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A machine vision system for the detection of missing fasteners on steel stampings

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Abstract

A machine vision system that has been developed for the detection of missing fasteners on steel stampings is described. The system has been tested using images generated by a commercial machine vision system installed on an assembly line for the production of automotive cross-car beams. This particular application was considered to be challenging due to variations in the operating conditions, such as the lighting, together with the fact that the appearance of the fasteners could change depending upon their angle relative to the camera. A neuro-fuzzy image classification algorithm was developed and tested against a threshold-based classification algorithm. Results indicate that both algorithms perform well when optimized, but the neuro-fuzzy algorithm was found to degrade in a less abrupt fashion when the input data deviated from the trained data. A graphical user interface (GUI) was designed to enable implementation on the assembly line. The developed system is adaptable to other machine vision-based automated inspection applications.

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Authors and Affiliations

  1. Department of Mechanical and Materials Engineering, Queen’s University, Kingston, ON, Canada

    J. Killing, B. W. Surgenor & C. K. Mechefske

Authors
  1. J. Killing
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  2. B. W. Surgenor
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  3. C. K. Mechefske
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Corresponding author

Correspondence to B. W. Surgenor.

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Killing, J., Surgenor, B.W. & Mechefske, C.K. A machine vision system for the detection of missing fasteners on steel stampings. Int J Adv Manuf Technol 41, 808–819 (2009). https://doi.org/10.1007/s00170-008-1516-3

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  • DOI: https://doi.org/10.1007/s00170-008-1516-3

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