Automated thermal fuse inspection using machine vision and artificial neural networks

Te-Hsiu Sun (), Fang-Cheng Tien (), Fang-Chih Tien () and Ren-Jieh Kuo ()
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Te-Hsiu Sun: Chaoyang University of Technology
Fang-Cheng Tien: Chung Hua University
Fang-Chih Tien: National Taipei University of Technology
Ren-Jieh Kuo: National Taiwan University of Science and Technology

Journal of Intelligent Manufacturing, 2016, vol. 27, issue 3, No 11, 639-651

Abstract: 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.

Keywords: Thermal fuse; Machine vision; Backpropagation neural networks; LVQ; Quality control (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (7)

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DOI: 10.1007/s10845-014-0902-y

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