UNIVERSITI PUTRA MALAYSIA OPTIMIZATION OF ...UNIVERSITI PUTRA MALAYSIA MOHAMAD MAAROFF BAHURDIN FK...

UNIVERSITI PUTRA MALAYSIA

MOHAMAD MAAROFF BAHURDIN

FK 2012 135

OPTIMIZATION OF CUTTING CONDITIONS ON TOOL LIFE IN FACE MILLING SPHEROIDAL GRAPHITE CAST IRON

USING TAGUCHI METHOD

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OPTIMIZATION OF CUTTING CONDITIONS ON TOOL LIFE

IN FACE MILLING SPHEROIDAL GRAPHITE CAST IRON

USING TAGUCHI METHOD


MASTER OF SCIENCE

UNIVERSITI PUTRA MALAYSIA

2012

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OPTIMIZATION OF CUTTING CONDITIONS ON TOOL LIFE IN FACE

MILLING SPHEROIDAL GRAPHITE CAST IRON USING TAGUCHI

METHOD

By


Thesis Submitted to the School of Graduate Studies, Universiti Putra

Malaysia, in Fulfilment of the Requirements for the Degree of Master of Science

October 2012

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Abstract of thesis presented to the Senate of Universiti Putra Malaysia in fulfilment

of the requirement for the degree of Master of Science

OPTIMIZATION OF CUTTING CONDITIONS ON TOOL LIFE IN FACE

MILLING SPHEROIDAL GRAPHITE CAST IRON USING TAGUCHI

METHOD

By


October 2012

Chairman: B.T. Hang Tuah Baharudin, PhD

Faculty: Engineering

ABSTRACT

The aim of this study was to determine the optimum cutting condition for face

milling when machining the nodular cast iron. The cutting conditions consist of the

cutting speed, the feed per tooth and the depth of cut. The original problem arose in

the crankshaft machining-line of the Proton engine shop where the face milling

cutting tool frequently changed due to premature of tool life. As a result, the

productivity of this production line was decreased. This problem occurred due to the

enhancement of the raw-cast hardness, and no detail study was made on the cutting

condition by the engine shop.

This study was carried in three experiments; the preliminary experiment,

optimization experiment and confirmation experiment. All experiments used the

same tool and the same material of the engine shop.The preliminary experiment

aimed to determine and benchmark the performance of the cutting tool and surface

roughness when machined in the laboratory environment. Taguchi optimization

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method was applied for optimization experiment. This experiment aims to regulate

the machining parameters of three parameters at three levels. A L9 orthogonal array,

signal-to-noise ratio and analysis of variance (ANOVA) were applied in order to

determine and analyzed the optimal cutting condition. The confirmation experiment

was conducted using the suggested cutting condition obtained from the optimization

experiment in order to validate the result.

The result of the preliminary experiment showed that the volume of material

removes using the current practice cutting condition was about two hundred and fifty

cubic centimetres. The average surface roughness measured was about point six

micrometers. The optimization result showed that the optimum cutting condition

capable to remove the material more than four hundred cubic centimetres with the

surface roughness measured about point six micrometers. Comparison between both

experiments shows that the result was different for about forty percent. This

indicated that the optimization experiment was successfully determined the optimal

cutting condition. Results obtained by Taguchi method match closely with ANOVA

and cutting speed is most influencing parameter. The confirmation experiment

conducted also agreed on the optimization experiment result where the result of

material removes volume and surface roughness was the same.

The results show that there is a proper way to determine the optimal cutting

conditions other than trial-and-error method, which indirectly saves operating costs.

It is recommended to the engine shop to adjust its current cutting conditions

according to the findings of this study. Research on the same topic by using another

grade inserts are recommended for further study.

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Abstrak tesis yang dikemukakan kepada Senat Universiti Putra Malaysia sebagai

memenuhi keperluan untuk ijazah Master Sains

PENGOPTIMUMAN KONDISI PEMESINAN PADA HAYAT MATA ALAT

DALAM PENGISARAN PERMUKAAN BESI TUANG BERGRAFIK BULAT

MENGGUNAKAN KAEDAH TAGUCHI

Oleh


Oktober 2012

Pengerusi: B.T. Hang Tuah Baharudin, PhD

Fakulti: Kejuruteraan

ABSTRAK

Tujuan kajian ini adalah untuk menentukan parameter pemotongan yang optimum

untuk operasi mesin pengisar apabila memesin besi tuang bergrafik bulat. Parameter

pemotongan terdiri daripada kelajuan pemotongan, suapan per gigi dan kedalaman

pemotongan.Masalah ini asalnya berlaku di kilang pembuatan enjin Proton pada

baris pemesinan aci engkol di mana mata alat mesin pengisar ini kerap kali ditukar

disebabkan oleh hayat mata alat yang pramatang. Hasil dari kerapnya penukaran ini,

produktiviti barisan pengeluaran ini telah menurun. Masalah ini berlaku disebabkan

oleh peningkatan kekerasan bahan yang hendak di mesin, dan tiada kajian terperinci

telah dibuat pada parameter pemotongan oleh pihak kilang.

Kajian ini telah dijalankan dalam tiga bentuk eksperimen; eksperimen permulaan,

eksperimen pengoptimuman dan eksperimen pengesahan. Semua eksperimen telah

menggunakan mata alat dan bahan yang sama seperti yang digunakan oleh kilang

enjin. Eksperimen awal bertujuan untuk menentukan dan sebagai penanda aras

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prestasi mata alat memotong dan kekasaran permukaan apabila dimesin dalam

persekitaran makmal. Kaedah pengoptimuman Taguchi telah digunakan untuk

eksperimen pengoptimuman. Eksperimen ini bertujuan untuk mengawalselia

parameter pemesinan yang terdiri dari tiga parameter pada tiga peringkat. Tatasusn

ortogon L9, nisbah isyarat-kepada-hingar dan analisis variasi (ANOVA) telah

digunakan untuk menentukan dan menganalisis keadaan pemotongan yang optimum.

Eksperimen pengesahan telah dijalankan menggunakan keadaan pemotongan yang

diperolehi dari eksperimen pengoptimuman untuk mengesahkan hasilnya.

Hasil eksperimen permulaan menunjukkan bahawa jumlah bahan terbuang adalah

kira-kira dua ratus lima puluh sentimeter padu. Kekasaran permukaan purata yang

diukur ialah sebanyak perpuluhan enam mikrometer. Hasil eksperimen

pengoptimuman menunjukkan bahawa keadaan pemesinan optimum mampu

memotong bahan lebih daripada empat ratus sentimeter padu dengan kekasaran

permukaan diukur sebanyak perpuluhan enam mikrometer. Perbandingan antara

eksperimen permulaan dan pengoptimuman menunjukkan bahawa isipadu bahan

yang dibuang adalah berbeza sebanyak kira-kira empat puluh peratus. Ini

menunjukkan bahawa eksperimen pengoptimuman berjaya menentukan keadaan

pemotongan yang optimum. Keputusan yang diperolehi oleh kaedah Taguchi

sepadan rapat dengan ANOVA dan kelajuan pemotongan adalah parameter paling

mempengaruhi keputusan eksperimen. Eksperimen pengesahan dijalankan juga

bersetuju pada hasil eksperimen pengoptimuman di mana hasil jumlah bahan

terbuang dan kekasaran permukaan adalah sama.

Keputusan kajian ini menunjukkan bahawa terdapat satu cara yang betul untuk

menentukan keadaan optimum pemotongan selain dari percubaan secara rawak, yang

secara tidak langsung menjimatkan kos operasi. Adalah disyorkan kepada pihak

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kilang enjin untuk menyesuaikan keadaan paras semasa pemesinan menurut hasil

dari kajian ini. Penyelidikan mengenai topik yang sama dengan menggunakan satu

lagi gred sisipan adalah disyorkan untuk kajian seterusnya.

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ACKNOWLEDGMENTS

I would like to acknowledge the advice and guidance of Dr. BT. Hang Tuah

Baharudin and Professor Datin Napsiah Ismail, chairman and member of the

supervisory committee.

I also thank the UPM’s lab staff, especially Mr. Tajul Ariffin Md. Tajuddin and

Muhammad Wildan Ilyas Mohamed Ghazali. Special thanks go to Mr. Amir Abidin

Samsuddin and Mr. Azman Ismail, without whose knowledge and assistance this

study would not have been successful.

I acknowledge the Dr. BT Hang Tuah Baharudin and the UniKL’s STRG scheme for

their financial support for this project.

The case and data for this study were provided by Engine and Transmission

Manufacturing, a division of Proton Tanjung Malim Sdn. Bhd. I appreciate their

support.

I would like to thank my family members, especially my wife, Suzana Wati Zulkepli,

my son, M. Adam Fareez, my parents and my siblings for supporting and

encouraging me to pursue this degree.

Without my wife’s encouragement, I would not have finished the degree.

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I certify that a Thesis Examination Committee has met on 29 October 2012 to

conduct the final examination of Mohamad Maaroff Bahurdin on his thesis entitled

“Optimization of Cutting Conditions on Tool Life in Face Milling Spheroidal

Graphite Cast Iron Using Taguchi Method” in accordance with the Universities and

University Colleges Act 1971 and the Constitution of the Universiti Putra Malaysia

[P.U.(A) 106] 15 March 1998. The Committee recommends that the student be

awarded the Master of Science.

Members of the Thesis Examination Committee were as follows:

Dr. Mohd Khairol Anuar Mohd Ariffin, PhD Associate Professor

Faculty of Engineering

Universiti Putra Malaysia

(Chairman)

Dr. Shamsuddin Sulaiman, PhD

Professor



(Internal Examiner)

Dr. Faieza Abdul Aziz, PhD Associate Professor



(Internal Examiner)

Dr. Safian Sharif, PhD

Professor


Universiti Teknologi Malaysia

(External Examiner)

_____________________________

SEOW HENG FONG, PhD

Professor and Deputy Dean

School of Graduate Studies


Date:

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This thesis was submitted to the Senate of Universiti Putra Malaysia and has been

accepted as fulfilment of the requirement for the degree of Master of Science. The

members of the Supervisory Committee were as follows:

B.T. Hang Tuah Baharudin, PhD

Senior Lecturer



(Chairman)

Napsiah Ismail, PhD

Professor



(Member)

_____________________________

BUJANG BIN KIM HUAT, PhD

Professor and Dean

School of Graduate Studies


Date:

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DECLARATION

I declare that the thesis is my original work except for quotation and citations, which

have been duly acknowledged. I also declare that it has not been previously, and is

not currently submitted for any other degree at Universiti Putra Malaysia or other

institutions.

_________________________________


Date: 29th October 2012

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TABLE OF CONTENTS

Page

ABSTRACT ii ABSTRAK iv

ACKNOWLEDGMENTS vii DECLARATION x

TABLE OF CONTENTS xi LIST OF TABLES xiii

LIST OF FIGURES xiv LIST OF ABBREVIATIONS xvii

CHAPTER

1. INTRODUCTION 1

1.1. Background of the Study 2

1.2. Crankshaft Machining Line 6

1.2.1. The roughing cells 7

1.2.2. The OP10 machining process 9

1.3. Problem Statement 12

1.4. Research Objectives 13

1.5. Scope of the Study 14

1.6. Significance of the Study 15

2. LITERATURE REVIEW 16

2.1. Introduction 16

2.2. The Cutting Condition Optimization 17

2.3. Milling of Cast Iron and Its Related Issues 20

2.4. Milling Process 23

2.4.1. Milling cutting parameters 23

2.4.2. Milling cutting tools 27

2.5. Cast Iron Alloys 39

2.5.1. Cast iron 40

2.5.2. Nodular cast iron and its machinability 42

2.5.3. The cast iron chip formation 45

2.6. Design of Experiment – Taguchi Method 46

2.6.1. Taguchi design method overview 47

2.6.2. Confirmation experiment 53

2.6.3. Signal-to-noise (S/N) ratio analysis 53

2.6.4. Analysis of variance (ANOVA) 55

2.7. Summary of Literature 58

3. MATERIALS AND METHODS 59


3.2. Cutting Conditions’ Transformation 60

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3.3. Experiment Material 62

3.3.1. Work piece 62

3.3.2. Cutting tool 64

3.4. Equipment and Testing Facilities 66

3.5. Experiment Methodology 68

3.5.1. Material hardness measurement 69

3.5.2. Experimental run process flow 70

3.5.3. Preliminary experiment 76

3.5.4. Optimization experiment 76

3.5.5. The confirmation experiment 82

4. RESULTS AND DISCUSSION 83


4.2. Preliminary Experiment 84

4.2.1. Preliminary experiment result and discussion 85

4.2.2. Tool wear analysis 87

4.2.3. Summary of the preliminary experiment 89

4.3. Optimization Experiment 89

4.3.1. Optimization experiment result 90

4.3.2. Signal-to-noise (S/N) ratio analysis 94

4.3.3. Analysis of variance (ANOVA) 97

4.3.4. Tool wear analysis 98

4.3.5. Discussion of the optimization experiment 100

4.4. Prediction and Confirmation Experiment 102

4.4.1. Removed volume (tool life) prediction 102

4.4.2. Confirmation experiment result and analysis 103

4.4.3. Discussion and conclusion of confirmation

experiment 107

4.5. Summary of Result and Discussion 108

5. CONCLUSIONS AND RECOMMENDATIONS 110

5.1. Conclusions 110

5.2. Recommendations for Further Studies 112

REFERENCES 113

APPENDICES 117 BIODATA OF STUDENT 121

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LIST OF TABLES

Table Page

Table 1.1: OP10 tool usage detail 9

Table 2.1: Mechanical properties of Nodular cast iron 43

Table 3.1: Current practice machining condition by the engine shop 60

Table 3.2: The initial machining condition for the study 61

Table 3.3: Chemical composition (wt %) for JIS G 5502 FCD 700 62

Table 3.4: Mechanical properties of JIS G 5502 FCD 700 62

Table 3.5: Cutting tool geometry and details (WGC4080R) 65

Table 3.6: Insert identification (SEMT13T3AGSN-G ACK300) 65

Table 3.7: Physical properties of insert coating 65

Table 3.8: The basic Taguchi L9 (34) orthogonal array 77

Table 3.9: Machining parameters and their levels 78

Table 3.10 Recommended cutting condition by the tool maker 78

Table 3.11: Experimental layout 79

Table 3.12: Complete randomization experimental run with replication 79

Table 3.13: Optimization experiment result summary 80

Table 4.1: Result of optimization experiment 90

Table 4.2: Detail study on experimental run #5 & #7 92

Table 4.3: Experimental result and S/N ratio 95

Table 4.4: S/N ratios response table - larger the better 96

Table 4.5: Analysis of Variance for S/N ratios 97

Table 4.6: Average removed volume of the optimization experiment. 100

Table 4.7: Parameter-level prediction for mean and S/N ratio. 102

Table 4.8: Result of confirmation experiment 104

Table 4.9: Comparison the prediction and conformation experiment result 104

Table 4.10: Summary of experiment result 108

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LIST OF FIGURES

Figure Page

Figure 1.1: Comparison of two-month production rate 3

Figure 1.2: Downtime analysis 4

Figure 1.3: Crankshaft S4PH 6

Figure 1.4: Crankshaft machining line layout 7

Figure 1.5: OP10 machining operation 8

Figure 1.6: OP20 operation, to turn the front shaft and counter weight 8

Figure 1.7: OP 10 tool usage 10

Figure 1.8: OP10 tool change frequency for two-month record 11

Figure 1.9: Tool deterioration frequency - not because of tool life 11

Figure 2.1: Illustration of axial depth of cut (ae) and radial depth of cut (ar) (Grote and

Antonsson, 2009) 26

Figure 2.2: Angles on an insert cutting tools (Stephenson and Agapiou, 2006) 27

Figure 2.3: Combination of principle angles: Double positive angles and its effect on

chips formation (Sumitomo Electric, 2010) 29

Figure 2.4: Combination of principle angles: Double negative angles and its effect on


Figure 2.5: Combination of principle angles: Negative-positive angles and its effect on


Figure 2.6: Approximate ranges of carbon and silicon for steel and various cast

irons.(“Ductile Iron Data - Section 12,” 2011) 41

Figure 2.7: Photomicrograph of Nodular Cast iron (Elgun, 2004) 42

Figure 2.8: Segmented chip formation 45

Figure 2.9: Discontinues chip formation 45

Figure 2.10: Built-up edge (BUE) 46

Figure 2.11: Taguchi DOE procedure 48

Figure 2.12: Fishbone diagram of parameters that may affect the machining desired

quality (Benardos and Vosniakos, 2003) 49

Figure 3.1: Work piece dimension 63

Figure 3.2: Cutting tool used 64

Figure 3.3: Indexable inserts 64

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Figure 3.4: Vertical CNC machining centre 66

Figure 3.5: Optical microscope for flank wears monitor and measurement 66

Figure 3.6: The surface roughness measurement device 67

Figure 3.7: Hardness testing machine 67

Figure 3.8: Hardness measurement points 69

Figure 3.9: Machining process flow 70

Figure 3.10: Cutting edges identification marks 71

Figure 3.11: Flank face and wear width measurement of an insert edge 71

Figure 3.12: Special jig for insert placement under microscope 71

Figure 3.13: Milling process with straddle cut method (Moltrecht, 1981) 73

Figure 3.14: Three spot for taking surface roughness measurement 74

Figure 3.15: The intersection graph to find the actual machining length 75

Figure 4.1: The preliminary experiment results; removed volume and its average 86

Figure 4.2: The preliminary experiment results; surface roughness and its average 86

Figure 4.3: Effect of tool life (removed volume) versus work piece hardness 86

Figure 4.4: Flank wears versus machining length 87

Figure 4.5: Flank wear 88

Figure 4.6: Abrasion wears on flank wear region 88

Figure 4.7: Discontinues chip formation 88

Figure 4.8: Optimization Experiment Result - Removed Volume 91

Figure 4.9: Feed per tooth effect on removed volume 93

Figure 4.10: Frequency of the different work piece hardness 94

Figure 4.11: S/N ratios response graph 96

Figure 4.12: Wear curves for Cutting Speed 99

Figure 4.13: Tool Life Curve 99

Figure 4.14: Tool wears of optimal condition 1 106

Figure 4.15: Tool wears of optimal condition 2 106

Figure 5.1: Tool wear - Experimental run 1 118

Figure 5.2: Tool wear – Experimental run 2 118




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LIST OF ABBREVIATIONS

Cases

: Cutting tool edge angle 28

0 : Cutting rake angle 28

f : Radial rake angle 28

p : Axial rake angle 28

ae : Axial depth of cut 24

Al2O3 : Aluminum Oxide 34

ar : Radial depth of cut 24

CF : Catastrophic failure 37

CH : Chipping 37

CNC : Computer Numerical Control 3, 7

Co : Cobalt 32

CPS : Cam Profile Switching 4

CR : Cracks 37

CVD : Chemical vapor deposition 33

D : Milling cutter diameter (mm) 24

fe : Degree of error 56

FL : Flaking 37

fp : Degree of freedom for parameter p 56

ft : Total degree of freedom 56

fz : Feed per tooth (mm) 24

HRC : Rockwell hardness scale C 70

KT : Face wear 36

MPR : Monthly Performance Report 3

PD : Plastic deformation 37

PVD : Physical vapor deposition 33

Ra : Arithmetic mean surface roughness 68

SS’P : Corrected sum of square 58

SSe : Sum of square error 57

SSP : Sum of square for parameter 57

SST : Sum of square 57

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TiC : Titanium Carbide 34

TiCN : Titanium Carbo-Nitride 34

TiN : Titanium Nitride 34

VB :Flank wear 36

Vc : Cutting speed (m/min) 24

Vf : Feed velocity vector (mm/min) 24

Vp : Variance of the parameter 57

WC : Tungsten Carbide 32

z : Number of teeth 24

ρ : Parameter contribution 58

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CHAPTER 1

INTRODUCTION

In metal machining industries, various methods and instruments are being used to

acquire desired shape and dimensions. Until today, either conventional or modern

machine tool, the exploration in machining technology is still in vast progress. These

include new discoveries in various machining technologies such as in cutting tool

technology, machine tool technology and tool management strategies.

The introduction of these new technologies is only been appreciated and utilized by

some manufacturer. The main hurdle is the involvement of significant capital

investment that reflects all economic perspective such as cost and time which most

manufacture would not be interested. Majority of them prefers to carry out research

that could improve or maximize manufacturing system performance using the

existing available resources. Therefore, the research in determining the values of the

process parameters that yield the high-quality product is the most desirable (Baskar

et al., 2006; Benardos and Vosniakos, 2003).

Face milling is frequently used process in industrial machining to machine large and

flat surfaces in a very rapid and precise way. Normally, the cutter is equipped by

multi-edge of indexable inserts that can easily be replaced whenever the inserts have

reached its useful life. Many machining constraints should be considered as well as

the selection of machining parameters. The selection of proper machining parameters

using one self’s experience or from the handbooks will not always give the best

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results. Conversely, this may lead to shorten the tool life and directly increased the

production cost and ravage the production time.

1.1. Background of the Study

This study is based on the actual case condition originated from Proton Engine and

Transmission Plant (Proton ETM) situated at Tanjung Malim, Perak. This engine

shop is responsible to produce four main engine parts, namely cylinder head,

cylinder block, crankshaft and camshaft. There are two varieties of part as the engine

size is different, 1.3 and 1.6 liter engine, and the parts are produced in batches as a

changeover in equipment programming is required between parts. The parts produce

by this shop then shipped to another plant for assembly.

This shop floor consists of six machining process lines where two lines dedicated for

cylinder head machining, another two lines for camshaft, and the rests are for

crankshaft and cylinder block. The layout design for each line can be classified as a

flexible manufacturing system (FMS) except for camshaft as the raw-casts were

handled manually. As explained by (Groover, 2008), the FMS means a production

system that has an ability to identify and distinguish between the different incoming

part or product styles processed, quick changeover of physical setup and quick

changeover of operating instruction. Each process line consists of a group of

Computer Numerical Controls (CNC) machine tools, interconnected by an automated

material handling system such as conveyor, gantry or robotic arm to move part

between machines in the cell. All these systems are controlled by a distributed

computer system.

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Production analysis is conducted in order to evaluate the performance of each

production line. The analysis is based on the engine shop’s Monthly Performance

Result (MPR) report as a variety of data was recorded on it, such as machining and

assembly production status, process and material defects, downtime and etc. The

two-month machining performance results were analyzed as shown in Figure 1.1 and

Figure 1.2.

Prod Line

Months

Cyl. Bloc

k

Cran

ksha

ft

Cms-CP

S

Cms-S4

P

Cyl. He

ad 2

Cyl. He

ad 1

Mon

2

Mon

1

Mon

2

Mon

1

Mon

2

Mon

1

Mon

2

Mon

1

Mon

2

Mon

1

Mon

2

Mon

1

40

30

20

10

0

Pro

d R

ate

19.34

16.25

19.3421.83

28.12

33.47

39.4738.38

22.9821.24

8.967.54

Comparison of two months production rateTwo months production line analysis

Cyl. Head 1: Cylinder head machining line 1 Cyl. Head 2: Cylinder head machining line 2 Cmc-S4P: Camshaft machining line for standard engine Cmc-CPS: Camshaft machining line for CPS engine Crankshaft: Crankshaft machining line Cyl. Block: Cylinder block machining line

Figure 1.1: Comparison of two-month production rate

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Down cause

Prod Line

Month

BA

Cyl. Bloc

k

Cranks

haft

Cms-CPS

Cms-S4P

Cyl. H

ead 2

Cy l.

Hea

d 1

Cyl. Bloc

k

Cranks

haft

Cms-CPS

Cms-S4P

Cyl. H

ead 2

Cy l.

Hea

d 1

Mon 2

Mon 1

Mon 2

Mon 1

Mon 2

Mon 1

Mon 2

Mon 1

Mon 2

Mon 1

Mon 2

Mon 1

Mon 2

Mon 1

Mon 2

Mon 1

Mon 2

Mon 1

Mon 2

Mon 1

Mon 2

Mon 1

Mon 2

Mon 1

20

15

10

5

0

% P

rod

ucti

on

Ra

te

2.943.

13

20.90

11.13

8.31

6.70

8.31

7.73

7.08

6.48

1.05

5.64

7.87

16.20

15.90

7.06

8.18

4.72

8.18

2.84

4.42

6.35

0.53

7.13

Downtime analysisTwo months production line analysis

A: Inspection and quality adjustment B: Tool broke / changes

Figure 1.2: Downtime analysis

Figure 1.1 shows that the camshaft and the crankshaft production line have a

decreased rate compared to previous month. This indicates the lack of productivity,

which gives an immense impact to the monthly production target. According to

(Wilson, 2011), the production rate analysis is a measurement of the typical amount

of time it takes for a manufacturing operation to produce its goods, and this gives an

understanding of the relative efficiency of each machining line’s operation.

Further investigation was made by analyzing the downtime for each machining line

for both months. The downtime or stoppage is classified into two highly related

causes; one is because of the inspection and quality adjustment, and the other one is

because of the tool deterioration. The analysis result was shown in Figure 1.2.

Clearly, the percentage value between the two-month records of the crankshaft’s

machining line has shown a rapid increment of the downtime in both causes. The

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inspection and quality adjustment was 7.06% for month-one and rapidly increased to

15.90%. It goes the same for the other cause of the downtime; the tool broke and

change stoppages, where in the first month was only 11.13% and suddenly increased

to 20.90% in the second month. Compared with the other machining line, the

crankshaft is regarded as the most problematic production line.

Based on these results and the discussion with crankshaft’s production personnel, the

main cause of the frequent tool broke is because the mechanical properties of the raw

material have been improved. The improvement commenced mid 2009 as to fulfill

the need of the new car model. According to (Ahmad Shahrir Halimi, Head of

Department, Engine & Transmission Department, Proton Tanjung Malim Sdn Bhd,

pers. comm. 16 December 2009), as the improvement in raw-cast is not too

enormous, the management decided the part should be processed according to

previous condition without any changes to the process parameters and further study.

Since then, the problem of the tool broke frequently appeared on almost the

machining time which subsequently affecting the production target. Consequently,

the crankshafts’ production team has to adjust the machine setting to meet the need

of newly improved raw-cast. The common method used is the trial-and-error, and

this does not only involve in adjusting the machining parameter but also various

other's adjustment such as changing the cutting tool dimension and the sequence of

the machining process. This obviously led to a worst scenario as the production line

has about 22 operations on 31 CNC machines.

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1.2. Crankshaft Machining Line

The crankshaft machining line is designed to fabricate the crankshaft named SP4H

(Figure 1.3), for 1.6 engine size and S4PE, for 1.3 engine size; both were produced in

different batch, and the projected production target is 27 units per hour.

Figure 1.3: Crankshaft S4PH

The line have six manufacturing cells in which three cells for roughing process and

the rest for finishing process. The layout is as illustrated in Figure 1.4. Every cell has

more than four workstations and an operator. The operator is the person responsible

to monitor and verify the quality of a processed work piece after every 20 pieces. If

quality problems arise, the machine and cutting tool had to be inspected for error,

correction being made and recorded. After the correction is made, the adjustment

process then takes place where a single raw-cast will go through the entire machining

process and inspected again afterwards. If the processed part meets the desired

quality requirement, the subsequent process will continue in full capacity. This

adjustment process also implemented after a replacement of cutting tool.

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Figure 1.4: Crankshaft machining line layout

This machining line consists of 22 operations, and the entire systems are fully

automated except for part loading at the beginning of the conveyor, unloading at the

end of the production line and part inspection at every cell, which is done manually

by an operator. The gantry system is used to transport the part between the conveyor

and CNC machines.

The roughing line is intended to machine the part until it near net dimension. It

consists of seven operations within three cells. Based on discussion and interview

with the production personnel, this roughing line contributed higher downtime due to

the tool change and the adjustment process.

1.2.1. The roughing cells

First cell consists of four CNC machine tools, named OP10A, OP10B, OP20A and

OP20B. There are two operations involve in this cell; the primary OP10 operation is

to face mill both end facing (Figure 1.5), centering and reference face milling. The

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OP20 operation is to turn the front shaft and counter weight as illustrated in Figure

1.6.

According to observation and response from the operator, machine OP10 often has a

downtime, mainly because of frequent cutting tool's changes due to premature end of

tool life. It occupied nine different cutting tools, and the total cycle time projected is

97 seconds per part; the same cycle time for both part family.

Figure 1.5: OP10 machining operation

Figure 1.6: OP20 operation, to turn the front shaft and counter weight

Face mill and centering

Counter weight turning Front shaft turning

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1.2.2. The OP10 machining process

There are three major machining processes for OP10 with help of nine different

cutting tools. The major machining processes are the face milling of both ends of the

crankshaft, the centering for front and rear shaft and the reference face milling. Table

1.1 and Figure 1.7 show the details of the cutting tool used for each process and its

time. The most employed tools were the tool-four (T4) with 22.9%, tool-one (T1)

with 20.7% and tool-three (T3) with 14.8% of machining time.

Table 1.1: OP10 tool usage detail

Tool number Process detail Process time

(sec/process) Tool life set

Tool 1 Both end facing – milling 18.5 150

Tool 2 Rear centering – drill 7.5 250

Tool 3 Rear centering – drill 13.2 150

Tool 4 Rear turning – turning 20.5 150

Tool 5 Rear chamfering – turning 5.5 300

Tool 6 Reference face milling – milling 7.5 200

Tool 7 Front turning – turning 7.5 200

Tool 8 Front drilling – drill 5.5 500

Tool 9 Front centering – drill 3.5 500

Extracted from Proton ETM Crankshaft Process Sheet.

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Tool 1

Tool 2

Tool 3

Tool 4

Tool 5

Tool 6

Tool 7

Tool 8

Tool 9

Category

Tool 93.9%Tool 8

6.2%

Tool 78.4%

Tool 68.4%

Tool 56.2%

Tool 423.0%

Tool 314.8%

Tool 28.4%

Tool 120.7%

OP 10 tool usage (sec/raw-cast)OP 10 tool analysis

Figure 1.7: OP 10 tool usage

Therefore, an analysis of the frequent tool change was made. The three tools: T4, T1

and T3 are predicted to have frequent changes. The two-month tool change record of

OP10 is analyzed and the result shows in Figure 1.8. As predicted, it clearly showed

that the most frequent tool change is T1 with 77 times, T4 with 70 times and T3 with

58 times. However, these change frequencies are not only counted for tools that

reach its service life but also for the tool that deteriorated before its end of life such

as fracture and deform.

Figure 1.9 shows the frequency of tool change because of premature tool life. The

highest frequent change because of this early tool life is T1 with 39 times, which is

about 50% of the total tool change recorded. The T4 is the second highest recorded

with 34 times of overall 70 times of tool change made. It is nearly 48% of the tool

change due to the premature tool life.

The T1 tool lifespan set as in Table 1.1 is infrequently achieved. On average, the T1

lifespan is only capable to machine up to 94 raw-casts. It is about 60.27% from the

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total lifespan set. Obviously, any improvement on T1 will provide positive outcomes

for the productivity.

Tool 9Tool 8Tool 7Tool 6Tool 5Tool 4Tool 3Tool 2Tool 1

80

70

60

50

40

30

20

10

0

Tool number

Ch

an

ge

fre

qu

en

cy

6

25

4341

27

70

58

33

77

OP 10 tool change frequency for two-month recordOP 10 tool analysis

Figure 1.8: OP10 tool change frequency for two-month record

Tool 9Tool 8Tool 7Tool 6Tool 5Tool 4Tool 3Tool 2Tool 1

40

30

20

10

0

Tool number

To

ol d

ete

rio

rati

on

fre

qu

en

cy

2

8

10

8

6

34

8

4

39

OP10 tool deteriorate frequencyTool change record analysis

Figure 1.9: Tool deterioration frequency - not because of tool life

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1.3. Problem Statement

The performance of the crankshaft machining line previously was very satisfying and

often achieves the desired production target without many hassles. The production

output target is 97 units per hour. However, the production performance declined

drastically since mid-2009 due to increase of the production downtime such as the

tool change, the adjustment and the inspection of quality. The frequent production

downtime is because of the mechanical properties of the raw-cast has been improved

where the hardness of the material was increased from averagely 230HB to 255HB.

This material improvement is mandatory as to meet the requirement of the new

engine specification.

An investigation was made to machine OP10 as it was the first machine of the

production chain and reported as a liability to the entire machining line. The result

found that some of the cutting tool had frequently failed prematurely. There are nine

different cutting tools equipped within this OP10, but the most failed cutting tool was

T1 which is responsible to face mill both-ends of the crankshaft. The tool life limit is

design to cut about 150 raw-casts, but currently, the tool only able to cut on average

94 raw-cast. For a record, there is no preliminary study made to the lifespan of the

cutting tool after the improvement of the raw-cast. Hence, this study will determine

the T1 performance when face milling the newly improved raw-cast.

The need for T1 cutting tool replacement to the other grade or other specification is

impractical as the current tool is still capable to withstand the material hardness up to

300HB (Sumitomo Electric, 2010). Moreover, if the conversion to other grades or

brand of the cutting tool, it will consume huge cost and time. Therefore, this study

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also seeks the possibility to improve the T1 machining length. The proposed

improvement was to determine the optimal cutting parameters in order to obtain the

maximum T1 lifespan. However, the material removal rate should remain the same

or higher than the current practices as any reduction in this rate will result in low

productivity and definitely will not be accepted by the engine shop management.

Therefore, the need for evaluating the T1 performance with the new raw cast is

essential as to be compared with previous performance. This performance evaluation

will show the new T1 lifespan with the current machining parameters. It is expected

that the T1 lifespan will be reduced. Since the T1 ability toward the new work piece

is the still broad, hence, further study in seeking the maximum T1 utilization is a

must. The optimal T1 cutting parameters should be determined for maximizing the

tool life in terms of removed volume.

1.4. Research Objectives

The aim of this study is to evaluate and improve the T1 performance. The desired

outcome of this study is to maximize the service life of T1 while maintaining the

surface finish quality. Therefore, the specific objectives of this study are as follows:

i. To evaluate the performance of T1 insert in terms of removed volume

and the average surface roughness when face mill the spheroidal

graphite cast iron using the current engine shop cutting condition (the

initial cutting condition) in the laboratory environment.

ii. To determine the optimal cutting condition for the tool lifespan (T1)

in terms of materials removed volume when face milling the

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spheroidal cast iron by controlling three parameters at three levels

using Taguchi optimization technique with the averaged machined

surface response not exceeds 50 µm.

1.5. Scope of the Study

This study focuses on finding the optimal machining conditions namely cutting

speed, feed rate and depth of cut for face milling operation to obtain the optimum

material removed volume. At the same time, the quality of machined surface should

be monitored as there is a tolerance limit where its Ra value should not exceed or

equal to 50 µm.

The optimization method used in this study is the Taguchi method as it was proven to

be the simplest yet efficient method by many researchers such as (Gopalsamy et al.,

2009; Kacal and Gulesin, 2011; Khorasani et al., 2011; Lin, 2002a, 2002b; Tosun et

al., 2004; Yang and Tarng, 1998; Zhang et al., 2007).

Only one type of milling insert and one type of material is employed during this

whole study. For every experimental run, only one insert is used at a time. According

to (Richetti et al., 2004), results from the milling test using lesser number of inserts

than the cutter capacity can be used as comparison index of the machinability

between two or more machining conditions. (Lin, 2002a, 2002b), mentioned that the

uses only one insert during milling is acceptable in order to simplify the tool life

analysis. Furthermore, it also reduces the experiment cost and time.

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1.6. Significance of the Study

This study provided better understanding of various manufacturing aspects, and also

method for process improvement. These include the machining tools' operation and

its dynamism, cutting tool performance measurement, and method for quality

improvement.

Furthermore, utilization of Taguchi technique in seeking the optimal value of

machining parameters is presented in proper step according to (Khorasani et al.,

2011; Lin, 2002a; Ross, 1995). The optimization technique proposed hopefully will

benefits and help future researchers as guidance.

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