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호감도 함수 기반 다특성 강건설계 최적화 기법

A Desirability Function-Based Multi-Characteristic Robust Design Optimization Technique

  • 박종필 (국립 한밭대학교 기계공학과) ;
  • 조재훈 (국립 한밭대학교 기계공학과) ;
  • 남윤의 (국립 한밭대학교 기계공학과)
  • Jong Pil Park (Department of Mechanical Engineering, Hanbat National University) ;
  • Jae Hun Jo (Department of Mechanical Engineering, Hanbat National University) ;
  • Yoon Eui Nahm (Department of Mechanical Engineering, Hanbat National University)
  • 투고 : 2023.11.04
  • 심사 : 2023.11.23
  • 발행 : 2023.12.31

초록

Taguchi method is one of the most popular approaches for design optimization such that performance characteristics become robust to uncontrollable noise variables. However, most previous Taguchi method applications have addressed a single-characteristic problem. Problems with multiple characteristics are more common in practice. The multi-criteria decision making(MCDM) problem is to select the optimal one among multiple alternatives by integrating a number of criteria that may conflict with each other. Representative MCDM methods include TOPSIS(Technique for Order of Preference by Similarity to Ideal Solution), GRA(Grey Relational Analysis), PCA(Principal Component Analysis), fuzzy logic system, and so on. Therefore, numerous approaches have been conducted to deal with the multi-characteristic design problem by combining original Taguchi method and MCDM methods. In the MCDM problem, multiple criteria generally have different measurement units, which means that there may be a large difference in the physical value of the criteria and ultimately makes it difficult to integrate the measurements for the criteria. Therefore, the normalization technique is usually utilized to convert different units of criteria into one identical unit. There are four normalization techniques commonly used in MCDM problems, including vector normalization, linear scale transformation(max-min, max, or sum). However, the normalization techniques have several shortcomings and do not adequately incorporate the practical matters. For example, if certain alternative has maximum value of data for certain criterion, this alternative is considered as the solution in original process. However, if the maximum value of data does not satisfy the required degree of fulfillment of designer or customer, the alternative may not be considered as the solution. To solve this problem, this paper employs the desirability function that has been proposed in our previous research. The desirability function uses upper limit and lower limit in normalization process. The threshold points for establishing upper or lower limits let us know what degree of fulfillment of designer or customer is. This paper proposes a new design optimization technique for multi-characteristic design problem by integrating the Taguchi method and our desirability functions. Finally, the proposed technique is able to obtain the optimal solution that is robust to multi-characteristic performances.

키워드

참고문헌

  1. Abdi, H. and Williams, L.J., Principal component analysis, Computational Statistics, Wiley Interdisciplinary Reviews, 2010, Vol. 2, pp. 433-459. https://doi.org/10.1002/wics.101
  2. Chakraborty, S. and Yeh, C.H., A simulation based comparative study of normalization procedures in multiattribute decision making, Proceedings of Conference on Artificial Intelligence, Knowledge Engineering and Data Bases, 2007.
  3. Chakraborty, S. and Yeh, C.H., A simulation comparison of normalization procedures for TOPSIS, Proceedings of International Conference on Computers & Industrial Engineering, 2009.
  4. Cristiano, J.J., Liker, J.K., and White, C.C., Key factors in the successful application of quality function deployment(QFD), IEEE Transactions on Engineering Management, 2001, Vol. 48, No. 1, pp. 81-95. https://doi.org/10.1109/17.913168
  5. Derringer, G. and Suich, R., Simultaneous optimization of several response variables, Journal of Quality Technology, 1980, Vol. 12, pp. 214-219. https://doi.org/10.1080/00224065.1980.11980968
  6. Fung, C.P. and Kang, P.C., Multi-response optimization in friction properties of PBT composites using Taguchi method and principle component analysis, Journal of Materials Processing Technology, 2005, Vol. 170, pp. 602-610. https://doi.org/10.1016/j.jmatprotec.2005.06.040
  7. Ho, L.H., Feng, S.Y. and Yen, T.M., A new methodology for customer satisfaction analysis: Taguchi's signal-to-noise ratio approach, Journal of Service Science and Management, 2014, Vol. 7, pp. 235-244 https://doi.org/10.4236/jssm.2014.73021
  8. Jeong, H.I., Lee, C.M., and Kim, D.H., Optimization of CFRP drilling conditions using the Taguchi method, Transactions of the KSME A, 2021, Vol. 45, No. 12, pp. 1077-1083. https://doi.org/10.3795/KSME-A.2021.45.12.1077
  9. Jo, J.H., Lee, J.H., Park, J.P., and Nahm, Y.E., A suty on the customer-oriented design using desirability function and Taguchi method, Journal of Korean Society of Industrial and Systems Engineering, 2022, Vol. 45, No. 4, pp. 99-108. https://doi.org/10.11627/jksie.2022.45.4.099
  10. Kano, N., Seraku, N., Takahashi, F., and Tsuji, S., Attractive quality and must-be quality, Journal of the Japanese Society for Quality Control(Hinshitsu), 1984, Vol. 14, No. 2, pp. 39-48.
  11. Kaushik, N. and Singhal, S., Hybrid combination of Taguchi-GRA-PCA for optimization of wear behavior in AA6063/SiCp matrix composite, Production & Manufacturing Research, 2018, Vol. 6, No. 1, pp. 171-189. https://doi.org/10.1080/21693277.2018.1479666
  12. Kim, J.W., Engineering Design: Creative New Product Development Methodology, Munundang, 2013.
  13. Kuo, Y., Yang, T. and Huang, G.W., The use of grey relational analysis in solving multiple attribute decision- making problems, Computers & Industrial Engineering, 2008, Vol. 55, pp. 80-93. https://doi.org/10.1016/j.cie.2007.12.002
  14. Kwon, Y.M., Robust design using desirability function in product-array, Journal of the Chosun Natrual Science, 2018, Vol. 11, No. 2, pp. 76-81.
  15. Lai, Y.J., Liu, T.Y. and Hwang, C.L., TOPSIS for MODM, European Journal of Operational Research, 1994, Vol. 76, No. 11, pp. 486-500. https://doi.org/10.1016/0377-2217(94)90282-8
  16. Lakshmi, T.M. and Venkatesan, V.P., A comparison of Various Normalization in Techniques for Order Performance by Similarity to Ideal Solution (TOPSIS), International Journal of Computing Algorithm, 2014, Vol. 3, No. 3, pp. 255-259. https://doi.org/10.20894/IJCOA.101.003.003.023
  17. Nahm, Y.E., Set-based multi-objective design optimization at the early phase of design (the third report): Application to environment-conscious automotive sidedoor assembly, Journal of Korean Society of Industrial and Systems Engineering, 2011, Vol. 34, No. 4, pp. 139-145.
  18. Nahm, Y.E., A novel approach to prioritize customer requirements in QFD based on customer satisfaction function for customer-oriented product design, Journal of Mechanical Science and Technology, 2013, Vol. 27, pp. 3765-3777. https://doi.org/10.1007/s12206-013-0921-1
  19. Najm, O., El-Hassan, H. and El-Dieb, A., Optimization of alkali-activated ladle slag composites mix design using Taguchi-based TOPSIS method, Construction and Building Materials, 2022, Vol. 327, p.126946.
  20. Niu, B., Shi, M., Zhang, Z., Li, Y., Cao, Y., and Pan, S., Multi-objective optimization of supply air jet enhancing airflow uniformity in data center with Taguchi-based grey relational analysis, Building and Environment, 2022, Vol. 208, p.108606.
  21. Sharma, A., Awasthi, A., Singh, T., Kumar, R., and Chauhan, R., Experimental investigation and optimization of potential parameters of discrete V down baffled solar thermal collector using hybrid Taguchi-TOPSIS method, Applied Thermal Engineering, 2022 Vol. 209, p.118250.
  22. Singarvel, B., Selvaraj, T., and Jeyapaulc, R., Multi objective optimization in turning of EN25 steel using Taguchi Based utility concept coupled with principal component analysis, Procedia Engineering, 2014, Vol. 97, pp. 158-165. https://doi.org/10.1016/j.proeng.2014.12.237
  23. Subrahmanyam, A.P.S.V.R., Rao, C.M., and Raju, B.N., Taguchi based desirability function analysis for the optimization of multiple performance characteristics, International Journal of Modern Trends in Engineering & Research, 2018, Vol. 5, No. 5, pp. 168-175. https://doi.org/10.21884/IJMTER.2018.5157.FY4QK
  24. van de Poel I.R., Methodological problems in QFD and directions for future development, Research in Engineering Design, 2007, Vol. 18, pp. 21-36. https://doi.org/10.1007/s00163-007-0029-7
  25. Viswanathan, R., Ramesh, S., Maniraj, S., Subburam, V., Measurement and multi-response optimization of turning parameters for magnesium alloy using hybrid combination of Taguchi-GRA-PCA technique, Measurement, 2020, Vol. 159, p.107800.
  26. Wu, L., Liu, J., Zhou, J., Zhang, Q., Song, Y., Du, S., and Tian, W., Evaluation of tar from the microwave co-pyrolysis of low-rank coal and corncob using orthogonal- test-based grey relational analysis(GRA), Journal of Cleaner Production, 2022, Vol. 337, p. 130362.
  27. Yum, B.J., Kim, S.J., Seo, S.K., Byun, J.H., and Lee S.H., The Taguchi robust design method: current status and future directions, Journal of the Korean Institute of Industrial Engineering, 2013, Vol. 39, No. 5, pp. 325-341. https://doi.org/10.7232/JKIIE.2013.39.5.325
  28. Youn, L.B. and Woo, L.H., Shape optimal design of an automotive pedal arm using the Taguchi method, Journal of the Korean Society for Precision Engineering, 2007, Vol. 24, No. 3, pp. 76-83.