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Study of Residual Wall Thickness and Multiobjective Optimization for Process Parameters of Water-Assisted Injection Molding
Advances in Polymer Technology ( IF 3.1 ) Pub Date : 2020-12-10 , DOI: 10.1155/2020/3481752
Jiangen Yang 1 , Shengrui Yu 2 , Ming Yu 3
Affiliation  

Residual wall thickness is an important indicator for water-assisted injection molding (WAIM) parts, especially the maximization of hollowed core ratio and minimization of wall thickness difference which are significant optimization objectives. Residual wall thickness was calculated by the computational fluid dynamics (CFD) method. The response surface methodology (RSM) model, radial basis function (RBF) neural network, and Kriging model were employed to map the relationship between process parameters and hollowed core ratio, and wall thickness difference. Based on the comparison assessments of the three surrogate models, multiobjective optimization of hollowed core ratio and wall thickness difference for cooling water pipe by integrating design of experiment (DOE) of optimized Latin hypercubes (Opt LHS), RBF neural network, and particle swarm optimization (PSO) algorithm was studied. The research results showed that short shot size, water pressure, and melt temperature were the most important process parameters affecting hollowed core ratio, while the effects of delay time and mold temperature were little. By the confirmation experiments for the best solution resulted from the Pareto frontier, the relative errors of hollowed core ratio and wall thickness are 2.2% and 3.0%, respectively. It demonstrated that the proposed hybrid optimization methodology could increase hollowed core ratio and decrease wall thickness difference during the WAIM process.

中文翻译:

水辅注射成型工艺参数的残余壁厚及多目标优化研究

残余壁厚是水辅助注射成型(WAIM)制件的重要指标,尤其是空心比的最大化和壁厚差异的最小化是重要的优化目标。剩余壁厚通过计算流体动力学 (CFD) 方法计算。采用响应面法(RSM)模型、径向基函数(RBF)神经网络和Kriging模型绘制工艺参数与空心比、壁厚差异之间的关系。基于三种替代模型的对比评估,通过优化拉丁超立方体(Opt LHS)实验设计(DOE)、RBF神经网络对冷却水管空心比和壁厚差进行多目标优化,并对粒子群优化(PSO)算法进行了研究。研究结果表明,短射量、水压和熔体温度是影响空心比的最重要工艺参数,而延迟时间和模具温度的影响很小。通过帕累托前沿得到的最佳解的验证实验,空心比和壁厚的相对误差分别为2.2%和3.0%。结果表明,所提出的混合优化方法可以在 WAIM 过程中增加空心比并减少壁厚差异。而延迟时间和模具温度的影响很小。由帕累托前沿得到的最佳解的验证实验表明,空心比和壁厚的相对误差分别为2.2%和3.0%。结果表明,所提出的混合优化方法可以在 WAIM 过程中增加空心比并减少壁厚差异。而延迟时间和模具温度的影响很小。由帕累托前沿得到的最佳解的验证实验表明,空心比和壁厚的相对误差分别为2.2%和3.0%。结果表明,所提出的混合优化方法可以在 WAIM 过程中增加空心比并减少壁厚差异。
更新日期:2020-12-10
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