Multi-Objective Optimization of Injection Molding Process Parameters for Junction Boxes Based on BP Neural Network and NSGA-II Algorithm

作     者：Hong, Tengjiao Huang, Dong Ding, Fengjuan Zhang, Liyong Dong, Fulong Chen, Lei 

作者机构：Anhui Sci & Technol Univ Coll Intelligent Mfg Chuzhou 233100 Peoples R China Stamford Int Univ Sch Business Adm Bangkok 10250 Thailand Fengyang Cty Sci & Technol Innovat Serv Ctr Chuzhou 233100 Peoples R China 

出 版 物：《MATERIALS》 (Mater.)

年 卷 期：2025年第18卷第3期

页      面：577-577页

核心收录：

学科分类：0806[工学-冶金工程] 08[工学] 0805[工学-材料科学与工程（可授工学、理学学位）] 080502[工学-材料学] 0703[理学-化学] 0702[理学-物理学] 

基　　金：Talent Introduction Project of Anhui Science and Technology University Natural Science Research Project of Higher Education Institutions in Anhui Province [2024AH050296] Research and Development of Fermentation Feed Drying Automatic Line Anhui Provincial Key Laboratory of Functional Agriculture and Functional Food, Anhui Science and Technology University [2024AH050318] RCYJ202105 

主　　题：junction box warping deformation volume shrinkage rate BP neural network multi-objective optimization process parameters 

摘      要：Many factors affect the quality of the injection molding of plastic products, including the process parameters, mold materials, type and geometry of plastic parts, cooling system, pouring system, etc. A multi-objective optimization method for injection molding process parameters based on the BP neural network and NSGA-II algorithm is proposed to address the problem of product quality defects caused by unreasonable process parameter settings. Taking the junction box shell as the object, numerical simulation was carried out using Moldflow2019 software and a six-factor five-level orthogonal experiment was designed to explore the influence of injection molding process parameters, such as the mold temperature, melt temperature, injection pressure, holding pressure, holding time, and cooling time, on the volume shrinkage rate and warpage deformation of the junction box. Based on a numerical simulation, the BP neural network and NSGA-II algorithm were used to optimize the optimal combination of injection molding process parameters, volume shrinkage rate, and warpage deformation. The research results indicate that the melt temperature has the most significant impact on the quality of the injection molding of junction boxes, followed by the holding time, holding pressure, cooling time, injection pressure, and mold temperature. After optimization using the BP neural network and the NSGA-II algorithm, the optimal process parameter combination was obtained with a melt temperature of 230.03 degrees C, a mold temperature of 51.27 degrees C, an injection pressure of 49.13 MPa, a holding pressure of 69.01 MPa, a holding time of 15.48 s, and a cooling time of 34.91 s. At this time, the volume shrinkage rate and warpage deformation of the junction box were 6.905% and 0.991 mm, respectively, which decreased by 33.2% and 3.8% compared to the average volume shrinkage rate (10.34884%) and warpage deformation (1.030764 mm) before optimization. The optimization effect was significant. In a