Abstract
In this paper, we investigate four methods that yield mathematical measures to analyze the precision of surfaces of manufactured parts. These four methods, namely the autocorrelation function, the Fourier spectrum, the Karhunen-Loève expansion, and a fractal-wavelet representation, are applied to surfaces produced from grinding processes. The first two methods are standard methods used in the surface analysis literature for qualitative signal characterization. The Karhunen-Loève expansion method, used in various signal processing applications, has never been applied to the field of surface characterization and representation. The fractal-wavelet representation has been previously proposed by the authors; its suitability to generate characteristic measures is investigated in this paper. The existence of characteristic measures of surface precision should aid designers in choosing process and design parameters and in comparing the precision between competing machining processes. The use of such measures is essential in taking a forward step towards integrating the fields of design and manufacturing.
Tissue segmentation on MR images of the brain by possibilistic clustering on a 3D wavelet representation
