High Definition Metrology Based Surface Quality Control and Applications (eBook)

Shichang Du, Lifeng Xi (Autoren)

eBook Download: PDF

2019 | 1st ed. 2019
XIV, 329 Seiten
Springer Singapore (Verlag)
978-981-15-0279-8 (ISBN)

Infos

This book provides insights into surface quality control techniques and applications based on high-definition metrology (HDM). Intended as a reference resource for engineers who routinely use a variety of quality control methods and are interested in understanding the data processing, from HDM data to final control actions, it can also be used as a textbook for advanced courses in engineering quality control applications for students who are already familiar with quality control methods and practices. It enables readers to not only assimilate the quality control methods involved, but also to quickly implement the techniques in practical engineering problems. Further, it includes numerous case studies to highlight the implementation of the methods using measured HDM data of surface features. Since MATLAB is extensively employed in these case studies, familiarity with this software is helpful, as is a general understanding of surface quality control methods.

Prof. Shichang Du received the B.S. and M.S.E. degrees in mechanical engineering from the Hefei University of Technology, Hefei, China, in 2000 and 2003, respectively, and the Ph.D. degree in industrial engineering and management from Shanghai Jiao Tong University, Shanghai, China, in 2008. Prof. Du was a Visiting Scholar with the University of Michigan, Ann Arbor, Michigan, USA, from 2006 to 2007. He is currently a Professor at Shanghai Jiao Tong University. His current research interests include quality and reliability engineering, quality control with analysis of error flow, and monitoring and diagnosis of manufacturing process. Since 2005, Prof. Du has published 72 peer-reviewed technical papers in international journals and conferences. He is a member of IEEE and served as a reviewer of several international journals. Meantime, he is an Area Editor of Computers and Industrial Engineering, and an Associate Editor of Flexible Services and Manufacturing journal. He was a recipient of Best Paper Award at the 12th International Conference on Industrial Engineering and Engineering Management in 2005. Prof. Du was also awarded Shanghai Science and Technology Progress Award (First Prize) in 2012 and Shanghai Young Scholar Science and Technology Rising-Star Award in 2013 by Shanghai Municipal Government, respectively.

Prof. Lifeng Xi received the B.S. degree from the University of Science and Technology of China, Hefei, China, in 1989, the M.S. degree from Chinese Academy of Sciences, Xi'an, China, in 1992, and the Ph.D. degree from Shanghai Jiao Tong University, Shanghai, China, in 1995, all in mechanical engineering. Prof. Xi is currently the vice president of Shanghai Jiao Tong University. His current research interests include quality and reliability engineering, theory and method of production system planning and design, and precision manufacturing of auto engine. Since 1996, Prof. Xi has published more than 100 peer-reviewed technical papers in international journals and conferences. He is a Fellow of the International Society of Engineering Asset Management (ISEAM), the Deputy Director of Academic Committee of China Association for Quality, and the Vice Chairman of Shanghai Society Automotive Engineers. Meanwhile, he is an Associate Editor of Industrial Engineering and Management journal. Prof. Xi was awarded National Science and Technology Progress Award by the State Council of the People's Republic of China. He was also awarded Shanghai Science and Technology Progress Award twice (First Prize in 2012 and Second Prize in 2009) by Shanghai Municipal Government.

Inhalt

Foreword 5
Preface 7
Acknowledgements 8
Contents 9
1 Introduction 13
1.1 History and Current Status of Surface Topography 13
1.2 Scope and Objectives 14
1.3 Organization 15
2 High-Definition Metrology 17
2.1 A Brief History of Measurement Technology 17
2.1.1 Contact Measurement 17
2.1.2 Noncontact Measurement 18
2.2 High-Definition Metrology 19
2.2.1 Definitions and Measurement Principles 19
2.2.2 Examples and Applications 20
References 23
3 Surface Characterization and Evaluation 25
3.1 A Brief History of Surface Evaluation 25
3.2 3D Surface Form Error Evaluation 26
3.2.1 Introduction 26
3.2.2 The Proposed Method 28
3.2.2.1 HDM Data Preprocessing Method 28
3.2.2.2 Modified GLCM Method 30
3.2.3 Case Study 33
3.2.3.1 Experimental Conditions 33
3.2.3.2 Experimental Results 34
3.2.4 Conclusions 35
3.3 Co-kriging Method for Form Error Estimation Incorporating Condition Variable Measurements 37
3.3.1 Introduction 37
3.3.2 Problem Description and Univariate Spatial Method for Form Error Estimation 39
3.3.3 The Proposed Multivariate Spatial Statistics Method 42
3.3.3.1 Variogram and Cross-Variogram 42
3.3.3.2 Multivariate Spatial Method 44
3.3.3.3 An Illustrated Example 46
3.3.4 Comparison of Univariate and Multivariate Spatial Statistics Methods Using Simulated Data 49
3.3.4.1 Simulation Comparison Procedure 49
3.3.4.2 Simulation Results and Discussion 51
3.3.5 Case Study 51
3.3.5.1 Case Study I 51
Experimental Setup 51
Data Preprocessing 54
Results and Discussion 57
3.3.5.2 Case Study II 62
Experimental Setup 62
Results and Discussion 63
3.3.6 Conclusions 65
Appendix 65
References 66
4 Surface Filtering 70
4.1 A Brief History of Surface Filtering 70
4.1.1 M-System and E-System 70
4.1.2 Current International Standards 71
4.2 A Shearlet-Based Filtering Method for 3D Engineering Surface 72
4.2.1 Introduction 72
4.2.2 The Construction of Shearlets 74
4.2.3 The Procedure of the Proposed 3D Surface Separation Method 77
4.2.3.1 Overview of the Proposed Method 77
4.2.3.2 The Procedure of Surface Decomposition with NSST 78
4.2.3.3 The Procedure of Surface Reconstruction with INSST 79
4.2.3.4 Transmission Characteristics of Shearlet Filter 83
4.2.4 Numerical Simulation 83
4.2.5 Case Studies 85
4.2.5.1 Case Study I 85
4.2.5.2 Case Study II 93
4.2.5.3 Case Study III 98
4.2.5.4 Discussions of Properties 100
4.2.6 Conclusions 103
4.3 A Diffusion-Based Filtering Method for 3D Engineering Surface 105
4.3.1 Introduction 105
4.3.2 Diffusion Filtering 107
4.3.2.1 Anisotropic Diffusion Filtering 107
4.3.2.2 Anisotropic Diffusion Filter with Edge Detectors 109
4.3.2.3 Parameters Setting 109
4.3.3 Simulation 110
4.3.4 Experiment 111
4.3.5 Conclusions 114
4.4 A Fast and Adaptive Bidimensional Empirical Mode Decomposition Based Filtering Method for 3D Engineering Surface 114
4.4.1 Introduction 114
4.4.2 Brief Introduction to BEMD 117
4.4.3 The Proposed Method 119
4.4.3.1 Overview of the Proposed Method 119
4.4.3.2 Detecting Local Extrema 120
4.4.3.3 Adaptive Window Algorithm to Select Window Width for Order Statistics Filters 121
4.4.3.4 Generating Envelopes 124
4.4.4 Simulation Experiment 126
4.4.5 Case Studies 127
4.4.5.1 Case Study I 129
4.4.5.2 Case Study II 134
4.4.5.3 Case Study III 138
4.4.5.4 Comparison with the Shearlet-Based Filter 140
4.4.6 Conclusions 145
References 146
5 Surface Classification 149
5.1 A Brief History of Surface Classification 149
5.2 A Selective Multiclass Support Vector Machine Ensemble Classifier for Engineering Surface Classification 151
5.2.1 Introduction 151
5.2.2 The Proposed Method 153
5.2.2.1 Feature Extraction with DT-CWT 153
5.2.2.2 SVMs 156
5.2.2.3 The Proposed MPO-SVME Classifier 158
5.2.3 Case Study 161
5.2.3.1 Feature Extraction 161
5.2.3.2 Classification Results 164
5.2.3.3 Sensitivity Analysis 172
5.2.4 Conclusions 175
5.3 An Adaptive Support Vector Machine Based Workpiece Surface Classification System 176
5.3.1 Introduction 176
5.3.2 The Framework of the Proposed Classification System 177
5.3.3 Feature Extraction Using NSCT 178
5.3.3.1 The Non-subsampled Contourlet Transform 179
5.3.3.2 Feature Extraction 179
5.3.4 The Proposed Adaptive SVM Classifier 180
5.3.4.1 Adaptive Particle Swarm Optimization (APSO) Algorithm 182
5.3.4.2 Varied Step-Length Pattern Search (VSPS) Algorithm 183
5.3.5 Case Study 186
5.3.5.1 Case 1: Engine Block Top Surface 186
5.3.5.2 Case 2: Cylinder Head Cover Surface 196
5.3.5.3 Case 3: Pump Valve Plate Top Surface 196
5.3.6 Conclusions 199
References 200
6 Surface Monitoring 205
6.1 A Brief History of Surface Monitoring 205
6.2 Tool Wear Monitoring of Wiper Inserts in Multi-insert Face Milling Using 3D Surface Form Indicators 206
6.2.1 Introduction 206
6.2.2 Measurement of Wiper-Insert Wear 208
6.2.3 Extraction of Wear Indicators 211
6.2.3.1 Gray Image Converting 212
6.2.3.2 Toolmark Straightening 213
6.2.3.3 Surface Feature Extracting 215
6.2.4 Results and Discussion 216
6.2.5 Conclusions 219
6.3 Detection and Monitoring of Defects on Three-Dimensional Curved Surfaces Based on High-Density Point Cloud Data 219
6.3.1 Introduction 219
6.3.2 The Proposed Method 222
6.3.2.1 Framework 222
6.3.2.2 Region Division of Curved Surfaces 222
6.3.2.3 Feature Evaluation of Each Sub-region 228
6.3.2.4 Quality Parameters Calculation of Curved Surfaces 233
6.3.2.5 Monitoring the Quality Parameters 235
6.3.3 Case Study 235
6.3.4 Conclusions 246
6.4 Leakage Monitoring in Static Sealing Interface Based on Three-Dimensional Surface Topography Indicator 247
6.4.1 Introduction 247
6.4.2 The Proposed Method 250
6.4.2.1 Surface Components Extraction 250
6.4.2.2 Virtual Gasket Generation 252
6.4.2.3 Leakage Parameters Definition 254
6.4.2.4 Threshold Determination and Monitoring the Leakage Condition of the Successive Machining Process 256
Threshold Determination 256
Monitoring the Leakage Condition of the Successive Machining Process 259
6.4.3 Case Study 260
6.4.3.1 Finite Element Model 261
6.4.3.2 Experimental Results 266
6.4.4 Conclusions 270
References 270
7 Surface Prediction 275
7.1 A Brief History of Surface Prediction 275
7.1.1 Theoretical Approach 275
7.1.2 Experimental Approach 276
7.2 A Space–Time Autoregressive Moving Average Based Predicting Method for 3D Engineering Surface 277
7.2.1 Introduction 277
7.2.2 STARIMA Model 279
7.2.2.1 Model Basis 279
7.2.2.2 Model Identification 280
7.2.2.3 Model Estimation and Diagnostic Checking 281
7.2.3 Experiment 282
7.2.4 Conclusions 285
7.3 A Space–Time Multi-output Support Vector Regression Based Predicting Method for 3D Engineering Surface 285
7.3.1 Introduction 285
7.3.2 The Procedure of STMSVR Model 288
7.3.2.1 Overview of the Model 288
7.3.2.2 Space–Time Surface Topography Data Preprocessing 288
7.3.2.3 STARMA and Model Identification 288
7.3.2.4 STMSVR and Model Building 290
7.3.2.5 Accuracy Evaluation 294
7.3.3 Case Study 295
7.3.3.1 Conclusions 299
References 299
8 Online Compensation Manufacturing 302
8.1 A Brief History of Online Compensation Manufacturing 302
8.2 A Systematic Method for Online Minimizing Volume Difference of Multiple Chambers in Machining Processes 303
8.2.1 Introduction 303
8.2.2 The Proposed Method 306
8.2.2.1 Datum Transformation 306
8.2.2.2 Accurate Volume Calculation of Multiple Chambers 310
8.2.2.3 The Model for Obtaining an Optimized Machining Parameter 316
8.2.2.4 Model Solution 320
8.2.3 Case Study 324
8.2.3.1 Machining Process Description 324
8.2.3.2 Results and Analysis 326
8.2.4 Conclusions 335
References 337

Produkt-Details

• Erscheint lt. Verlag: 18.10.2019
• Zusatzinfo: XIV, 329 p. 224 illus., 166 illus. in color.
• Sprache: englisch
• Themenwelt: Naturwissenschaften
• Themenwelt: Technik ► Bauwesen
• Themenwelt: Technik ► Maschinenbau
• Themenwelt: Wirtschaft ► Betriebswirtschaft / Management ► Logistik / Produktion
• Schlagworte: High definition metrology • Optical Measurement • quality control • Quality Control, Reliability, Safety and Risk • Surface characterization • Surface classification • Surface filtering • surface metrology • Surface monitoring • Surface prediction • surface topography • Tool wear monitoring
• ISBN-10: 981-15-0279-X / 981150279X
• ISBN-13: 978-981-15-0279-8 / 9789811502798