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Process parameters optimization of injection molding using a fast strip analysis as a surrogate model

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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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Authors and Affiliations

  1. Institute of Advanced Manufacturing Engineering, Zhejiang University, Hangzhou, 310027, Zhejiang, People’s Republic of China

    Peng Zhao

  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

    Huamin Zhou, Yang Li & Dequn Li

Authors
  1. Peng Zhao
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  2. Huamin Zhou
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  3. Yang Li
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  4. Dequn Li
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Correspondence to Huamin Zhou.

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Zhao, P., Zhou, H., Li, Y. et al. Process parameters optimization of injection molding using a fast strip analysis as a surrogate model. Int J Adv Manuf Technol 49, 949–959 (2010). https://doi.org/10.1007/s00170-009-2435-7

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  • DOI: https://doi.org/10.1007/s00170-009-2435-7

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