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A Data Mining-Based Engineering Design Support System: A Research Agenda

  • Carol J. Romanowski
  • Rakesh Nagi
Part of the Massive Computing book series (MACO, volume 3)

Abstract

Currently, designers do not have access to product life cycle information and other feedback, causing costly design iterations and increased time-to-market. Our research proposes using data mining to incorporate this heterogeneous and distributed information into the beginning stages of design — thereby reducing iterations and lowering the cost of product design.

Keywords

Data Mining Product Life Cycle Life Cycle Cost Data Mining Algorithm Component Library 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. Anglade, E., and MacRae, A. U., “End-product hardware design quality metrics–definitions,” in International Conference on Communications, pp. 1182–1187, 1987.Google Scholar
  2. Arciszewski, T., Mustafa, M., and Ziarko, W., “A methodology for design knowledge acquisition for use in learning expert systems,” International Journal of Man-Machine Studies, 27, pp. 23–32, 1987.CrossRefGoogle Scholar
  3. Borg, J. C., Yan, X-t., and Juster, N. P., “Guiding component form design using decision consequence knowledge support,” Artificial Intelligence for Engineering Design, Analysis and Manufacturing, 13, pp. 387–403, 1999.Google Scholar
  4. Cook, D. J., and Holder, L. B., “Graph-based Data Mining,” IEEE Intelligent Systems, (March/April), pp. 32–41, 2000.Google Scholar
  5. Court, A. W., Culley, S. J., and McMahon, C. A., “The influence of information technology in new product development: observations of an empirical study of the access of engineering design information,” International Journal of Information Management, 17 (5), pp. 359–375, 1997.CrossRefGoogle Scholar
  6. Court, A. W., Ullamn, D. G., and Culley, S. J., “A comparison between the provision of information to engineering designers in the UK and the USA,” International Journal of Information Management, 18 (6), pp. 409–425, 1998.CrossRefGoogle Scholar
  7. Diteman, M., and Stauffer, L., “Usability Analysis of a Computer Tool for Evaluating Design Concepts.” Design Theory and Methodology, 42, pp. 41–44, 1992.Google Scholar
  8. Dong, A., and Agogino, A. M., “Managing design information in enterprise-wide CAD using ‘smart drawings’,” Computer-Aided Design, 30 (6), pp. 425–435, 1998.CrossRefGoogle Scholar
  9. Duffy, S. M., and Duffy, A. H. B., “Sharing the learning activity using intelligent CAD,” Artificial Intelligence for Engineering Design, Analysis and Manufacturing, 10, pp. 83–100, 1996.Google Scholar
  10. Dym, C. L., Engineering Design: A Synthesis of Views. Cambridge: Cambridge University Press, 1994.Google Scholar
  11. Dym, C. L., and Levitt, R. E., Knowledge-Based Systems in Engineering. New York: McGraw-Hill, 1991.Google Scholar
  12. Ertas, A., and Jones, J., The Engineering Design Process. New York: John Wiley & Sons, 1996.Google Scholar
  13. Fayyad, U. M., Piatetsky-Shapiro, G., Smyth, P., and Uthurusamy, R., Advances in Knowledge Discovery and Data Mining. Menlo Park, CA: AAAI Press/The MIT Press, 1996.Google Scholar
  14. Ferguson, C.-J., Lees, B., MacArthur, E., and Irgens, C, C., “An Application of Data Mining for Product Design,” IEE Colloquium Digest, 434, pp. 5/1–5/5, 1998.Google Scholar
  15. French, M. E., Form, Structure and Mechanism. London: MacMillan, 1992.CrossRefGoogle Scholar
  16. Gomes, P., Bento, C., and Gago, P., “Learning to verify design solutions from failure knowledge,” Artificial Intelligence for Engineering Design, Analysis and Manufacture, 12, pp. 107–115, 1998.Google Scholar
  17. Hazelrigg, G. A., Systems Engineering: An Approach to Information-Based Design. Upper Saddle River, NJ: Prentice-Hall, Inc., 1996.Google Scholar
  18. Hyde, R. S., and Stauffer, L., “The comparison of the reliability of three psychometric scales for measuring design quality,” in Design Theory and Methodology, 40, pp. 349–354, 1990.Google Scholar
  19. Ivezic, N., and Garrett, J. H., “A neural-network-based machine learning approach for supporting synthesis,” Artificial Intelligence for Engineering Design, Analysis and Manufacture, 8, pp. 143–161, 1994.Google Scholar
  20. Lu, S., and Chen, K., “A machine learning approach to the automatic synthesis of mechanistic knowledge for engineering decision-making,” Artificial Intelligence for Engineering Design, Analysis and Manufacturing, 1 (2), pp. 109–118, 1987.Google Scholar
  21. Madni, A. M., “The Role of Human Factors in Expert Systems Design and Acceptance,” Human Factors, 30 (4), pp. 395–414, 1988.Google Scholar
  22. McLaughlin, S., and Gero, J. S., “Acquiring expert knowledge from characterised designs,” Artificial Intelligence for Engineering Design, Analysis and Manufacturing, 1, pp. 73–87, 1987.Google Scholar
  23. Moczulski, W., “Inductive Learning in Design: A Method and Case Study Concerning Design of Antifriction Bearing Systems,” in Machine Learning and Data Mining (Michalski, R., Bratko, I., and Kubat, M., Eds.), pp. 203–219, John H. Wiley & Sons, Ltd.: Chichester, 1998.Google Scholar
  24. Nahmias, S., Production and Operations Analysis. Burr Ridge IL: Irwin, 1993. Pahl, G., and Beitz, W., Engineering Design. London: Springer-Verlag, 1999.Google Scholar
  25. Prasad, B., “Survey of life-cycle measures and metrics for concurrent product and process design,” Artificial Intelligence for Engineering Design, Analysis and Manufacturing, 14, pp. 163–176, 2000.Google Scholar
  26. Rabins, M. J., Ardayfio, D., Balzar, R., Fenves, S., Nadler, G., Richardson, H., Rinard, I., Roth, B., Seireg, A., and Wozny, M, M., “Goals and Priorities for Research in Engineering Design: A Report to the Design Research Community,” New York, NY: American Society of Mechanical Engineers, 1986.Google Scholar
  27. Raghavan, V., and Hafez, A., “Dynamic Data Mining,” in Intelligent Problem Solving-Methodologies and Approaches: Proc. of Thirteenth International Conference on Industrial Engineering Applications of AI & Expert Systems, pp. 220–229, 2000.CrossRefGoogle Scholar
  28. Reich, Y., “The development of BRIDGER: A methodological study of research on the use of machine learning in design,” Artificial Intelligence in Engineering, 8, pp. 217–231, 1993.CrossRefGoogle Scholar
  29. Reich, Y., “Layered models of research methodologies,” Artificial Intelligence for Engineering Design, Analysis and Manufacturing, 8, pp. 263–274, 1994.Google Scholar
  30. Reich, Y., “Learning in design: From characterizing dimensions to working systems,” Artificial Intelligence for Engineering Design, Analysis and Manufacturing, 12, pp. 161–172, 1998.Google Scholar
  31. Reich, Y., Konda, S., Levy, S., Monarch, I., and Subrahmanian, E., “New roles for machine learning in design,” Artificial Intelligence in Engineering, 8, pp. 165–181, 1993.CrossRefGoogle Scholar
  32. Rychener, M. D., “Research in Expert Systems for Engineering Design,” in Expert Systems for Engineering Design, pp. 1–33, Academic Press, Inc.: Boston, 1988.CrossRefGoogle Scholar
  33. Siemieniuch, C. E., and Sinclair, M. A., “Implications of concurrent engineering for organizational knowledge and structure: a European, ergonomics perspective,” Journal of Design and Manufacturing, 3, pp. 189–200, 1993.Google Scholar
  34. Sim, S. K., and Duffy, A. H. B., “A foundation for machine learning in design,” Artificial Intelligence for Engineering Design, Analysis and Manufacture, 12, pp. 193–209, 1998.Google Scholar
  35. Thurston, D. L. “Subjective Design Evaluation with Multiple Attributes,” in Design Theory and Methodology–DTM ‘80, pp. 355–361, 1990.Google Scholar
  36. Wang, K., “Discovering patterns from large and dynamic sequential data,” Journal of Intelligent Information Systems, 9 (1), pp. 33–56, 1997.CrossRefGoogle Scholar
  37. Witten, I. H., Moffat, A., and Bell, T. C., Managing Gigabytes: Compressing and Indexing Documents and Images. San Francisco, CA: Morgan Kaufmann Publishers, 1999.MATHGoogle Scholar
  38. Yang, M. C., Wood, W. H., and Cutkosky, M. R., “Data mining for thesaurus generation in informal design information retrieval,” in Congress on Computing in Civil Engineering, pp. 189–200, 1998.Google Scholar
  39. Zhou, A., Jin, W., Zhou, S., Qian, W., and Tian, Z., “Incremental mining of the schema of semi-structured data,” Journal of Computer Science and Technology, 15 (3), pp. 241–248, 2000.CrossRefMATHGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2001

Authors and Affiliations

  • Carol J. Romanowski
    • 1
  • Rakesh Nagi
    • 1
  1. 1.Department of Industrial EngineeringState University of New York at BuffaloBuffaloUSA

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