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An enhanced optimization approach based on Gaussian process surrogate model for process control in injection molding

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Abstract

A stepwise optimization approach based on Gaussian process (GP) surrogate model is proposed to determine the process parameters and improve the quality control for injection molding. In order to improve the global performance in this optimization, an enhanced probability of improvement criterion is also introduced. Firstly, GP surrogate model is constructed with the initial samples which are obtained from an optimal design of experiment method. GP is capable of giving both a prediction and an estimate of the confidence for the prediction simultaneously. Secondly, an enhanced probability of improvement criterion is used to find the direction of adding training samples and optimize the surrogate model. Since the global optimal region of the model become accurate efficiently after steps of optimizing the surrogate model, the proposed enhanced probability of improvement criterion can switch more swiftly to global optima compared with other improvement criterion. Finally, an auto front grille molding process is taken as an example to illustrate the method. The results show that the proposed optimization method can effectively decrease the warpage of injection-molded parts.

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Authors and Affiliations

  1. Key Laboratory of New Processing Technology for Nonferrous Metals and Materials, Ministry of Education, Guangxi University, 100 Daxue Road, Nanning, 530004, People’s Republic of China

    Wei Xia & Bin Luo

  2. College of Mechanical Engineering, Guangxi University, 100 Daxue Road, Nanning, 530004, People’s Republic of China

    Xiao-ping Liao

Authors
  1. Wei Xia
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  2. Bin Luo
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  3. Xiao-ping Liao
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Correspondence to Bin Luo.

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Xia, W., Luo, B. & Liao, Xp. An enhanced optimization approach based on Gaussian process surrogate model for process control in injection molding. Int J Adv Manuf Technol 56, 929–942 (2011). https://doi.org/10.1007/s00170-011-3227-4

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  • DOI: https://doi.org/10.1007/s00170-011-3227-4

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