Intelligent Machine Vision Model for Defective Product Inspection Based on Machine Learning

Quality control is one of the industrial tasks most susceptible to be improved by implementing technological innovations. As an innovative technology, machine vision enables reliable and fast 24/7 inspections and helps producers to improve the efficiency of manufacturing operations. The accessible data by vision equipment will be used to identify and report defective products, understand the causes of deficiencies and allow rapid and efficient intervention in smart factories. From this perspective, the proposed machine vision model in this paper combines 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 suggested model exploits all generated data by various integrated technologies in the manufacturing chain, thus meeting the requirements of quality management in the context of Industry 4.0, based on predictive analysis to identify patterns in data and suggest corrective actions to ensure product quality. In addition, a comparative study between several machine learning algorithms, both for product classification and process improvement models, is performed in order to evaluate the designed system. The results of this study show that the proposed model largely meets the requirements for the proper implementation of these techniques.

Automated summary

Quality control in industrial tasks can be significantly improved through the implementation of technological innovations, such as machine vision technology. This innovative technology enables reliable and fast inspections, helps identify defective products, and allows for rapid interventions to improve manufacturing processes in smart factories. The data obtained through machine vision can be used to predict and suggest suitable production parameters to ensure defect-free items and meet quality management requirements in Industry 4.0.