5,9608,764

8,1847,815

9,8273,929

40%

Saµm

0,3150,328

0,3310,324

0,3750,373

0,3600,335

0,3280,361

0,4220,317

0,3340,320

0,3070,294

0,2980,317

0,3160,298

0,3330,032

9%

Smr (c = 1 µm

under the highest peak)%

0,1100,002

0,9930,043

0,1000,160

0,0230,073

0,1140,046

0,0103,261

0,0240,015

7,3125,271

1,6496,794

2,1884,488

1,6342,443

150%

Smc (p = 10%

)µm

0,4980,518

0,5310,506

0,5480,571

0,5610,512

0,5040,549

0,6110,504

0,5120,503

0,4840,461

0,4740,503

0,4920,463

0,5150,037

7%

Sxp (p = 50%, q = 97.5%

)µm

0,8050,827

0,8510,906

1,0180,990

0,9420,858

0,8630,979

1,1440,827

0,9090,858

0,8200,759

0,7850,838

0,8320,824

0,8820,093

11%

Sal (s = 0.2)µm

4,4833,080

4,6063,792

2,8912,748

3,8733,657

3,3813,288

1,2524,718

3,2853,307

4,1763,203

4,3154,377

3,5383,594

3,5780,798

22%

Str (s = 0.2)<no unit>

0,6250,038

0,7260,516

0,2890,137

0,6490,606

0,5010,202

0,1260,687

0,3310,122

0,4860,365

0,6360,528

0,1970,291

0,4030,219

54%

Std (Reference angle = 0°)°

119,006110,995

111,24785,247

93,253107,246

84,503100,502

139,99234,502

39,501100,992

119,00285,257

104,49672,997

78,747100,245

98,75284,495

93,54925,031

27%

Sdq<no unit>

0,9271,479

0,8971,186

1,9641,921

1,2191,033

1,1951,686

3,1980,910

1,3811,385

1,0921,107

0,8981,020

1,2681,094

1,3430,538

40%

Sdr%

20,65528,144

22,63926,655

40,77337,438

31,54924,437

27,31634,760

61,26520,536

29,12126,919

23,58822,997

19,41520,994

25,11722,752

28,3549,661

34%

Vm

(p = 10%)

µm³/µm

²0,022

0,0200,019

0,0180,035

0,0220,025

0,0280,025

0,0250,030

0,0200,021

0,0190,017

0,0170,018

0,0190,019

0,0170,022

0,00522%

Vv (p = 10%

)µm

³/µm²

0,5200,538

0,5500,524

0,5820,593

0,5870,540

0,5290,574

0,6400,524

0,5330,521

0,5010,478

0,4920,522

0,5120,480

0,5370,041

8%

Vm

p (p = 10%)

µm³/µm

²0,022

0,0200,019

0,0180,035

0,0220,025

0,0280,025

0,0250,030

0,0200,021

0,0190,017

0,0170,018

0,0190,019

0,0170,022

0,00522%

Vm

c (p = 10%, q = 80%

)µm

³/µm²

0,3400,334

0,3660,341

0,3550,371

0,3840,355

0,3390,362

0,3520,344

0,3440,329

0,3280,311

0,3220,340

0,3290,315

0,3430,019

5%

Vvc (p = 10%

, q = 80%)

µm³/µm

²0,468

0,4700,497

0,4600,496

0,5050,523

0,4830,467

0,4950,507

0,4700,464

0,4540,443

0,4210,440

0,4630,448

0,4210,470

0,0286%

Vvv (p = 80%

)µm

³/µm²

0,0520,069

0,0530,065

0,0870,088

0,0630,056

0,0620,080

0,1340,054

0,0690,068

0,0580,057

0,0520,059

0,0640,059

0,0670,019

28%

Spd (pruning = 5%)

1/µm²

0,0190,004

0,0400,012

0,0050,005

0,0210,018

0,0080,004

0,0020,025

0,0060,008

0,0160,024

0,0260,010

0,0110,015

0,0140,010

70%

Spc (pruning = 5%)

1/µm3,766

4,5133,309

4,6527,390

5,0305,891

5,2504,905

6,72749,040

3,3827,770

3,9803,456

3,0823,275

3,0683,565

3,5686,781

10,049148%

S10z (pruning = 5%)

µm5,329

9,4594,042

7,04911,429

10,5846,074

5,5617,960

10,43419,058

4,5298,340

7,8495,586

5,4214,763

7,0316,800

6,6227,696

3,40044%

S5p (pruning = 5%)

µm1,571

1,6941,201

1,5533,134

1,7192,134

2,2212,393

2,6067,263

1,1392,707

1,4310,991

1,0361,084

1,0641,171

1,2231,967

1,40071%

S5v (pruning = 5%)

µm3,759

7,7642,842

5,4968,295

8,8653,940

3,3395,567

7,82811,795

3,3895,633

6,4184,595

4,3853,679

5,9675,629

5,3995,729

2,25339%

Sda (pruning = 5%)

µm²

19,07325,241

12,01217,832

19,73723,279

13,93919,188

20,49122,256

20,55014,706

21,27923,693

16,09717,226

16,46122,536

22,36018,760

19,3363,510

18%

Sha (pruning = 5%)

µm²

53,311224,642

24,92988,080

188,482188,378

46,78656,014

119,902185,574

256,07539,785

177,296132,669

62,87442,440

39,996106,226

88,97870,341

109,63970,382

64%

Sdv (pruning = 5%)

µm³

0,5830,959

0,3830,598

0,7901,067

0,5270,635

0,7550,865

1,0460,418

0,8060,876

0,5130,552

0,4780,643

0,7310,569

0,6900,201

29%

Shv (pruning = 5%)

µm³

2,33315,472

1,0505,050

13,22413,540

2,7953,248

8,89615,232

30,4711,624

11,5286,536

3,1461,856

1,6465,400

4,6813,601

7,5677,258

96%

Parameters

MSG

158M

EAN

SDRSD

MSG 157 MSG 158

Appendix 6: Interferometer Readings

71

12

34

56

78

910

1112

1314

1516

1718

1920

Sqµm

0,2220,407

0,2490,222

0,2630,244

0,3210,236

0,2480,413

0,2930,302

0,3430,299

0,2530,326

0,2650,270

0,3570,258

0,2900,056

19%

Ssk<no unit>

-0,777-5,068

0,997-1,092

-2,210-2,229

-6,358-1,053

-0,713-7,598

-3,397-2,765

-5,813-4,060

-1,606-6,187

-2,5591,132

-5,538-2,692

-2,9792,478

-83%

Sku<no unit>

4,97567,570

58,2868,478

19,63322,785

79,3277,673

5,49989,302

36,74725,664

70,38243,935

14,90176,304

25,60635,443

74,17624,858

39,57728,063

71%

Spµm

0,8813,808

6,6030,726

0,9091,033

0,9610,846

0,9390,782

0,9971,018

1,0251,253

1,7062,548

1,1074,487

4,0001,370

1,8501,610

87%

Svµm

1,6507,739

3,5942,607

4,2404,629

6,0432,223

1,9768,155

6,1264,852

6,9806,170

4,0496,499

5,0342,631

7,5644,487

4,8622,011

41%

Szµm

2,53111,547

10,1973,333

5,1505,663

7,0043,069

2,9158,937

7,1235,870

8,0047,423

5,7559,046

6,1417,118

11,5635,857

6,7122,664

40%

Saµm

0,1700,223

0,1710,166

0,1880,175

0,1860,178

0,1910,206

0,1990,207

0,2070,191

0,1810,189

0,1840,186

0,1940,177

0,1880,015

8%

Smr (c = 1 µm

under the highest peak)%

73,9690,054

0,00790,986

69,36147,815

62,80677,955

62,83287,356

55,20950,980

52,09312,827

0,1980,021

33,6230,047

0,0294,070

39,11233,659

86%

Smc (p = 10%

)µm

0,2640,331

0,2680,259

0,2880,271

0,2820,277

0,2940,305

0,3070,316

0,3100,281

0,2740,280

0,2870,280

0,2890,275

0,2870,018

6%

Sxp (p = 50%, q = 97.5%

)µm

0,5090,620

0,4560,464

0,5320,491

0,4890,486

0,5140,525

0,5480,582

0,5710,553

0,5400,544

0,5260,503

0,5540,485

0,5250,041

8%

Sal (s = 0.2)µm

6,5171,607

4,7086,155

5,7625,276

2,4785,131

6,5571,063

5,1596,527

3,1033,594

5,5612,711

4,6934,327

1,6383,854

4,3211,742

40%

Str (s = 0.2)<no unit>

0,7340,020

0,5100,540

0,4750,402

0,0300,631

0,5440,013

0,3350,213

0,0380,044

0,4930,033

0,4280,525

0,0200,209

0,3120,245

78%

Std (Reference angle = 0°)°

93,51595,245

93,50084,252

87,00193,259

84,75484,506

94,00187,484

80,24584,748

74,99385,249

93,99493,752

85,75593,512

93,00286,001

88,4385,582

6%

Sdq<no unit>

0,3321,162

0,5610,378

0,5030,470

0,8190,412

0,4011,173

0,6330,634

0,8500,730

0,4540,842

0,5610,517

1,0220,557

0,6510,253

39%

Sdr%

3,88415,658

5,8034,410

5,8635,172

8,6335,446

5,10414,064

7,6447,135

10,1398,256

5,1939,591

7,0256,823

12,5247,403

7,7883,213

41%

Vm

(p = 10%)

µm³/µm

²0,008

0,0150,011

0,0090,009

0,0090,009

0,0090,010

0,0090,010

0,0110,009

0,0110,010

0,0080,010

0,0130,011

0,0090,010

0,00217%

Vv (p = 10%

)µm

³/µm²

0,2720,346

0,2790,268

0,2970,280

0,2910,286

0,3040,314

0,3160,327

0,3190,292

0,2850,288

0,2970,293

0,3000,284

0,2970,019

7%

Vm

p (p = 10%)

µm³/µm

²0,008

0,0150,011

0,0090,009

0,0090,009

0,0090,010

0,0090,010

0,0110,009

0,0110,010

0,0080,010

0,0130,011

0,0090,010

0,00217%

Vm

c (p = 10%, q = 80%

)µm

³/µm²

0,1880,213

0,1800,178

0,2030,188

0,1880,195

0,2140,195

0,2100,218

0,2130,196

0,1920,188

0,1950,196

0,1840,185

0,1960,012

6%

Vvc (p = 10%

, q = 80%)

µm³/µm

²0,240

0,2900,247

0,2370,259

0,2460,247

0,2530,271

0,2570,274

0,2840,271

0,2480,247

0,2410,258

0,2580,248

0,2480,256

0,0156%

Vvv (p = 80%

)µm

³/µm²

0,0320,057

0,0320,032

0,0380,035

0,0440,033

0,0330,058

0,0420,044

0,0490,044

0,0380,047

0,0390,035

0,0520,037

0,0410,008

20%

Spd (pruning = 5%)

1/µm²

0,0140,002

0,0010,010

0,0040,002

0,0010,019

0,0190,001

0,0030,002

0,0020,001

0,0040,002

0,0050,004

0,0010,005

0,0050,006

111%

Spc (pruning = 5%)

1/µm2,168

10,70324,647

1,7602,094

1,9802,192

1,8911,733

2,6432,985

2,6313,094

3,2853,616

6,4352,887

4,17816,170

2,4514,977

5,827117%

S10z (pruning = 5%)

µm1,758

7,5345,221

2,0922,767

3,3074,364

2,0992,051

6,7324,344

3,5664,353

5,4732,444

6,0663,639

4,0858,239

3,1314,163

1,88245%

S5p (pruning = 5%)

µm0,542

1,7893,068

0,5180,603

0,6310,624

0,5420,612

0,6560,746

0,5690,653

0,7330,618

1,6990,834

1,7082,472

0,8691,024

0,72771%

S5v (pruning = 5%)

µm1,216

5,7452,153

1,5742,164

2,6763,740

1,5571,439

6,0763,598

2,9973,700

4,7401,825

4,3682,805

2,3775,767

2,2623,139

1,52549%

Sda (pruning = 5%)

µm²

36,88769,136

230,47442,762

73,16384,437

110,73830,426

32,982106,086

86,14570,748

89,56374,374

70,74991,018

56,76063,964

93,49248,345

78,11242,754

55%

Sha (pruning = 5%)

µm²

65,019181,772

6,90898,104

219,891315,186

375,39849,562

50,397292,407

291,381369,921

424,570499,652

222,134209,246

151,553192,666

103,270188,779

215,391135,601

63%

Sdv (pruning = 5%)

µm³

0,4001,462

6,7030,671

1,3461,792

2,7490,531

0,5192,950

2,2720,980

2,0691,475

1,2711,937

0,9921,491

2,4471,013

1,7531,380

79%

Shv (pruning = 5%)

µm³

1,15312,509

1,9412,077

7,5409,652

13,3730,937

0,98013,771

13,80510,992

15,15521,455

6,88610,568

5,2866,503

9,6546,715

8,5485,591

65%

Parameters

MSG

160M

EAN

SDRSD

MSG 160

Appendix 6: Interferometer Readings

72

#1

23

45

67

89

1011

1213

1415

1617

1819

20

Sqµ

m0,331

0,3940,326

0,3710,409

0,5020,555

0,2940,424

0,4000,343

0,3880,441

0,3290,504

0,4790,306

0,3140,281

0,3140,385

0,07920%

Ssk<n

o u

nit>

-5,256-6,763

-4,442-4,595

-6,638-7,060

-6,602-3,568

-6,668-6,785

-4,777-6,268

-6,723-5,528

-5,557-7,837

-3,789-4,958

-3,042-4,706

-5,5781,320

-24%

Sku<n

o u

nit>

48,11577,760

38,15640,683

72,14771,421

69,19328,886

65,51276,634

43,60959,739

75,43658,206

56,07188,767

31,49048,790

23,85343,960

55,92118,450

33%

Spµ

m0,763

1,1330,786

1,1090,902

1,7021,664

0,7860,792

0,8210,822

1,0920,937

0,9072,706

0,8190,823

0,6850,880

0,8581,049

0,47645%

Svµ

m5,177

8,5524,566

6,2437,439

9,0589,210

3,8337,016

7,9645,668

6,2928,521

7,1787,853

8,5564,210

5,7923,451

5,0966,584

1,81027%

Szµ

m5,940

9,6855,352

7,3528,340

10,76110,874

4,6197,808

8,7846,490

7,3849,458

8,08510,559

9,3755,033

6,4774,331

5,9547,633

2,07627%

Saµ

m0,186

0,2080,194

0,2190,212

0,2330,260

0,1880,212

0,2110,204

0,1970,232

0,1890,233

0,2180,192

0,1860,187

0,1850,207

0,02110%

Smr (c = 1 µ

m u

nd

er th

e h

ighe

st pe

ak)%

90,11233,674

86,36938,426

74,6040,759

3,10185,801

86,54682,847

82,04640,166

66,63572,965

0,41285,808

82,69893,403

74,71780,073

63,05831,610

50%

Smc (p

= 10%)

µm

0,2820,308

0,2960,327

0,3090,333

0,3580,287

0,3100,316

0,3080,288

0,3510,289

0,3100,309

0,2880,284

0,2830,277

0,3060,023

7%

Sxp (p

= 50%, q

= 97.5%)

µm

0,5010,563

0,5320,597

0,6220,612

0,6910,499

0,5400,564

0,5590,521

0,5940,533

0,6130,652

0,5520,501

0,5100,528

0,5640,054

10%

Sal (s = 0.2)µ

m2,597

1,5302,240

2,8631,216

0,9401,109

3,1481,103

1,1162,675

1,1151,459

2,3751,248

0,9224,381

3,2314,710

3,0032,149

1,15454%

Str (s = 0.2)<n

o u

nit>

0,0320,019

0,0270,035

0,0150,012

0,0140,039

0,0140,014

0,0330,014

0,0180,029

0,0150,011

0,0540,040

0,0580,037

0,0260,014

54%

Std (R

efe

ren

ce an

gle = 0°)

°103,006

94,50785,005

84,25178,754

113,50153,006

84,00594,749

95,746114,496

100,75278,758

107,501121,252

81,74994,757

104,751107,251

95,00594,640

15,74717%

Sdq

<no

un

it>0,890

1,1420,862

0,9961,193

1,5031,692

0,7171,209

1,1690,903

1,1411,307

0,9941,529

1,5320,747

0,8240,642

0,8221,091

0,30328%

Sdr

%11,133

14,83211,559

13,79316,626

20,06723,967

9,60315,656

15,60512,363

15,16817,945

13,36320,813

21,7489,142

10,3678,170

10,58814,625

4,49431%

Vm

(p = 10%

)µ

m³/µ

m²

0,0090,010

0,0090,010

0,0090,013

0,0240,009

0,0090,009

0,0090,009

0,0100,009

0,0210,008

0,0090,009

0,0090,010

0,0110,004

39%

Vv (p

= 10%)

µm

³/µm

²0,291

0,3180,306

0,3370,319

0,3460,382

0,2960,318

0,3250,316

0,2970,361

0,2980,331

0,3170,297

0,2930,292

0,2870,316

0,0258%

Vm

p (p

= 10%)

µm

³/µm

²0,009

0,0100,009

0,0100,009

0,0130,024

0,0090,009

0,0090,009

0,0090,010

0,0090,021

0,0080,009

0,0090,009

0,0100,011

0,00439%

Vm

c (p = 10%

, q = 80%

)µ

m³/µ

m²

0,1790,197

0,1930,215

0,1960,201

0,2170,194

0,1970,202

0,2020,181

0,2220,183

0,1940,189

0,1910,185

0,1940,179

0,1960,012

6%

Vvc (p

= 10%, q

= 80%)

µm

³/µm

²0,242

0,2600,257

0,2800,255

0,2720,301

0,2530,257

0,2660,264

0,2390,298

0,2490,258

0,2440,250

0,2470,250

0,2390,259

0,0187%

Vvv (p

= 80%)

µm

³/µm

²0,049

0,0570,049

0,0570,063

0,0740,080

0,0430,062

0,0580,052

0,0580,063

0,0490,073

0,0730,047

0,0460,042

0,0480,057

0,01120%

Spd

(pru

nin

g = 5%)

1/µm

²0,002

0,0010,005

0,0010,001

0,0000,001

0,0080,002

0,0010,003

0,0020,001

0,0020,001

0,0010,004

0,0020,007

0,0030,002

0,00290%

Spc (p

run

ing = 5%

)1/µ

m1,469

3,4161,607

1,5772,614

9,9808,232

1,3582,467

3,5552,569

2,4713,966

4,0174,917

3,5482,031

2,2871,583

0,9823,232

2,28171%

S10z (pru

nin

g = 5%)

µm

4,9416,758

4,5465,480

6,6898,689

8,5743,890

6,7716,860

5,4866,195

7,6296,090

7,9958,494

3,9924,714

3,2944,495

6,0791,662

27%

S5p (p

run

ing = 5%

)µ

m0,574

0,7550,586

0,6360,675

0,9470,920

0,5120,628

0,6680,648

0,7650,733

0,6810,934

0,6050,587

0,5730,516

0,6280,679

0,13019%

S5v (pru

nin

g = 5%)

µm

4,3686,003

3,9604,844

6,0147,741

7,6543,377

6,1426,192

4,8385,429

6,8955,409

7,0627,888

3,4064,141

2,7783,868

5,4011,561

29%

Sda (p

run

ing = 5%

)µ

m²

54,86355,394

41,87144,903

43,96870,543

49,19332,790

70,85258,291

45,29348,324

49,69737,358

51,67834,702

52,77749,045

37,06045,204

48,69010,234

21%

Sha (p

run

ing = 5%

)µ

m²

451,776508,631

202,325626,965

778,998268,587

1713,598116,406

420,9601007,261

277,963797,761

1222,836666,085

7,7131036,910

256,257372,695

140,305329,440

560,174429,343

77%

Sdv (p

run

ing = 5%

)µ

m³

1,0471,132

0,8581,074

1,0561,849

1,3160,694

1,6741,405

1,0350,957

1,1130,719

1,0740,900

0,9260,978

0,6321,009

1,0720,302

28%

PA

RA

METER

SM

SG186

MEA

NSD

RSD

#1

23

45

67

89

1011

1213

1415

1617

1819

20

Sqµ

m0,658

0,5130,515

0,5210,534

0,4620,469

0,3670,412

2,0270,406

0,4380,380

0,4660,450

0,4890,384

0,3680,676

0,4140,547

0,35865%

Ssk<n

o u

nit>

-0,890-6,237

-5,508-4,528

-5,186-5,339

-5,229-2,825

-4,569-0,783

-4,647-4,638

-4,102-4,704

-4,644-5,798

-3,466-3,585

-4,534-3,687

-4,2451,426

-34%

Sku<n

o u

nit>

54,18463,127

52,46741,156

53,22954,108

53,67824,356

46,55313,726

49,35742,983

45,82843,798

42,66662,527

29,48232,932

47,64931,094

44,24512,627

29%

Spµ

m7,597

1,3431,245

1,9691,997

1,1541,124

1,1142,392

10,4871,177

1,0831,274

1,2851,253

1,2751,124

1,0244,986

1,1582,303

2,512109%

Svµ

m6,962

9,5068,312

9,3709,049

9,4207,509

4,9777,621

16,4517,861

7,4277,020

8,1627,932

9,2725,776

6,84110,317

5,4108,260

2,39929%

Szµ

m14,559

10,8499,557

11,33911,045

10,5748,633

6,09110,013

26,9379,039

8,5108,294

9,4469,185

10,5466,900

7,86615,303

6,56810,563

4,49643%

Saµ

m0,307

0,2700,287

0,3010,301

0,2660,274

0,2490,253

1,0430,252

0,2610,243

0,2750,268

0,2740,248

0,2390,331

0,2620,310

0,17456%

Smr (c = 1 µ

m u

nd

er th

e h

ighe

st pe

ak)%

0,07714,273

26,1000,719

0,59735,409

39,63138,182

0,0010,085

30,73843,412

17,92020,678

23,27421,486

37,17451,138

0,10333,272

21,71416,896

78%

Smc (p

= 10%)

µm

0,4110,399

0,4300,450

0,4440,403

0,4130,382

0,3901,120

0,3820,403

0,3780,426

0,4090,424

0,3830,368

0,4630,405

0,4440,161

36%

Sxp (p

= 50%, q

= 97.5%)

µm

0,7460,703

0,7410,799

0,8460,699

0,7680,663

0,6784,226

0,6690,652

0,6370,708

0,6890,706

0,6760,624

0,8660,661

0,8880,788

89%

Sal (s = 0.2)µ

m2,040

1,2962,593

3,3473,114

2,7683,378

4,5813,764

6,3283,719

3,0184,198

3,3913,390

2,5084,228

3,8792,089

3,7753,370

1,07532%

Str (s = 0.2)<n

o u

nit>

0,3920,016

0,0320,041

0,0380,034

0,0410,056

0,0460,635

0,0460,037

0,0510,041

0,0410,031

0,0520,047

0,0260,046

0,0870,151

173%

Std (R

efe

ren

ce an

gle = 0°)

°106,000

100,99884,252

98,01070,995

94,75571,998

95,50293,497

125,75483,999

85,99484,262

59,00439,753

95,49358,994

85,24995,494

84,76285,738

18,89322%

Sdq

<no

un

it>1,943

1,6031,490

1,5721,678

1,3341,373

0,8651,046

6,3131,075

1,2450,974

1,3671,277

1,4340,980

0,9482,324

1,0991,597

1,16773%

Sdr

%26,805

23,44922,053

25,12326,882

20,32021,257

12,77015,081

143,17714,882

18,16613,737

21,08318,636

20,97814,552

13,77939,578

16,34026,432

28,188107%

Vm

(p = 10%

)µ

m³/µ

m²

0,0360,012

0,0130,018

0,0190,012

0,0130,013

0,0120,278

0,0120,013

0,0140,013

0,0130,013

0,0120,012

0,0290,013

0,0280,059

207%

Vv (p

= 10%)

µm

³/µm

²0,447

0,4110,443

0,4680,463

0,4150,426

0,3950,402

1,3980,394

0,4160,392

0,4400,422

0,4380,395

0,3790,492

0,4190,473

0,22046%

Vm

p (p

= 10%)

µm

³/µm

²0,036

0,0120,013

0,0180,019

0,0120,013

0,0130,012

0,2780,012

0,0130,014

0,0130,013

0,0130,012

0,0120,029

0,0130,028

0,059207%

Vm

c (p = 10%

, q = 80%

)µ

m³/µ

m²

0,2660,255

0,2770,288

0,2840,261

0,2700,262

0,2560,619

0,2570,259

0,2500,271

0,2650,266

0,2550,248

0,2820,268

0,2830,080

28%

Vvc (p

= 10%, q

= 80%)

µm

³/µm

²0,365

0,3350,367

0,3910,383

0,3470,355

0,3420,342

1,0590,336

0,3530,340

0,3720,356

0,3680,338

0,3270,395

0,3600,392

0,15840%

Vvv (p

= 80%)

µm

³/µm

²0,082

0,0760,076

0,0770,080

0,0680,070

0,0520,060

0,3390,058

0,0630,052

0,0680,066

0,0700,057

0,0530,098

0,0590,081

0,06276%

Spd

(pru

nin

g = 5%)

1/µm

²0,002

0,0010,002

0,0010,002

0,0020,003

0,0070,001

0,0080,002

0,0030,003

0,0020,002

0,0010,004

0,0040,001

0,0050,003

0,00272%

Spc (p

run

ing = 5%

)1/µ

m48,743

3,5312,239

4,9122,431

3,7921,197

2,2662,402

80,2134,053

3,4662,818

4,0651,976

1,1901,665

2,18227,839

1,70210,134

20,097198%

S10z (pru

nin

g = 5%)

µm

14,5889,301

7,3008,307

9,0437,556

7,8714,442

6,06928,162

6,8656,686

7,4567,598

7,1687,638

5,1345,263

11,0145,776

8,6625,105

59%

S5p (p

run

ing = 5%

)µ

m5,300

0,9950,922

1,1531,168

0,8760,803

0,7511,147

10,9611,000

0,8260,950

0,9420,951

0,9230,827

0,7131,951

0,8481,700

2,397141%

S5v (pru

nin

g = 5%)

µm

9,2888,306

6,3797,154

7,8766,680

7,0683,691

4,92317,201

5,8655,860

6,5066,656

6,2176,715

4,3074,550

9,0634,928

6,9622,837

41%

Sda (p

run

ing = 5%

)µ

m²

50,61949,109

47,39734,460

28,05441,885

30,94538,117

70,55315,100

67,44945,181

48,76534,749

48,15042,363

39,98748,895

24,91738,605

42,26513,037

31%

Sha (p

run

ing = 5%

)µ

m²

548,839616,469

509,7541049,295

659,959549,906

303,951152,874

644,29816,701

572,324378,373

413,258641,377

418,181819,603

198,706272,022

487,552213,950

473,370243,451

51%

Sdv (p

run

ing = 5%

)µ

m³

1,3901,355

1,1710,899

0,7721,036

0,7860,815

1,5951,318

1,5921,045

1,1020,862

1,1731,071

0,8701,175

0,8310,832

1,0840,260

24%

PA

RA

METER

SM

SG187

RSD

MEA

NSD

MSG 186 MSG 187 Work Package 2 Reading

Appendix 6: Interferometer Readings

73

#1

23

45

67

89

1011

1213

1415

1617

1819

20

Sqµm

0,6900,546

0,4890,584

0,5490,775

0,7510,780

0,9661,461

0,9570,621

0,8550,838

0,7631,166

0,7870,812

0,7130,695

0,7900,225

29%

Ssk<no unit>

-5,229-6,834

-7,110-6,254

-6,518-4,673

-6,544-4,298

-4,517-4,611

-2,296-3,186

-4,209-4,643

-5,212-2,371

-5,244-2,820

-6,579-6,682

-4,9921,514

-30%

Sku<no unit>

41,72567,113

69,94970,148

68,85744,043

72,95136,502

35,40029,937

29,83224,681

38,50437,397

43,22629,400

47,07526,992

61,65761,417

46,84016,693

36%

Spµm

1,9791,485

1,0885,112

2,8786,177

2,6252,971

4,8245,697

8,3547,766

3,4753,354

3,5699,432

4,7173,629

2,1072,253

4,1752,326

56%

Svµm

8,8769,203

8,0168,240

9,8769,841

12,7219,551

10,11412,805

10,7507,022

12,13911,767

9,87712,184

11,3299,671

12,72011,524

10,4111,695

16%

Szµm

10,85410,688

9,10513,352

12,75416,018

15,34612,522

14,93818,501

19,10414,788

15,61415,121

13,44721,617

16,04613,301

14,82613,777

14,5862,933

20%

Saµm

0,3340,253

0,2250,241

0,2460,401

0,3240,358

0,4250,598

0,4290,362

0,4000,401

0,3460,475

0,3530,437

0,3170,303

0,3610,090

25%

Smr (c = 1 µm

under the highest peak)%

1,1333,547

43,9710,075

0,2200,003

0,9580,760

0,0300,006

0,0130,002

0,4510,352

0,1630,074

0,0310,433

0,1620,022

2,6209,766

373%

Smc (p = 10%

)µm

0,4950,358

0,3240,323

0,3420,664

0,4170,469

0,5670,736

0,4640,562

0,5260,561

0,4710,535

0,4550,598

0,4230,413

0,4850,111

23%

Sxp (p = 50%, q = 97.5%

)µm

1,3790,823

0,5950,602

0,6341,009

0,9281,221

2,0604,889

1,5111,506

1,3711,716

1,4782,942

1,4361,623

1,4281,315

1,5230,959

63%

Sal (s = 0.2)µm

1,3830,911

0,9560,856

0,9914,457

1,8741,147

0,9961,257

2,3573,249

1,2781,606

1,3311,332

1,3536,120

0,8781,016

1,7671,360

77%

Str (s = 0.2)<no unit>

0,0170,011

0,0120,010

0,0120,151

0,0230,089

0,3520,276

0,3080,538

0,0160,105

0,1300,346

0,0170,420

0,0110,012

0,1430,168

117%

Std (Reference angle = 0°)°

121,00479,252

84,50586,251

105,49540,754

118,499100,006

140,9932,755

31,00094,758

145,25725,998

78,75194,498

176,99760,992

73,25178,753

86,98842,517

49%

Sdq<no unit>

2,6681,742

1,4441,839

1,7202,220

2,2702,952

3,8925,318

3,3812,215

3,3453,259

2,8884,241

2,9192,850

2,6462,395

2,8100,938

33%

Sdr%

52,94327,888

20,15425,114

24,46533,803

33,53858,601

87,216126,072

70,63349,132

69,49668,538

55,99791,376

55,24160,056

50,29442,647

55,16026,145

47%

Vm

(p = 10%)

µm³/µm

²0,022

0,0120,010

0,0170,018

0,0370,041

0,0550,056

0,0710,088

0,0210,064

0,0370,029

0,0680,036

0,0670,016

0,0140,039

0,02461%

Vv (p = 10%

)µm

³/µm²

0,5180,370

0,3340,341

0,3600,702

0,4580,524

0,6230,806

0,5530,584

0,5900,598

0,5000,603

0,4910,664

0,4390,427

0,5240,127

24%

Vm

p (p = 10%)

µm³/µm

²0,022

0,0120,010

0,0170,018

0,0370,041

0,0550,056

0,0710,088

0,0210,064

0,0370,029

0,0680,036

0,0670,016

0,0140,039

0,02461%

Vm

c (p = 10%, q = 80%

)µm

³/µm²

0,2650,212

0,1950,192

0,2040,341

0,2370,251

0,2790,294

0,2790,318

0,2820,298

0,2490,273

0,2520,339

0,2360,227

0,2610,044

17%

Vvc (p = 10%

, q = 80%)

µm³/µm

²0,403

0,2850,261

0,2570,281

0,5890,346

0,4060,465

0,5300,409

0,4790,461

0,4560,370

0,4060,361

0,5360,316

0,3110,396

0,09624%

Vvv (p = 80%

)µm

³/µm²

0,1150,085

0,0730,083

0,0790,113

0,1120,119

0,1580,277

0,1440,104

0,1290,142

0,1290,197

0,1300,128

0,1230,116

0,1280,045

35%

Spd (pruning = 5%)

1/µm²

0,0020,001

0,0020,001

0,0000,001

0,0010,005

0,0070,006

0,0040,003

0,0050,001

0,0020,005

0,0020,002

0,0010,001

0,0030,002

75%

Spc (pruning = 5%)

1/µm11,514

9,6193,967

50,8568,181

49,53078,393

12,97232,265

66,01039,626

45,03722,749

26,60137,861

98,58713,109

24,03314,003

17,52633,122

25,49277%

S10z (pruning = 5%)

µm10,168

8,8278,028

11,1139,177

13,06112,859

11,46915,474

18,95615,030

10,95613,542

13,47612,310

23,72512,406

11,24012,273

12,24812,817

3,55828%

S5p (pruning = 5%)

µm1,791

0,9350,788

4,4231,247

4,2052,864

2,2085,828

5,6225,923

4,9512,616

3,5803,105

12,0092,089

2,2221,490

2,1643,503

2,57674%

S5v (pruning = 5%)

µm8,377

7,8917,240

6,6907,931

8,8569,995

9,2619,646

13,3349,107

6,00510,925

9,8959,205

11,71610,317

9,01810,783

10,0849,314

1,72419%

Sda (pruning = 5%)

µm²

14,36632,396

59,49275,992

51,46242,152

42,22918,422

13,44113,780

16,11611,215

15,40913,104

17,03414,821

18,51811,400

17,45521,816

26,03118,488

71%

Sha (pruning = 5%)

µm²

369,356652,953

468,59190,233

619,196224,511

0,744117,024

105,62487,184

66,130241,025

61,048612,522

184,9319,093

245,152359,161

388,365213,966

255,841206,350

81%

Sdv (pruning = 5%)

µm³

0,7061,142

1,8532,098

1,4961,324

1,4811,062

1,0521,301

1,0040,565

0,9900,871

0,8641,049

1,0440,679

0,9981,025

1,1300,378

33%

Shv (pruning = 5%)

µm³

24,79228,227

13,1325,583

30,60536,607

0,2045,439

7,6918,826

5,14221,443

4,07148,602

12,5163,390

9,34418,122

29,17312,165

16,25413,024

80%

PARA

METERS

MSG

189

MEA

NSD

RSD

#1

23

45

67

89

1011

1213

1415

1617

1819

20

Sqµ

m0,000

0,0000,000

0,0000,000

0,3370,385

0,4210,316

0,2970,382

0,2640,504

0,3270,341

0,3910,249

0,2900,391

0,2530,257

0,16464%

Ssk<n

o u

nit>

-3,024-8,386

-8,336-5,812

-6,856-6,185

-8,703-7,724

-7,147-7,963

-7,694-5,388

-4,993-7,979

-4,294-8,778

-6,038-6,510

-7,801-5,012

-6,7311,603

-24%

Sku<n

o u

nit>

35,349104,585

109,04057,941

73,11464,898

109,27983,667

84,568103,642

81,60649,741

80,66699,803

73,303108,034

57,06468,984

89,44945,997

79,03622,560

29%

Spµ

m2,798

1,4404,536

0,9490,715

1,8560,836

1,9130,510

2,0050,909

0,8924,482

0,8038,028

0,8220,578

1,3681,778

0,6191,892

1,85898%

Svµ

m3,310

9,9708,105

5,1536,763

6,8938,467

7,2587,469

6,7826,928

4,76610,412

7,3715,522

8,2193,730

6,1857,974

4,0906,768

1,91628%

Szµ

m6,109

11,41012,640

6,1027,478

8,7499,303

9,1717,979

8,7867,838

5,65814,894

8,17413,550

9,0404,307

7,5529,752

4,7098,660

2,79932%

Saµ

m0,131

0,1950,171

0,1550,190

0,1720,173

0,1660,166

0,1550,172

0,1510,197

0,1630,167

0,1740,134

0,1540,189

0,1490,166

0,01811%

Smr (c = 1 µ

m u

nd

er th

e h

ighe

st pe

ak)%

0,0521,678

0,00071,878

91,5790,280

86,5470,535

97,7720,001

79,45180,371

0,21288,597

0,00287,843

97,1030,265

0,01996,381

44,02845,244

103%

Smc (p

= 10%)

µm

0,1710,247

0,2200,210

0,2650,225

0,2290,201

0,2280,214

0,2230,197

0,2250,225

0,2170,227

0,1670,204

0,2520,200

0,2170,024

11%

Sxp (p

= 50%, q

= 97.5%)

µm

0,4660,581

0,5160,511

0,5720,525

0,5190,489

0,5200,484

0,5210,511

0,5250,513

0,5230,546

0,5080,508

0,5870,494

0,5210,031

6%

Sal (s = 0.2)µ

m3,379

0,8930,979

1,4671,155

1,1000,899

0,8661,154

1,1370,805

2,4031,509

0,9211,132

0,8902,074

1,5111,081

2,4251,389

0,67749%

Str (s = 0.2)<n

o u

nit>

0,0410,011

0,0120,018

0,0140,013

0,0110,011

0,0140,014

0,0100,029

0,0180,011

0,0140,011

0,0250,018

0,0130,030

0,0170,008

49%

Std (R

efe

ren

ce an

gle = 0°)

°106,254

129,00084,751

48,00250,750

125,499126,252

126,74848,748

71,996125,252

49,506133,999

60,248133,008

121,24578,752

53,752109,252

126,99895,501

34,16136%

Sdq

<no

un

it>0,526

1,3621,191

0,7210,993

0,9231,117

1,2220,840

0,7961,122

0,6661,612

0,9230,978

1,1220,644

0,7591,108

0,6260,962

0,27529%

Sdr

%5,861

17,31613,648

8,27612,113

11,08112,610

13,6409,912

8,79412,913

7,69220,981

10,58811,322

12,7616,901

8,89314,024

7,22911,328

3,68233%

Vm

(p = 10%

)µ

m³/µ

m²

0,0060,011

0,0070,006

0,0060,010

0,0080,013

0,0050,005

0,0090,005

0,0260,006

0,0080,005

0,0040,004

0,0050,005

0,0080,005

65%

Vv (p

= 10%)

µm

³/µm

²0,177

0,2580,227

0,2150,271

0,2340,236

0,2140,233

0,2180,232

0,2010,251

0,2310,225

0,2320,171

0,2080,257

0,2060,225

0,02511%

Vm

p (p

= 10%)

µm

³/µm

²0,006

0,0110,007

0,0060,006

0,0100,008

0,0130,005

0,0050,009

0,0050,026

0,0060,008

0,0050,004

0,0040,005

0,0050,008

0,00565%

Vm

c (p = 10%

, q = 80%

)µ

m³/µ

m²

0,1270,165

0,1390,149

0,1820,157

0,1500,125

0,1590,148

0,1460,147

0,1480,151

0,1510,150

0,1210,144

0,1720,146

0,1490,015

10%

Vvc (p

= 10%, q

= 80%)

µm

³/µm

²0,137

0,1910,164

0,1690,214

0,1800,178

0,1510,182

0,1720,172

0,1550,184

0,1800,172

0,1710,125

0,1590,194

0,1620,171

0,02012%

Vvv (p

= 80%)

µm

³/µm

²0,039

0,0670,063

0,0470,057

0,0540,059

0,0630,051

0,0460,060

0,0460,067

0,0510,053

0,0610,046

0,0490,062

0,0430,054

0,00815%

Spd

(pru

nin

g = 5%)

1/µm

²0,001

0,0000,001

0,0010,000

0,0000,000

0,0000,000

0,0000,000

0,0000,001

0,0000,001

0,0000,001

0,0000,000

0,0010,001

0,00077%

Spc (p

run

ing = 5%

)1/µ

m8,016

9,66721,887

3,8476,893

22,16111,311

4,2140,397

8,46412,126

3,00719,999

10,70151,822

8,3743,292

8,3887,615

2,72211,245

11,359101%

S10z (pru

nin

g = 5%)

µm

3,6580,000

7,3563,429

5,4550,000

0,0000,000

0,0004,655

6,4053,195

11,3505,844

7,1085,599

2,8330,000

7,3032,697

3,8443,243

84%

S5p (p

run

ing = 5%

)µ

m1,013

0,0002,106

0,4670,542

0,0000,000

0,0000,000

1,0230,656

0,4322,478

0,5593,420

0,5640,373

0,0000,974

0,3850,750

0,921123%

S5v (pru

nin

g = 5%)

µm

2,6458,889

5,2502,962

4,9134,716

5,8704,924

3,1663,632

5,7492,763

8,8725,285

3,6885,035

2,4602,914

6,3292,312

4,6191,922

42%

Sda (p

run

ing = 5%

)µ

m²

71,55566,228

254,44969,144

72,867134,110

151,627201,624

98,558140,640

118,63754,521

56,13194,496

237,517124,546

56,95090,441

79,53146,170

110,98760,712

55%

Sha (p

run

ing = 5%

)µ

m²

3,6590,000

93,957951,158

0,0000,000

0,0000,000

0,00023,903

7,15037,690

75,81476,393

29,2460,000

251,9140,000

217,198497,457

113,277232,774

205%

Sdv (p

run

ing = 5%

)µ

m³

1,4241,530

7,8391,513

1,8113,431

3,7895,240

2,6773,188

2,7951,107

1,3722,381

6,7330,000

0,7460,000

1,9730,810

2,5182,084

83%

Shv (p

run

ing = 5%

)µ

m³

0,2260,000

1,16720,884

0,0000,000

0,0000,000

0,0001,132

0,4501,827

1,6451,169

0,7170,000

4,4500,000

0,68413,274

2,3815,282

222%

PA

RA

METER

SM

SG191

MEA

NSD

RSD

MSG 189 MSG 190

Appendix 6: Interferometer Readings

74

#1

23

45

67

89

1011

1213

1415

1617

1819

20

Sqµm

0,2170,455

0,4050,458

0,4350,321

0,4560,385

0,3610,290

0,4030,307

0,4300,346

0,2620,373

0,3190,392

0,3440,390

0,3670,067

18%

Ssk<no unit>

-5,928-8,841

-6,673-7,605

-6,179-6,330

-7,371-7,521

-6,489-5,372

-4,818-7,632

-7,993-7,955

-6,045-7,318

-7,535-8,524

-9,244-6,396

-7,0891,156

-16%

Sku<no unit>

60,634100,716

64,51785,750

61,13468,028

81,93574,302

64,58254,346

42,01583,400

91,91286,895

64,650107,124

78,12693,311

123,89892,496

78,98919,691

25%

Spµm

0,3280,664

0,9821,046

1,1431,256

2,9350,603

0,8031,891

1,0630,576

1,0310,579

0,7565,634

0,5280,513

0,8898,712

1,5972,053

129%

Svµm

4,3678,182

6,79310,782

7,9885,970

8,4866,263

6,4745,852

6,0575,560

8,9427,450

5,3387,810

6,3817,496

8,0576,866

7,0561,471

21%

Szµm

4,6958,846

7,77511,829

9,1327,226

11,4216,866

7,2777,743

7,1196,137

9,9738,029

6,09413,444

6,9098,009

8,94615,578

8,6522,658

31%

Saµm

0,1230,183

0,1920,212

0,2210,171

0,2210,169

0,1840,167

0,2250,146

0,1950,158

0,1450,162

0,1450,161

0,1530,151

0,1740,029

17%

Smr (c = 1 µm

under the highest peak)%

99,00795,629

65,20352,611

32,5059,029

0,00196,969

87,2610,003

47,55197,445

56,78097,469

92,3870,011

97,79398,049

82,9390,001

60,43239,837

66%

Smc (p = 10%

)µm

0,1590,233

0,2540,299

0,3140,246

0,3110,220

0,2600,243

0,3170,186

0,2630,205

0,1970,218

0,1850,208

0,2020,193

0,2360,047

20%

Sxp (p = 50%, q = 97.5%

)µm

0,4400,545

0,5580,587

0,6540,485

0,6810,507

0,5200,500

0,6610,485

0,5640,483

0,4720,492

0,4930,471

0,4590,421

0,5240,073

14%

Sal (s = 0.2)µm

3,5030,834

1,1140,996

1,1271,138

1,0750,991

1,0071,891

2,6860,995

0,9210,919

1,2031,013

0,9830,885

0,9240,803

1,2500,682

55%

Str (s = 0.2)<no unit>

0,0430,010

0,0140,012

0,0140,014

0,0130,012

0,0120,023

0,0330,012

0,0110,011

0,0150,012

0,0120,011

0,0110,010

0,0150,008

55%

Std (Reference angle = 0°)°

120,992125,248

157,75243,997

50,251121,251

121,009129,750

47,49752,484

119,75693,747

121,25173,254

115,755101,251

133,25449,752

50,498129,747

97,92536,666

37%

Sdq<no unit>

0,5361,337

1,1471,339

1,2840,870

1,3131,102

1,0010,780

1,0910,848

1,2630,974

0,6871,052

0,8861,134

0,9891,278

1,0460,228

22%

Sdr%

5,38414,984

14,52817,345

18,8419,894

17,37512,988

12,4629,578

15,4598,915

16,07310,752

7,66111,881

9,48912,428

10,42013,981

12,5223,560

28%

Vm

(p = 10%)

µm³/µm

²0,003

0,0070,012

0,0110,013

0,0090,009

0,0070,009

0,0080,015

0,0050,008

0,0050,007

0,0070,006

0,0050,008

0,0110,008

0,00336%

Vv (p = 10%

)µm

³/µm²

0,1620,240

0,2660,309

0,3270,255

0,3200,227

0,2690,251

0,3320,191

0,2710,210

0,2040,225

0,1910,213

0,2100,204

0,2440,049

20%

Vm

p (p = 10%)

µm³/µm

²0,003

0,0070,012

0,0110,013

0,0090,009

0,0070,009

0,0080,015

0,0050,008

0,0050,007

0,0070,006

0,0050,008

0,0110,008

0,00336%

Vm

c (p = 10%, q = 80%

)µm

³/µm²

0,1180,147

0,1640,184

0,1960,161

0,1980,140

0,1690,162

0,2120,129

0,1680,138

0,1360,137

0,1200,130

0,1320,113

0,1530,028

19%

Vvc (p = 10%

, q = 80%)

µm³/µm

²0,124

0,1710,203

0,2400,258

0,2060,249

0,1660,214

0,2050,269

0,1410,206

0,1550,160

0,1690,138

0,1530,159

0,1480,187

0,04323%

Vvv (p = 80%

)µm

³/µm²

0,0380,069

0,0630,069

0,0680,049

0,0710,061

0,0550,046

0,0630,050

0,0650,054

0,0440,056

0,0530,060

0,0510,056

0,0570,009

16%

Spd (pruning = 5%)

1/µm²

0,0000,000

0,0010,000

0,0010,001

0,0000,000

0,0010,001

0,0020,000

0,0000,000

0,0010,001

0,0000,000

0,0000,001

0,0010,000

87%

Spc (pruning = 5%)

1/µm4,146

13,6421,658

0,9195,810

2,65810,364

8,8045,779

9,4901,953

4,3323,352

29,0182,715

24,8585,782

15,86511,361

42,43910,247

10,693104%

S10z (pruning = 5%)

µm*****

*****6,024

7,1857,207

4,6307,283

5,5864,979

4,5725,974

*****7,284

*****2,868

8,6464,109

5,160*****

10,1516,111

3,15852%

S5p (pruning = 5%)

µm*****

*****0,719

0,7650,802

0,7771,131

0,4870,631

0,8630,801

*****0,697

*****0,460

2,6840,423

0,569*****

4,1061,061

0,98092%

S5v (pruning = 5%)

µm2,532

5,7485,304

6,4206,405

3,8546,152

5,0994,347

3,7095,173

3,3336,588

4,2922,409

5,9623,687

4,5915,403

6,0454,853

1,29127%

Sda (pruning = 5%)

µm²

69,143163,569

64,07483,153

42,230112,937

69,89477,507

79,08374,002

43,68099,076

79,465140,625

90,614186,857

143,204168,907

166,206258,282

110,62555,851

50%

Sha (pruning = 5%)

µm²

**********

257,834*****

846,856337,312

907,148*****

558,997723,437

303,284*****

967,779*****

6135,1023,511

22,4620,338

*****37,561

853,9711358,479

159%

Sdv (pruning = 5%)

µm³

0,9162,578

1,4651,789

1,0782,578

1,7351,699

1,9031,513

1,1301,573

2,3222,956

1,5503,437

2,0963,604

2,6243,490

2,1020,816

39%

Shv (pruning = 5%)

µm³

**********

19,637*****

48,61616,171

64,389*****

19,22652,888

29,883*****

59,287*****

231,4660,686

1,1290,071

*****1,027

41,88353,058

127%

PARA

METERS

MSG

190M

EAN

SDRSD

MSG 191

Appendix 7: Insert Geometry and wear

75

Appendix 7: Insert Geometry and wear

In order to achieve better understanding for the cutting inserts that used in this task and how

the inserts look. Below in figure is the geometrical definition of the insert used in the project

are described

The flank side, flank face is the surfaces of the cutting tool against which the newly

produced Work piece surface passes.

Rake Face, it is defined as the whole upper side of the insert, where the chips breaks

Edge Rounding (ER), is the definition of the radius of the cutting edge. An increased

radius on the edge make it more concave or not sharp, which increases the risk of built

up edges. .

Figure.11details of interest of the cutting inserts (Sandvik Coromant)

Wear Type

Wear occur due to damage or removal material from either one or both surfaces Material wear

processes are found at all places where materials are in mechanical contact with each other

[38]. Depending on how the inserts are used different types of wear occurs, this will affect the

life of the insert. During the metal cutting process , cutting tools suffer different kind of wear

The most Cutting tools wear are flank wear, crater wear, chipping, fracture and notch wear

Crater wear is wear located at the rake face of the tool, in the form of a crater; it is caused by

the chip that is creating an abrasive wear on the chip surface of the insert it is caused by the

chip that is creating an abrasive wear on the chip surface of the insert

. Figure 2: Crater wear on the rake side

(http://www.Sandvik.coromant.com/enus/knowledge/materials/cutting_tool_materials/wear_on_cutting_edges)

Flank Face

k Face Nose radius

k Face Edge Rounding

Face k Face

Rake Face

k Face

Appendix 7: Insert Geometry and wear

76

Flank wear Flank wear appears on the flank face of the cutting tool, caused mainly by

abrasive mechanisms. Flank wear is measured as the width of flank wear land, VBB, and is

often measured microscopically [39]

Figure3: Flank wear (http://www.Sandvik.coromant.com/en-

us/knowledge/materials/cutting_tool_materials/wear_on_cutting_edges)

Notch wear Notching happens when excessive localized damage occurs at the flank and rake

Face simultaneously, causing a single groove formation, its occurs on inserts used in harder

materials that subject to deformation hardening under the surface

Figure 4: Notch wear

(http://www.Sandvik.coromant.com/en-us/knowledge/materials/cutting_tool_materials/wear_on_cutting_edges)

Chipping Wear it is a small edge fracture, it occurs due to unpredictable wear mechanism

that could occur when the cutting tool is subjected to sudden loads or thermal shocks due to

low fracture toughness [38,39]. Chipping is when a small material piece of the cutting tool

edge breaks loose.

Figure 5: Chipping wear

(http://www.Sandvik.coromant.com/en-us/knowledge/materials/cutting_tool_materials/wear_on_cutting_edges)

Fracture Wear is the breaking down of the cutting edge under tough cutting conditions. The

Wear-induced change of tool geometry, weakening of the cutting edge due to high

temperatures and high resultant forces, leads to the cutting-edge breakage [40].

WP 1 Characterization of pre-treatment variants

MSG157 manufactured through standard production procedure. The rounding of the edge is

done through blasting. The blasting slurry is a mixture of Alumina oxide (corundum) and

Appendix 7: Insert Geometry and wear

77

water. The abrasive particles have a high kinetic energy when they hit the surface of the

inserts and therefore some WC grains can break or crack.

Figure 6: SEM images in RBSD mode of variant MSG157 in magnifications of 5kX (left) and 20 KX (right).

[Sandvik Coromant]

MSG158: Variant MSG158 is first ER blasted; also blasted with a finer grit size of media,

since the media size is significantly smaller in the second step of blasting, the kinetic energy of

the particles is lower and thus the blasting should not fracture any new grains of WC. It is

thought that the adhesion of the coating by the degree of crushed WC grains on the surface,

which is why these two variants are manufactured. As seen in Fig.1 cracks are seen in two

rather large grains (marked in right image).

Figure 7: SEM images in RBSD mode of variant MSG158 in magnifications of 5kX (left) and 20 kX (right). [Sandvik

Coromant]

MSG160: also round edge treated with blasting the same way as MSG157 and MSG158 but

before coating, it was polished. The polishing is done through shooting out rubber particles

covered in a fine grit abrasive material through a nozzle with an airstream. As can be seen

from figure 3.5 there is a large difference of the surfaces.

78

Figure 7: First row; MSG157 in SE mode at three different magnifications, second row; MSG160 in SEM mode at

three different magnifications. [Sandvik Coromant]

WP2 Characterization of pre-treatment and post treatment variants

Coating by CVD technology

Coating layers from WC-Co substrate and upwards: Ti(C,N)/Alpha Al2O3/TiN. Total

thickness approximately 6 microns.

MSG 186

Standard production procedure for post coating treatment: Blasting with 220 grit size

Al2O3 media at 2.0 bar pressure with a concentration of approximately volume 20 %

of media in the slurry

Variant

ER

Method Pre coating treatment Post coating treatment

MSG186 (R) Blasting

Blasting

MSG187 Blasting Fine grain blasting Blasting

MSG189 Blasting Polishing Blasting

MSG190 Blasting Polishing Blasting, Polishing

MSG191 Blasting

Blasting, Polishing

79

Blasting stream directed perpendicular to the insert rake face, removing the TiN

coating on same face of insert

Typical appearance is a surface that on a microscopic level has indentations from the

blasting particles. Cracks can also be found in some cases

MSG 187

Pre coating treatment in two steps:

Step 1: Standard production procedure for post coating treatment. Blasting

with 220 grit sizes Al2O3 media at 2.0 bar air pressure and 1.8 bar slurry

pressure with a concentration of approximately volume 20 % of media in

the slurry.

Step 2: Fine grain blasting with a 500 mesh grit size of Al2O3 media at a

pressure of 2.5 bar air pressure and 1.0 bar slurry pressure

The pre coating treatment of fine grained blasting is not thought to impact the

roughness of the coating surface, See comparison of reference MSG186 and MSG189

in cross sectional images

MSG 189

Pre coating treatment in two steps:

Step 1: Standard production procedure for blasting.

Step 2: Polishing.

Post treatment Blasting with 220 grit size Al2O3 media at 2.0 bar pressure with a

concentration of approximately volume 20 % of media in the slurry

There could be a difference in the smoothness of the coating surface due to the fact

that the pre coating treatment results in a surface with less peaks and valleys. See

comparison of reference MSG186 and MSG189 in cross sectional images

MSG 190

Pre coating treatment in two steps:

Step 1: Standard production procedure for blasting.

Step 2: Polishing.

MSG186 MSG189

MSG187 MSG186

80

Post coating treatment in two steps:

Step 1: Blasting with 220 grit size Al2O3 media at 2.0 bar pressure with a

concentration of approximately volume 20 % of media in the slurry

Step 2: Polishing in a brushing machine with a media consisting of fine grit

abrasive particles. Three brushes rotating in same direction that are fed down

onto the rake face of the inserts

For comparison, see cross section images of MSG186 and MSG190

MSG 191

Pre coating treatment Standard production procedure for blasting Post coating

treatment in two steps.

Step 1: Blasting with 220 grit size Al2O3 media at 2.0 bar pressure with a

concentration of approximately volume 20 % of media in the slurry.

Step 2: Polishing in a brushing machine with a media consisting of fine grit

abrasive particles. Three brushes rotating in same direction that are fed down

onto the rake face of the inserts

See cross section images of MSG186 and MSG191

MSG186 MSG190

MSG186 MSG191

PO Box 823, SE-301 18 HalmstadPhone: +35 46 16 71 00E-mail: registrator@hh.sewww.hh.se

Shobin JohnE-mail: johnsho777@gmail.comTel: 0726707785

Zoel-fikar ElghoulE-mail: zoelfi@hotmail.comTel: 0728398771