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An inverse model for injection molding of optical lens using artificial neural network coupled with genetic algorithm

Author

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  • Kuo-Ming Tsai

    (National Chin-Yi University of Technology)

  • Hao-Jhih Luo

    (National Chin-Yi University of Technology)

Abstract

This study combined the artificial neural network (ANN) with a genetic algorithm (GA) to establish an inverse model of injection molding for optical lens form accuracy. The Taguchi parameter design was used for screening experiments of the injection molding parameters, and the significant factors influencing lens form accuracy were found to be mold temperature, cooling time, packing pressure, and packing time. These significant factors were used for full factorial experiments, and the experimental data then were used as training and checking data sets for the ANN prediction model. Finally, the ANN prediction model was combined with the GA to construct an inverse model of injection molding. Lens form accuracies of 0.5, 0.7, and $$1\,\upmu \hbox {m}$$ 1 μ m were taken as examples for validation, and when the error of the set lens form accuracy target value was within 2 % there were 26, 17, and six sets of the injection molding parameters, respectively, that met the desired form accuracy obtained by using the inverse model. The result indicated that the proposed strategy was successful in identifying process parameters for products with reliable accuracy. In addition, using the GA as a global search algorithm for the optimal solution could further optimize the Taguchi optimal process parameters. The validation experiments revealed that the form accuracy of the lens was improved.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:joinma:v:28:y:2017:i:2:d:10.1007_s10845-014-0999-z
    DOI: 10.1007/s10845-014-0999-z
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    Citations

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    Cited by:

    1. İhsan Yanıkoğlu & Erinç Albey & Serkan Okçuoğlu, 2022. "Robust Parameter Design and Optimization for Quality Engineering," SN Operations Research Forum, Springer, vol. 3(1), pages 1-36, March.
    2. Myeongso Kim & Minyoung Lee & Minjeong An & Hongchul Lee, 2020. "Effective automatic defect classification process based on CNN with stacking ensemble model for TFT-LCD panel," Journal of Intelligent Manufacturing, Springer, vol. 31(5), pages 1165-1174, June.
    3. Jiyoung Jung & Kundo Park & Byungjin Cho & Jinkyoo Park & Seunghwa Ryu, 2023. "Optimization of injection molding process using multi-objective bayesian optimization and constrained generative inverse design networks," Journal of Intelligent Manufacturing, Springer, vol. 34(8), pages 3623-3636, December.
    4. Ohyung Kwon & Hyung Giun Kim & Min Ji Ham & Wonrae Kim & Gun-Hee Kim & Jae-Hyung Cho & Nam Il Kim & Kangil Kim, 2020. "A deep neural network for classification of melt-pool images in metal additive manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 31(2), pages 375-386, February.
    5. 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.
    6. Liang Hou & Roger J. Jiao, 2020. "Data-informed inverse design by product usage information: a review, framework and outlook," Journal of Intelligent Manufacturing, Springer, vol. 31(3), pages 529-552, March.
    7. Ahmed Ktari & Mohamed El Mansori, 2022. "Digital twin of functional gating system in 3D printed molds for sand casting using a neural network," Journal of Intelligent Manufacturing, Springer, vol. 33(3), pages 897-909, March.

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