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Optimization of injection molding process parameters using integrated artificial neural network model and expected improvement function method

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Abstract

In this study, an adaptive optimization method based on artificial neural network model is proposed to optimize the injection molding process. The optimization process aims at minimizing the warpage of the injection molding parts in which process parameters are design variables. Moldflow Plastic Insight software is used to analyze the warpage of the injection molding parts. The mold temperature, melt temperature, injection time, packing pressure, packing time, and cooling time are regarded as process parameters. A combination of artificial neural network and design of experiment (DOE) method is used to build an approximate function relationship between warpage and the process parameters, replacing the expensive simulation analysis in the optimization iterations. The adaptive process is implemented by expected improvement which is an infilling sampling criterion. Although the DOE size is small, this criterion can balance local and global search and tend to the global optimal solution. As examples, a cellular phone cover and a scanner are investigated. The results show that the proposed adaptive optimization method can effectively reduce the warpage of the injection molding parts.

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Acknowledgments

The authors gratefully acknowledge financial support for this work from the Major program (10590354) of the National Natural Science Foundation of China and wish to thank Moldflow Corporation for making their simulation software available for this study.

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Authors and Affiliations

  1. State Key Laboratory of Structural Analysis for Industrial Equipment, Dalian University of Technology, Dalian, 116024, Liaoning, China

    Huizhuo Shi, Yuehua Gao & Xicheng Wang

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  1. Huizhuo Shi
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  2. Yuehua Gao
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  3. Xicheng Wang
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Correspondence to Xicheng Wang.

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Shi, H., Gao, Y. & Wang, X. Optimization of injection molding process parameters using integrated artificial neural network model and expected improvement function method. Int J Adv Manuf Technol 48, 955–962 (2010). https://doi.org/10.1007/s00170-009-2346-7

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  • DOI: https://doi.org/10.1007/s00170-009-2346-7

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