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Multi-objective optimal approach for injection molding based on surrogate model and particle swarm optimization algorithm

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Abstract

An integrated optimization strategy based on Kriging model and multi-objective particle swarm optimization (PSO) algorithm was constructed. As a new surrogate model technology, Kriging model has better fitting precision for nonlinear problem. The Kriging model was adopted to replace computer aided engineering (CAE) simulation as fitness function of multi-objective PSO algorithm, and the computation cost can be reduced greatly. By introducing multi-objective handling mechanism of crowding distance and mutation operator to multi-objective PSO algorithm, the entire Pareto front can be approximated better. It is shown that the multi-objective optimization strategy can get higher solving accuracy and computation efficiency under small sample.

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Authors and Affiliations

  1. National Die and Mold CAD Engineering Research Center, Shanghai Jiaotong University, Shanghai, 200030, China

    Wei Chen  (陈 巍), Xiong-hui Zhou  (周雄辉) & Wan Wang  (王 婉)

  2. School of Material Science and Engineering, University of Science and Technology Beijing, Beijing, 100083, China

    Hui-feng Wang  (王会风)

Authors
  1. Wei Chen  (陈 巍)
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  2. Xiong-hui Zhou  (周雄辉)
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  3. Hui-feng Wang  (王会风)
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  4. Wan Wang  (王 婉)
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Corresponding author

Correspondence to Wei Chen  (陈 巍).

Additional information

Foundation item: the National Natural Science Foundation of China (No. 50873060)

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Chen, W., Zhou, Xh., Wang, Hf. et al. Multi-objective optimal approach for injection molding based on surrogate model and particle swarm optimization algorithm. J. Shanghai Jiaotong Univ. (Sci.) 15, 88–93 (2010). https://doi.org/10.1007/s12204-010-9517-4

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  • DOI: https://doi.org/10.1007/s12204-010-9517-4

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