Machine Vision and Deep Learning Based Rubber Gasket Defect Detection

  • Chao-Ching Ho Graduate Institute of Manufacturing Technology and Department of Mechanical Engineering, National Taipei University of Technology, Taipei, Taiwan
  • Eugene Su Graduate Institute of Manufacturing Technology and Department of Mechanical Engineering, National Taipei University of Technology, Taipei, Taiwan
  • Po-Chieh Li Graduate Institute of Manufacturing Technology and Department of Mechanical Engineering, National Taipei University of Technology, Taipei, Taiwan
  • Matthew J. Bolger Graduate Institute of Manufacturing Technology and Department of Mechanical Engineering, National Taipei University of Technology, Taipei, Taiwan
  • Huan-Ning Pan Graduate Institute of Manufacturing Technology and Department of Mechanical Engineering, National Taipei University of Technology, Taipei, Taiwan
Keywords: traditional rule-based strategy, deep learning, convolutional neural networks (CNN), image recognition, image processing, deep residual learning

Abstract

This study develops an automated optical inspection system for silicone rubber gaskets using traditional rule-based and deep learning detection techniques. The specific object of interest is a 5 mm × 10 mm × 5 mm  mobile device power supply connector gasket that provides protection against foreign body inclusion and water ingression. The proposed system can detect a total of five characteristic defects introduced during the mold-based manufacture process, which range from 10-100 μm. The deep learning detection strategies in this system employ convolutional neural networks (CNN) developed using the TensorFlow open-source library. Through both high dynamic range image capture and image generation techniques, accuracies of 100% and 97% are achieved for notch and residual glue defect predictions, respectively.

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Published
2020-04-01
How to Cite
[1]
C.-C. Ho, E. Su, P.-C. Li, M. J. Bolger, and H.-N. Pan, “Machine Vision and Deep Learning Based Rubber Gasket Defect Detection”, Adv. technol. innov., vol. 5, no. 2, pp. 76-83, Apr. 2020.
Section
Articles