Automation of Quality Control in the Automotive Industry Using Deep Learning Algorithms

Abstract

Quality control is an essential operation for an automotive company like Faurecia. A vast number of references is produced, and many regions of interest need to be checked. For that, quality control is necessary and should be applied to every reference part. Visual inspection is achieved by the operator who checks each part manually. After several checks per day, the operator gets tired and thus may misqualify a welding seam or a component control. To avoid that, Faurecia is trying to integrate automatic quality control to obtain better overall equipment effectiveness (OEE), especially to avoid performance degradation over the operator's shift. Researches demonstrate the ability of a neural network to reach high precision in detecting object presence or absence. We have been able to achieve an accuracy of 99% with ResNet-50. Apart from accuracy, the other performance matrices used in this work are reliability and cycle time. Our contribution will help the current state of manufacturing by offering an automatic visual inspection, which will lead to other innovative projects in the automotive industry.