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| موضوع: كتاب Geometric Tolerances - Impact on Product Design, Quality Inspection and Statistical Process Monitoring الإثنين 18 ديسمبر 2017, 9:44 pm | |
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أخوانى فى الله أحضرت لكم Geometric Tolerances Impact on Product Design, Quality Inspection and Statistical Process Monitoring Bianca M. Colosimo Nicola Senin
ويتناول الموضوعات الأتية :
Contents Part I Impact on Product Design 1 Geometric Tolerance Specification 3 Antonio Armillotta and Quirico Semeraro 1.1 Introduction . 4 1.2 From Linear to Geometric Tolerances . 6 1.3 Description of the Product . 9 1.3.1 Geometric Data 9 1.3.2 Design Requirements . 11 1.4 General Approach to Tolerance Specification . 17 1.4.1 Empirical Specification Rules 17 1.4.2 Classification of Tolerancing Cases . 19 1.5 Generative Specification Methods . 23 1.5.1 Technologically and Topologically Related Surfaces 25 1.5.2 Degrees of Freedom . 27 1.5.3 Mirrors . 28 1.5.4 Function Decomposition 29 1.5.5 Positioning Table . 31 1.5.6 Variational Loop Circuit 32 1.6 Conclusions . 33 References . 34 2 Geometric Tolerance Analysis . 39 Wilma Polini 2.1 Introduction . 39 2.2 The Reference Case Study . 42 2.3 The Vector Loop Model 44 2.3.1 Results of the Case Study with Dimensional Tolerances . 47 2.3.2 Results of the Case Study with Geometric Tolerances 50xiv Contents 2.4 Further Geometric Tolerance Analysis Models . 54 2.4.1 The Variational Model . 54 2.4.2 The Matrix Model 56 2.4.3 The Jacobian Model . 58 2.4.4 The Torsor Model 59 2.5 Comparison of the Models 61 2.6 Guidelines for the Development of a New Tolerance Analysis Model 65 2.7 Conclusions . 67 References . 67 Part II Impact on Product Quality Inspection 3 Quality Inspection of Microtopographic Surface Features with Profilometers and Microscopes . 71 Nicola Senin and Gianni Campatelli 3.1 Introduction . 72 3.2 Profilometers and 3D Microscopes for Microtopography Analysis . 74 3.2.1 Stylus-based Profilometers 75 3.2.2 Performance and Issues of Measuring with Stylus-based Profilometers 79 3.2.3 Optical Profilometers and Optical 3D Microscopes . 82 3.2.4 Performance and Issues of Measuring with Optical Profilometers and Microscopes . 92 3.2.5 Nonoptical Microscopes 94 3.2.6 Performance and Issues of Measuring with Nonoptical Microscopes 96 3.2.7 Scanning Probe Microscopes . 98 3.2.8 Performance and Issues of Measuring with Scanning Probe Microscopes 100 3.3 Application to the Inspection of Microfabricated Parts and Surface Features 101 3.3.1 Aspects and Issues Peculiar to the Application of Profilometers and Microscopes . 102 3.3.2 Aspects and Issues That Are Shared with Quality Inspection of Average-sized Mechanical Parts with Conventional Instruments 105 3.4 Conclusions . 106 References . 107 Standard under Development 110Contents xv 4 Coordinate Measuring Machine Measurement Planning . 111 Giovanni Moroni and Stefano Petr? 4.1 Introduction . 112 4.1.1 What Is a CMM? 112 4.1.2 Traceability of CMMs 116 4.1.3 CMM Inspection Planning . 118 4.2 Measurement Strategy Planning 119 4.3 Sampling Patterns 123 4.3.1 Blind Sampling Strategies 123 4.3.2 Adaptive Sampling Strategies 129 4.3.3 Manufacturing-signature-based Strategies . 132 4.3.4 Effectiveness of Different Sampling Patterns: Case Studies . 139 4.4 Sample Size Definition 148 4.4.1 An Economic Criterion for the Choice of the Sample Size 151 4.4.2 Case Studies: Roundness and Flatness . 153 4.5 Conclusions . 154 References . 155 Standard under Development 158 5 Identification of Microtopographic Surface Features and Form Error Assessment 159 Nicola Senin, Stefano Pini, and Roberto Groppetti 5.1 Introduction . 160 5.1.1 Scenario . 160 5.1.2 Main Terminology and Outline of the Proposed Approach 161 5.2 Previous Work . 162 5.2.1 Previous Work on Feature Identification and Extraction 162 5.2.2 Previous Work on Geometry Alignment and Form Error Assessment . 163 5.3 Outline of the Proposed Approach 164 5.3.1 Simulated Case Study 164 5.3.2 Overall Schema of the Proposed Approach . 166 5.4 Feature Identification and Extraction 167 5.4.1 The Main Scanning Loop . 168 5.4.2 Template Preparation . 168 5.4.3 Template and Candidate Region Preprocessing . 168 5.4.4 Template and Candidate Region Comparison Through Pattern Matching . 171 5.4.5 Some Considerations on the Sensitivity and Robustness of the Preprocessed-shape Comparison Substep 173 5.4.6 Final Identification of the Features 173xvi Contents 5.4.7 Feature Extraction 174 5.5 Nominal Versus Measured Feature Comparison . 175 5.5.1 Coarse and Fine Alignment 177 5.5.2 Template and Candidate Geometry Preprocessing for Alignment Purposes . 177 5.5.3 Coarse Rotational Alignment with Diametral Cross-section Profile Comparison 177 5.5.4 Fine Alignment with ICP . 179 5.5.5 Comparison of Aligned Geometries . 180 5.6 Validation of the Proposed Approach 181 5.6.1 Feature Identification and Extraction . 182 5.6.2 Feature Alignment and Form Error Assessment 184 5.7 Conclusions . 185 5.7.1 Issues Related to Feature Identification . 185 5.7.2 Issues in Feature Alignment and Form Error Assessment . 186 References . 186 Standards under Development 187 6 Geometric Tolerance Evaluation Using Combined Vision – Contact Techniques and Other Data Fusion Approaches . 189 Gianni Campatelli 6.1 Introduction to Hybrid Coordinate Measuring Machine Systems . 189 6.1.1 Brief Description of Optical Measurement Systems 192 6.2 Starting Problem: Precise Data Registration . 194 6.3 Introduction to Serial Data Integration, Data Fusion, and the Hybrid Model 196 6.4 Serial Data Integration Approaches . 198 6.4.1 Serial Data Integration: Vision-aided Reverse Engineering Approach . 198 6.4.2 Serial Data Integration: Serial Bandwidth . 201 6.5 Geometric Data Integration Approaches . 203 6.5.1 Geometric Approach: Geometric Reasoning . 204 6.5.2 Geometric Approach: Self-organizing Map Feature Recognition . 206 6.6 Data Fusion Approach . 208 6.7 Concluding Remarks . 211 References . 212 7 Statistical Shape Analysis of Manufacturing Data 215 Enrique del Castillo 7.1 Introduction . 215 7.2 The Landmark Matching Problem . 216Contents xvii 7.3 A Review of Some SSA Concepts and Techniques . 222 7.3.1 Preshape and Shape Space . 223 7.3.2 Generalized Procrustes Algorithm . 224 7.3.3 Tangent Space Coordinates 228 7.4 Further Work . 231 Appendix: Computer Implementation of Landmark Matching and the GPA and PCA . 233 References . 233 Part III Impact on Statistical Process Monitoring 8 Statistical Quality Monitoring of Geometric Tolerances: the Industrial Practice 237 Bianca Maria Colosimo and Massimo Pacella 8.1 Introduction . 237 8.2 Shewhart’s Control Chart 238 8.2.1 Two Stages in Control Charting . 241 8.3 Geometric Tolerances: an Example of a Geometric Feature Concerning Circularity 242 8.4 Control Chart of Geometric Errors 245 8.4.1 Control Limits of the Individuals Control Chart 245 8.4.2 An Example of Application to the Reference Case Study . 246 8.5 Monitoring the Shape of Profiles . 249 8.5.1 The Location Control Chart . 250 8.5.2 Control Limits of the Location Control Chart 251 8.5.3 An Example of Application of the Location Control Chart . 251 8.6 Conclusions . 254 References . 254 9 Model-based Approaches for Quality Monitoring of Geometric Tolerances 257 Bianca Maria Colosimo and Massimo Pacella 9.1 Introduction . 257 9.2 Linear Profile Monitoring 261 9.2.1 A Control Chart Approach to Linear Profile Monitoring 263 9.2.2 A Numerical Example for Linear Profile Monitoring 264 9.3 Profile Monitoring for Geometric Tolerances . 269 9.3.1 Regression Model with Spatially Correlated Errors . 270 9.3.2 The PCA-based Model . 277 9.4 Conclusions . 281 References . 282xviii Contents 10 A Model-free Approach for Quality Monitoring of Geometric Tolerances 285 Massimo Pacella, Quirico Semeraro, Alfredo Anglani 10.1 Introduction . 286 10.2 An Introduction to Machine Learning . 288 10.2.1 Supervised and Unsupervised Learning . 289 10.2.2 Neural Networks 290 10.2.3 Supervised Learning: the MLP Model . 291 10.2.4 Unsupervised Learning: the ART Model . 292 10.3 Neural Networks for Quality Monitoring 294 10.3.1 Control Chart Pattern Recognition . 294 10.3.2 Unnatural Process Behavior Detection 295 10.4 A Neural Network Approach for Profile Monitoring 297 10.4.1 Input and Preprocessing Stage . 298 10.4.2 Training 298 10.4.3 The Method for Implementing the Neural Network 299 10.5 A Verification Study 300 10.5.1 Implementation of the Fuzzy ART Neural Network 301 10.5.2 Run Length Performance . 303 10.6 Conclusions . 305 Appendix 307 References . 308 11 Quality Monitoring of Geometric Tolerances: a Comparison Study . 311 Bianca Maria Colosimo and Massimo Pacella 11.1 Introduction . 311 11.2 The Reference Case Study . 314 11.2.1 The Registration of Profiles . 318 11.3 Production Scenarios . 320 11.4 Out-of-Control Models 322 11.5 Performance Comparison in Phase II of Process Monitoring 323 11.5.1 Production Scenario with the Random-Effect Model 325 11.5.2 Production Scenario with the Fixed-Effect Model . 327 11.5.3 Overall Performance Measure 328 11.6 Conclusions . 330 References . 331 Index . 333Part
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