A non-conformance rate prediction method supported by machine learning and ontology in reducing underproduction cost and overproduction cost

References

• Ameri, F., and D. Dutta. 2008a. “A Matchmaking Methodology for Supply Chain Deployment in Distributed Manufacturing Environments.” Journal of Computing and Information Science in Engineering 8 (1): 11002–11010. doi: 10.1115/1.2830849
   Web of Science ®Google Scholar
• Ameri, F., and D. Dutta. 2008b. “An Upper Ontology for Manufacturing Service Description.” ASME 2006 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, Philadelphia, USA, June 3.
   Google Scholar
• Ameri, F., C. Urbanovsky, and C. McArthur. 2012. “A Systematic Approach to Developing Ontologies for Manufacturing Service Modelling.” Proceedings of the Workshop on Ontology and Semantic Web for Manufacturing. Graz, Austria, July 24.
   Google Scholar
• Antani, K. R. 2014. “A Study of the Effects of Manufacturing Complexity on Product Quality in Mixed-Model Automotive Assembly.” PhD diss., Clemson University.
   Google Scholar
• Arp, R., B. Smith, and A. Spear. 2015. Building Ontologies with Basic Formal Ontology. Cambridge: MIT Press.
   Google Scholar
• Bishop, C. M. 1994. “Mixture Density Networks.” Technical Report. Aston University.
   Google Scholar
• Boothroyd, G. 1994. “Product Design for Manufacture and Assembly.” Computer-Aided Design 26 (7): 505–520. doi: 10.1016/0010-4485(94)90082-5
   Web of Science ®Google Scholar
• Bzdok, D., N. Altman, and M. Krzywinski. 2018. “Statistics Versus Machine Learning.” Nature Methods 15 (4): 233–234. doi: 10.1038/nmeth.4642
   PubMed Web of Science ®Google Scholar
• Carney, M., P. Cunningham, J. Dowling, and C. Lee. 2005. “Predicting Probability Distributions for Surf Height Using an Ensemble of Mixture Density Networks.” Proceedings of the 22nd International Conference on Machine Learning. Bonn, Germany, August 7–11.
   Google Scholar
• Chaturvedi, A., P. E. Green, and J. D. Caroll. 2001. “K-modes Clustering.” Journal of Classification 18 (1): 35–55. doi: 10.1007/s00357-001-0004-3
   Web of Science ®Google Scholar
• Chen, X. M., W. Q. Wu, and S. S. Li. 2013. “Random Yield Model in Integrated Circuit Design.” Applied Mechanics and Materials 397: 1837–1843. doi: 10.4028/www.scientific.net/AMM.397-400.1837
   Google Scholar
• Flack, V. F. 1985. “Introducing Dependency into IC Yield Models.” Solid-State Electronics 28 (6): 555–559. doi: 10.1016/0038-1101(85)90125-X
   Web of Science ®Google Scholar
• Gardner, R. M., J. Bieker, and S. Elwell. 2000. “Solving Tough Semiconductor Manufacturing Problems Using Data Mining.” 2000 IEEE/SEMI Advanced Semiconductor Manufacturing Conference and Workshop. Boston, USA, September 12–14.
   Google Scholar
• Gruber, T.R. 1993. “A Translation Approach to Portable Ontologies.” Knowledge Acquisition 5 (2): 199–200. doi: 10.1006/knac.1993.1008
   Web of Science ®Google Scholar
• Grüninger, M., and M. S. Fox. 1995. “Methodology for the Design and Evaluation of Ontologies.” Proceedings of the Workshop on Basic Ontological Issues in Knowledge Sharing held in conjunction with IJCAI-95. Montreal, Canada, August 20–25.
   Google Scholar
• Guarino, N. 2017. “BFO and DOLCE: So Far, So Close … .” COSMOS + TAXIS 4 (4): 10–18.
   Google Scholar
• Gupta, P., and E. Papadopoulou. 2008. “Yield Analysis and Optimization, Chap. 37.” In Handbook of Algorithms for Physical Design Automation, edited by Charles Alpert, Dinesh Mehta, and Sachin Sapatnekar, 771–790. Florida: CRC Press. doi:10.1201/9781420013481.ch37.
   Google Scholar
• Hinckley, C. M. 1994. “A Global Conformance Quality Model. A New Strategic Tool for Minimizing Defects Caused by Variation, Error, and Complexity.” PhD diss., Stanford University.
   Google Scholar
• Jiang, J. J., and D. W. Conrath. 1997. “Semantic Similarity Based on Corpus Statistics and Lexical Taxonomy.” Proceedings of International Conference Research on Computational Linguistics. Taipei, Taiwan, August.
   Google Scholar
• Karray, M. H., F. Ameri, M. Hodkiewicz, and L. T. Romain. 2019. “Towards a BFO Compliant Reference Ontology for Industrial Maintenance.” Applied Ontology 14 (2): 155–177. doi: 10.3233/AO-190208
   Web of Science ®Google Scholar
• Krugh, M., K. Antani, L. Mears, and J. Schulte. 2016. “Prediction of Defect Propensity for the Manual Assembly of Automotive Electrical Connectors.” Procedia Manufacturing 5: 144–157. doi: 10.1016/j.promfg.2016.08.014
   Google Scholar
• Lee, S. F., and C. J. Spanos. 1995. “Prediction of Wafer State After Plasma Processing Using Real-Time Tool Data.” IEEE Transactions on Semiconductor Manufacturing 8 (3): 252–261. doi: 10.1109/66.400999
   Web of Science ®Google Scholar
• Li, T.-S., C.-L. Huang, and Z.-Y. Wu. 2006. “Data Mining Using Genetic Programming for Construction of a Semiconductor Manufacturing Yield Rate Prediction System.” Journal of Intelligent Manufacturing 17 (3): 355–361. doi: 10.1007/s10845-005-0008-7
   Web of Science ®Google Scholar
• Lyonnet, B., and R. Toscano. 2012. “Towards an Adapted Lean System – a Push-Pull Manufacturing Strategy.” Production Planning & Control 25 (4): 346–354. doi: 10.1080/09537287.2012.702867
   Web of Science ®Google Scholar
• Maedche, A., B. Motik, L. Stojanovic, R. Studer, and R. Volz. 2003. “Ontologies for Enterprise Knowledge Management.” IEEE Intelligent Systems 18 (2): 26–33. doi: 10.1109/MIS.2003.1193654
   Web of Science ®Google Scholar
• Manna, A. K., J. K. Dey, and S. K. Mondal. 2017. “Imperfect Production Inventory Model with Production Rate Dependent Defective Rate and Advertisement Dependent Demand.” Computer & Industrial Engineering 104: 9–22. doi: 10.1016/j.cie.2016.11.027
   Web of Science ®Google Scholar
• Masolo, C., S. Borggo, A. Gangemi, N. Guarino, and A. Olteramari. 2002. “Wonderweb Deliverable D17.” Technical Report, ISTC-CNR.
   Google Scholar
• Murphy, B. T. 1964. “Cost-Size Optima of Monolithic Integrated Circuits.” Proceedings of the IEEE 52 (12): 1537–1545. doi: 10.1109/PROC.1964.3442
   Web of Science ®Google Scholar
• Niles, I., and A. Pease. 2001. “Towards a Standard Upper Ontology.” Proceedings of the International Conference on Formal Ontology in Information Systems, 2–9. New York: ACM.
   Google Scholar
• Noy, N. F., and D. L. McGuinness. 2001. “Ontology Development 101: A Guide to Creating Your First Ontology.” Technical Report, Stanford University.
   Google Scholar
• Ohno, T. 1978. Toyota Production System: Beyond Large-Scale Production. New York: Production press.
   Google Scholar
• Rada, R., H. Mili, E. Bicknell, and M. Blettner. 1989. “Development and Application of a Metric on Semantic Nets.” IEEE Transactions on Systems, man, and Cybernetics 19 (1): 17–30. doi: 10.1109/21.24528
   Web of Science ®Google Scholar
• Resnik, P. 1999. “Semantic Similarity in a Taxonomy: An Information-Based Measure and its Application to Problems of Ambiguity in Natural Language.” Journal of Artificial Intelligence Research 11: 95–130. doi: 10.1613/jair.514
   Web of Science ®Google Scholar
• Rudnicki, R., B. Smith, T. Malyuta, and C. W. Mandrick. 2016. “Best Practices of Ontology Development.” White Paper, CUBRC, Inc.
   Google Scholar
• Schittenkopf, C., G. Dorffner, and E. J. Dockner. 1998. “Volatility Prediction with Mixture Density Networks.” International Conference on Artificial Neural Networks, edited by Lars Niklasso, 929-934. London: Springer.
   Google Scholar
• Seeds, R. B. 1968. “Yield and Cost Analysis of Bipolar LSI.” IEEE Transactions on Electron Devices 15 (6): 409–409. doi: 10.1109/T-ED.1968.16203
   Web of Science ®Google Scholar
• Sheldon, R. 2014. A First Course in Probability. Boston: Pearson.
   Google Scholar
• Shibata, H. 2003. “Global Assembly Quality Methodology: A New Method for Evaluating Assembly Complexities in Globally Distributed Manufacturing.” ASME 2003 International Mechanical Engineering Congress and Exposition, Washington, DC, USA, November 15–21.
   Google Scholar
• Shin, J., B. Kulvatunyou, and Y. Lee. 2013. “Concept Analysis to Enrich Manufacturing Service Capability Models.” Procedia Computer Science 16: 648–657. doi: 10.1016/j.procs.2013.01.068
   Google Scholar
• Stapper, C. H. 1976. “LSI Yield Modeling and Process Monitoring.” IBM Journal of Research and Development 20 (3): 228–234. doi: 10.1147/rd.203.0228
   Web of Science ®Google Scholar
• Su, Q., L. Liu, and S. Lai. 2009. “Measuring the Assembly Quality from the Operator Mistake View: A Case Study.” Assembly Automation 29 (4): 332–340. doi: 10.1108/01445150910987745
   Web of Science ®Google Scholar
• Tversky, A. 1977. “Features of Similarity.” Psychological Review 84: 327–352. doi: 10.1037/0033-295X.84.4.327
   Web of Science ®Google Scholar
• Uschold, M., and M. Gruinger. 1996. “Ontologies: Principles, Methods and Applications.” Technical Report-University of Edinburgh Artificial Intelligence Applications Institute.
   Google Scholar
• Usman, Z. 2012. “A Manufacturing Core Concepts Ontology to Support Knowledge Sharing.” PhD diss., Loughborough University.
   Google Scholar
• Virkki-Hatakka, T., and G. Reniers. 2009. “A Case-Based Reasoning Safety Decision-Support Tool: Nextcase/Safety.” Expert System with Applications 36 (7): 10374–10380. doi: 10.1016/j.eswa.2009.01.059
   Web of Science ®Google Scholar
• Wu, L., and J. Zhang. 2010. “Fuzzy Neural Network Based Yield Prediction Model for Semiconductor Manufacturing System.” International Journal of Production Research 48 (11): 3225–3268. doi: 10.1080/00207540902795307
   Web of Science ®Google Scholar
• Zen, H., and A. Senior. 2014. “Deep Mixture Density Networks for Acoustic Modelling in Statistical Parametric Speech Synthesis.” IEEE International Conference on Acoustic Modelling in Statistical Parametric Florence, May 4–9.
   Google Scholar