Curvelet transforms and flower pollination algorithm based machine vision system for roughness estimation

Abstract

Machine vision based systems aided in approximation of the surface roughness in a nondestructive means and relieved the process of automation. The advent of computers and computational tools in manufacturing, assisted in reduction of ideal time by their computational knack to estimate the micron level and uncertain in line values of surface roughness. In the present work, to develop a machine vision based intelligent roughness estimation system; high-resolution surface images of the machine surface are captured. The images captured are processed in MATLAB image processing toolbox for Curvelet Transform texture features. The measured surface roughness values and the texture features are mapped using the advanced computational tool Flower Pollination Algorithm (FPA). The FPA closely estimated the surface roughness with precise percentile of error when compared with another support vector machine model.