Surface Defects Classification Using Artificial Neural Networks in Vision Based Polishing Robot

One of the highly skilled tasks in manufacturing is the polishing process. The purpose of polishing is to get uniform surface roughness. In order to reduce the polishing time and to cope with the shortage of skilled workers, robotic polishing technology has been investigated. This paper proposes a vision system to measure surface defects that have been classified to some level of surface roughness. Artificial neural networks are used to classify surface defects and to give a decision in order to drive the actuator of the arm robot. Force and rotation time have been chosen as output parameters of artificial neural networks. The results show that although there is a considerable change in both parameter values acquired from vision data compared to real data, it is still possible to obtain surface defects classification using a vision sensor to a certain limit of accuracy. The overall results of this research would encourage further developments in this area to achieve robust computer vision based surface measurement systems for industrial robotics, especially in the polishing process.