Optimization of the quality control for pieces recognition in manufacturing flexible cells by the implementation of neural networks

Abstract

The high levels of automation in the companies that use manufacturing cells for their fabrication processes guarantee a high efficiency in production with high quality standards. The main objective of this paper is to develop an optimization for the tasks performed by the quality control station through artificial vision belonging to the manufacturing cell FMS-200; the development of the proposal focuses on optimizing the inspection process and control of quality by adapting image processing techniques and adaptive resonance neural networks. For this particular case, a review of the location of the optical system and a detailed time study for each movement of the station actuators was carried out, attempting to determinate a better position for the optical detection system (artificial vision camera) and the possibility elimination of some components of the station; all of this in order to considerably reduce the operating time and increasing the efficiency for the quality control of the system.