A new surface roughness measurement method based on QR-SVM

In this paper, a method for detecting surface roughness in machining processes is proposed to solve the problem of low detection accuracy caused by a small sample size in machine vision detection. The proposed method combines QR decomposition with the support vector machine (SVM) classifier to accurately assess surface roughness. First, a contact roughness detector is used to measure the surface roughness value, and a CCD camera is used to capture the processed surface image to obtain the sample. Subsequently, an improved QR decomposition method is employed to generate virtual samples and expand the sample size. Texture feature values of the image are then extracted using the grayscale level co-occurrence matrix, and the correlation between roughness and texture features is determined. Finally, SVM is employed to classify the surface roughness of machined components. Experimental results demonstrated that the accuracy of the machine vision-based surface roughness detection method increased from 80.6 to 96.5%, thus validating the feasibility of the proposed method and providing a theoretical basis for on-site detection of small-sample surface roughness. This method has potential for practical engineering applications.