optimization warpage defects of pencil box by using

By

EKO ARI WIBOWO 2-1952-055

MASTER’S DEGREE in

MECHANICAL ENGINEERING - MECHATRONICS

FACULTY OF ENGINEERING & INFORMATION TECHNOLOGY

SWISS GERMAN UNIVERSITY The Prominence Tower

Jalan Jalur Sutera Barat No. 15, Alam Sutera Tangerang, Banten 15143 - Indonesia

JANUARY 2021

Revision after Thesis Defense on January 28^th, 2021

Eko Ari Wibowo STATEMENT BY THE AUTHOR

I hereby declare that this submission is my own work and to the best of my knowledge, it contains no material previously published or written by another person, nor material which to a substantial extent has been accepted for the award of any other degree or diploma at any educational institution, except where due acknowledgement is made in the thesis.

Eko Ari Wibowo

_____________________________________________

Student Date

Approved by:

Edi Sofyan, B.Eng., M.Eng., Ph.D.

_____________________________________________

Thesis Advisor Date

Ary Syahriar, Ph.D, DIC

_____________________________________________

Thesis Co-Advisor Date

Dr. Maulahikmah Galinium, S.Kom, M.Sc

_____________________________________________

Dean Date

Eko Ari Wibowo ABSTRACT

OPTIMIZATION WARPAGE DEFECTS OF PENCIL BOX BY USING FINITE ELEMENT ANALYSIS AND ARTIFICIAL NEURAL NETWORKS

By

Eko Ari Wibowo

Edi Sofyan, B.Eng., M.Eng., Ph.D., Advisor Ary Syahriar, Ph.D., DIC, Co-Advisor

SWISS GERMAN UNIVERSITY

The use of plastic products is currently increasing rapidly, starting from automotive components, electronics, and office equipment. Injection molding process is a method of making plastic products by injecting the material into the mold. One of the products is a pencil box, but this product has a warpage defect. Defect is indicated by a deflection in the wall, causing misassemble. This study aims to eliminate these defects with parameter optimization. Taguchi method with the L27 (3⁴) orthogonal array was used to make the data input design. Data that has been designed is simulated with the Finite Element Analysis method using MoldFlow to get value of deflection. Results of the experiment were analyzed with Backpropagation Neural Network to determine the pattern of the relationship between process parameters and response, while Genetic Algorithm method was used for parameter optimization. Composition of the recommended parameters, namely: mold temperature 15°C, melt temperature 200°C, packing pressure 120% and injection time 6 seconds. As a result, optimization of deflection reached 44%. Previous maximum deflection of 2.779 mm has decreased to 1.554 mm.

Keywords: Plastic injection molding, Warpage defect, Taguchi Method, Backpropagation Neural Network, Genetic Algorithm

Eko Ari Wibowo

© Copyright 2021 by Eko Ari Wibowo

All rights reserved

Eko Ari Wibowo DEDICATION

I dedicate this work for my beloved wife and son who constantly encourage and inspire me, patiently wait as well as give the best prayers for my struggle.

Allah subhanahu wa ta'ala is the One Who created seven heavens in layers, and likewise for the earth. The divine command descends between them so you may know

that Allah is Most Capable of everything and that Allah certainly encompasses all things in His knowledge. (Q.S. At-Talaq: 12)

Eko Ari Wibowo ACKNOWLEDGEMENTS

Praise and gratitude to Allah subhanahu wa ta'ala for all His blessings and guidance, so that until now they are still given the opportunity and health. By His will the writer is also able to complete the preparation of a thesis and complete study assignments in this beloved campus.

The author's gratitude goes to Mr. Edi Sofyan, B.Eng., M.Eng., Ph.D. and Mr.

Ary Syahriar, Ph.D., DIC for his patience and sincerity in guiding authors and always give advice like parents themselves. The author is very proud to study at this campus with the support of lecturers, employees and colleagues in the Master of Mechanical Engineering (Mechatronics) study program.

The author also expresses his gratitude to Mr. Ir. Tony Harley Silalahi, M.A.B., E.M.B.A., Mr. Tonny Pongoh, S.H., LL.M, Mr. Budi Hartono, S.T., M.T., and all colleagues at Astra Manufacturing Polytechnic who have provided opportunities and support so that this study assignment can be carried out and completed.

The author realizes that this thesis is still far from perfect, so the writer is open to constructive criticism and suggestions. Hopefully this thesis can be useful for Swiss German University students in general and Mechanical Engineering (Mechatronics) students.

Tangerang, January 2021 Eko Ari Wibowo

Eko Ari Wibowo TABLE OF CONTENTS

Page

TITTLE ... 1

STATEMENT BY THE AUTHOR ... 2

ABSTRACT ... 3

DEDICATION ... 5

ACKNOWLEDGEMENTS ... 6

TABLE OF CONTENTS ... 7

LIST OF FIGURES ... 11

LIST OF TABLES ... 13

LIST OF APPENDICES ... 15

CHAPTER 1 – INTRODUCTION ... 16

1.1. Background ... 16

1.2. Research Problem ... 21

1.3. Research Objectives ... 21

1.4. Significance of Study ... 21

1.5. Research Questions ... 22

1.6. Hypothesis ... 22

CHAPTER 2 - LITERATURE REVIEW ... 23

2.1. Plastic Injection Molding ... 23

2.1.1. Plastic injection molding machine ... 23

2.1.2. Injection molding process ... 24

2.2. Plastic Material ... 29

2.2.1. Thermoplastic ... 30

2.2.2. Thermoset ... 31

2.2.3. Elastomer ... 31

2.3. Parameter of Process Injection Molding ... 31

2.3.1. Temperature ... 31

2.3.2. Pressure ... 32

Eko Ari Wibowo

2.3.4. Stroke ... 32

2.4. Warpage Defect ... 33

2.4.1. Material characteristics ... 33

2.4.2. Differential orientation ... 33

2.4.3. Differential cooling ... 33

2.4.4. Differential crystallinity ... 34

2.4.5. Differential thermal strain ... 34

2.4.6. Mold condition ... 34

2.5. Finite Element Analysis ... 34

2.5.1 Types of Finite Element Analysis ... 35

2.6. Taguchi Method ... 36

2.7.1. Degrees of freedom ... 36

2.6.1. Orthogonal array ... 37

2.6.4. Replication ... 40

2.6.5. Randomization ... 40

2.7. Artificial Neural Network (ANN) ... 40

2.7.1. Basic concept of Artificial Neural Network ... 41

2.7.2. Component of Artificial Neural Network ... 41

2.8. Backpropagation Neural Network ... 44

2.8.1. Network initialization ... 46

2.8.2. Initialize weights ... 47

2.8.3. Network simulation ... 47

2.8.4. Backpropagation Network standard training ... 47

2.8.5. Backpropagation Neural Network training termination criteria ... 48

2.8.6. Selection of Neural Network Backpropagation training functions ... 50

2.8.7. Experimental data preprocessing ... 50

2.9. Algorithm Genetic Optimization Method ... 50

2.9.1. Selection process ... 51

2.9.2. Crossover process ... 52

2.9.3. Mutation process ... 53

2.9.4. Confirmation experiments ... 53

Eko Ari Wibowo

CHAPTER 3 – RESEARCH METHODS ... 57

3.1. Research Flow ... 57

3.2. Data Collection ... 58

3.2.1. Plastic Material ... 58

3.2.2. Plastic Injection Molding ... 59

3.2.3. Plastic Injection Molding Machine ... 60

3.2.4. Testing Parameters ... 61

3.2.5. Response Parameters ... 63

3.2.6. Constant Parameters ... 63

3.3. Experimental Design ... 63

3.3.1. Parameter setting for warpage defect simulation ... 63

3.3.2. Calculation and determination of the orthogonal matrix ... 64

3.4. Simulation of Finite Element Analysis Using MoldFlow ... 66

3.5. Training Backpropagation Neural Network ... 67

3.6. Optimization using Genetic Algorithm ... 68

3.7. Confirmatory Experiment ... 69

CHAPTER 4 – RESULTS AND DISCUSSIONS ... 70

4.1. Data Analysis ... 70

4.2. Data processing with Backpropagation Neural Network ... 74

4.2.1. Pre-processing Data ... 74

4.2.2. Network Determination ... 78

4.2.3. Number of Neurons on Hidden Layer ... 78

4.2.4. Network Initialization ... 79

4.2.5. Initialize Weight and Bias Values ... 79

4.2.6. Stopping criterion ... 81

4.2.7. Percentage of Training Data and Test Data ... 82

4.2.8. Learning Rate ... 83

4.2.9. Network Architecture Forms ... 83

4.2.10. Backpropagation Neural Network Results ... 84

4.3. Optimization of Response Parameters Using Genetic Algorithm Method .... 87

4.3.1. Determination of Process Parameter Boundaries ... 87

Eko Ari Wibowo

4.3.3. Determination of Fitness Functions ... 88

4.3.4. Determination of the Options Structure ... 88

4.3.5. Result of Optimization of Genetic Algorithm ... 88

4.4. Confirm Trial ... 90

4.4.1. Optimization Results ... 90

CHAPTER 5 – CONCLUSIONS AND RECCOMENDATIONS ... 92

5.1. Conclusions ... 92

5.2. Recommendations ... 92

GLOSSARY ... 95

REFERENCES ... 96

APPENDIX ... 100

CURRICULUM VITAE ... 119

Eko Ari Wibowo LIST OF FIGURES

Figures Page

Figure 1 Warpage Defects on Pencil Box ... 16

Figure 2 Data of Experiment Injection for Pencil Box ... 17

Figure 3 Simulation Result of Warpage Indicator at Cover of The Pencil Box ... 17

Figure 4 Simulation Result of Warpage Indicator at Base of The Pencil Box ... 18

Figure 5 Warpage measurement on the pencil box (a) top surface area (b) side surface area ... 18

Figure 6 General Layout of Machine Injection Molding ... 23

Figure 7 Mold Close Position ... 24

Figure 8 Fill Injection ... 26

Figure 9 Holding Injection Position ... 27

Figure 10 Charging and Cooling Position ... 27

Figure 11 Mold Open ... 28

Figure 12 Variations in Molecular Structure in Monomers ... 30

Figure 13 Engineering Structures with Interconnected Nodules ... 35

Figure 14 3D Cover of Pencil Box ... 35

Figure 15 Neuron ... 41

Figure 16 Artificial Neural Network Architecture ... 42

Figure 17 Sigmoidal Curve as Activation Function... 43

Figure 18 One Point Crossover Model ... 52

Figure 19 Two Point Crossover Model ... 53

Figure 20 Pie Diagram of Parameter Process for Optimization Warpage ... 56

Figure 21 Research Flowchart of Warpage Defect Optimization on A Pencil Box .... 57

Figure 22 a. Assy of Mold, b. Sub Assy of Core, c. Sub Assy of Cavity ... 59

Figure 23 Hwa Chin 160 SE Injection Molding Machine ... 60

Figure 24 Flow Chart of The Simulation Data Collection Process ... 66

Figure 25 Flow Chart of The Data Training Process ... 67

Figure 26 Flow Chart of The Decision-Making Process ... 68

Eko Ari Wibowo

Figure 28 Architecture of Network Formation 4-9-9-1 ... 84

Figure 29 Deflection Optimization Results on Pencil Box ... 90

Figure 30 Rib Shape on Base Component ... 93

Figure 31 Warpage Analysis with Adding Rib ... 93

Eko Ari Wibowo LIST OF TABLES

Table Page

Table 1 Recommendation of Matrix Orthogonal Array... 38

Table 2 Orthogonal Array (three-level, L27) ... 38

Table 3 Recommendation of Matrix Orthogonal Array... 40

Table 4 Previous Research of Parameter Optimization in PMI ... 54

Table 5 PP7555KNE2 ExxonMobil ... 58

Table 6 Specifications of Mold Pencil Box ... 60

Table 7 Specification data of the Hwa Chin machine type HC-Series 160 SE ... 61

Table 8 1^st Parameter of Plastic Box Testing ... 62

Table 9 2^nd Parameter of Plastic Box Testing ... 62

Table 10 3^rd Parameter of Plastic Box Testing ... 62

Table 11 Constant Parameter ... 63

Table 12 Degrees of Freedom for Variable Process and Level ... 64

Table 13 Orthogonal Array L27 (3⁴) at Plastic Box ... 65

Table 14 1^st Design Matrix Orthogonal for Deflection Simulation on Base ... 71

Table 15 2^nd Design Matrix Orthogonal for Deflection Simulation on Base... 72

Table 16 3^rd Design Matrix Orthogonal for Deflection Simulation on Base ... 73

Table 17 Pre-processing at 1^st Design Data Matrix Orthogonal ... 75

Table 18 Pre-processing at 2^nd Design Data Matrix Orthogonal ... 76

Table 19 Pre-processing at 3^rd Design Data Matrix Orthogonal ... 77

Table 20 Number of Neuron Used ... 79

Table 21 Network Function ... 79

Table 22 Weight and Bias Value Criteria ... 80

Table 23 Weight and Bias values from Hidden Layer 1 to Hidden Layer 2... 80

Table 24 Weight and Bias values from Hidden Layer 1 to Hidden Layer 2... 81

Table 25 Weight and Bias values from Hidden Layer 2 to Output Layer ... 81

Table 26 Stopping Criterion ... 81

Table 27 Composition of The Percentage Data ... 82

Eko Ari Wibowo

Table 29 Combination of Error Calculation Parameters ... 83

Table 30 Comparison of Experimental Data with BPPN Training Results Data ... 85

Table 31 Error value in comparison of experimental data and BPPN training data .... 86

Table 32 Variable Limit of the Genetic Algorithm Testing Process ... 87

Table 33 Genetic Algorithm Structure Options ... 88

Table 34 Recommended Parameters for MoldFlow Simulation ... 89

Table 35 Optimal Composition of Parameters for the Moldflow Simulation... 90

Table 36 One Sample T-Test Analysis ... 91

Table 37 Hypotheses for Warpage Defect Testing ... 91

Table 38 Rib Specifications on Pencil Box Products ... 93

Eko Ari Wibowo LIST OF APPENDICES

Appendix Page

Appendix 1 Research Resume with Theme of Warpage Defect Optimization... 100

Appendix 2 Datasheet of Polyprophylene ... 103

Appendix 3 Hwa Chin 160 SE Injection Molding Machine Specification ... 105

Appendix 4 Injection Mold Data Sheet of Pencil Box ... 106

Appendix 5 Script Program ... 107

Appendix 6 Result of Analysis with MATLAB ... 110

Appendix 7 Neural Network Training for Architecture 4-9-9-1... 112

Appendix 8 Best Training Performance for Architecture 4-9-9-1 ... 113

Appendix 9 Training State for Architecture 4-9-9-1 ... 114

Appendix 10 Regression for Architecture 4-9-9-1 ... 115

Appendix 11 Optimal Paramter for Simulation ... 116

Appendix 12 Result of Optimal Paramter ... 117