Abstract
Three-dimensional (3D) surfaces of wear particles were reconstructed through multi-view contour fitting and dense point-cloud interpolation with an aim to obtain comprehensive features for wear debris analysis. Multiple image frames at different views were captured when the particles move with rotations through a micro-sized flow channel. The particle contours were extracted from multi-view images to build a 3D model based on particle contour fitting. The 3D modeling accuracy was then improved through an interpolated dense point-cloud. To validate the 3D model, an experiment was carried out to compare its performance to that of results obtained using laser scanning confocal microscopy. The results show that the errors of quantitative surface characterization using the arithmetical mean height (Sa) and the root-mean-square height (Sq) are less than 5.86%, and less than 17.12% for the kurtosis (Sku). The proposed method has great potential values in online condition monitoring of wear particles.
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