Multi‐objective optimization of injection molding parameters, based on the Gkriging‐NSGA‐vague method,Journal of Applied Polymer Science

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Multi‐objective optimization of injection molding parameters, based on the Gkriging‐NSGA‐vague method
Journal of Applied Polymer Science ( IF 2.7 ) Pub Date : 2019-11-08 , DOI: 10.1002/app.48659
Sai Li ₁ , Xiying Fan ₁ , Haiyue Huang ₁ , Yanli Cao ₁

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Plastic production quality, manufacturing cost, and molding efficiency are three important indices for a new product development. In addition to injection molding process parameters (IMPP), runner system also has an important role in the injection molding process. In this study, the plastic production quality, manufacturing costs, and molding efficiency are considered as the optimized objectives. The design parameters include runner diameters and IMPP. The improved Kriging surrogate model (Gkriging), nondominated sorting genetic algorithm (NSGA‐II), and multicriteria fuzzy decision‐making approach (vague sets) are combined, and the Gkriging‐NSGA‐vague scheme is proposed to optimize the runner diameters and the IMPP. Firstly, the Gkriging model is established to map the correlation between design parameters and optimized objectives. Based on the Gkriging model, the NSGA‐II is combined with predictive models to obtain the Pareto‐optimal solutions. Then, the optimal Pareto‐optimal solution is obtained by the vague approach. A multicavity mold with two different plastic parts is utilized as the design case. The optimization results indicate that the Gkriging‐NSGA‐vague method is a powerful method for solving the multi‐objective optimization problems. © 2019 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2019, 137, 48659.

中文翻译：

基于Gkriging-NSGA-vague方法的注塑参数多目标优化

塑料的生产质量，制造成本和成型效率是新产品开发的三个重要指标。除注塑工艺参数（IMPP）外，流道系统在注塑工艺中也起着重要作用。在这项研究中，塑料的生产质量，制造成本和成型效率被认为是最优化的目标。设计参数包括流道直径和IMPP。结合改进的Kriging替代模型（Gkriging），非支配排序遗传算法（NSGA-II）和多准则模糊决策方法（vague集），并提出了Gkriging-NSGA-vague方案来优化流道直径和IMPP。首先，建立了Gkriging模型来映射设计参数和优化目标之间的相关性。基于Gkriging模型，将NSGA-II与预测模型相结合，以获得帕累托最优解。然后，通过模糊方法获得最优的帕累托最优解。具有两个不同塑料零件的多腔模具被用作设计案例。优化结果表明，Gkriging-NSGA-vague方法是解决多目标优化问题的有力方法。分级为4 +©2019 Wiley Periodicals，Inc.J.Appl。Polym。科学优化结果表明，Gkriging-NSGA-vague方法是解决多目标优化问题的有力方法。分级为4 +©2019 Wiley Periodicals，Inc.J.Appl。Polym。科学优化结果表明，Gkriging-NSGA-vague方法是解决多目标优化问题的有力方法。分级为4 +©2019 Wiley Periodicals，Inc.J.Appl。Polym。科学2019，137，48659。

更新日期：2020-02-12

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