Machine vision for correlating Tool status and machined Surface in Turning Nickel-base super alloy

Abstract

Substantial amount of research has been performed on automated tool status monitoring systems. The research has tended to focus on the development of Tool and work Surface Texture Monitoring using machine vision. However, there has been relatively less effort to monitor surface texture. This paper presents machine vision system, capable of providing surface texture information in Turning Inconel 718 material. Images of the turned surface specimens were acquired using the machine vision system. The images were pre-processed to eliminate noise present in the image. An image histogram quantifies the distribution of all image pixels against the grey level and is a measure of the reflectance properties of the surface under monitoring. The histogram shape changes as the wear state of tool increases. From the analysis of the intensity distribution in the region of interest of the tool and surface, a good correlation between the tool image and corresponding surface image was found. It is expected that these results would support further to establish a criteria for tool replacement in turning operation.