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Defect Detection Model Using CNN and Image Augmentation for Seat Foaming Process

Author

Listed:
  • Nak-Hun Choi

    (Department of Future Convergence Engineering, Kongju National University, Cheonan 31080, Chungnam, Republic of Korea)

  • Jung Woo Sohn

    (Department of Mechanical Design Engineering, Kumoh National Institute of Technology, Gumi 39177, Gyeongbuk, Republic of Korea)

  • Jong-Seok Oh

    (Department of Future Convergence Engineering, Kongju National University, Cheonan 31080, Chungnam, Republic of Korea
    Department of Future Automotive Engineering, Kongju National University, Cheonan 31080, Chungnam, Republic of Korea)

Abstract

In the manufacturing industry, which is facing the 4th Industrial Revolution, various process data are being collected from various sensors, and efforts are being made to construct more efficient processes using these data. Many studies have demonstrated high accuracy in predicting defect rates through image data collected during the process using two-dimensional (2D) convolutional neural network (CNN) algorithms, which are effective in image analysis. However, in an environment where numerous process data are recorded as numerical values, the application of 2D CNN algorithms is limited. Thus, to perform defect prediction through the application of a 2D CNN algorithm in a process wherein image data cannot be collected, this study attempted to develop a defect prediction technique that can visualize the data collected in numerical form. The polyurethane foam manufacturing process was selected as a case study to verify the proposed method, which confirmed that the defect rate could be predicted with an average accuracy of 97.32%. Consequently, highly accurate defect rate prediction and verification of the basis of judgment can be facilitated in environments wherein image data cannot be collected, rendering the proposed technique applicable to processes other than those in this case study.

Suggested Citation

  • Nak-Hun Choi & Jung Woo Sohn & Jong-Seok Oh, 2023. "Defect Detection Model Using CNN and Image Augmentation for Seat Foaming Process," Mathematics, MDPI, vol. 11(24), pages 1-13, December.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:24:p:4894-:d:1295450
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    References listed on IDEAS

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    1. Yao, Jiachi & Han, Te, 2023. "Data-driven lithium-ion batteries capacity estimation based on deep transfer learning using partial segment of charging/discharging data," Energy, Elsevier, vol. 271(C).
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