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Deep Learning-Based Defect Detection for Sustainable Smart Manufacturing

Author

Listed:
  • Sang-Hyun Park

    (Department of Computer Science and Engineering, Dongguk University, Seoul 04620, Korea)

  • Kang-Hee Lee

    (Department of Computer Science and Engineering, Dongguk University, Seoul 04620, Korea)

  • Ji-Su Park

    (Department of Computer Science and Engineering, Jeonju University, Jeonju 55069, Korea)

  • Youn-Soon Shin

    (Department of Computer Science and Engineering, Dongguk University, Seoul 04620, Korea)

Abstract

In manufacturing a product, product defects occur at several stages. This study makes the case that one can build a smart factory by introducing it into the manufacturing process of small-scale scarce products, which mainly solves the defect problem through visual inspection. By introducing an intelligent manufacturing process, defects can be minimized, and human costs can be lowered to enable sustainable growth. In this paper, in order to easily detect defects occurring in the manufacturing process, we studied a deep learning-based automatic defect detection model that can train product characteristics and determine defects using open sources. To verify the performance of this model, it was applied to the disposable gas lighter manufacturing process to detect the liquefied gas volume defect of the lighter, and it was confirmed that the detection accuracy and processing time were sufficient to apply to the manufacturing process.

Suggested Citation

  • Sang-Hyun Park & Kang-Hee Lee & Ji-Su Park & Youn-Soon Shin, 2022. "Deep Learning-Based Defect Detection for Sustainable Smart Manufacturing," Sustainability, MDPI, vol. 14(5), pages 1-15, February.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:5:p:2697-:d:758777
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