Surface roughness modeling based on laser speckle imaging

Abstract

Surface roughness is an important parameter in measuring the roughness of surface formed by laser irradiation on the workpiece. Speckle images of rough surfaces in different classes and different surface roughness values are obtained by constructing a set of laser speckle image acquisition systems. First, the texture features of speckle images including coarseness, contrast and direction are extracted using Tamura texture theory. Then, the interactions these three features with the surface roughness are analyzed. Based on the analyses of their monotonic relations, the surface roughness functions, including flat grinding, external grinding and mill grinding craftworks, are established respectively between the texture coarseness feature of the speckle image Fcrs and surface roughness Ra. Through the establishment of surface roughness function for the above three classes of workpieces, the value of surface roughness can be computed directly. However, before obtaining the value of surface roughness, the classes of processing technic should be determined because of the inconsistency of function expressions for different classes. And based on the specific connection and related dependencies between Tamura texture features and workpiece class, Bayes network is proposed to describe this uncertainty relation among different classes. Through network structure learning and parameter learning, a model for reasoning is found which can be used to determine the class of workpiece after obtaining texture coarseness feature Fcrs. Thus, not only can the value of surface roughness be measured, also the class of work-piece can be recognized. Experiments are conducted to confirm the feasibility of the proposed model for measurement. The detection results indicate that high precision and accuracy are achieved for both workpiece class recognition and roughness measurement.