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Intelligent setting of process parameters for injection molding based on case-based reasoning of molding features

Author

Listed:
  • Shengrui Yu

    (Jingdezhen Ceramic Institute
    University of Wisconsin-Madison)

  • Tianfeng Zhang

    (Jingdezhen Ceramic Institute)

  • Yun Zhang

    (Huazhong University of Science and Technology)

  • Zhigao Huang

    (Huazhong University of Science and Technology)

  • Huang Gao

    (Huazhong University of Science and Technology)

  • Wen Han

    (Jingdezhen Ceramic Institute)

  • Lih-Sheng Turng

    (University of Wisconsin-Madison
    University of Wisconsin-Madison)

  • Huamin Zhou

    (Huazhong University of Science and Technology)

Abstract

Process parameters of injection molding are the key factors affecting the final quality and the molding efficiency of products. In the traditional automatic setting of process parameters based on case-based reasoning, only the geometric features of molds are considered, which may not be the representative feature of products and cause the reasoning process to fail. This problem of failure manifests itself in that the molding process parameters inferred by the reasoning system may be very different between molds with similar geometric features or very similar between molds with different geometric features. Therefore, this paper proposes a case-based-reasoning method based on molding features in order to overcome this problem by a method of dimensionality reduction, composed of three stages which (1) obtain the injection pressure profile data through actual injection molding or filling simulation analysis, (2) calculate the similarity of the pressure profiles between target case and each of source cases in case database using the nearest neighbor method, and sort according to the value of similarity, (3) find the case with a maximum of similarity out as the one closest to the target case, and take the process parameters of the most similar case as the solution of the target case according to case modification strategies. This method simplifies the high-dimensional molding features to the pressure profile at the injection location with two-dimensional data features. Experiments show that the new method has a high retrieval accuracy and sensitivity. Moreover, even slight differences in molding can be captured easily.

Suggested Citation

  • Shengrui Yu & Tianfeng Zhang & Yun Zhang & Zhigao Huang & Huang Gao & Wen Han & Lih-Sheng Turng & Huamin Zhou, 2022. "Intelligent setting of process parameters for injection molding based on case-based reasoning of molding features," Journal of Intelligent Manufacturing, Springer, vol. 33(1), pages 77-89, January.
    • Handle: RePEc:spr:joinma:v:33:y:2022:i:1:d:10.1007_s10845-020-01658-y
    DOI: 10.1007/s10845-020-01658-y
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    References listed on IDEAS

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    1. Kuo-Ming Tsai & Hao-Jhih Luo, 2017. "An inverse model for injection molding of optical lens using artificial neural network coupled with genetic algorithm," Journal of Intelligent Manufacturing, Springer, vol. 28(2), pages 473-487, February.
    2. Zhigang Jiang & Ya Jiang & Yan Wang & Hua Zhang & Huajun Cao & Guangdong Tian, 2019. "A hybrid approach of rough set and case-based reasoning to remanufacturing process planning," Journal of Intelligent Manufacturing, Springer, vol. 30(1), pages 19-32, January.
    3. Mohammad Reza Khosravani & Sara Nasiri, 2020. "Injection molding manufacturing process: review of case-based reasoning applications," Journal of Intelligent Manufacturing, Springer, vol. 31(4), pages 847-864, April.
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