Objective speckle pattern-based surface roughness measurement using matrix factorization

Abstract

A method for the measurement of profile parameters of both isotropic and anisotropic surfaces is presented using the objective laser speckle imaging technique. The surface parameters are characterized in terms of a singular value decomposition method-based metric derived from the initial key contributing singular values of the speckle pattern. A simulation study is performed with random Gaussian anisotropic surfaces generated as a function of the correlation lengths in both x and y directions. In the experimental demonstration, the proposed method is verified with metallic samples having distinct surface roughness processed through widely used machining operations viz., vertical milling, and grinding. A brief discussion about the extent to which the minimum number of singular values that are sufficient to evaluate the profile parameters in the context of experimental results is provided. The method supports the measurement of profile parameters of higher magnitude in the realm of non-contact topographic measurement techniques. The experimental results substantiate the practical applicability of the proposed method.