Please use this identifier to cite or link to this item: http://ir.library.ui.edu.ng/handle/123456789/1929
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dc.contributor.authorAlabi, B.-
dc.contributor.authorSalau, T. A. O.-
dc.contributor.authorOke, S. A.-
dc.date.accessioned2018-10-11T09:07:48Z-
dc.date.available2018-10-11T09:07:48Z-
dc.date.issued2007-
dc.identifier.issn1392-1207-
dc.identifier.issnui_art_alabi_surface_2007-
dc.identifier.otherMechanika 2(64), pp. 65- 71-
dc.identifier.urihttp://ir.library.ui.edu.ng/handle/123456789/1929-
dc.description.abstract"A new method on machined surface finish quality characterization using fractal analysis is proposed. This seems to be an improvement on Olaosebikan's spectral analysis index method for surface finish assessment. Mathematical model based on disk count-Monte Carlo approach is developed' and tested with simulated results from computer programme written in Fortran. Test cases Involve five-finished machine surfaces (work pieces) that are ranked based on fractal dimensions obtained for the respective machined surface spectral trace. The work pieces, made using different machining operations (milling, grinding, etc.), have their quality of finishing described as a function of the machine operation that! each work piece passes through. The respective spectral fractal dimensions of six fractal images (A, B, C, D, E and F) were then obtained. The conjecture is that the ranked results will agree with ranking obtained by both CLA and spectral index methods. Contrarily, the ranked results disagreed with both CLA and spectral trace results. The new method seems superior to both CLA and spectral trace approaches since a higher accuracy and much less computation time is observed. The maximum percentage relative absolute difference is 13.1 %, and the computation time is as short as 3 minutes. "en_US
dc.language.isoenen_US
dc.titleSurface finish quality characterisation of machined workpieces using fractal analysisen_US
dc.typeArticleen_US
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