Robust Design Method Using Adjustable Control Factors
Takeo Kato*, Masatoshi Muramatsu*, Suguru Kimura**,
and Yoshiyuki Matsuoka***
*Department of Mechanical Engineering, Tokai University, 4-1-1 Kitakaname, Hiratsuka, Kanagawa 259, Japan
**Graduate School of Science and Technology, Keio University, Japan
***Department of Mechanical Engineering, Keio University, 3-14-1 Hiyoshi Kohoku-ku, Yokohama, Kanagawa 238, Japan
Design that ensures robust performance for diverse users and environments has received much attention. Previous research proposed a Robust Design Method (RDM) that considered the adjustable control factors (ACFs) of mechanisms such as the servo mechanisms of machine tools or recliner mechanisms of public seats. This method derived the optimum adjustment ranges of the ACFs only after both these factors and their (dependent or independent) relationships had been identified in the design problem. This research improves on the previous RDM to enable designers to select ACFs and their relationships. This method contains two indices. One is the standard deviation of each control factor, used for finding ACFs that need not be adjusted. The other is the contribution ratio of the eigenvalues calculated from the variance-covariance matrix of the ACFs, used for finding dependent relationships. This method effectively derives the optimum adjustment ranges of the ACFs and their relationships based on these indices. Numerical and design examples are presented to demonstrate the practicality of the proposed method.
-  Y. Matsuoka, “Design Science,” Maruzen, Tokyo, 2010.
-  S. Sundaresan, K. Ishii, and D. R. Houser, “Design optimization forrobustness using performance simulation programs,” Proc. of theASME ADA, Vol.DE-65-1, pp. 249-256, 1991.
-  G. Taguchi, “Taguchi on robust technology development,” ASMEPress, New York, 1993.
-  J. C. Yu and K. Ishii, “Design optimization for robustness usingquadrature factorial models,” Engineering optimization, Vol.30,pp. 203-225, 1998.
-  J. C. Yu and K. Ishii, “Design for robustness based on manufacturingvariation patterns,” ASME J. of Mechanical Design, Vol.120, No.2, pp. 196-202, 1998.
-  M. Arakawa and H. Yamakawa, “A study on Optimum Design Using Fuzzy Numbers as Design Variables,” Proc. of the ASME DETC, DE-82, pp. 463-470, 1995.
-  A. D. Belegundu and S. Zhang, “Robustness of Design Through Minimum Sensitivity,” ASME J. of Mechanical Design, Vol.114, No.2, pp. 213-217, 1992.
-  G. Emch and A. Parkinson, “Robust optimal design for worstcase tolerances,” ASME J. of Mechanical Design, Vol.116, No.4, pp. 1019-1025, 1994.
-  A. Parkinson, C. Sorensen, and N. Pourhassan, “A general approach for robust optimal design,” ASME J. of Mechanical Design, Vol.115, No.1, pp. 74-80, 1993.
-  A. Parkinson, “Robust mechanical design using engineering models,” ASME J. of Mechanical Design, Vol.117B, pp. 48-54, 1995.
-  B. Ramakrishnan and S. S. Rao, “A general loss function based optimization procedure for robust design,” Engineering optimization, Vol.25, pp. 255-276, 1996.
-  J. Zhu and K. L. Ting, “Performance distribution analysis and robust design,” ASME J. of Mechanical Design, Vol.123, No.1, pp. 11-17, 2001.
-  R. J. Eggert and R. W. Mayne, “Probabilistic optimal design using successive surrogate probability density functions,” ASME J. of Mechanical Design, Vol.115, No.3, pp. 385-391, 1993.
-  A.Watai, S. Nakatsuka, T. Kato, Y. Ujiie, and Y. Matsuoka, “Robust Design Method for Diverse Conditions,” Proc. of the ASME DETC (DETC/CIE2009-87108), 2009.
-  T. Kato, A. Watai, Y. Ujiie, and Y. Matsuoka, “Robust Design Method for Objective Characteristics with Nonnormal Distributions and Control Factors with Adjustable Range,” UMTIK 2010 Int. Conf. on Machine Design and Production, 2010.
-  S. Kimura, T. Kato, and Y. Matsuoka, “A proposed Robust Design Method for Deriving Adjustable Range,” Advanced Materials Research, Vol.311-313, pp. 2332-2335, 2011.
-  Y. Tanimizu, K. Amano, K. Harada, C. Ozawa, and N. Sugimura, “Multi-Objective Production and Transportation SchedulingConsidering Carbon Dioxide Emissions Reductions in Dynamic Supply Chains,” Int. J. of Automation Technology, Vol.6, No.3, pp. 322-330, 2012.
-  I. Bouserhane, A. Hazzab, A. Boucheta, B.Mazari, and R.Mostefa, A. M. Rubinov, and J. Zhang, “Optimal Fuzzy Self-Tuning of PI Controller Using Genetic Algorithm for Induction Motor Speed Control,” Int. J. of Automation Technology, Vol.2, No.2, pp. 85-95, 2008.
-  A. O. Griewank, “Generalized Descent for Global Optimization,” J Optimization Theory Appl, Vol.34, No.1, pp. 11-39, 1981.
-  A. M. Bagirov, A. M. Rubinov, and J. Zhang, “A Multidimensional Descent Method for Global Optimization,” A J. of Mathematical Programming and Operations Research, Vol.58, No.5, pp. 611-625, 2009.