ABSTRACT
Manufacturing is an imperfect process that requires frequent checks and verifications to ensure products are being produced properly. In many cases, such as visual inspection, these checks can be automated to a certain degree. Incorporating advanced inspection techniques (i.e., via deep learning) into real-world inspection pipelines requires different mechanical, machine vision, and process-level considerations. In this work, we present an approach that builds upon prior work at an automotive gear facility located in Guelph, Ontario, which is looking to expand its defect detection capabilities. We outline a set of inspection-cell changes, which has led to full-gear surface scanning and inspection at a rate of every 7.5 s, and which is currently able to detect three common types of surface-level defects.
ABSTRACT
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%.
