Intelligent machine vision model for defective product inspection based on machine learning
Tajeddine Benbarrad, Soukaina Bakhat Kenitar, Mounir Arioua
Abstract
The accelerated growth of new technologies, along with the optimization of the manufacturing systems, are forcing companies to change their traditional methods of production and step up to industry 4.0. Quality control is one of the production processes most susceptible to be enhanced by introducing technological innovations. Accordingly, machine vision will be a critical part of automation systems in Industry 4.0. The data accessible by vision equipment will be used to identify and report defective products, understand the causes of deficiencies and allow rapid and efficient intervention in the Industry 4.0 factory. From this perspective, the machine vision model proposed in this paper combines between the identification of defective products and the continuous improvement of manufacturing processes by predicting the most suitable parameters of production processes to obtain a defect-free item. The proposed model exploits all data generated by various technologies integrated in manufacturing chain, thus meeting the requirements of quality management in the context of Industry 4.0, based on the predictive analysis to identify patterns in data and suggest corrective actions to ensure product quality.