Machine-Vision-Assisted Performance Monitoring in Turning Inconel 718 Material Using Image Processing

Abstract

Machining performance monitoring is the utilization of different sensors to determine the condition of processes. Machine vision system has been used to monitor the state of both cutting tool and workpiece during turning process. The turning experiments on Inconel 718 material have been performed in a precision lathe using coated carbide cutting tool in dry conditions. Cutting tool and machined surface images were acquired using machine vision. Image features of machined surface and cutting tool were extracted by processing the images. Image features such as wear area and perimeter have been considered to characterize tool wear state; consequently, machined surface state was characterized by means of image histogram frequency. Further, trends have been plotted with image features extracted from both tool and machined surfaces. Results indicate that monitoring of turning performance could be effectively accomplished by plotted trends.