ANALYSIS OF ENGINEERED SURFACES FOR PRODUCT QUALITY MONITORING

摘要

Surface texture is the key consideration affecting the function and reliability of engineering components. The engineered textured surfaces have various quantitative roughness measures that specify the quality of the surface. In industrial product quality monitoring systems, it is increasingly important to devise the automated techniques for evaluation of surface roughness. The proposed method is the novel approach for surface roughness evaluation that implements discrete wavelet transform (DWT) and dual-tree complex wavelet transform (DT-CWT) for analysing surface textures. Further, the method emphasizes selection of effective texture descriptors for classification of surfaces according to their roughness values. The experiments are carried out using surfaces manufactured by the machining processes namely milling, casting, shaping, grinding and blasting. We propose combinations of texture descriptors namely standard deviation, kurtosis, the properties of grey-level co-occurrence matrix (GLCM) and the Canny edge descriptor to form a robust feature set. The Canberra distance metric is used as similarity measure. The algorithm and the results are presented with both DWT and DT-CWT. The feature set comprising standard deviation, kurtosis and GLCM properties gives the correct classification performances of 94.45%, 92.22%, 97.5%, 95%, 95% and 94.38% for milling, casting, shaping, grinding, grit blasting and shot blasting, respectively, with DT-CWT.