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An Approach for Designing an Optimal CNN Model Based on Auto-Tuning GA with 2D Chromosome for Defect Detection and Classification

Author

Listed:
  • Nhat-To Huynh

    (Division of Industrial Engineering and Management, The University of Danang—University of Science and Technology, Danang 550000, Vietnam)

  • Duong-Dong Ho

    (Division of Industrial Engineering and Management, The University of Danang—University of Science and Technology, Danang 550000, Vietnam)

  • Hong-Nguyen Nguyen

    (Division of Industrial Engineering and Management, The University of Danang—University of Science and Technology, Danang 550000, Vietnam)

Abstract

Defect detection and classification on the final products are necessary for the manufacturers to ensure the quality of the final product before delivering it to the end customers. With rapid changes in manufacturing technologies, most of the companies have changed their operation methods toward industry 4.0. On this road, developing an automatic detection system based on the surface images can enhance the productivity and ensure the quality of the product. However, only a few studies have developed the models for solving this problem. Due to its complicated structure and parameters, designing an optimal convolution neural network (CNN) is still a challenge. Thus, this study aims to propose an autotuning genetic algorithm with two-dimension chromosomes for designing an optimal CNN model efficiently. In particular, a two-dimension chromosome is developed to represent a CNN’s structure and parameters. To enhance the searching process, the crossover rate and mutation rate are tuned automatically according to the generation. A two-dimension crossover method is proposed to create offspring for selecting the next population. In addition, a case of ceramic textile manufacturing is constructed to validate the proposed approach. The accuracy of the proposed approach is up to 95.5 percent on the testing dataset.

Suggested Citation

  • Nhat-To Huynh & Duong-Dong Ho & Hong-Nguyen Nguyen, 2023. "An Approach for Designing an Optimal CNN Model Based on Auto-Tuning GA with 2D Chromosome for Defect Detection and Classification," Sustainability, MDPI, vol. 15(6), pages 1-14, March.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:6:p:5455-:d:1102179
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    References listed on IDEAS

    as
    1. Hui Lin & Bin Li & Xinggang Wang & Yufeng Shu & Shuanglong Niu, 2019. "Automated defect inspection of LED chip using deep convolutional neural network," Journal of Intelligent Manufacturing, Springer, vol. 30(6), pages 2525-2534, August.
    2. Haiyong Chen & Yue Pang & Qidi Hu & Kun Liu, 2020. "Solar cell surface defect inspection based on multispectral convolutional neural network," Journal of Intelligent Manufacturing, Springer, vol. 31(2), pages 453-468, February.
    3. Olatomiwa Badmos & Andreas Kopp & Timo Bernthaler & Gerhard Schneider, 2020. "Image-based defect detection in lithium-ion battery electrode using convolutional neural networks," Journal of Intelligent Manufacturing, Springer, vol. 31(4), pages 885-897, April.
    4. Domen Tabernik & Samo Šela & Jure Skvarč & Danijel Skočaj, 2020. "Segmentation-based deep-learning approach for surface-defect detection," Journal of Intelligent Manufacturing, Springer, vol. 31(3), pages 759-776, March.
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