Process parameters optimization of injection molding using a fast strip analysis as a surrogate model |
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Authors: | Peng Zhao Huamin Zhou Yang Li Dequn Li |
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Affiliation: | 1. Institute of Advanced Manufacturing Engineering, Zhejiang University, Hangzhou, 310027, Zhejiang, People’s Republic of China 2. State Key Laboratory of Material Processing and Die & Mould Technology, Huazhong University of Science and Technology, Wuhan, 430074, Hubei, People’s Republic of China
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Abstract: | Injection molding process parameters such as injection temperature, mold temperature, and injection time have direct influence on the quality and cost of products. However, the optimization of these parameters is a complex and difficult task. In this paper, a novel surrogate-based evolutionary algorithm for process parameters optimization is proposed. Considering that most injection molded parts have a sheet like geometry, a fast strip analysis model is adopted as a surrogate model to approximate the time-consuming computer simulation software for predicating the filling characteristics of injection molding, in which the original part is represented by a rectangular strip, and a finite difference method is adopted to solve one dimensional flow in the strip. Having established the surrogate model, a particle swarm optimization algorithm is employed to find out the optimum process parameters over a space of all feasible process parameters. Case studies show that the proposed optimization algorithm can optimize the process parameters effectively. |
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