基于代理模型和参数不确定性的冲压件容差稳健设计
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摘要
为了提高冲压件的可靠性和稳定性,减少代理模型不确定性和参数不确定性对冲压产品质量的影响,需要进行稳健设计。提出了一种具有内外层结构的容差稳健模型,有效地降低了可控因素和不可控因素不确定性引起的质量波动。通过似然函数因素筛选法确定关键的可控因素和不可控因素,采用改进的贝叶斯估计对代理模型不确定性进行量化。以NUMISHEET93方盒件为研究对象进行稳健优化设计,利用目标函数和约束函数的均值、方差建立容差稳健模型。将稳健优化结果与忽略模型不确定性的优化结果分别进行蒙特卡洛模拟并对比。结果表明,综合考虑代理模型不确定性和参数不确定性的稳健设计方法能有效地提高冲压件的成形质量。
In order to increase the reliability and stability of stamping parts and reduce the influences of metamodel uncertainty and parameter uncertainty on the quality of stamping parts,a robust design is needed. Then,a tolerance robust model with inner and outer loop structures was proposed,and the quality fluctuation caused by the uncertainty of controllable factors and uncontrollable factors was reduced effectively. Furthermore,the key controllable factors and uncontrollable factors were confirmed by likelihood function factor selection method,and the metamodel uncertainty was quantitated by revised Bayesian estimation. For the square box of NUMISHEET 93,the robustness optimization design was conducted,and the tolerance robust model was established by the mean and variance of objective and constraint functions. In addition,the robust optimization results were compared with the optimization results of ignoring metamodel uncertainty by Monte Carlo simulation. The results show that the robust design method considering comprehensively metamodel uncertainty and parameter uncertainty can effectively improve the forming quality of stamping parts.
In order to increase the reliability and stability of stamping parts and reduce the influences of metamodel uncertainty and parameter uncertainty on the quality of stamping parts,a robust design is needed. Then,a tolerance robust model with inner and outer loop structures was proposed,and the quality fluctuation caused by the uncertainty of controllable factors and uncontrollable factors was reduced effectively. Furthermore,the key controllable factors and uncontrollable factors were confirmed by likelihood function factor selection method,and the metamodel uncertainty was quantitated by revised Bayesian estimation. For the square box of NUMISHEET 93,the robustness optimization design was conducted,and the tolerance robust model was established by the mean and variance of objective and constraint functions. In addition,the robust optimization results were compared with the optimization results of ignoring metamodel uncertainty by Monte Carlo simulation. The results show that the robust design method considering comprehensively metamodel uncertainty and parameter uncertainty can effectively improve the forming quality of stamping parts.
引文
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[2]张骥超,刘罡,林忠钦,等.侧围外板冲压工艺稳健性优化设计[J].上海交通大学学报,2012,46(7):1005-1010.Zhang J C,Liu G,Lin Z Q,et al.Robust optimization for an autobody outer panel stamping process[J].Journal of Shanghai Jiao Tong University,2012,46(7):1005-1010.
[3]韦东来,崔振山,杨晨光,等.稳健回归随机响应面法与冲压成形性容差预测[J].上海交通大学学报,2008,42(1):20-23.Wei D L,Cui Z S,Yang C G,et al.Robust regression stochastic response surface method and tolerance prediction for stamping process[J].Journal of Shanghai Jiao Tong University,2008,42(1):20-23.
[4]张勇.基于Dyna Form技术和正交试验的引擎盖内板冲压工艺参数优化[J].锻压技术,2018,43(4):64-69.Zhang Y.Optimization on stamping process parameters of engine cover inner plate based on orthogonal test and DynaForm technology[J].Forging&Stamping Technology,2018,43(4):64-69.
[5]Ouyang L H,Ma Y Z,Wang J J,et al.An interval approach to robust design with parameter uncertainty[J].International Journal of Production Research,2016,54(11):3201-3215.
[6]Miró-Quesada G,Castillo E D,Peterson J J.A Bayesian approach for multiple response surface optimization in the presence of noise variables[J].Journal of Applied Statistics,2004,31(3):251-270.
[7]Apley D W,Kim J B.A cautious approach to robust design with model parameter uncertainty[J].IIE Transactions,2011,43(7):471-482.
[8]汪建均,马义中,欧阳林寒,等.多响应稳健参数设计的贝叶斯建模与优化[J].管理科学学报,2016,19(2):85-94.Wang J J,Ma Y Z,Ouyang L H,et al.Bayesian modeling and optimization of multi-response robust parameter design[J].Journal of Management Sciences in China,2016,19(2):85-94.
[9]要强壮.基于贝叶斯理论的近似模型不确定性量化方法及其应用研究[D].武汉:华中科技大学,2015.Yao Q Z.Research and Application of Metamodel Uncertainty Quantification Based on Bayer'Theorem[D].Wuhan:Huazhong University of Science and Technology,2015.
[10]Bostr9m P,HeikkilM,Huova M,et al.Bayesian statistical analysis for performance evaluation in real-time control systems[A].12th International Conference on Quantitative Evaluation of Systems[C].Berlin:Springer Verlag,2015.
[11]Jiang C,Zhang Z G,Zhang Q F,et al.A new nonlinear interval programming method for uncertain problems with dependent interval variables[J].European Journal of Operational Research,2014,238(1):245-253.
[12]Joachim D.Experimental investigation of a square-cup deep-drawing process[J].Journal of Materials Processing Technology,1995,50(1):375-384.
[13]Wang H,Li E G,Li G Y.Parallel boundary and best neighbor searching sampling algorithm for drawbead design optimization in sheet metal forming[J].Structural&Multidisciplinary Optimization,2010,41(2):309-324.
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