Characterization and Assessment of Surface Roughness Using NovelData Representation Methods

摘要

In this paper, we propose novel data representation methods for characterization and assessment of surfacernroughness of machined parts. The surface texture images acquired by a high resolution surface imaging system arernfirst translated into a one-dimensional signal by scanning the image along the Hilbert curve pattern. These onedimensionalrnsignals are passed through a novel multi-resolution characterization procedure to extract signal features.rnIn the next step a rich library of texture image templates is built using a probabilistic neural network that is trained inrnauto-associative mode. When a new surface texture sample is given, a closely matching surface roughness templaternis retrieved from the surface roughness library. The experimental results show that the proposed data representationrnmethods give better performance than traditional two-dimensional texture characterization methods. The newrnapproach offers a good means for measurement and estimation of surface roughness of engineering surfaces andrnlends itself for integrating into automated manufacturing systems to meet the growing demand for product qualityrnand cost reduction.