Although lighting is recognized as critical to the success of any machine vision inspection system, the choice of lighting is often based on experience and intuition rather than scientific research.
To assess the effects of lighting in a more quantitative fashion, Queen's University (Kingston, Ontario, Canada) student Michael T. Yan has compared three basic lighting geometries in a vision inspection system that checked for the presence of J-clips on aluminum carriers used in the automotive industry.
Two different National Instruments (Austin, TX, USA) LabVIEW machine vision algorithms were used to evaluate samples of the aluminum carriers that were lit using using backlighting, bright field and dark field illumination techniques to determine whether the J-clips were present or absent.
Results showed that there were clear differences in performance. Tan's experiments proved that -- in his particular system set up -- backlighting offered the best solution for both the inspection algorithms that were used to analyze the images of the parts.
Tan described the results from his experiments in a thesis submitted to the Department of Mechanical and Materials Engineering at Queen's University for his Master of Applied Science degree in 2012. His thesis can be downloaded here.
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