[题目] 优化注塑工艺参数以提高聚合物产品抗冲击机械性能

[作者] Yingjie Xu & QingWen Zhang & Weihong Zhang & Pan Zhang

[期刊] 国际先进制造技术杂志

[摘要] 注塑是聚合物产品中应用最广泛的工艺。注塑过程中引起的变形对注塑产品的机械性能有重要影响。因此，如何优化工艺参数成为提高产品机械性能达到预期使用条件的关键问题。本文提出了一种将人工神经网络和粒子群优化（PSO）算法相结合的方法来优化注射成型过程。首先对注塑过程、装配过程中弯曲产生的残余应力和维修产品的机械性能进行了综合有限元分析。然后建立了一个反向传播神经网络模型，以绘制过程参数与产品力学性能之间的复杂非线性关系。PSO算法与该预测模型相结合，以优化工艺参数，从而显著提高机械性能。

[关键词] 注塑成型、翘曲、影响、BP神经网络、PSO算法

[Title] Optimization of injection molding process parameters to improve the mechanical performance of polymer product against impact

[Author] Yingjie Xu & QingWen Zhang & Weihong Zhang &Pan Zhang

[Abstract] Injection molding is the most widely used process in manufacturing polymer products. The warpage induced during injection molding process has an important influence on the mechanical performance of injection molded products. Therefore, how to optimize process parameters becomes the key issue in improving the mechanical performance of the product towards the expected service conditions. In this paper, a combined artificial neural network and particle swarm optimization (PSO) algorithm method is proposed to optimize the injection molding process. An integrated finite element analysis of the injection molding process, the warpage-induced residual stresses during assembly, and mechanical performance of serviced product is firstly proposed. A back propagation neural network model is then developed to map the complex nonlinear relationship between process parameters and mechanical performance of the product. The PSO algorithm is interfaced with this predictive model to optimize process parameters and thereby significantly improve the mechanical performance. A case study of vehicle window made of polycarbonate (PC) is presented. Optimum values of process parameters are determined to minimize the maximum von Mises stress within the PC vehicle window under impact loading.

[Keywords] Injection molding 、Warpage、Impact、BP neural network 、 PSO algorithm