A neural network approach to determining optimal inspection sampling size for CMM |
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| Affiliation: | 1. Burjeel Hospital for Advanced Surgery, Dubai, United Arab Emirates;2. School of Medicine, University of Pretoria, Pretoria, South Africa;3. Department of Anatomy, School of Medicine, Faculty of Health Sciences, University of Pretoria, Pretoria, South Africa;4. Centre for Asset Integrity Management (C-AIM), Department of Mechanical and Aeronautical Engineering, University of Pretoria, Pretoria, South Africa;5. Department of Health Sciences, Clinical Anatomy and Imaging, Warwick Medical School, University of Warwick, Coventry, UK;6. Department of Orthopaedic Surgery, Royal Brisbane Hospital, Herston, Australia;7. Orthopaedic Research Centre of Australia, Brisbane, Australia;8. Department of Surgery, School of Medicine, University of Queensland, Brisbane, Australia;9. Department of Orthopaedic Surgery, Royal Brisbane Hospital, Herston, Australia;10. Limb Reconstruction Center, Macquarie University Hospital, Macquarie Park, Australia;11. Department of Orthopaedics, University of Texas Health Science Center, San Antonio, Texas, U.S.A.;12. Department of Orthopaedic Surgery, Harvard Medical School, Massachusetts General Hospital, Boston, Massachusetts, U.S.A.;1. Structural Bioinformatics Lab, CSIR-Institute of Himalayan Bioresource Technology (CSIR-IHBT), Palampur, HP 176061, India;2. Biotechnology division, CSIR-IHBT, Palampur, HP 176061, India;3. Academy of Scientific & Innovative Research (AcSIR), Ghaziabad, 201002, India;1. Univ. Paris-Est, CSTB, F-77447 Marne La Vallée, France;2. University of Cergy-Pontoise, L2MGC, EA 4114, F-95000 Cergy-Pontoise, France;3. Mines Douai, LGCgE – GCE, F-59508 Douai, France;4. SIAME, Université de Pau, Allée du Parc Montaury, 64 600 Anglet, France;1. Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA;2. Lawrence Livermore National Laboratory, Livermore, CA 94550, USA;3. Centre for Energy Research, Hungarian Academy of Sciences, H-1525 Budapest, Hungary;1. School of Chemistry and Chemical Engineering, Yancheng Institute of Technology, Yancheng, 224051, China;2. School of Biotechnology, Jiangsu University of Science and Technology, Zhenjiang, 212018, China;3. National & Local Joint Engineering Research Center for Deep Utilization Technology of Rock-salt Resource, Huaiyin Institute of Technology, Huai’an, 223003, China;4. State Key Laboratory of Analytical Chemistry for Life Science, School of Chemistry and Chemical Engineering, Nanjing University, Nanjing, 210023, China |
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| Abstract: | This paper reports a neural network approach to determining the optimal inspection sampling size of ‘hole’ features using the Coordinate Measuring Machine (CMM). Factors which could affect sample size due to design, manufacturing, and measurement related factors, i.e. size, dimensional and geometrical tolerances, machining processes, and confidence levels, have been studied. Machining process type, size, and tolerance band have been identified as the known factors which may affect the sample size required. Experiments have been carried out to collect sampling size data versus the variation of these factors for different ‘hole’ features. The implicit correlation between the sample size and these factors, has been achieved by training a back-propagation neural network using the collected data. The neural network architecture is described, and the test of the trained neural network on a few new ‘hole’ features is presented to highlight the applicability of this approach. |
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