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An approach to the analysis of design concepts using the rough set theory

Published online by Cambridge University Press:  07 June 2005

D. ALISANTOSO
Affiliation:
School of Mechanical and Production Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798
L.P. KHOO
Affiliation:
School of Mechanical and Production Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798
I.B.H. LEE
Affiliation:
SIMTech Institute of Manufacturing Technology, 72 Nanyang Drive, Singapore 638075

Abstract

This paper describes an approach to the analysis of design concepts (DCs) using the rough set theory. The proposed approach attempts to extract design knowledge from past designs, and used the knowledge obtained to perform DC–capability mapping in a dynamic design evolution environment. The mapping enables designers to estimate the feasibility of a DC to meet stipulated design specifications. The proposed approach encompasses two algorithms, namely, the dissimilar objects algorithm and the attribute decomposition algorithm, to deal with an information system with unavailable information and multidecision attributes, respectively. The details of these algorithms are presented. A case study on the design of vacuum cleaners is used to illustrate the capability of the proposed approach.

Type
Research Article
Copyright
© 2004 Cambridge University Press

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References

REFERENCES

Arciszewski, T., Ziarko, W., & Khan, T.L. (1993). Learning conceptual design rules: A rough set approach. In Rough Sets, Fuzzy Sets and Knowledge Discovery (Ziarko, W.P., Ed.), pp. 444449. New York: Springer–Verlag.
Felix, R. & Ushio, T. (1999). Rules induction from inconsistent and incomplete data using rough sets. Proc. IEEE Int. Conf. Systems, Man, and Cybernetics, Vol. 5, pp. 154158, Tokyo, October 12–15.
Grzymala–Busse, J.W. & Hu, M. (2000). A Comparison of several approaches to missing attribute values in data mining. Second Int. Conf. Rough Sets and Current Trends in Computing, pp. 340347, Banff, Canada, October 16–19.
Hundal, M.S. (1997). Systematical Mechanical Designing: A Cost and Management Perspective. New York: American Society of Mechanical Engineers.
Khoo, L.P. & Zhai, L.Y. (2001). Multiconcept classification of diagnostic knowledge to manufacturing systems: analysis of incomplete data with continuous-valued attributes. International Journal of Production Research 39(17), 39413957.CrossRefGoogle Scholar
Komorowski, J., Pawlak, Z., Polkowski, L., & Skowron, A. (1999). Rough sets: A tutorial. In Rough Fuzzy Hybridization (Skowron, A., Ed.), pp. 398. New York: Springer–Verlag.
Kryszkiewicz, M. (1998). Rough set approach to incomplete information systems. Information Sciences 113, 3949.CrossRefGoogle Scholar
Kryszkiewicz, M. (1999). Rules in incomplete information systems. Information Sciences 112, 271292.Google Scholar
Kusiak, A., Kern, J.A., Kernstine, K.H., & Tseng, B.T.L. (2000). Autonomous decision-making: a data mining approach. IEEE Transactions on Information Technology in Biomedicine 4(4), 274284.Google Scholar
Lee, S. & Vachtsevanos, G. (2002). An application of rough set theory to defect detection of automotive glass. Mathematics and Computers in Simulation 60, 225231.CrossRefGoogle Scholar
Liang, J. & Xu, C. (2000). Uncertainty measures of roughness of knowledge and rough sets in incomplete information systems. Proc. Third World Congr. on Intelligent Control and Automation, pp. 25262529, Hefei, China, June 28–July 2.
Mollestad, T. & Komorowski, J. (1999). A Rough set framework for mining propositional default rules. In Rough Fuzzy Hybridization (Skowron, A., Ed.), pp. 233262. New York: Springer–Verlag.
Nguyen, H.P., Le, L.P., Santiprabhob, P., & De Baets, B. (2001). Approach to generating rules for expert systems using rough set theory. Joint 9th IFSA World Congr. and 20th NAFIPS Int. Congr., Vol. 2, pp. 877882, Vancouver, Canada, July 27.
Pawlak, Z. (1982). Rough sets. International Journal of Computer and Information Sciences 11, 341356.CrossRefGoogle Scholar
Pawlak, Z. (1996). Why rough sets? Proc. Fifth Int. Conf. Fuzzy Systems, Vol. 2, pp. 738743, New Orleans, LA, September 8–11.
Pawlak, Z. (1998). Granularity of knowledge, indiscernibility and rough sets. Proc. IEEE World Congr. Computational Intelligence and IEEE International Conf. Fuzzy Systems, Vol. 1, pp. 106110, Anchorage, AK, May 4–9.
Reich, Y. & Barai, S.V. (1999). Evaluating Machine learning models for engineering problems. Artificial Intelligence in Engineering 13, 257272.Google Scholar
Ulrich, K.T. & Eppinger, S.D. (2000). Product Design and Development. New York: McGraw–Hill.
Wang, G. (2002). Extension of rough set under incomplete information systems. Proc. IEEE Int. Conf. Fuzzy Systems, Vol. 2, pp. 10981103, Honolulu, HI, May 12–17.
Wills, L.M. & Kolodner, J.L. (1994). Towards more creative case-based design systems. Proc. 12th National Conf. Artificial Intelligence, Seattle, WA, August.
Zhai, L.Y., Khoo, L.P., & Fok, S.C. (2001). Derivation of decision rules for the evaluation of product performance using genetic algorithms and rough set theory. In Data Mining for Design and Manufacturing (Braha, D., Ed.), pp. 337353. Boston: Kluwer.