Startseite Quality prediction and control of thin-walled shell injection molding based on GWO-PSO, ACO-BP, and NSGA-II
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Quality prediction and control of thin-walled shell injection molding based on GWO-PSO, ACO-BP, and NSGA-II

  • Dezhao Wang , Xiying Fan ORCID logo EMAIL logo , Yonghuan Guo , Xiangning Lu , Changjing Wang und Wenjie Ding
Veröffentlicht/Copyright: 21. Juni 2022
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Abstract

ECG recorders are precision medical devices, but their thin-walled shells are susceptible to warpage and shrinkage during injection molding production due to the injection molding process, which greatly shortens their service life. To address this problem, a multiobjective optimization method for injection molding process parameters based on a combination of a BP neural network model optimized by an ant colony algorithm (ACO-BP) and an improved non-dominated sorting genetic algorithm (NSGA-II) is proposed. The study takes the warpage deformation amount and volume shrinkage rate of the plastic part as the optimization objectives, and the melt temperature, mold temperature, injection pressure, holding pressure, holding time, and cooling time as the design variables. However, for BP neural networks, it is crucial to choose an appropriate number of hidden layer neurons, so the particle swarm algorithm combined with the grey wolf algorithm (GWO-PSO) is used to solve for the optimal number of hidden layer neurons. Firstly, the number of hidden layer neurons of the BP network model was solved based on the samples obtained from the Box–Behnken experimental design and the GWO-PSO algorithm, and the ACO-BP algorithm was used to build the prediction models for warpage and volume shrinkage, respectively, and then combined with NSGA-II for global optimisation. The pareto optimal solution set was subjected to CRITIC analysis and the optimal process parameters were finally obtained, with a minimum warpage of 0.3293 mm and minimum volume shrinkage of 4.993%, a reduction of 8.93 and 6.95% respectively compared to the pre-optimisation period. At the same time, injection molding tests were carried out on the optimum process parameters, and it was found that the molding quality of the plastic parts was better and met the actual production requirements through measurement. The research in this paper provides a theoretical basis for further improving the quality defects of the thin-walled injection molded parts.


Corresponding author: Xiying Fan, School of Mechanical and Electrical Engineering, Jiangsu Normal University, 221116 Xuzhou, China, E-mail:

Funding source: Graduate Research Innovation Program of Jiangsu Normal University

Award Identifier / Grant number: 2021XKT0362

Funding source: National Natural Science Foundation of China

Award Identifier / Grant number: 52075231

  1. Author contributions: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.

  2. Research funding: The authors would like to express sincere thanks to National Natural Science Foundation of China (no. 52075231) and Graduate Research Innovation Program of Jiangsu Normal University (2021XKT0362) for the financial support of this research.

  3. Conflict of interest statement: The authors declare that they have no financial and personal relationships with other people or organizations that can inappropriately influence their work, there is no professional or other personal interest of any nature or kind in any product, service and/or company that could be construed as influencing the position presented in, or the review of, this article.

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Received: 2022-04-22
Accepted: 2022-04-30
Published Online: 2022-06-21
Published in Print: 2022-10-26

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Heruntergeladen am 28.11.2025 von https://www.degruyterbrill.com/document/doi/10.1515/polyeng-2022-0085/html
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