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Quality Monitoring and Multi-Objective Optimization of the Glass Fiber-Reinforced Plastic Injection Molded Products
- Xin Liu - Jiangsu Normal University, School of Mechanical and Electrical Engineering, China ,
- Xiying Fan - Jiangsu Normal University, School of Mechanical and Electrical Engineering, China ,
- Yonghuan Guo - Jiangsu Normal University, School of Mechanical and Electrical Engineering, China ,
- Ziqi Liu - Shenyang University of Technology, Chemical Equipment Institute, China ,
- Wenjie Ding - Jiangsu Normal University, School of Mechanical and Electrical Engineering, China
ISSN: 1946-3979, e-ISSN: 1946-3987
Published September 15, 2022 by SAE International in United States
Citation: Liu, X., Fan, X., Guo, Y., Liu, Z. et al., "Quality Monitoring and Multi-Objective Optimization of the Glass Fiber-Reinforced Plastic Injection Molded Products," SAE Int. J. Mater. Manf. 16(1):35-47, 2023, https://doi.org/10.4271/05-16-01-0004.
Compared with traditional plastics, glass fiber-reinforced plastic (GFRP) has more outstanding performance advantages, which is more and more widely used. To improve the quality of the products manufactured by the GFRP injection molding, the injection parameters are optimized in two stages. In the first stage, the range of optimization parameters including the glass fiber content and six molding parameters is selected by the Moldflow recommendation. The warpage and shrinkage of each orthogonal experiment are obtained by the Moldflow simulation. Then, a comprehensive evaluation method called GRA-TOPSIS and the range analysis method are utilized to identify the optimal level values of all optimization parameters. According to the order of influence of each parameter, the range of these parameters is adjusted for the second stage. In the second stage, the orthogonal array table is also arranged for the training samples, and the Latin hypercubic sampling (LHS) table is arranged for the prediction samples. The regular extreme learning machine based on the improved particle swarm optimization (IPSO-RELM) is utilized to construct the surrogate models of the warpage and shrinkage, which replaces expensive experimental time and cost. Then, the multi-objective firefly algorithm (MOFA) is performed to find the Pareto-optimal front, and the GRA-TOPSIS method is performed to obtain the final injection scheme. Through the simulation verification, the warpage and shrinkage are reduced by 0.0857 mm and 0.1893% compared with the scheme of the first stage, which indicates the effectiveness of the proposed multi-objective optimization method.