كتاب Geometric Tolerances - Impact on Product Design, Quality Inspection and Statistical Process Monitoring
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 كتاب Geometric Tolerances - Impact on Product Design, Quality Inspection and Statistical Process Monitoring

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عدد المساهمات : 14836
التقييم : 24326
تاريخ التسجيل : 01/07/2009
العمر : 30
الدولة : مصر
العمل : مدير منتدى هندسة الإنتاج والتصميم الميكانيكى
الجامعة : المنوفية

مُساهمةموضوع: كتاب Geometric Tolerances - Impact on Product Design, Quality Inspection and Statistical Process Monitoring   الإثنين 18 ديسمبر 2017, 9:44 pm

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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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