Machine Vision and Deep Learning Based Rubber Gasket Defect Detection
作者: Chao-Ching HoEugene SuPo-Chieh LiMatthew J. BolgerHuan-Ning Pan
作者单位: 1Graduate Institute of Manufacturing Technology and Department of Mechanical Engineering, National Taipei University of Technology, Taipei, Taiwan
刊名: Advances in Technology Innovation, 2020, Vol.5 (2), pp.76-83
来源数据库: Taiwan Association of Engineering and Technology Innovation
原始语种摘要: 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 �gm. 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...
全文获取路径: 台湾工程与科技创新学会 

  • inspection 检查
  • learning 学识
  • system 
  • protection 保护
  • proposed 建议的
  • range 射程
  • dynamic 动力学的
  • total 总和
  • process 过程
  • developed 开发