基于最优拉丁超立方抽样方法和NSGA–II算法的注射成型多目标优化

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提出应用最优拉丁超立方抽样的方法并结合响应面模型和带精英策略的非支配排序遗传算法(NSGA–II算法)实现注射工艺多目标优化,缩短寻找最优工艺参数的时间,提高塑件生产效率.以充电宝上盖塑件的体积收缩率、缩痕指数为优化目标,以模具温度、熔体温度、保压时间、保压压力、冷却时间作为影响因素.通过模流分析获得优化目标值;建立体积收缩率、缩痕指数与影响因素之间的响应面模型并通过复相关系数评价了响应面模型的有效性;基于NSGA–II算法在响应面模型内自主寻优,获得了满足塑件注塑成型质量的一组最优工艺参数组合.优化后的体积收缩率为5.584%,比优化前的体积收缩率6.337%降低了11.88%;优化后的缩痕指数为1.458%,比优化前的缩痕指数1.681%降低了13.27%,优化效果明显.
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