基于灰色关联分析的注塑成型工艺多目标优化
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摘要
针对复杂曲面构件注塑成型过程中体积收缩率、翘曲变形量和缩痕指数最小化等多目标优化问题,采用正交试验法设计了六因素五水平的聚甲醛叶轮注塑加工试验;基于灰色关联分析将多目标优化问题转化为单目标优化问题,利用主成分分析法确定体积收缩率、翘曲变形量和缩痕指数对灰色关联度的影响权重;通过对试验数据的回归分析,建立了灰色关联度与注塑成型主要工艺参数的二阶预测模型;基于各工艺参数对体积收缩率、翘曲变形量和缩痕指数影响规律的分析,确定了注塑工艺参数的优化方案。利用响应曲面求解注塑成型参数优化问题并进行注塑成型仿真实验,结果表明:由该优化方法获得的注塑成型工艺参数组合可以使制品的体积收缩率、翘曲变形量和缩痕指数均大幅减小。
Aiming at the multi-objective optimization problems of volume shrinkage, warping deformation and sink marks in injection molding of POM impeller with complex surface, 25 experimental runs based on Taguchi method orthogonal arrays were studied to pick the best factor level condition. Based on grey relational analysis, the multiple objective optimization problem was transformed into a single objective optimization problem, and the infl uence weights of volume contraction, warping deformation and sink marks on grey relational degree were determined by principal component analysis. Based on the regression analysis of experimental data, a second-order prediction model was established between grey relational degree and main process parameters of injection molding. And the optimization scheme of injection molding process parameters was determined by the analysis of the infl uence of each process parameter. Finally, the results show that the warpage deformation, volume shrinkage and shrinkage index of the product can be greatly reduced by using the optimized injection molding process parameters based on the CAE simulation experiment of injection molding.
Aiming at the multi-objective optimization problems of volume shrinkage, warping deformation and sink marks in injection molding of POM impeller with complex surface, 25 experimental runs based on Taguchi method orthogonal arrays were studied to pick the best factor level condition. Based on grey relational analysis, the multiple objective optimization problem was transformed into a single objective optimization problem, and the infl uence weights of volume contraction, warping deformation and sink marks on grey relational degree were determined by principal component analysis. Based on the regression analysis of experimental data, a second-order prediction model was established between grey relational degree and main process parameters of injection molding. And the optimization scheme of injection molding process parameters was determined by the analysis of the infl uence of each process parameter. Finally, the results show that the warpage deformation, volume shrinkage and shrinkage index of the product can be greatly reduced by using the optimized injection molding process parameters based on the CAE simulation experiment of injection molding.
引文
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[3]Park H S,Nguyen T T.Optimization of injection molding process for car fender in consideration of energy efficiency and product quality[J].J Comput Des Eng,2014,1(4):256-265.
[4]徐征,王继章,吕治斌,等.工艺参数对平板微小器件注塑翘曲的影响[J].光学精密工程,2013,21(7):1 825-1 830.
[5]王超房.注塑成型模拟关键理论与算法研究及Z-Mold软件改进[D].郑州:郑州大学,2016.
[6]Ozcelik B,Erzurumlu T.Comparison of the warpage optimization in the plastic injection molding using ANOVA,neural network model and genetic algorithm[J].J Mater Process Tech,2006,171:437-445.
[7]胡广洪.微细发泡注塑成型工艺的关键技术研究[D].上海:上海交通大学,2009.
[8]Yin F,Mao H,Hua L,et al.Back Propagation neural network modeling for warpage prediction and optimization of plastic products during injection molding[J].Materials&Design,2011,32:1 844-1 850.
[9]Ozcelik B,Sonat I.Warpage and structural analysis of thin shell plastic in the plastic injection molding[J].Materials&Design,2009,30:367-375.
[10]Hakmian E,Sulonu A B.Analysis of warpage and shrinkage properties of injection-molded micro gears polymer composites using numerical simulations assisted by the Taguchi method[J].Materials&Design,2012,42:62-71.
[11]时慧焯,王希诚.基于改进的BP神经网络的注塑成型翘曲优化设计[J].化工学报,2011,62(9):2 562-2 568.
[12]潘秀石,林桂霞.基于正交试验法的吸尘器电机罩板注塑工艺优化设计[J].塑料科技,2014,42(12):90-93.
[13]Subrata Mondal,C P Paul,L M Kukreja,et al.Application of Taguchi-based gray relational analysis for evaluating the optimal laser cladding parameters for AISI1040 steel plane surface[J].International Journal of Advanced Manufacturing Technology,2013,66:91-96.
[14]张军锋,史耀耀,蔺小军,等.基于灰色关联分析的叶片砂带抛光参数优化[J].计算机集成制造系统,2017,23(4):806-814.
[15]车应田,刘泓滨,火寿平.基于灰色关联度和BP神经网络的多级注塑成型工艺参数优化[J].塑料科技,2018,46(3):91-96.
[16]刘春景,唐敦兵,何华,等.基于灰色关联和主成分分析的车削加工多目标优化[J].农业机械学报,2013,44(4):293-298,292.
[17]刘思峰,谢乃明,等.灰色系统理论及其应用[M].北京:科学出版社,2008.
[18]Abdi H,Williams L J.Principal component analysis[J].Wiley Interdisciplinary Reviews:Computational Statistics,2010,2(4):433-459.