Deep Learning-Based Defect Detection for Sustainable Smart Manufacturing

Sang-Hyun Park, Kang-Hee Lee, Ji-Su Park and Youn-Soon Shin
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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

Sustainability, 2022, vol. 14, issue 5, 1-15

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.

Keywords: deep learning; image processing; smart factory; sustainable computing; Internet of Things (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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