TEXTURE CLASSIFICATION OF ENGINEERING SURFACES WITH NANOSCALE ROUGHNESS

The spatial structure of the surface layer, or texture is important fo r surface topography characterization. In many respects a texture dete rmines contact behavior of the rough surfaces. Despite increasing role of the precision mechanics, the texture of engineering surfaces have not been adequately investigated. In this paper pattern recognition th eory is introduced to perform surface textures classification. The hei ght-coded images obtained by atomic force microscopy were used as init ial data. The images represent the surface textures of various materia ls formed by various processes. We take the following procedure for th e texture classification. First, the texture was characterized by a ma trix of co-occurrence of image contrast. Next, the matrix is transform ed into feature vector by the Karhunen-Loeve transformation. The featu re vector was considered as coordinates of a point in the multidimensi onal feature space. The location of the point depends on the peculiari ties of the surface texture. The set of the points form clusters that correspond to different classes of textures. The mutual arrangement of the points and structure of the clusters were analyzed by the multidi mensional scaling procedure. It was founded that there is at least fou r classes of surface relives. The first three of them related to the p roperties of surface material and the Last to the process of growth an d crystallization on the interface of different materials. (C) 1998 El sevier Science Ltd.