期刊
SENSORS
卷 21, 期 24, 页码 -出版社
MDPI
DOI: 10.3390/s21248480
关键词
automated inspection; automotive gears inspection; gear defect detection; machine vision inspection
Gears are crucial in many mechanical systems, including vehicle transmissions, but defects in their manufacturing process can lead to catastrophic failure. The current manual inspection process used by an automotive gear manufacturer in Guelph suffers from poor scalability and the risk of missing defects.
Gears are a vital component in many complex mechanical systems. In automotive systems, and in particular vehicle transmissions, we rely on them to function properly on different types of challenging environments and conditions. However, when a gear is manufactured with a defect, the gear's integrity can become compromised and lead to catastrophic failure. The current inspection process used by an automotive gear manufacturer in Guelph, Ontario, requires human operators to visually inspect all gear produced. Yet, due to the quantity of gears manufactured, the diverse array of defects that can arise, the time requirements for inspection, and the reliance on the operator's inspection ability, the system suffers from poor scalability, and defects can be missed during inspection. In this work, we propose a machine vision system for automating the inspection process for gears with damaged teeth defects. The implemented inspection system uses a faster R-CNN network to identify the defects, and combines domain knowledge to reduce the manual inspection of non-defective gears by 66%.
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