Iowa State University Capstones, Theses and Retrospective Theses and Dissertations Dissertations 2001 The development of in-process surface roughness prediction systems in turning operation using accelerometer Hanming Huang Iowa State University Follow this and additional works at: https://lib.dr.iastate.edu/rtd Part of the Industrial Engineering Commons Recommended Citation Huang, Hanming, "The development of in-process surface roughness prediction systems in turning operation using accelerometer " (2001). Retrospective Theses and Dissertations. 435. https://lib.dr.iastate.edu/rtd/435 This Dissertation is brought to you for free and open access by the Iowa State University Capstones, Theses and Dissertations at Iowa State University Digital Repository. It has been accepted for inclusion in Retrospective Theses and Dissertations by an authorized administrator of Iowa State University Digital Repository. For more information, please contact [email protected]. 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Photographs included in the original manuscript have been reproduced xerographically in this copy. Higher quality 6" x 9" black and white photographic prints are available for any photographs or illustrations appearing in this copy for an additional charge. Contact UMI directly to order. Bell & Howell Information and Learning 300 North Zeeb Road, Ann Arbor, Ml 48106-1346 USA 800-521-0600 The development of in-process surface roughness prediction systems in turning operation using accelerometer by Hanming Huang A dissertation submitted to the graduate faculty in partial fulfillment of the requirements for the degree of DOCTOR OF PHILOSOPHY Major: Industrial Education and Technology Major Professor: Joseph C. Chen Iowa State University Ames. Iowa 2001 Copyright © Hanming Huang, 2001. All right reserved. UMI Number: 3003249 Copyright 2001 by Huang, Hanming All rights reserved. ® UMI UMI Microform 3003249 Copyright 2001 by Bell & Howell Information and Learning Company. All rights reserved. This microform edition is protected against unauthorized copying under Title 17, United States Code. Bell & Howell Information and Learning Company 300 North Zeeb Road P.O. Box 1346 Ann Arbor, Ml 48106-1346 11 Graduate College Iowa State University This is to certify that the Doctoral dissertation of Hanming Huang has met the dissertation requirements of Iowa State University Signature was redacted for privacy. Major Professor Signature was redacted for privacy. Signature was redacted for privacy. Ill TABLE OF CONTENTS LIST OF FIGURES viii LIST OF TABLES xi ABSTRACT xiii CHAPTER 1. INTRODUCTION 1 1.1. Role of Turning Operation in Manufacturing 1 1.2. Existing Problems about Turning Operation in Manufacturing 3 1.3. Resolutions for the Problem 5 1.4. Objectives of the Study h 1.5. Impact of the Study 7 1.6. Limitations of the Study 8 1.7. Dissertation Organization 8 CHAPTER 2. LITERATURE REVIEW 10 2.1. Turning Operation 10 2.2. Surface Characterization and Measurement Techniques 14 2.3. Application of Sensor Techniques 18 2.4. Fuzzy Theory and Its Application 23 2.4.1. Fuzzy logic theory 23 2.4.2. The application of fuzzy logic 26 2.4.3. Fuzzy nets theory 29 2.4.4. Application of fuzzy nets 31 iv 2.5. Summary of the Literature Review 33 CHAPTER 3. METHODOLOGY 34 3.1. Experimental Setup 34 3.2. Detection of Vibration 36 3.3. Detection of Spindle Rotation 40 3.4. Conversion of Analogue Signal to Digital Signal 42 3.5. Process of Vibration Data 43 3.6. Experimental Design in Data Collection 45 3.7. Data Organization 47 3.8. Multiple Regression Modeling 49 3.8.1. Multiple regression based modeling 49 3.8.2. Fuzzy logic modeling 51 3.8.2.1 Fuizification 51 3.8.2.2. Inference 56 3.8.2.3. Defuzzificaiion 58 3.8.3. Fuzzy Nets Modeling 59 3.8.3.1. Divide the input space into fuzzy regions 60 3.8.3.2. Generate fuzzy rules from input-output given data pairs 62 3.8.3.3. .4voidconjlicting rules 64 3.8.3.4. Develop a combined fuzzy rule base 65 3.8.3.5. Defuzzification 67 3.9. Determination of Prediction Accuracy 68 V 3.10. Comparison of Model Accuracies 69 CHAPTER 4. A MULTIPLE REGRESSION MODEL TO PREDICT IN-PROCESS SURFACE ROUGHNESS IN TURNING OPERATION VIA ACCELEROMETER 71 Introduction 71 Purpose of Study 73 Experimental Setup 73 Processing Vibration Signals 78 Multiple Regression Modeling 79 The Model Accuracy 83 Verification of Using Vibration Information 85 Conclusion and Discussion 87 References 88 CHAPTER 5. DEVELOPMENT OF AN IN-PROCESS SURFACE ROUGHNESS PREDICTION SYSTEM FOR TURNING OPERATION USING FUZZY LOGIC MODELING 89 ABSTRACT 89 1. Introduction 89 2. The Structure of the System 92 3. Experimental Setup and Sampling 93 3.1. Experimental setup 93 3.2. Treatment of the vibration data 96 3.3. Data structure 97 4. Fuzzy Logic Modeling 98 vi 4.1. Fuzzification 98 4.2. Inference 104 4.3. Defuzzification 108 5. Accuracy Calculation 108 6. Computation of Fuzzy Logic Inference 10L) 7. The Model Accuracy 111 8. Conclusion 112 References 112 CHAPTER 6. A FUZZY-NETS-BASED IN-PROCESS SURFACE ROUGHNESS PREDICTION SYSTEM IN TURNING OPERATIONS 115 Abstract 115 1. Introduction 115 2. Experimental Setup 118 2.1. Hardware setup 119 2.2. Experimental design for data sampling 121 2.3. Software setup for collecting vibration signals 122 3. The Architecture of the FISRP System 124 4. The Test Results for the FISRP System 140 4.1. Test results 140 4.2. The system accuracy effect from each parameter 141 5. Conclusion 142 References 143 CHAPTER 7. CONCLUSIONS 146 vii 7.1. Comparison of Model Accuracies 146 7.2. Conclusions 147 7.3. Suggestions for Further Research 149 APPENDIX 1. ORIGINAL TRAINING DATA FOR THE CUTTER WITH A NOSE RADIUS OF 0.016 INCHES 150 APPENDIX 2. ORIGINAL TESTING DATA FOR THE CUTTER WITH A NOSE RADIUS OF 0.016 INCHES 152 APPENDIX 3. ORIGINAL TRAINING DATA FOR THE CUTTER WITH A NOSE RADIUS OF 0.031 INCHES 153 APPENDIX 4. ORIGINAL TESTING DATA FOR THE CUTTER WITH A NOSE RADIUS OF 0.031 INCHES 155 APPENDIX 5. THE VISUAL BASIC PROGRAM FOR THE FUZZY-LOGIC- BASED SYSTEM 156 APPENDIX 6. FUZZY RULE BANK OF THE FUZZY-LOGIC-BASED SYSTEM FOR THE TOOL WITH A NOSE RADIUS OF 0.016 INCHES 162 APPENDIX 7. FUZZY RULE BANK OF THE FUZZY-LOGIC-BASED SYSTEM FOR THE TOOL WITH A NOSE RADIUS OF 0.031 INCHES 163 APPENDIX 8. THE VISUAL BASIC PROGRAM FOR THE FUZZY-NETS- BASED SYSTEM 164 APPENDIX 9. FUZZY RULE BANK OF THE FUZZY-NETS-BASED SYSTEM FOR THE TOOL WITH A NOSE RADIUS OF 0.016 INCHES 169 APPENDIX 10. FUZZY RULE BANK OF THE FUZZY-NETS-BASED SYSTEM FOR THE TOOL WITH A NOSE RADIUS OF 0.031 INCHES 172 BIBLIOGRAPHY 175 ACKNOWLEDGEMENTS 182 viii LIST OF FIGURES CHAPTER 1 Figure 1-1. Values of shipments of machine tool products in the last decade 2 CHAPTER 2 Figure 2-1. Surface roughness (Ra) produced by common production methods 11 Figure 2-2. Illustration of Ra 15 Figure 2-3. Operation of stylus profiler 17 CHAPTER 3 Figure 3-1. The experimental set up 35 Figure 3-2. Wiring chart 35 Figure 3-3. Setup of the work piece and the measuring zone 36 Figure 3-4. Illustration of the sensing principles of an accelerometer 37 Figure 3-5. Principle structure of a piezoelectric accelerometer 40 Figure 3-6. The principle structure of a proximity sensor 41 Figure 3-7. Signal conditioning circuitry for a proximity sensor 41 Figure 3-8. The six screw holes and the corresponding signals from the proximity sensor 42 Figure 3-9. A schematic diagram of a flash-comparator A/D converter 43 Figure 3-10. Signals from the accelerometer and the proximity sensor 44 Figure 3-11. Data organization 48 Figure 3-12. Structure of the prediction system 49 Figure 3-13. Membership functions of feed rate 55 ix Figure 3-14. The five layer structure of the fuzzy nets algorithm 59 Figure 3-15. Plots of triangular membership functions of input features and the output class 61 Figure 3-16. Membership functions of surface roughness 62 Figure 3-17. Illustration of a 2-dimension fuzzy rule array 66 CHAPTER 4 Figure 1. Hardware set up 74 Figure 2. Wiring of the signal system 74 Figure 3. Identifications of revolution in the vibration signals 75 Figure 4. Setup of the work piece and the measuring zone 76 Figure 5. Sampling data structure 77 CHAPTER 5 Figure 1. Structure of the in-process surface roughness prediction system 93 Figure 2. Experimental setup 94 Figure 3. Method to count the spindle revolution 95 Figure 4. Wiring of the hardware 95 Figure 5. Setup of the work piece and the measuring zone 96 Figure 6. Data organization 98 Figure 7. Membership functions of feed rate 102 Figure 8.