Machined surface generation using wavelet filtering

Abstract

The surface geometry of a workpiece after machining is a major concern in the industry. Thus, it is important to generate its geometry, reconfiguring and modifying it by a wavelet-based filtering technique with confocal laser scanning microscopy (CLSM). This technique is well-suited to construct the three-dimensional surface of a machined workpiece. The problem of noise on the surface always occurs when treating the raw height data (HEI: Height Encoded Image) acquired by CLSM. The noise occurs in constructing the surface height image (HEI) due to an electronic and photographic image process and must be filtered. In this paper, a wavelet-based filtering algorithm is used to eliminate noise in the shape features of the surface on a micro scale so as to realize an accurate geometry for further topographic analysis. The filtered surface patterns enable us to predict the machined surface and analyze surface characteristics in varied shapes of different levels. Also, to consider the tilting that inevitably occurs in mounting a specimen on the table, the surface is compensated and flattened in the x and y-directions simultaneously. This newly developed technique combines CLSM and wavelet filtering generates various micro machined surfaces over a wide range of surface resolutions on a micro scale

Study on the extraction and reconstruction of arbitrary frequency topography from precision machined surfaces

“…Many researchers employed filter characteristics of DWT method to estimate the surface roughness and obtained waviness topography in different decomposition level from variously machined surfaces. 15–18 Wang et al 19 decomposed and reconstructed the two-dimensional (2D) frequency profiles in the cutting direction with multi-level decomposition of 1D DWT. Wavelet method was also suitable to analyze various time series signal in machining process.…”

Using Wavelet Packet Transform for Surface Roughness Evaluation and Texture Extraction

“…Rosenboom et al and Doshi et al applied wavelet analysis for surface description, defect detection and texture classification [ 24 , 25 , 26 , 27 ]. Yang et al used wavelet filter for generating surface with confocal laser scanning microscope (CLSM) [ 28 ], demonstrating a powerful tool for eliminating image noise. Wavelet transform provides flexible time-frequency resolution, whereas it suffers from a relatively low resolution in high frequency regions, leading to difficulty in differentiating high frequency transient components [ 7 ].…”