Knowledge-based transfer fuzzy clustering with non-local spatial information for surface roughness measurement

Abstract

Surface roughness is an important quality indicator of machining processing, which is often needed as a feedback. To meet the requirement of on-line roughness measurement, the machine vision-based measurement method has emerged in recent decades. In this paper, a new surface roughness measurement method is developed. It measures the surface roughness by evaluating the aliasing degree of the images reflected by the machined surface. To analyze the aliasing degree more accurately, a knowledge-based transfer fuzzy clustering with non-local spatial information (KTFCM_NLS) is proposed. The KTFCM_NLS incorporates both the auxiliary knowledge and non-local spatial information into the fuzzy c-means (FCM) algorithm. It can preserve more image details, and is robust to image noise. The results show that the proposed algorithm performs better than other FCM algorithms, and the corresponding index is strongly related to surface roughness. The average relative error of the proposed method is only 5.95% on the testing samples.