Colosimo / Senin Geometric Tolerances

Impact on Product Design, Quality Inspection and Statistical Process Monitoring
1. Auflage 2010
ISBN: 978-1-84996-311-4
Verlag: Springer
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 336 Seiten

Geometric tolerances are changing the way we design and manufacture industrial products. Geometric Tolerances covers their impact on the world of design and production, highlighting new perspectives, possibilities, current issues and future challenges. The topics covered are designed to be relevant to readers from a variety of backgrounds, ranging from product designers and manufacturers to quality inspection engineers and quality engineers involved in statistical process monitoring. Areas included are: • selection of appropriate geometric tolerances and how they stack up in assembled products; • inspection of parts subjected to geometric tolerancing from the macro to the micro and sub-micro scales; and • enhancement of efficiency and efficacy of quality monitoring. Geometric Tolerances provides the reader with the most recent scientific research in the field, as well as with a significant amount of real-life industrial case studies, delivering a multidisciplinary, synoptic view of one of the hottest and most strategic topics in industrial production.

Bianca M. Colosimo is an associate professor in the Department of Mechanical Engineering at the Politecnico di Milano, Milan, Italy, where she received both her M.S. degree in Industrial Engineering and her PhD degree in Manufacturing and Production Systems. Since 2001, she has collaborated with the Engineering Statistics Laboratory of the Industrial Engineering Department of the Pennsylvania State University. She is a member of the editorial board of the Journal of Quality Technology. She is the author of about 60 papers in international and national journals and conference proceedings, including over 20 refereed papers (in the Journal of Quality Technology, Technometrics, Communications in Statistics and Journal of Applied Statistics, among others). She is a senior member of the American Society for Quality (www.asq.org), a member of Informs (http://www.informs.org/) and of the AITEM (Italian association of manufacturing engineers (www.aitem.org)). Her research interests are mainly in the area of quality monitoring and process adjustment, with special attention to discrete part manufacturing. Further research activity is devoted to manufacturing process optimization. Her main research target is to take full advantage of new methods and tools developed in the area of applied or industrial statistics for solving industrial manufacturing problems. Nicola Senin is an associate professor in the Department of Industrial Engineering at the University of Perugia, Perugia, Italy, where he received his M.S degree in Mechanical Engineering. Since 1995 he has collaborated with the Computer-aided Design Laboratory at the Massachusetts Institute of Technology (MIT), in Cambridge (MA), USA. He is the author of about 30 publications in international and national journals and conference proceedings, including about 10 refereed papers in international journals (in the ASME Journal of Mechanical Design, the Computer Aided Design Journal, the Journal of Robotics and Computer-integrated Manufacturing, and Wear, among others). He is a member of the AITEM (Italian association of manufacturing engineers (www.aitem.org)). His research interests are mainly in the area of inspection of manufactured surfaces, with particular reference to three-dimensional surface topography analysis at the micro and sub-micro scales. Additional research activities are related to the development of computer-based frameworks for supporting collaborative product design and manufacturing.

Autoren/Hrsg.

Senin, Nicola

Colosimo, Bianca M.

Inhaltsverzeichnis

1;Preface;5
2;Contents;13
3;Part I - Impact on Product Design;19
3.1;Chapter 1 - Geometric Tolerance Specification;20
3.1.1;1.1 Introduction;21
3.1.2;1.2 From Linear to Geometric Tolerances;23
3.1.3;1.3 Description of the Product;26
3.1.3.1;1.3.1 Geometric Data;26
3.1.3.2;1.3.2 Design Requirements;28
3.1.3.2.1;1.3.2.1 Fit and Function;28
3.1.3.2.2;1.3.2.2 Classification and Modeling of Requirements;30
3.1.3.2.3;1.3.2.3 Identification and Treatment of Requirements;32
3.1.4;1.4 General Approach to Tolerance Specification;34
3.1.4.1;1.4.1 Empirical Specification Rules;34
3.1.4.2;1.4.2 Classification of Tolerancing Cases;36
3.1.5;1.5 Generative Specification Methods;40
3.1.5.1;1.5.1 Technologically and Topologically Related Surfaces;42
3.1.5.2;1.5.2 Degrees of Freedom;44
3.1.5.3;1.5.3 Mirrors;45
3.1.5.4;1.5.4 Function Decomposition;46
3.1.5.5;1.5.5 Positioning Table;48
3.1.5.6;1.5.6 Variational Loop Circuit;49
3.1.6;1.6 Conclusions;50
3.1.7;References;51
3.2;Chapter 2 - Geometric Tolerance Analysis;55
3.2.1;2.1 Introduction;55
3.2.2;2.2 The Reference Case Study;58
3.2.3;2.3 The Vector Loop Model;60
3.2.3.1;2.3.1 Results of the Case Study with Dimensional Tolerances;63
3.2.3.2;2.3.2 Results of the Case Study with Geometric Tolerances;66
3.2.4;2.4 Further Geometric Tolerance Analysis Models;70
3.2.4.1;2.4.1 The Variational Model;70
3.2.4.2;2.4.2 The Matrix Model;72
3.2.4.3;2.4.3 The Jacobian Model;74
3.2.4.4;2.4.4 The Torsor Model;75
3.2.5;2.5 Comparison of the Models;77
3.2.6;2.6 Guidelines for the Development of a New Tolerance Analysis Model;81
3.2.7;2.7 Conclusions;83
3.2.8;References;83
4;Part II - Impact on Product Quality Inspection;85
4.1;Chapter 3 - Quality Inspection of Microtopographic Surface Features with Profilometers and Microscopes;86
4.1.1;3.1 Introduction;87
4.1.2;3.2 Profilometers and 3D Microscopes for Microtopography Analysis;89
4.1.2.1;3.2.1 Stylus-based Profilometers;90
4.1.2.2;3.2.2 Performance and Issues of Measuring with Stylus-based Profilometers;94
4.1.2.2.1;3.2.2.1 Measurement Performance;95
4.1.2.2.2;3.2.2.2 Constraints on Material, Geometry, and Surface Topography of the Part To Be Measured;96
4.1.2.3;3.2.3 Optical Profilometers and Optical 3D Microscopes;97
4.1.2.3.1;3.2.3.1 Single-point Focus-detection Profilometers;99
4.1.2.3.2;3.2.3.2 Wide-field Focus-detection 3D Microscopes;100
4.1.2.3.3;3.2.3.3 Confocal Laser Scanning Microscopes;100
4.1.2.3.4;3.2.3.4 Chromatic Aberration Profilometers;102
4.1.2.3.5;3.2.3.5 Interferometric Profilometers;102
4.1.2.3.6;3.2.3.6 Conoscopic Holography Profilometers;105
4.1.2.4;3.2.4 Performance and Issues of Measuring with Optical Profilometers and Microscopes;107
4.1.2.4.1;3.2.4.1 Measurement Performance;107
4.1.2.4.2;3.2.4.2 Constraints on Material Geometry and Surface Topography of the Part To Be Measured;108
4.1.2.5;3.2.5 Nonoptical Microscopes;109
4.1.2.6;3.2.6 Performance and Issues of Measuring with Nonoptical Microscopes;111
4.1.2.6.1;3.2.6.1 Measurement Performance;111
4.1.2.6.2;3.2.6.2 Constraints on Material, Geometry, and Surface Topography of the Part To Be Measured;112
4.1.2.7;3.2.7 Scanning Probe Microscopes;113
4.1.2.7.1;3.2.7.1 Scanning Tunneling Microscopes;113
4.1.2.7.2;3.2.7.2 Atomic Force Microscopes;114
4.1.2.8;3.2.8 Performance and Issues of Measuring with Scanning Probe Microscopes;115
4.1.2.8.1;3.2.8.1 Measurement Performance;115
4.1.2.8.2;3.2.8.2 Constraints on Material, Geometry and Surface Topography of the Parts To Be Measured;116
4.1.3;3.3 Application to the Inspection of Microfabricated Parts and Surface Features;116
4.1.3.1;3.3.1 Aspects and Issues Peculiar to the Application of Profilometers and Microscopes;117
4.1.3.1.1;3.3.1.1 Different Measurement Performance in x, y and z;117
4.1.3.1.2;3.3.1.2 Unidirectional Probing;118
4.1.3.1.3;3.3.1.3 Raster Scanning;119
4.1.3.1.4;3.3.1.4 Image-inspired Data Processing;119
4.1.3.2;3.3.2 Aspects and Issues That Are Shared with Quality Inspection of Average-sized Mechanical Parts with Conventional Instruments;120
4.1.3.2.1;3.3.2.1 Registration with Nominal Geometry;120
4.1.3.2.2;3.3.2.2 Data Stitching;121
4.1.4;3.4 Conclusions;121
4.1.5;References;122
4.1.6;Standard under Development;125
4.2;Chapter 4 - Coordinate Measuring Machine Measurement Planning;126
4.2.1;4.1 Introduction;127
4.2.1.1;4.1.1 What Is a CMM?;127
4.2.1.1.1;4.1.1.1 Mechanical Setup;127
4.2.1.1.2;4.1.1.2 CMM Sensors;128
4.2.1.1.3;4.1.1.3 Control Unit;129
4.2.1.1.4;4.1.1.4 Computer and Software for Data Processing;130
4.2.1.2;4.1.2 Traceability of CMMs;131
4.2.1.2.1;4.1.2.1 Performance Verification;131
4.2.1.2.2;4.1.2.2 Uncertainty Evaluation;132
4.2.1.3;4.1.3 CMM Inspection Planning;133
4.2.2;4.2 Measurement Strategy Planning;134
4.2.3;4.3 Sampling Patterns;138
4.2.3.1;4.3.1 Blind Sampling Strategies;138
4.2.3.2;4.3.2 Adaptive Sampling Strategies;144
4.2.3.3;4.3.3 Manufacturing-signature-based Strategies;147
4.2.3.3.1;4.3.3.1 Sampling Strategies Based on Process Raw Data;149
4.2.3.3.2;4.3.3.2 Sampling Strategies Based on the Manufacturing Signature Model;151
4.2.3.3.3;4.3.3.3 Reconstruction Strategies;153
4.2.3.4;4.3.4 Effectiveness of Different Sampling Patterns: Case Studies;154
4.2.3.4.1;4.3.4.1 Strategies for Flatness: Face-milled Planes;154
4.2.3.4.2;4.3.4.2 Process-raw-data-based Sampling Strategy;156
4.2.3.4.3;4.3.4.3 An Example: Sampling Strategy for Face-milled Planes;158
4.2.3.4.4;4.3.4.4 Strategies for Roundness;161
4.2.4;4.4 Sample Size Definition;163
4.2.4.1;4.4.1 An Economic Criterion for the Choice of the Sample Size;166
4.2.4.2;4.4.2 Case Studies: Roundness and Flatness;168
4.2.5;4.5 Conclusions;169
4.2.6;References;170
4.2.7;Standard under Development;173
4.3;Chapter 5 - Identification of Microtopographic Surface Features and Form Error Assessment;174
4.3.1;5.1 Introduction;175
4.3.1.1;5.1.1 Scenario;175
4.3.1.2;5.1.2 Main Terminology and Outline of the Proposed Approach;176
4.3.2;5.2 Previous Work;177
4.3.2.1;5.2.1 Previous Work on Feature Identification and Extraction;177
4.3.2.2;5.2.2 Previous Work on Geometry Alignment and Form Error Assessment;178
4.3.3;5.3 Outline of the Proposed Approach;179
4.3.3.1;5.3.1 Simulated Case Study;179
4.3.3.2;5.3.2 Overall Schema of the Proposed Approach;181
4.3.4;5.4 Feature Identification and Extraction;182
4.3.4.1;5.4.1 The Main Scanning Loop;183
4.3.4.2;5.4.2 Template Preparation;183
4.3.4.3;5.4.3 Template and Candidate Region Preprocessing;183
4.3.4.3.1;5.4.3.1 Height-based Binarization;184
4.3.4.3.2;5.4.3.2 Attribute-based n-segmentation;185
4.3.4.4;5.4.4 Template and Candidate Region Comparison Through Pattern Matching;186
4.3.4.5;5.4.5 Some Considerations on the Sensitivity and Robustness of the Preprocessed-shape Comparison Substep;188
4.3.4.6;5.4.6 Final Identification of the Features;188
4.3.4.7;5.4.7 Feature Extraction;189
4.3.5;5.5 Nominal Versus Measured Feature Comparison;190
4.3.5.1;5.5.1 Coarse and Fine Alignment;192
4.3.5.2;5.5.2 Template and Candidate Geometry Preprocessing for Alignment Purposes;192
4.3.5.3;5.5.3 Coarse Rotational Alignment with Diametral Cross-section Profile Comparison;192
4.3.5.4;5.5.4 Fine Alignment with ICP;194
4.3.5.5;5.5.5 Comparison of Aligned Geometries;195
4.3.6;5.6 Validation of the Proposed Approach;196
4.3.6.1;5.6.1 Feature Identification and Extraction;197
4.3.6.2;5.6.2 Feature Alignment and Form Error Assessment;199
4.3.7;5.7 Conclusions;200
4.3.7.1;5.7.1 Issues Related to Feature Identification;200
4.3.7.2;5.7.2 Issues in Feature Alignment and Form Error Assessment;201
4.3.8;References;201
4.3.9;Standards under Development;202
4.4;Chapter 6 - Geometric Tolerance Evaluation Using Combined Vision – Contact Techniques and Other Data Fusion Approaches;204
4.4.1;6.1 Introduction to Hybrid Coordinate Measuring Machine Systems;204
4.4.1.1;6.1.1 Brief Description of Optical Measurement Systems;207
4.4.2;6.2 Starting Problem: Precise Data Registration;209
4.4.3;6.3 Introduction to Serial Data Integration, Data Fusion, and the Hybrid Model;211
4.4.4;6.4 Serial Data Integration Approaches;213
4.4.4.1;6.4.1 Serial Data Integration: Vision-aided Reverse Engineering Approach;213
4.4.4.2;6.4.2 Serial Data Integration: Serial Bandwidth;216
4.4.5;6.5 Geometric Data Integration Approaches;218
4.4.5.1;6.5.1 Geometric Approach: Geometric Reasoning;219
4.4.5.2;6.5.2 Geometric Approach: Self-organizing Map Feature Recognition;221
4.4.6;6.6 Data Fusion Approach;223
4.4.7;6.7 Concluding Remarks;226
4.4.8;References;227
4.5;Chapter 7 - Statistical Shape Analysisof Manufacturing Data;229
4.5.1;7.1 Introduction;229
4.5.2;7.2 The Landmark Matching Problem;230
4.5.3;7.3 A Review of Some SSA Concepts and Techniques;236
4.5.3.1;7.3.1 Preshape and Shape Space;237
4.5.3.2;7.3.2 Generalized Procrustes Algorithm;238
4.5.3.3;7.3.3 Tangent Space Coordinates;242
4.5.4;7.4 Further Work;245
4.5.5;Appendix: Computer Implementation of Landmark Matching and the GPA and PCA;247
4.5.6;References;247
5;Part III - Impact on Statistical Process Monitoring;249
5.1;Chapter 8 - Statistical Quality Monitoring of Geometric Tolerances: the Industrial Practice;250
5.1.1;8.1 Introduction;250
5.1.2;8.2 Shewhart’s Control Chart;251
5.1.2.1;8.2.1 Two Stages in Control Charting;254
5.1.3;8.3 Geometric Tolerances: an Example of a Geometric Feature Concerning Circularity;255
5.1.4;8.4 Control Chart of Geometric Errors;258
5.1.4.1;8.4.1 Control Limits of the Individuals Control Chart;258
5.1.4.2;8.4.2 An Example of Application to the Reference Case Study;259
5.1.5;8.5 Monitoring the Shape of Profiles;262
5.1.5.1;8.5.1 The Location Control Chart;263
5.1.5.2;8.5.2 Control Limits of the Location Control Chart;264
5.1.5.3;8.5.3 An Example of Application of the Location Control Chart;264
5.1.6;8.6 Conclusions;267
5.1.7;References;267
5.2;Chapter 9 - Model-based Approaches for Quality Monitoring of Geometric Tolerances;269
5.2.1;9.1 Introduction;269
5.2.2;9.2 Linear Profile Monitoring;273
5.2.2.1;9.2.1 A Control Chart Approach to Linear Profile Monitoring;275
5.2.2.2;9.2.2 A Numerical Example for Linear Profile Monitoring;276
5.2.3;9.3 Profile Monitoring for Geometric Tolerances;281
5.2.3.1;9.3.1 Regression Model with Spatially Correlated Errors;282
5.2.3.1.1;9.3.1.1 Control Limits of the SARX-based Control Chart;283
5.2.3.1.2;9.3.1.2 A Numerical Example;284
5.2.3.2;9.3.2 The PCA-based Model;289
5.2.3.2.1;9.3.2.1 Control Limits of PCA-based Control Charts;290
5.2.3.2.2;9.3.2.2 A Numerical Example;291
5.2.4;9.4 Conclusions;293
5.2.5;References;294
5.3;Chapter 10 - A Model-free Approach for Quality Monitoring of Geometric Tolerances;296
5.3.1;10.1 Introduction;297
5.3.2;10.2 An Introduction to Machine Learning;299
5.3.2.1;10.2.1 Supervised and Unsupervised Learning;300
5.3.2.2;10.2.2 Neural Networks;301
5.3.2.3;10.2.3 Supervised Learning: the MLP Model;302
5.3.2.4;10.2.4 Unsupervised Learning: the ART Model;303
5.3.3;10.3 Neural Networks for Quality Monitoring;305
5.3.3.1;10.3.1 Control Chart Pattern Recognition;305
5.3.3.2;10.3.2 Unnatural Process Behavior Detection;306
5.3.4;10.4 A Neural Network Approach for Profile Monitoring;308
5.3.4.1;10.4.1 Input and Preprocessing Stage;309
5.3.4.2;10.4.2 Training;309
5.3.4.3;10.4.3 The Method for Implementing the Neural Network;310
5.3.5;10.5 A Verification Study;311
5.3.5.1;10.5.1 Implementation of the Fuzzy ART Neural Network;312
5.3.5.2;10.5.2 Run Length Performance;314
5.3.6;10.6 Conclusions;316
5.3.7;Appendix;318
5.3.8;References;319
5.4;Chapter 11 - Quality Monitoring of Geometric Tolerances: a Comparison Study;322
5.4.1;11.1 Introduction;322
5.4.2;11.2 The Reference Case Study;325
5.4.2.1;11.2.1 The Registration of Profiles;329
5.4.3;11.3 Production Scenarios;331
5.4.4;11.4 Out-of-Control Models;333
5.4.5;11.5 Performance Comparison in Phase II of Process Monitoring;334
5.4.5.1;11.5.1 Production Scenario with the Random-Effect Model;336
5.4.5.2;11.5.2 Production Scenario with the Fixed-Effect Model;338
5.4.5.3;11.5.3 Overall Performance Measure;339
5.4.6;11.6 Conclusions;341
5.4.7;References;342
6;Index;343