Automating inspection & applying AI to improve process performance
Eliminate the overhead & risk of manual work
To date, visual inspection of automotive equipment has typically labor intensive manual inspection of images or potentially inaccurate static computer vision algorithms embedded in the inspection machine.This approach is extremely time consuming, inefficient, prone to error, and does not enable a feedback mechanism. The implication is high yield loss in later processes, higher potential for in-field failures and an increase in rework costs , as well as safety liabilities due to issues going undetected.The key to overcoming the challenge is to deploy machine learning based inspection algorithms to the edge where they operate inline with the equipment. This way manufacturers can detect issues such as weld defects and cracks faster and more accurately, reduce false-calls and process escapes, and ensure that only quality products make it to the field.