Characterization Of Ultraprecision Machined Surfaces Using Spectral Density And Fractal Analysis

Ultra-precision machining is a front-edge technology in advanced manufacturing technology area, which has been the key technology for each field to succeed in international competitions. It plays an important role in manufacturing precision parts for national defense, astronautics and space, nuclear energy, and the other advanced technological areas of national economy. Evaluating a ultra-precision technology is mainly based on the characterization of the corresponding manufactured surface quality. Therefore, measuring and characterizing of machined surfaces is a key in ultra-precision machining.In the project, Nanoscope-III(Dimension 3100)AFM and Taylorsurf CCI 2000 WLI were used to measure the ultra-precision ground and turned surfaces to obtain micro morphologies and three dimensional (3D) data.Features of using the conventional method to characterize the machined surfaces was firstly analyzed and summarized, and then the power spectral density (PSD) and fractal theory were applied to characterize the ultra-precision machined surfaces. PSD graph was exported as horizontal axis using spatial wavelength, so the distribution of spatial wavelength and its proportion on the machined surface was simply and institutionally analyzed.The structural function method and variation method were used to calculate the 2D and 3D fractal dimensionality accordingly, then fractal characters of ultra-precision machined surface were identified through slop coefficient of fit straight line which was exported from log-log grid of structural function curves. 2D and 3D fractal dimensionality were used to evaluate those machined surfaces accordingly to comparatively analyze the relationship between the fractal dimensionality and machining parameters. Based on the experiments, the relationship between 3D and and 2D fractal dimensionality was analyzed in contrast, as well as the relationship between 3D fraction and 3D roughness.By characterizing the ultra-precision machined surfaces using PSD and fraction, the rich information used to select the appropriate machining methods in ultra-precision machining can be provided.