Moayyedian, Mehdi, Chalak Qazani, Mohamad Reza, Amirkhizi, Parisa Jourabchi, Asadi, Houshyar, and Hedayati-Dezfooli, Mohsen (2024) Multiple objectives optimization of injection-moulding process for dashboard using soft computing and particle swarm optimization. Scientific Reports, 14. 23767.

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Abstract

This research focuses on utilizing injection moulding to assess defects in plastic products, including sink marks, shrinkage, and warpages. Process parameters, such as pure cooling time, mould temperature, melt temperature, and pressure holding time, are carefully selected for investigation. A full factorial design of experiments is employed to identify optimal settings. These parameters significantly affect the physical and mechanical properties of the final product. Soft computing methods, such as finite element (FE), help mitigate behaviour by considering different input parameters. A CAD model of a dashboard component integrates into an FE simulation to quantify shrinkage, warpage, and sink marks. Four chosen parameters of the injection moulding machine undergo comprehensive experimental design. Decision tree, multilayer perceptron, long short-term memory, and gated recurrent units models are explored for injection moulding process modelling. The best model estimates defects. Multiple objectives particle swarm optimisation extracts optimal process parameters. The proposed method is implemented in MATLAB, providing 18 optimal solutions based on the extracted Pareto-Front.

Item ID:	86701
Item Type:	Article (Research - C1)
ISSN:	2045-2322
Keywords:	Injection moulding, Warpage/shrinkage/sink mark, Soft computing, Multiple objectives particle swarm optimisation, Pareto front
Copyright Information:	Open Access. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/
Date Deposited:	28 Aug 2025 01:11
FoR Codes:	40 ENGINEERING > 4014 Manufacturing engineering > 401408 Manufacturing processes and technologies (excl. textiles) @ 60% 46 INFORMATION AND COMPUTING SCIENCES > 4611 Machine learning > 461104 Neural networks @ 30% 46 INFORMATION AND COMPUTING SCIENCES > 4602 Artificial intelligence > 460203 Evolutionary computation @ 10%
SEO Codes:	28 EXPANDING KNOWLEDGE > 2801 Expanding knowledge > 280110 Expanding knowledge in engineering @ 70% 28 EXPANDING KNOWLEDGE > 2801 Expanding knowledge > 280115 Expanding knowledge in the information and computing sciences @ 30%
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