面向复杂质量特性的容差设计研究

摘要/Abstract

摘要：

在传统“设计-试验-建模-优化”模式基础上,首先在试验阶段采用CCD优化试验设计以同时考虑多个质量特性;然后在优化阶段运用置信域方法解决容差设计问题,运用蒙特卡罗仿真方法解决计算复杂性问题;最后通过算例验证了方法的有效性,以期为解决该类问题提供有益探索。

关键词: 复杂质量特性, 容差设计, 中心复合设计, 置信域, 蒙特卡罗仿真

Abstract:

On the basis of traditional way of “design- experiment-model-optimization”, CCD was employed to consider multi quality characteristics simultaneously at stage of experiment. Trust region and simulation approaches were used to design tolerance and overcome the computation complexity at stage of optimization. An example was discussed to verify the effectiveness of proposed method.

Key words: complex quality characteristics, tolerance design, CCD(central composite design), trust region, Monte Carlo simulation

中图分类号:

TH122

蒲国利1, 2, 苏秦2. 面向复杂质量特性的容差设计研究[J]. 中国机械工程, 2012, 23(23): 2864-2868.

BO Guo-Li-1, 2, SU Qin-2. Tolerance Design of Complex Quality Characteristics[J]. China Mechanical Engineering, 2012, 23(23): 2864-2868.

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参考文献

[1]Kondapalli S P, Chalamalasetti S R, Damera N R. Review on Application of Response Surface Method based Design of Experiments to Welding Processes [J]. Journal for Manufacturing Science and Produc- tion, 2012, 12(1) :17-24.
[2]曹衍龙[1],杨将新[2],吴昭同[2],吴立群[3].面向制造环境的稳健公差设计方法[J].中国机械工程,2003,14(2):134-137.
[3]潘双夏[1],陈入领[2],邱清盈[2],冯培恩[2].稳健优化设计进程策略的研究及实践[J].中国机械工程,2003,14(20):1717-1721.
[4]Pal S, Gauri S K. Assessing Effectiveness of the Various Performance Metrics for Multi-response Optimization Using Multiple Regression[J]. Com- puters & Industrial Engineering, 2010, 59(4) :976- 985.
[5]杨志宏[1],黄克正[1],吕良敏[1].产品概念结构设计中的公差进化模型和算法[J].中国机械工程,2004,15(8):693-697.
[6]Myers R H, Montgomery D C, Anderson-Cook C M. Response Surface Methodology: Process and Product Optimization Using Designed Experiments (3rd ed. )[M]. New York: Wiley, 2009.
[7]Tong L I, Chen C C, Wang C H. Optimization of Multi- response Processes Using the VIKOR Method [J]. International Journal on Advanced Manufacturing Technology, 2007, 31 ( 11/12): 1049-1057.
[8]Pan L K, Wang C C, Wei S L, et al. Optimizing Multiple Quality Characteristics Via Taguchi Meth- od-based Grey Analysis[J]. Journal of Materials Processing Technology, 2007, 182(1/3): 107-116.
[9]Jamshidi S F, Amiri M, Karimi N. Nonlinear Con- tinuous Multi-response Problems: a Novel Two- phase Hybrid Genetie Based Metaheuristic[J]. Op- timisation Methods & Applications in Deeision-Making Processes, 2010, 10(4):1274-1283.
[10]张宇[1].面向质量目标的统计公差的表达方式及应用分析[J].中国机械工程,2006,17(24):2595-2599.
[11]Kovach J, Cho B R. A D-optimal Design Ap- proach to Constrained Multiresponse Robust De- sign with Prioritized Mean and Variance Consider- ations[J]. Computers & Industrial Engineering, 2009, 57(1): 237-245.
[12]Montgomery D C, Loredo E N, Jearkpaporn D, et al. Experimental Designs for Constrained Regions [J]. Quality Engineering, 2002, 14(4) :587-601.
[13]Box G E P, Wilson K B. On the Experimental At- tainment of Optimum Conditions [J]. Journal of the Royal Statistical Society, Series B, 1951, 13 (1) :1-45.
[14]I.in D K J,Tu W. Dual Response Surface Optimi- zation[J]. Journal of Quality Technology, 1995, 27(1) :34-39.
[15]Tang L C, Xu K. A Unified Approach for Dual Response Surface Optimization[J].Journal of Quality Technology, 2002, 34(4) :437-447.
[16]Kim Y J, Cho B R. Development of Priority- based Robust Design [J]. Quality Engineering, 2002, 14(3): 355-363.
[17]Shin S, Cho B R. Bias-specified Robust Design Optimization and Its Analytical Solutions[J]. Computers and Industrial Engineering, 2005, 48 (1) : 129-140.
[18]Cho B R, Park C. Robust Design Modeling and Opti- mization with Unbalanced Data[J]. Computers and Industrial Engineering, 2005,48(2) : 173-180.
[19]Kovach J, Cho B R. A D proach to Robust Design optimal Design Ap Under Constraints: A New Design for Six Sigma Tool[J]. International Journal of Six Sigma and Competitive Advantage, 2006, 2(4) :389-403.
[20]Kovaeh J, Cho B R. Constrained Robust Design Experiments and Optimization with the Considera- tion of Uncontrollable Factors [J]. International Journal of Advanced Manufacturing Technology, 2008, 38(1/2):7-18.
[21]Pal S, Gauri S K. Multi-response Optimization Using Multiple Regression Based Weighted Signal-to - noise Ratio (MRWSN) [J]. Quality Engineering, 2010, 22(4) :336-350.
[22]Montgomery D C. Design and Analysis of Experi- ments(6th ed. )[M]. New York: Wiley, 2005.
[23]Liao H C. Multi-response Optimization Using Weighted Principal Component [J]. International Journal on Advanced Manufacturing Technology,2006, 27(7/8): 720-725.
[24]Tong L I, Wang C H, Chen H C, Optimlzanon ot Multiple Responses Using Principal Component A- nalysis and Technique for Order Preference by Similarity to Ideal Solution[J]. International Jour- nal of Advanced Manufacturing Technology, 2005, 27(3/4) : 407-414.
[25]Zellner A. An Efficient Method of Estimating Seemingly Unrelated Regressions and Tests for Aggregation Bias[J]. Journal of American Statisti- cal Association, 1962, 57(298) :348-368.
[26]Wu C F J. Optimization of Correlated Multiple Quality Characteristics Using Desirability Function [J]. Quality Engineering, 2005, 17(1) :119-126.
[27]Derringer G, Suich R. Simultaneous Optimization of Several Response Variables [J]. Journal of Quality Technology, 1980, 12(4): 214-219.
[28]Yamashita H, Yabe H. Superlinear and Quadratic Convergence of Some Primal-dual Interior Point Methods for Constrained Optimization[J]. Mathe- matical Programming, 1996, 75(3) :377-397.
[29]Rubinstein R Y. Simulation and theMonte Carlo Method [M]. New York: John Wiley & Sons,1981.