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Automated thermal fuse inspection using machine vision and artificial neural networks

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Abstract

Machine vision is an excellent tool for inspecting a variety of items such as textiles, fruit, printed circuit boards, electrical components, labels, integrated circuits, machine tools, etc. This paper presents an intelligent system that incorporates machine vision with artificial intelligent networks to automatically inspect thermal fuses. An effective inspection flow is proposed to detect four commonly seen defects, including black-dot, small-head, bur, and flake during the production of thermal fuses. Backpropagation neural networks and learning vector quantization performance is compared in detecting the bur defect because of its illegibility. Different numbers of defective samples were screened out from a production line in a case study company and used to demonstrate the efficacy of the proposed system. Currently, the proposed inspection system is operating at the case study company, replacing four to six human inspectors. The system not only ensures the quality of the thermal fuses produced, but also reduced the cost of manual visual inspection.

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Authors and Affiliations

  1. Department of Industrial Engineering and Management, Chaoyang University of Technology, 168, Gifeng E. Rd. Wufeng, Taichung County, 413, Taiwan, ROC

    Te-Hsiu Sun

  2. Center of General Education, Chung Hua University, 707, Sec. 2, WuFu Rd., Hsinchu, 30012, Taiwan, ROC

    Fang-Cheng Tien

  3. Department of Industrial Engineering and Management, National Taipei University of Technology, No. 1, Section 3, Chung-Hsiao E. Rd., Taipei, 106, Taiwan, ROC

    Fang-Chih Tien

  4. Department of Industrial Management, National Taiwan University of Science and Technology, No. 43, Section 4, Kee-Lung Road, Taipei, 106, Taiwan, ROC

    Ren-Jieh Kuo

Authors
  1. Te-Hsiu Sun
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  2. Fang-Cheng Tien
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  3. Fang-Chih Tien
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  4. Ren-Jieh Kuo
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Correspondence to Fang-Chih Tien.

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Sun, TH., Tien, FC., Tien, FC. et al. Automated thermal fuse inspection using machine vision and artificial neural networks. J Intell Manuf 27, 639–651 (2016). https://doi.org/10.1007/s10845-014-0902-y

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  • DOI: https://doi.org/10.1007/s10845-014-0902-y

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