Automotive Gear Manufacturing
The current inspection course of utilized by an automotive gear manufacturer in Guelph, Ontario, requires human operators to visually inspect all gear produced. In this work, we propose a machine imaginative and prescient system for automating the inspection course of for gears with broken enamel defects. The applied inspection system makes use of a faster R-CNN network to identify the defects, and combines area knowledge to reduce the handbook inspection of non-defective gears by 66%.
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