Multi-objective optimization of injection molded parts with insert based on IFOA-GRNN-NSGA-II
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Chunxiao Li
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Yonghuan Guo
Abstract
The physical properties of plastic products, such as local strength, wear resistance and electrical properties, can be improved by adding embedded parts in the appropriate position of the products, and the precision of plastic parts can also be improved. However, due to the addition of inserts, the flow and shrinkage around inserts will be affected. Compared with traditional injection molding products, the quality is difficult to predict. To solve this problem, the injection molded parts with inserts (electrostatic test box) was used as an example, according to the product structure, three objectives of volume shrinkage, warpage in the X direction, and warpage in the Z direction were optimized. A generalized regression neural network (GRNN) model was established with molding parameters as input and quality objectives as output. Improved fruit fly optimization algorithm (IFOA) was proposed to select the optimal smoothing parameters dynamically. Through the prediction of samples, the experimental results show that the model is superior to two comparative models. Non-dominated sorting genetic algorithm (NSGA-II) was used to solve the model, and the Pareto-optimal front was obtained. The entropy TOPSIS method was used to evaluate the Pareto-optimal front, and the optimal solution was obtained. The results show that IFOA-GRNN-NSGA is a reliable multi-objective optimization method.
Funding source: National Natural Science Foundation of China
Award Identifier / Grant number: 51475220
Funding source: Postgraduate Research & Practice Innovation Program of Jiangsu Normal University
Award Identifier / Grant number: 2020XKT188
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Author contributions: Chunxiao Li: writing of original manuscript and translation; Xiying Fan: revising the paper and verifying the results; Yonghuan Guo: software, verifying; Xin Liu: testing of code; Changjing Wang: cooperation to build the finite element model; Dezhao Wang: test and data processing. All the authors have read and approved the final version submitted and agreed to publish this paper.
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Research funding: This research was financially supported by the National Natural Science Foundation of China (grant 51475220) and the Postgraduate Research & Practice Innovation Program of JSNU (Grant 2020XKT188).
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Conflict of interest statement: The authors declare that they have no conflicts of interest regarding this article.
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Ethics approval: Not applicable.
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Consent to participate: Not applicable.
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Data availability: The data in this paper were obtained from experiments and simulations. All data generated during this study are included in this manuscript.
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Code availability: All coding was done in MATLAB, and it will be sent if needed.
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Articles in the same Issue
- Frontmatter
- Material Properties
- Development and characterization of eco-friendly biopolymer gellan gum based electrolyte for electrochemical application
- Structural transitions and rheological properties of poly-d-lysine hydrobromide: effect of pH, salt, temperature, and shear rate
- Carbon dioxide adsorption onto modified polyvinyl chloride with ionic liquid
- Synergistic effect of organic-Zn(H2PO2)2 and lithium containing polyhedral oligomeric phenyl silse-squioxane on flame-retardant, thermal and mechanical properties of poly(ethylene terephthalate)
- Preparation and Assembly
- Network structural hardening of polypropylene matrix using hybrid of 0D, 1D and 2D carbon-ceramic nanoparticles with enhanced mechanical and thermomechanical properties
- An environment friendly hemp fiber modified with phytic acid for enhancing fire safety of automobile parts
- Flexible silicone rubber/carbon fiber/nano-diamond composites with enhanced thermal conductivity via reducing the interface thermal resistance
- In situ synthesis of Ag NPs in the galactomannan based biodegradable composite for the development of active packaging films
- Engineering and Processing
- Multi-objective optimization of injection molded parts with insert based on IFOA-GRNN-NSGA-II
