Machine vision assisted characterization of machined surfaces

Abstract

This paper demonstrates the feasibility of using an inexpensive machine vision system to compute non-contact, optical parameters for the characterization of surface roughness of machined surfaces. Two parameters were selected for online analysis, where surface roughness is measured during the rotation of a specimen on a lathe. The sensitivity of the vision-based optical parameters to differences in surface roughness, ambient light and spindle speed of a lathe during measurement was evaluated. Statistical analysis of data collected through experimentation revealed that the vision parameters can discriminate different surface roughness heights and are insensitive to changes in ambient lighting and speed of rotation during measurement. The results of the experimental analysis were used to conclude the feasibility of using machine vision for the evaluation of surfaces.