Image Processing Method Based Quality Test on a Smart Flexible Assembly Mechatronic System With Component Recovery

Abstract

. The article presents a new approach for image processing algorithms based on image processing techniques: edge detection, normal cross correlation (NCC) and M-estimator Sample Consensus (MASC). The new algorithm is integrated into the station dedicated to the quality test (QT) on a SMART flexible assembly mechatronic system with component recovery. As a result of the QT analysis, the manufacturing flow can continue with either the following operations: disassembly and recovery of components or transport and storage of good products. It is obvious the importance of implementing a high accuracy analysis function for products quality. The new algorithm increases the performance of the detection function both in terms of identification but also in the speed of QT execution. The results obtained in this article will be used in future research for the development of a machine vision system adapted to modern Industry 4.0 technologies, in which it will have the control structure specific to an integrated IoT sensor.