Surface Roughness Evaluation in Turning of Nimonic C263 Super Alloy Using 2D DWT Histogram Equalization

Abstract

Surface roughness of specimens is an important area of research since it influences the performance of machined parts. Meanwhile, employing a vision system to judge the roughness of the machined surface of specimens via captured images acquired from the specimen is an innovative and extensively used method. In this investigation, a vision system is used to capture the SEM images of the machined surface. The two-dimensional images of the machined surface of the Nimonic263 alloy are used to approximate the profile of the surface of specimens in finish turning. Surface roughness was detected in simulated images of specimens in a variety of machining conditions using the imaging technology. In this research work, the surface texture is extracted using a technique that combines 2D surface images and wavelet transform approach. The 2D wavelet transform has the capability to disintegrate a machined surface image into multiresolution depiction for several surface characteristics and can be utilized for surface evaluation. The difference in the histogram frequency of an illuminated region of interest (ROI) from turned surface images was analyzed to aid in the evaluation of surface roughness with an average prediction error of less than 3.2%.