基于Grey-Taguchi方法的汽车内置储物盒注塑工艺参数多目标优化
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摘要
以某汽车内置储物盒为研究实例,运用CAE软件建立了储物盒注塑成型的数值模型。选取模具温度、熔体温度、保压时间、保压压力及注射时间5个注塑工艺参数为影响因素,以储物盒的翘曲变形量及缩痕指数为优化指标,通过L_(16)(4~5)正交试验分析,获得翘曲变形及缩痕指数最小的最优工艺参数组合及影响趋势,并结合灰色关联分析法,将多目标优化转化为单目标优化问题,获得兼顾两目标的最优工艺参数组合。仿真验证结果表明:优化后的翘曲变形量减小了9.25%,缩痕指数降低了33.42%,获得了较高品质的塑件。
Taking a vehicle built-in storage box as the example, a numerical model for injection molding of storage box was established by CAE software. In order to get the warpage and the shrink mark index of the product, the mold temperature, melt temperature, packing time, packing pressure and filling time were chosen as the objects of research. The optimum combination of process parameters and the influence trend of warpage and shrink mark index were obtained respectively through L_(16)(4~5) orthogonal experiment analysis. Finally, the multi-objective optimization could be converted into single objective optimization based on the grey relational analysis, and the optimum combination of process parameters with two objectives was obtained. The simulation results show that, through simulation verification, the warpage is reduced by 9.25%, and the shrink mark index is reduced by 33.42%, and the high quality plastic parts are obtained.
Taking a vehicle built-in storage box as the example, a numerical model for injection molding of storage box was established by CAE software. In order to get the warpage and the shrink mark index of the product, the mold temperature, melt temperature, packing time, packing pressure and filling time were chosen as the objects of research. The optimum combination of process parameters and the influence trend of warpage and shrink mark index were obtained respectively through L_(16)(4~5) orthogonal experiment analysis. Finally, the multi-objective optimization could be converted into single objective optimization based on the grey relational analysis, and the optimum combination of process parameters with two objectives was obtained. The simulation results show that, through simulation verification, the warpage is reduced by 9.25%, and the shrink mark index is reduced by 33.42%, and the high quality plastic parts are obtained.
引文
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[3]陆广华.基于BP神经网络的注塑成型翘曲分析及工艺优化[J].塑料工业,2017,45(2):58-60.
[4]娄天祥,何修旭,董娇.基于稳健设计的注塑成型工艺参数多目标优化[J].塑料科技,2017,45(8):63-66.
[5]杨波,孙玲,辛勇.基于试验设计法的汽车大型面板件注塑成型翘曲预测[J].塑料工业,2015,43(3):85-89.
[6]刘思峰,等.灰色系统理论及其应用[M].北京:科学出版社,2017.
[7]吕大刚,王力,张鹏,等.结构方案设计模糊多属性决策的灰色关联度方法[J].哈尔滨工业大学学报,2007,39(6):841-844.
[8]陈丽君,王宏松.一模两腔不对称薄壁注塑件的工艺参数的Taguchi方法优化[J].塑料工业,2012,40(11):56-59.
[9]刘荣亮,胡泽豪.基于CAE的薄壁注塑件工艺参数多目标优化研究[J].塑料科技,2014,42(6):89-92.
[10]Huang S J,Chiu N H,Chen L W.Intergration of the grey relational analysis with genetic algorithm for software effort estimation[J].European Journal of Operational Research,2008,188(3):898-909.
[11]Teixeira A,Cunha A E,Clement J J.Modeling and optimization of a recombinant BHK-21 cultivation process using hybrid grey-box system[J].Journal of Biotechnology,2005,118(3):209-303.
[12]汪建敏.车身前翼子板成形质量控制与工艺参数优化[D].镇江:江苏大学,2016.
[2]Huang M C,Tai C C.The effective factors in the warpage problem of an injection molded part with a thin shell feature[J].Mater Process Technol,2001,110(1):1-9.
[3]陆广华.基于BP神经网络的注塑成型翘曲分析及工艺优化[J].塑料工业,2017,45(2):58-60.
[4]娄天祥,何修旭,董娇.基于稳健设计的注塑成型工艺参数多目标优化[J].塑料科技,2017,45(8):63-66.
[5]杨波,孙玲,辛勇.基于试验设计法的汽车大型面板件注塑成型翘曲预测[J].塑料工业,2015,43(3):85-89.
[6]刘思峰,等.灰色系统理论及其应用[M].北京:科学出版社,2017.
[7]吕大刚,王力,张鹏,等.结构方案设计模糊多属性决策的灰色关联度方法[J].哈尔滨工业大学学报,2007,39(6):841-844.
[8]陈丽君,王宏松.一模两腔不对称薄壁注塑件的工艺参数的Taguchi方法优化[J].塑料工业,2012,40(11):56-59.
[9]刘荣亮,胡泽豪.基于CAE的薄壁注塑件工艺参数多目标优化研究[J].塑料科技,2014,42(6):89-92.
[10]Huang S J,Chiu N H,Chen L W.Intergration of the grey relational analysis with genetic algorithm for software effort estimation[J].European Journal of Operational Research,2008,188(3):898-909.
[11]Teixeira A,Cunha A E,Clement J J.Modeling and optimization of a recombinant BHK-21 cultivation process using hybrid grey-box system[J].Journal of Biotechnology,2005,118(3):209-303.
[12]汪建敏.车身前翼子板成形质量控制与工艺参数优化[D].镇江:江苏大学,2016.