کاربرد داده‌کاوی در مهندسی تولید محصول از طراحی مفهومی تا تولید نهایی

[1] A. Choudhary, J. Harding, and H. Lin., “Engineering moderator to universal knowledge moderator for moderating collaborative projects”, Global Journal of e-Business & Knowledge Management, vol. 3, no. 1, pp. 5-12, 2007.

[2] J. Harding, and K. Popplewell., “Knowledge reuse and sharing through data mining manufacturing data”, In Proceedings of IERC  Industrial Engineering Research Conference, The Institute of Industrial Engineers, Orlando,  pp. 1, 2006.

[3] A. Choudhary, J. Harding, and K. Popplewell., “Knowledge discovery for moderating collaborative projects”, In Proceedings of the 4th IEEE International Conference on Industrial Informatics, Singapore,  pp. 519-524, 2006.

[4] Y. Elovici, and D. Braha., “A decision-theoretic approach to data mining”, IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans, vol. 33, no. 1, pp. 42-51, 2003.

[5] S. Mitra, S. K. Pal, and P. Mitra., “Data mining in soft computing framework: a survey”, IEEE Transactions on Neural Networks, vol. 13, no. 1, pp. 3-14, 2002.

[6] D. Zhang, and L. Zhou., “Discovering golden nuggets: data mining in financial application”, IEEE Transactions on Systems,Man, and Cybernetics, Part C: Applications and Reviews, vol. 34, no. 4, pp. 513-522, 2004.

[7] J. Harding, M. Shahbaz, and A. Kusiak., “Data mining in manufacturing: a review”, Journal of Manufacturing Science and Engineering, vol. 128, no. 4, pp. 969-976, 2006.

[8] J.-X. Feng, and A. Kusiak., “Data mining applications in engineering design, manufacturing and logistics”, International Journal of Production Research, vol. 44, no. 14, pp. 2689-2694, 2006.

[9] J. Han, and M. Kamber., “Data mining: concepts and techniques”, Morgan Kaufmann Publisher, pp. 89, 2000.

[10] D. Pham, and A. Afify., “Machine-learning techniques and their applications in manufacturing”, Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, vol. 219, no. 5, pp. 395-412, 2005.

[11] U. M. Fayyad, G. Piatetsky-Shapiro, P. Smyth, and R. Uthurusamy., “Advances in knowledge discovery and data mining”, Menlo Park, CA: AAAI/MIT Press, 1996.

[12] F. MacGarry., “Violence, Citizenship and Virility: The Making of an Irish Fascist”, History Ireland, pp. 30-35, 2005.

[13]      A.-L. Huyet., “Optimization and analysis aid via data-mining for simulated production systems”, European journal of operational research, vol. 173, no. 3, pp. 827-838, 2006.

[14]      Y.-H. Liu, H.-P. Huang, and Y.-S. Lin., “Attribute selection for the scheduling of flexible manufacturing systems based on fuzzy Set-theoretic approach and genetic algorithm”, Journal of the Chinese Institute of Industrial Engineers, vol. 22, no. 1, pp. 46-55, 2005.

[15] D.-C. Li, C. Wu, and F. M. Chang., “Using data continualization and expansion to improve small data set learning accuracy for early flexible manufacturing system (FMS) scheduling”, International Journal of Production Research, vol. 44, no. 21, pp. 4491-4509, 2006.

[16] X. Li, and S. Olafsson., “Discovering dispatching rules using data mining”, Journal of Scheduling, vol. 8, no. 6, pp. 515-527, 2005.

[17] D. Koonce, and S.-C. Tsai., “Using data mining to find patterns in genetic algorithm solutions to a job shop schedule”, Computers & industrial engineering, vol. 38, no. 3, pp. 361-374, 2000.

[18] R. Belz, and P. Mertens., “Combining knowledge-based systems and simulation to solve rescheduling problems”, Decision Support Systems, vol. 17, no. 2, pp. 141-157, 1996.

[19] S. Lee, and Y. Ng., “Hybrid case-based reasoning for on-line product fault diagnosis”, The International Journal of Advanced Manufacturing Technology, vol. 27, no. 7-8, pp. 833-840, 2006.

[20] T. Fountain, T. Dietterich, and B. Sudyka., “Data mining for manufacturing control: an application in optimizing IC tests”, Morgan Kaufmann Publisher, San Francisco, pp. 381-400.

[21] H. Maki, and Y. Teranishi., “Development of automated data mining system for quality control in manufacturing”, Data Warehousing and Knowledge Discovery, pp. 93-100: Springer, 2001.

[22] L. Shen, F. E. Tay, L. Qu, and Y. Shen., “Fault diagnosis using rough sets theory”, Computers in Industry, vol. 43, no. 1, pp. 61-72, 2000.

[23] A. Kusiak, and C. Kurasek., “Data mining of printed-circuit board defects”, IEEE Transactions on Robotics and Automation, vol. 17, no. 2, pp. 191-196, 2001.

24] O. Dengiz, A. E. Smith, and I. Nettleship., “Two-stage data mining for flaw identification in ceramics manufacture”, International Journal of Production Research, vol. 44, no. 14, pp. 2839-2851, 2006.

[25] R. Menon, L. H. Tong, and S. Sathiyakeerthi., “Analyzing textual databases using data mining to enable fast product development processes”, Reliability Engineering & System Safety, vol. 88, no. 2, pp. 171-180, 2005.

[26] E. I. Neaga, and J. A. Harding*., “An enterprise modeling and integration framework based on knowledge discovery and data mining”, International Journal of Production Research, vol. 43, no. 6, pp. 1089-1108, 2005.

[27] N. Chen, D. D. Zhu, and W. Wang., “Intelligent materials processing by hyperspace data mining”, Engineering Applications of Artificial Intelligence, vol. 13, no. 5, pp. 527-532, 2000.

[28] T. Holden, and M. Serearuno., “A hybrid artificial intelligence approach for improving yield in precious stone manufacturing”, Journal of Intelligent manufacturing, vol. 16, no. 1, pp. 21-38, 2005.

[29] C. Gertosio, and A. Dussauchoy., “Knowledge discovery from industrial databases”, Journal of Intelligent manufacturing, vol. 15, no. 1, pp. 29-37, 2004.

[30] D. Batanov, N. Nagarur, and P. Nitikhunkasem., “EXPERT-MM: A knowledge-based system for maintenance management”, Artificial intelligence in engineering, vol. 8, no. 4, pp. 283-291, 1993.

[31] C. J. Romanowski, and R. Nagi., “Analyzing maintenance data using data mining methods”, Data mining for design and manufacturing, pp. 235-254: Springer, 2001.

[32] F. Bergeret, and C. Le Gall., “Yield improvement using statistical analysis of process dates”, IEEE Transactions on Semiconductor Manufacturing,vol. 16, no. 3, pp. 535-542, 2003.

[33] R. M. Dabbas, and H.-N. Chen., “Mining semiconductor manufacturing data for productivity improvement—an integrated relational database approach”, Computers in Industry, vol. 45, no. 1, pp. 29-44, 2001.

[34] S. K. Murthy., “Automatic construction of decision trees from data: A multi-disciplinary survey”, Data mining and knowledge discovery, vol. 2, no. 4, pp. 345-389, 1998.

[35] J.-L. Hou, and S.-T. Yang., “Technology-mining model concerning operation characteristics of technology and service providers”, International Journal of Production Research, vol. 44, no. 16, pp. 3345-3365, 2006.

[36] U. Jung, M. K. Jeong, and J.-c. Lu., “Data reduction for multiple functional data with class information”, International Journal of Production Research, vol. 44, no. 14, pp. 2695-2710, 2006.

[37] Y. Zhang, and M. S. Dudzic., “Online monitoring of steel casting processes using multivariate statistical technologies: From continuous to transitional operations”, Journal of Process Control, vol. 16, no. 8, pp. 819-829, 2006.

[38] K. R. Caskey., “A manufacturing problem solving environment combining evaluation, search, and generalisation methods”, Computers in Industry, vol. 44, no. 2, pp. 175-187, 2001.

[39] R.-S. Guh., “Real-time pattern recognition in statistical process control: a hybrid neural network/decision tree-based approach”, Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, vol. 219, no. 3, pp. 283-298, 2005.

[40] L. Rokach, and O. Maimon., “Data mining for improving the quality of manufacturing: a feature set decomposition approach”, Journal of Intelligent manufacturing, vol. 17, no. 3, pp. 285-299, 2006.

[41] C. Kwak, and Y. Yih., “Data-mining approach to production control in the computer-integrated testing cell”, IEEE Transactions on Robotics and Automation, vol. 20, no. 1, pp. 107-116, 2004.

[42] K. B. Irani, J. Cheng, U. M. Fayyad, and Z. Qian., “Applying machine learning to semiconductor manufacturing”, IEEE Expert, vol. 8, no. 1, pp. 41-47, 1993.

[43] V. A. Skormin, V. I. Gorodetski, and L. J. Popyack., “Data mining technology for failure prognostic of avionics”, IEEE Transactions on Aerospace and Electronic Systems,vol. 38, no. 2, pp. 388-403, 2002.

[44] M. K. Jeong, J.-C. Lu, X. Huo, B. Vidakovic, and D. Chen., “Wavelet-based data reduction techniques for process fault detection”, Technometrics, 2012.

[45] A. Rojas, and A. K. Nandi., “Practical scheme for fast detection and classification of rolling-element bearing faults using support vector machines”, Mechanical Systems and Signal Processing, vol. 20, no. 7, pp. 1523-1536, 2006.

[46] C. J. McDonald., “New tools for yield improvement in integrated circuit manufacturing: can they be applied to reliability?”, Microelectronics Reliability, vol. 39, no. 6, pp. 731-739, 1999.

[47] W.-C. Chen, S.-S. Tseng, and C.-Y. Wang., “A novel manufacturing defect detection method using data mining approach”, Innovations in Applied Artificial Intelligence, pp. 77-86: Springer, 2004.

[48] U. Purintrapiban, and V. Kachitvichyanukul., “Detecting patterns in process data with fractal dimension”, Computers & industrial engineering, vol. 45, no. 4, pp. 653-667, 2003.

[49] Y. Peng., “Intelligent condition monitoring using fuzzy inductive learning”, Journal of Intelligent manufacturing, vol. 15, no. 3, pp. 373-380, 2004.

[50] Z. Pasek., “Exploration of rough sets theory use for manufacturing process monitoring”, Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, vol. 220, no. 3, pp. 365-374, 2006.

[51] T.-H. T. Hou, W.-L. Liu, and L. Lin., “Intelligent remote monitoring and diagnosis of manufacturing processes using an integrated approach of neural networks and rough sets”, Journal of Intelligent manufacturing, vol. 14, no. 2, pp. 239-253, 2003.

[52] T.-H. T. Hou, and C.-C. Huang., “Application of fuzzy logic and variable precision rough set approach in a remote monitoring manufacturing process for diagnosis rule induction”, Journal of Intelligent manufacturing, vol. 15, no. 3, pp. 395-408, 2004.

[53] D. Braha, and A. Shmilovici., “Data mining for improving a cleaning process in the semiconductor industry”, IEEE Transactions on Semiconductor Manufacturing, vol. 15, no. 1, pp. 91-101, 2002.

[54] T. Liao, G. Wang, E. Triantaphyllou, and P. Chang., “A data mining study of weld quality models constructed with MLP neural networks from stratified sample data”, 2006.

[55] A. Kusiak., “Data mining and decision making”, International Society for Optics and Photonics, pp. 155-165, 2002.

[56] A. Kusiak., “A data mining approach for generation of control signatures”, Transactions-American Society of Mechanical Engineers Journal of Manufacturing Science and Engineering, vol. 124, no. 4, pp. 923-926, 2002.

[57] J. W. Grzymala-Busse, J. Stefanowski, and S. Wilk., “A comparison of two approaches to data mining from imbalanced data”, Journal of Intelligent Manufacturing, vol. 16, no. 6, pp. 565-573, 2005

[58] S.-C. Horng, and S.-Y. Lin., “A hybrid classification tree for products of complicated machines in flexible manufacturing systems”, IEEE International Conference on Systems, Man and Cybernetics,vol. 4, pp.3775-3780, 2005.

[59] C.-H. Hsu, and M.-J. J. Wang., “Using decision tree-based data mining to establish a sizing system for the manufacture of garments”, The International Journal of Advanced Manufacturing Technology, vol. 26, no. 5-6, pp. 669-674, 2005.

[60] J. Jiao, and Y. Zhang., “Product portfolio identification based on association rule mining”, Computer-Aided Design, vol. 37, no. 2, pp. 149-172, 2005.

[61] R. Xu, and D. Wunsch., “Survey of clustering algorithms”, Neural Networks, IEEE Transactions on, vol. 16, no. 3, pp. 645-678, 2005.

[62] S.-H. Liao, and C.-H. Wen., “Artificial neural networks classification and clustering of methodologies and applications–literature analysis from 1995 to 2005”, Expert Systems with Applications, vol. 32, no. 1, pp. 1-11, 2007.

[63] M. Gardner, and J. Bieker., “Data mining solves tough semiconductor manufacturing problems”,  Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 376-383, 2000.

[64] C.-F. Chien, W.-C. Wang, and J.-C. Cheng., “Data mining for yield enhancement in semiconductor manufacturing and an empirical study”, Expert Systems with Applications, vol. 33, no. 1, pp. 192-198, 2007.

[65] Y. Ishino, and Y. Jin., “Data mining for knowledge acquisition in engineering design”, Data mining for design and manufacturing, pp. 145-160: Springer, 2001.

[66] P. Kim, and Y. Ding., “Optimal engineering system design guided by data-mining methods”, Technometrics, vol. 47, no. 3, pp. 336-348, 2005.

[67] O. Torkul, I. H. Cedimoglu, and A. Geyik., “An application of fuzzy clustering to manufacturing cell design”, Journal of Intelligent & Fuzzy Systems, vol. 17, no. 2, pp. 173-181, 2006.

[68] C. Romanowski, and R. Nagi., “A data mining for knowledge acquisition in engineering design”, Kluwer Academic, Dordrecht, pp. 161-178, 2001

[69] C. J. Romanowski, and R. Nagi., “On comparing bills of materials: a similarity/distance measure for unordered trees”, Systems, Man and Cybernetics, Part A: Systems and Humans, IEEE Transactions on, vol. 35, no. 2, pp. 249-260, 2005.

[70] C. J. Romanowski, and R. Nagi., “A data mining approach to forming generic bills of materials in support of variant design activities”, Journal of Computing and Information Science inEngineering, vol. 4, no. 4, pp. 316-328, 2004.

[71] A. Kusiak, K. Kernstine, J. Kern, K. McLaughlin, and T. Tseng., “Data mining: medical and engineering case studies”, pp. 1-7.

[72] Y. Sebzalli, and X. Wang., “Knowledge discovery from process operational data using PCA and fuzzy clustering”, Engineering Applications of Artificial Intelligence, vol. 14, no. 5, pp. 607-616, 2001.

[73] J.-H. Lee, and S.-C. Park., “Data mining for high quality and quick response manufacturing”, Data mining for design and manufacturing, pp. 179-205: Springer, 2001.

[74] F. Crespo, and R. Weber., “A methodology for dynamic data mining based on fuzzy clustering”, Fuzzy Sets and Systems, vol. 150, no. 2, pp. 267-284, 2005.

[75] T. Liao, D.-M. Li, and Y.-M. Li., “Detection of welding flaws from radiographic images with fuzzy clustering methods”, Fuzzy sets and Systems, vol. 108, no. 2, pp. 145-158, 1999.

[76] C. Huang, T. Li, and T. Peng., “A hybrid approach of rough set theory and genetic algorithm for fault diagnosis”, The International Journal of Advanced Manufacturing Technology, vol. 27, no. 1-2, pp. 119-127, 2005.

[77] S. C. Hui, and G. Jha., “Data mining for customer service support”, Information & Management, vol. 38, no. 1, pp. 1-13, 2000.

[78] A. L. Symeonidis, D. D. Kehagias, and P. A. Mitkas., “Intelligent policy recommendations on enterprise resource planning by the use of agent technology and data mining techniques”, Expert Systems with Applications, vol. 25, no. 4, pp. 589-602, 2003.

[79] Z. Qian, W. Jiang, and K.-L. Tsui., “Churn detection via customer profile modelling”, International Journal of Production Research, vol. 44, no. 14, pp. 2913-2933, 2006.

[80] W.-C. Chen, S.-S. Tseng, and C.-Y. Wang., “A novel manufacturing defect detection method using association rule mining techniques”, Expert Systems with Applications, vol. 29, no. 4, pp. 807-815, 2005.

[81] M.-C. Chen, and H.-P. Wu., “An association-based clustering approach to order batching considering customer demand patterns”, Omega, vol. 33, no. 4, pp. 333-343, 2005.

[82] M. Caramia, and G. Felici., “Mining relevant information on the Web: a clique-based approach”, International Journal of Production Research, vol. 44, no. 14, pp. 2771-2787, 2006.

[83] M. Inada, and T. Terano., “QC chart mining: Extracting systematic error patterns from quality control charts”, IEEE International Conference on Systems, Man and Cybernetics, vol. 4, pp. 3781-378, 2005.

[84] C.-X. J. Feng, Z.-G. Yu, and A. Kusiak., “Selection and validation of predictive regression and neural network models based on designed experiments”, IIE Transactions, vol. 38, no. 1, pp. 13-23, 2006.

[85] A. Eswaradass, X.-H. Sun, and M. Wu., “A neural network based predictive mechanism for available bandwidth”, 19th IEEE International Proceedings Parallel and Distributed Processing Symposium, pp. 33a-33a, 2005.

[86] C.-X. J. Feng, and X.-F. Wang., “Data mining techniques applied to predictive modeling of the knurling process”, IIE Transactions, vol. 36, no. 3, pp. 253-263, 2004.

[87] S.-J. T. Hsieh., “Artificial neural networks and statistical modeling for electronic stress prediction using thermal profiling”, IEEE Transactions on Electronics Packaging Manufacturing, vol. 27, no. 1, pp. 49-58, 2004.

[88] J. Ordieres Meré, A. González Marcos, J. González, and V. Lobato Rubio., “Estimation of mechanical properties of steel strip in hot dip galvanising lines”, Ironmaking & steelmaking, vol. 31, no. 1, pp. 43-50, 2004.

[89] B. Yuan, X. Wang, and T. Morris., “Software analyser design using data mining technology for toxicity prediction of aqueous effluents”, Waste Management, vol. 20, no. 8, pp. 677-686, 2000.

[90] Y. Ren, Y. Ding, and S. Zhou., “A data mining approach to study the significance of nonlinearity in multistation assembly processes”, IIE Transactions, vol. 38, no. 12, pp. 1069-1083, 2006.