基于BP-NSGA-Ⅱ的汽车仪表板注塑工艺优化

Optimization of Injection Molding Process of Automobile Dashboard based on BP-NSGA-Ⅱ

根据大型注塑件产品汽车仪表板的生产质量和成本都有较高要求的特点,以某汽车仪表板为研究对象,研究其注塑工艺参数对体积收缩率和翘曲变形量的影响并进行工艺参数的优化。利用Moldflow软件对其进行数值模拟仿真,采用Box-Behnken试验设计方法进行数据采集,以开模时间、模具温度、注塑时间、熔体温度、速度压力切换、保压压力、保压恒压时间、保压衰减时间、冷却时间9个工艺参数为影响因子,体积收缩率和翘曲变形量为优化目标,运用BP神经网络模型和非支配排序遗传算法(NSGA-Ⅱ)获得最佳工艺参数组合,结合Moldflow仿真验证了最佳工艺参数组合的准确性,最终两个目标值分别降低了8.58%和8.83%,汽车仪表板的成型质量得到了有效提高。

According to the characteristics that a large injection molded part—automobile dashboard had high requirements for its production quality and cost,taking an automobile dashboard as the research object,the influences of its injection molding process parameters on volume shrinkage and warpage deformation were studied,and the process parameters were optimized.Moldflow software was used for numerical simulation,and Box-Behnken experimental design method was used for data collection.Nine process parameters,including mould opening time,mould temperature,injection time,melt temperature,speed pressure switching,holding pressure,holding constant pressure time,holding attenuation time and cooling time,were taken as influence factors,and volume shrinkage and warpage deformation were taken as optimization objectives.The best combination of process parameters was obtained by using BP neural network model and non-dominated sorting genetic algorithm(NSGA-Ⅱ).Combined with Moldflow simulation,the accuracy of the best combination of process parameters was verified.Finally,the two target values are reduced by 8.58%and 8.83%respectively,and the forming quality of automobile dashboard is effectively improved.