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International Journal of Interdisciplinary Research Centre (IJIRC) ISSN: 2455-2275(E)

Volume II, Issue 6 June 2016

All rights are reserved 1

SOME STUDY OF MACHINABILITY BY TURNING PROCESS-A

REVIEW

* Saadat Ali Rizvi and

* * Wajahat Ali and* * * Mumtaz Ahmad

* Research Scholar, Department of Mechanical Engineering, IIT (BHU), Varanasi (U.P) and

faculty member in U.P, J.M.I,New Delhi, INDIA

**Mechanical Engineering Department, SCRIET, (CCS University, Meerut) , INDIA

***Faculty member in U.P, J.M.I,New Delhi , INDIA

Abstract

In this review paper an attempt has to been made to study of different parameters which

affect the mahinability of different materials which are commonly used to produce

automotive parts such as shafts, gears and bearings. Surface quality is one of the most

impellent customer requirements in machining of soft and hard turning. The main aspect of

surface quality on machined parts is probably surface integrity, such as roughness and

residual stresses. There are three controllable factors in turning process viz. cutting speed,

feed and depth of cut. It is seen that the insert radius, feed rate, and depth of cut have

different effects on the surface roughness. In this research paper various optimization

technique such as Taguchi, RSM, and ANN etc method as well as insert are reviewed.

Taguchi method a powerful tool for experiment design is also used to optimize the cutting

parameters to achieve better surface finish and to identify the most effective parameter for

cost evolution during turning.

Keywords: CNC turning, Taguchi method, ANOVA, Machining, Machinability.

1. Introduction:

In modern industry the basic goal of manufacturer is to manufacture the product at low cost,

high quality products in short time. For this purpose Automated and flexible manufacturing

systems are employed with computerized numerical control (CNC) machines that are capable

of achieving high degree accuracy and low processing time. Among the entire machining

process turning is the first most common method for cutting and especially for the finishing

machined parts. In a turning operation, it is important task to select cutting parameters for

achieving high cutting performance.

Cutting parameters affect the surface roughness, surface texture and dimensional deviations

of the product. Surface roughness, which is used to determine and to evaluate the quality of a

product, is one of the major quality attributes of a turning product. Three cutting parameters

namely, insert radius, feed rate, and depth of cut, are optimized with considerations of surface

roughness. The mechanism behind the formation of surface roughness is very complicated

and process dependent [1]




Fig. 1. Surface roughness profile [4]

N. Satheesh Kumar et al [2] investigates the effect of process parameters in turning of

Carbon Alloy Steels in a CNC lathe. The parameters namely the spindle speed and feed rate

are varied to study their effect on surface roughness. The experiments are conducted using

one factor at a time approach. The five different carbon alloy steels used for turning are

SAE8620, EN8, EN19, EN24 and EN47. The study reveals that the surface roughness is

directly influenced by the spindle speed and feed rate and at the last they observed that the

surface roughness increases with increased feed rate and is higher at lower speeds and vice

versa for all feed rates.

Surface Roughness Profile of EN8 at 339 rpm and 0.05 mm/rev







Fig 2 Surface roughness graph of different material at different speed

W.S. Lin et al [3] develop a model for surface roughness and cutting force This network is

composed of a number of functional nodes, which are self-configured to form an optimal

network hierarchy by using a predicted square error (PSE) criterion. Once the process

parameters (cutting speed, feed rate and depth of cut) are given, the surface roughness and

cutting force can be predicted by this network. To verify the accuracy of the abdicative

network, regression analysis has been adopted in the paper to develop a second prediction

model for surface roughness and cutting force. Comparison of the two models indicates that

the prediction model developed by the abdicative network is more accurate than that by

regression analysis. Experimental results are provided to confirm the effectiveness of this

approach and they conclude that Crucial factors that control the cutting force are the feed rate

and the depth of cut, where the cutting force tends to increase with an increased feed rate and

depth of cut

The models for surface roughness Ra and cutting force F are derived from the data shown in

Table 1 and as defined below:

Ra =-2.172026+ 0.035321V – 0.000103V2+ 86.164152f

2 – 0.037214Vf + 3.856817fd

F=-161.988683+ 2.351115V+ 429.783951f+ 423.407407d- 0.009119V2 -

47.407407d2+1692.283951fd

Fig 3.Adductive network for surface roughness




Fig 4.Adductive network for cutting forces

Uma Maheshwera Reddy Paturi et al [4] studied the effect of solid lubricant assisted

minimum quantity lubrication (MQL) turning of Inconel 718 on the finish quality of

machined surface by turning process. In MQL application, micron sized tungsten disulfide

(WS2) solid lubricant powder particles were dispersed (0.5% wt.) in emulsifier oil based

cutting fluid (20:1). To comprehend the finish quality of machined surface with the presence

of WS2 solid lubricant, turning tests are conducted. The effect of cutting parameters on the

finish quality of work surface is evaluated using statistical design approach. Multiple linear

regression models are developed and validated to understand the correlations between cutting

parameter .and they find out that surface quality of machined work material during WS2 solid

lubricant assisted MQL machining showed a much improvement on the finish quality of work

material by on average about 35% when compared to MQL machining alone.

Gabriel Medrado Assis Acayaba , Patricia Mun˜oz de Escalona[5] developed a prediction

models to predicting the surface roughness by using Multiple Linear Regression and

Artificial Neural Network methodologies. Results show that the neural network outperforms

the linear model by a fair margin (1400%). Moreover, the developed Artificial Neural

Network model has been integrated with an optimisation algorithm, known as Simulated

Annealing (SA), this is done in order to obtain a set of cutting parameters that result in low

surface roughness. A low value of surface roughness and the set of parameters resulting on it,

are successfully yielded by the SA algorithm and finally they conclude that the cutting

conditions obtained by the SA algorithm for low values of surface roughness are V = 49.3

m/min, f = 0.156 mm/ rev, d = 0.52 mm and r = 0.57 mm.

Fig. 5. Schematic drawings of turning insert geometry




The proposed regression equation with potential adjustment is given by

Ra = 101:31

V-0:424

f 0:734

d0:187

r-0:499

R2 =0:466,R

2adj = 0:460 and σ = 1:33

Anupam Agrawal et al [6]studied the effect of cutting parameters in influencing the

machined surface roughness The machining outcome was used as an input to develop various

regression models to predict the average machined surface roughness on this material. Three

regression models – Multiple regression, Random forest, and Quantile regression were

applied to the experimental outcomes The performance of these models was compared to

ascertain how feed, depth of cut, and spindle speed affect surface roughness and finally to

obtain a mathematical equation correlating these variables and they concluded that the

random forest regression model is a superior choice over multiple regression models for

prediction of surface roughness during machining of AISI 4340 steel (69 HRC)

Table no 1[6]

2. Taguchi method

Mustafa Gunay & Emre Yucel [7] optimizing the cutting conditions for the average surface

roughness (Ra) obtained in machining of high-alloy white cast iron (Ni-Hard) at two different

hardness levels (50 HRC and 62 HRC). Machining experiments were performed at the CNC

lathe using ceramic and cubic boron nitride (CBN) cutting tools on Ni-Hard materials.

Cutting speed, feed rate and depth of cut were chosen as the cutting parameters. Taguchi L18

orthogonal array was used to design of experiment. Optimal cutting conditions was

determined using the signal-to-noise (S/N) ratio which was calculated for Ra according to the

„„the-smaller-the-better‟‟ approach. The effects of the cutting parameters and tool materials

on surface roughness were evaluated by the analysis of variance and they finally conclude

that the smallest Ra values occurred during machining of Ni-Hard with 62 HRC and Ni-Hard

with 50 HRC are obtained as 0.262 lm and 0.280 lm with CBN cutting tool, respectively.




M. Nalbant et al [8] study, the Taguchi method is used to find the optimal cutting parameters

for surface roughness in turning. The orthogonal array, the signal-to-noise ratio, and analysis

of variance are employed to study the performance characteristics in turning operations of

AISI 1030 steel bars using TiN coated tools. Three cutting parameters namely, insert radius,

feed rate, and depth of cut, are optimized with considerations of surface roughness.

Experimental results are provided to illustrate the effectiveness of this approach and they told

that in turning, use of greater insert radius (1.2 mm), low feed rate (0.15 mm/rev) and low

depth of cut (0.5 mm) are recommended to obtain better surface roughness for the specific

test range.

Total mean of the S/N ratio g can be calculated as

Where m is the number of experiments in the orthogonal array, .g., m = 9 and gi is the mean

S/N ratio for the ith

experiment

Squared deviations SST can be calculated as

where p represent one of the experiment parameters, j the level number of this parameter p, t

the repetition of each level of the parameter p, sgj the sum of the S/N ratio involving this

parameter p and level j.

The sum of squares from error parameters SSe is

The corrected sum of squares SP can be calculated as

The estimated S/N ratio ƞ using the optimal level of the process parameters can be calculated

as

Bouacha et al [9] Investigated the Ra values created in hard turning of 64 HRC hardness

AISI 52100 bearing steel with CBN cutting tool. They performed the machining tests




according to the L27 orthogonal array of Taguchi experimental design method. They reported

that the cutting parameter which is the most effective on Ra is the feed rate and cutting speed.

Asilturk and Akkus[10] uses the Taguchi method for minimizing the average Surface

roughness (Ra) and the arithmetic mean value of the single roughness depths of consecutive

sampling lengths (Rz) in turning of hardened AISI 4140 (51 HRC) with coated carbide

cutting tools. Their study focused on effects of cutting speed, feed rate and depth of cut on

surface roughness. Statistical analysis of experimental data indicated that the feed rate was

the most significant effect on Ra and Rz. The authors were also found that the optimum

cutting conditions was different for Ra and Rz.

Mangesh R. Phate and V.H.Tatwawadi [11] developed a model to calculate the Material

Removal Rate (MRR) & Power Consumption (PC) using Dimensional Analysis (DA). DA

models of MRR & PC have been developed with machining field parameters. The

parameters were the operator performing the turning operation, the cutting tool used to

remove the material, the work piece, the cutting process parameters, such as cutting speed,

feed rate, depth of cut etc., and lathe machining specifications and the turning

environmental parameters, such as humidity, atmospheric temperature, air circulation,

noise level, and light illumination etc and finally they told that the cutting condition and the

lathe machine parameters have significant effects on the material removal rate and the

power consumption, while the tool and the environment have the least effect.

Model Formulation

Model: Material Removal Rate (MRR) Model

𝜋D1= 0.00026743 × 𝜋10.1408× 𝜋2 −0.0293 × 𝜋3

0.324 × 𝜋40.520 × 𝜋5

−0.0415 × 𝜋60.4412

• Correlation Coefficient = 0.982915516

• Root Mean Square Errors=0.034305626

• Reliability = 98.25331313%

It is obvious that the predicted values by DA are very close to the experimental readings.

Model: Power Consumption (PC) Model

𝜋D2= 9.65 𝐸−05× 𝜋1−0.0545× 𝜋2 −0.0495 × 𝜋3

0.5267 × 𝜋4−0.1369 × 𝜋5

0.1072 × 𝜋6−0.1983

• Correlation Coefficient = 0.98203603

• Root Mean Square =0.03476825

• Reliability = 98.1258013%

Z= Log (Π D1), K= Log (K1), X1= Log (Π1), X2 = Log (Π2) X3= Log (Π3), X4= Log (Π4), X5= Log (Π5

and X6=Log (Π6)

Z1 (Max MRR) = -8.228 +0.1408X1 -0.0293 X2 +0.324X3 +0.5200 X4 - 0.0415X5 +0.4412X6

Z2 (Min PC) = -9.2455 -0.0545X1 -0.0495 X2 +0.5267X3 -0.1369 X4 +0.1072 X5 -0.1983 X6




Fig 6.Flow Chart for regression Analysis

M. Gunay, A. Kacal, Y. Turgut [12] studied about the Taguchi method, which is used in the

industry to decrease the product development period for the design and production which also

decrease the costs and increase the profit of the company. Taguchi method also allows

controlling the variations caused by the uncontrollable factors which are not taken into

consideration at conventional design of experiment. Taguchi converts the objective function

values to signal-to-noise (S/N) ratio for measure the performance characteristics of the levels

of control factors against these factors. S/N ratio is defined as the desired signal ratio for the

undesired random noise value and shows the quality characteristics of the experimental data.




S. A. Hussain et al [13] discusses the use of fuzzy logic for modeling turning parameters in

turning of glass fiber reinforced plastics by carbide tool (K-20). Experiments were conducted

based on the established Taguchi‟s Design of Experiments (DOE) L25 orthogonal array on an

all geared lathe. The cutting parameters considered were cutting speed, feed, depth of cut, and

work piece (fiber orientation). Fuzzy based model is developed for correlating the cutting

parameters with surface roughness (Ra). And finally they find that the model can be

effectively used for predicting the surface roughness (Ra) in turning of GFRP composites.

Kopac et al. [24] considered cutting speed, cutting tool materials, feed rate and depth of cut

as cutting parameters in machining C15 E4 steel on a lathe. They used the Taguchi

orthogonal array of L16 (25), which has two levels and a degree of freedom of 13 in the

experimental design. The quality determinant of „„the smaller the better‟‟ was used in

calculating the signal–noise ratio. It was observed that the control parameter having the

highest effect on surface roughness is the cutting speed, and better surface roughness values

were obtained at higher cutting speeds.

3. Response Surface Methodology (RSM)

Satish Chinchainkar et al [14] investigate effect of different cooling medium and cutting

parameters on surface roughness during the turning of AISI 52100 steel. They generate a

mathematical model. experiment conduct by using a PVD-coated TiSiN-TiAlN carbide tool

under dry, with water based and coconut oil-based cutting fluids and they find out that hard

turning under dry conduction produced lower values of surface roughness.hower at higher

cutting speed hard turning using coconut oil produced lower value of surface roughness. It is

also observed that surface roughness mostly affected by feed and increased when cutting

speed exceeds 150-160 m/min irrespective of the cooling medium used. Stat-Ease Design

Expert software use to calculate the regression equation. Analysis of the experimental result

was performed using standard Response Surface Methodology (RSM).

Surface roughness: Dry condition

Surface roughness: Water based cutting fluid

Surface roughness: Coconut oil based cutting fluid




Fig 7. Response surface showing interactions effects of cutting speed and feed on surface

roughness for (a) Dry ;(b) water based;(c)Coconut oil based cutting fluids

Varaprasad Bhemuni and Varaprasad Bhemuni [15] studied the effects of cutting speed;

feed rate and depth of cut on surface roughness are investigated in hard turning of AISI D3

steel. AISI D3 steel is hardened to 62 HRC and is machined using a mixed ceramic tool.

Mathematical models for surface roughness are developed using the Response Surface

Methodology (RSM). Central Composite Design (CCD) is applied as an experimental design.

Al2O

3/TiC mixed ceramic tool with corner radius of 0.8 mm is employed to accomplish 20

tests with six centre points. The range of each parameter is set at three different levels,

namely low, medium and high. The main effects of the factors and their interactions were

considered in the present study using Analysis of variance (ANOVA). Various graphs and

plots are drawn to evaluate the influence of the process parameters on surface roughness.

Feed rate is the most influencing factor on surface roughness. Results revealed that the use of

lower feed, lower depth of cut and higher cutting speed, while hard turning of AISI D3

hardened steel, ensures a better surface roughness.

In the present study, the quantitative form of the relationship between the desired response

and independent input process parameters can be represented by [9]

Y=Φ (Vc, f, ap) 𝑌=𝑎0+𝑎1𝑉𝑐+𝑎2𝑓+𝑎3𝑎𝑝+𝑎12𝑉𝑐𝑓+𝑎13𝑉𝑐𝑎𝑝 +𝑎23𝑓𝑎𝑝+𝑎11𝑉

2𝑐+𝑎22𝑓2+𝑎33𝑎

2𝑝

Where Y is the desired response and is the response function. In the present investigation,

the RSM-based mathematical models for surface roughness Ra, have been φ developed with




cutting speed Vc, feed rate f and depth of cut (Doc) as the process parameters. The response

surface equation for three factors is given by [16]

Where Y is the desired response and a0

is the free term of the regression equation, the

coefficients a1, a

2, a

3 and a

11, a

22, ...a

33 are the linear and quadratic terms, respectively, while

a12

, a13

,..a23

are the interacting terms. The experimental plan is developed to assess the

influence of cutting speed (Vc), feed rate (f), and depth of cut (Doc) on the surface roughness

(Ra), regression coefficients to be determined for each response. The regression coefficients

of linear, quadratic, and interaction terms of RSM-based mathematical models are determined

by [16]

Fig 8. Workpiece with carbide insert CC6050[16]

Murat Sarıkaya and Abdulkadir Güllü[17] studied the effect of the main turning

parameters such as cooling condition, cutting speed, feed rate and depth of cut on arithmetic

average roughness (Ra) and average maximum height of the profile (Rz) when turning of AISI

1050 steel. Experiments have been performed under dry cutting (DC), conventional wet

cooling (CC) and MQL. Tests are designed according to Taguchi‟s L16 (43X 2

1) orthogonal

array. ANOVA analysis was performed to determine the importance of machining parameters

on the Ra and Rz and they conclude that the most effective parameters are feed rate on the

surface roughness. Cooling conditions are significantly effective on the surface roughness.

MQL is a good tool in order to increase of the machined surface quality for cutting operations




Fig 9. A schematic diagram of the experimental set-up.[17]

RSM based modeling for surface roughness: In this paper, MINITAB 16 and DESIGN

EXPERT 8.0 were used for modeling purpose: a second order model response surface can be

fitted into the following Eq

Surface roughness and turning parameters are given below

Relationship between surface roughness and machining variables

RSM surface roughness Ra model is given by

Shetty et al. [18] used Taguchi and Response Surface Methodologies for minimizing the

surface roughness in turning of discontinuously reinforced aluminum composites (DRACs)

having aluminum alloy 6061 as the matrix and containing 15 vol.% of silicon carbide

particles of mean diameter 25 lm under pressured steam jet approach. The effect of cutting

parameters on surface roughness was

Evaluated and the optimum cutting condition for minimizing the surface roughness was also

determined




4. ANN Technique

Ilhan Asiltürk and Mehmet Çunkas[19] measure the surface roughness during turning at

different cutting parameters such as speed, feed, and depth of cut. Full factorial experimental

design is implemented to increase the confidence limit and reliability of the experimental

data. Artificial neural networks (ANN) and multiple regression approaches are used to model

the surface roughness of AISI 1040 steel. Multiple regression and neural network-based

models are compared using statistical methodsand they proposed models which are capable

of prediction of the surface roughness. The ANN model estimates the surface roughness with

high accuracy compared to the multiple regression model

Fig 10.Mathematical model of neuron[19]

Fig.11 ANN structure[19]

Konanki M. Naidu[20] developed a model of surface roughness in turning of AA 6351

alloy with carbide tool. Cutting speed, feed and depth of cut were considered as machining

parameters and surface roughness was considered as the response. Experiments were

conducted to develop the linear regression equations based on Taguchi‟s experimental design

methodology. Moreover, Artificial Neural Network (ANN) model was also developed for the

surface roughness. Further, the performance of the developed model has been tested with the

help of ten experimental test cases and they concluded that the multiple linear regression

analysis the interaction terms of speed, feed and depth of cut are not significant on the

response surface roughness.




Fig 12.Structure of feed-forward neural network [20]

B. Anuja Beatrice et al [21] predict the Surface Roughness of AISI H13 Steel by using

Artificial Neural Network in Hard Turning with Minimal Cutting Fluid Application. In this

research work an attempt was made to develop a model based on Artificial Neural Network

to simulate hard turning .This model is expected to predict the surface roughness in terms of

cutting parameters. Networks with different architecture were trained using a set of training

data for a fixed number of cycles and were tested using a set of input / output data reserved

for this purpose. The root mean square error was determined for the selected architectures.

The model with 3-7-7-1 architecture gave the minimum RMSE value. The ability of ANN

model to predict surface roughness (Ra) was analyzed. It was found that the predictions made

by the ANN model matched well with the experimental results.In this present work,

„learngdm‟ was considered as the learning function and „trainlm‟ as the training function. The

transfer function of the ANN model was considered as “tansig” and the sigmoid function

used in this experimentation is shown in equation f(x)

5. Analysis of Variance (ANOVA)

A.M.Badadhe et al [22] studied Four parameters viz. spindle speed, feed, depth of cut and

length to diameter (L/D) ratio of boring bar has been taken as control factors for optimizing

cutting parameters. The cutting trials were performed as per Taguchi 34 (L9 ) orthogonal

array method to deal with the response from multi-variables. AISI 1041 (EN9) carbon steel

was used as a job material which was cut by using standard boring bars of various sizes each

having a tungsten carbide inserts of same insert radius. The Analysis of Variance (ANOVA)

was carried out to find the significant factors and their individual contribution in the response

function i.e. surface roughness and they conclude that optimal turning operation parameters

for surface roughness under varying conditions through the use of the Taguchi parameter

design process and they showed that the control factors had varying effects on the response

variable. The use of the Taguchi parameter design technique was considered successful as an

efficient method to optimize machining parameters




6. Inserts or Cutting Tools

D.M. D'Addona and Sunil J Raykar[23] investigate performance of wiper inserts in hard

turning of oil hardening non-shrinking steel. The oil hardening non-shrinking steel is

commonly used material for making measuring instruments and gauges wherein surface

roughness is very important aspect. The major emphasis here is given to study and compare

performance of wiper insert in terms of surface finish with conventional inserts. Influence of

process parameters such as speed, feed, depth of cut and nose radius (for wiper and

conventional inserts) on surface roughness is analyzed using analysis of variance (ANOVA)

and analysis of means (AOM) plots. And finally they result that wiper inserts produce a very

good machined surface compared to conventional inserts.

K. Palanikumar et al [24] studied the factors which affect the cutting parameters of surface

roughness such as Ra, Rt, Rq, Rp and R3z in turning of glass fiber reinforced composite

materials. Empirical models are developed to correlate the machining parameters with

surface roughness .Empirical models are developed to correlate the machining parameters

with surface roughness. Analysis of experimental results is carried out through area graphs

and three-dimensional surface plot and finally the concluded that surface roughness

increases with the increase of feed rate and almost decreases with the increase of cutting

speed.

Fig13. Polycrystalline diamond tool and tool holder used in experiment[24]

Junyun Chen, Qingliang Zhao [25] tries to improve a method to evaluate the actual relative

tool-work vibration. By using this method the vibration information obtained is more

credible, as it contains the components caused by machine tool error, cutting force, material

property and changing of cutting parameters. Moreover, the swelling effect is analyzed using

a new evaluating method and taken into account for predicting surface roughness and they

prove that this model is a closer approximation of the actual turning process as compared to

the previous models and shows a higher predicting accuracy of surface roughness.




Fig 14. CNC ultra-precision machine tool (left) and diamond tools used in tests (right)[25]

The arithmetic roughness value Rat calculated from the profile of tool locus and the arithmetic

roughness value Rah calculated from the curve of simple harmonic motion can be given

respectively.

&

Then equivalent amplitude A can be calculated by

Fig 15.Three-dimensional topography and surface profile of machined surface. (a) Material:

NiP; (b) material: Cu; (c) material: Al7075[25]

A.Srithar et al [26] carried out the machining of AISI D2 steel workpiece having 66 HRC

hardness using coated carbide insert. The microstructure shows rolled grains of the steel

along the direction of the material. The microstructure shows fine grains of cementite with

the grain boundary chromium and other alloys and the presence of carbide, which increases

strength and wear resistant. Investigations were carried out on conventional lathe using the

prefixed cutting conditions. The graph shows the feed rate is the main impact with increasing

feed rate, but reduces with larger cutting speed and rapidly increasing depth of cut. The

responses studied in the investigation of surface roughness are studied in the investigation of

surface roughness parameters (Ra,Rt and Rz) on responses are studied and presented in detail.




Fig.16. Surface roughness measurement by roughness tester[26]

Hamza Bensouilah et al [27] study of the effects of cutting speed, feed rate and depth of cut

on the performance of machining which traditionally named „„machinability with CC6050

and CC650 ceramic inserts. The planning of experiments was based on Taguchi‟s L16

orthogonal array. The analysis of variance (ANOVA), the signal-to-noise ratio and response

surface methodology (RSM) were adopted and finally they find that the surface quality

obtained with the coated CC6050 ceramic insert is 1.6 times better than the one obtained with

uncoated CC650 ceramic insert. However, the uncoated ceramic insert was useful in reducing

the machining force.

RMS-based first order mathematical model is given by

where b0 is the free term of the regression equation, the coefficients, b1; b2; . . . ; bk are the

linear terms. The experimental plan is developed to assess the influence of cutting speed (Vc),

feed rate (f) and depth of cut (ap) on the surface roughness parameter (Ra) and cutting force

component

Taguchi method: For smaller-the-better, the S/N ratio is defined as

For nominal-the-best, the S/N ratio is defined as:

For larger-the-better, the S/N ratio is defined as




Fig 17. Illustration of measured force components, surface roughness and tool wear [27].

7. Conclusions

A From the above literature survey we find that there are some latest techniques for

optimization, Taguchi technique, Fuzzy Logic, Scatter Search technique, ANN, Genetic

Algorithm, Response Surface Methodology, Ant Colony technique, etc. are being applied

successfully in industrial applications for optimal selection of process variables for evaluation

the surface roughness of different materials. Among all these methods it is observed that

Taguchi Method is the most widely used method due to its simplicity and accuracy. The use

of other methods like Genetic Algorithm, Response Surface Method and Artificial Neural

Network are gradually increasing. In optimization of surface roughness feed is found to be

the most affecting factor followed by depth of cut and cutting speed




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