A Fundamental Study on the Digital Recognition of Grinding Wheel


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In this paper, the 3D morphology of a grinding wheel was modeled by the depth from focus. Firstly, the picture information of different heights was extracted by the up-down moving of the microscope. The operator Laplacian was adopted to distinguish the distinct and fuzzy areas in a picture. Then, the distinct image and height information was obtained. The information of height was distorted due to the occurrence of noise. In order to reconstruct 3D surface, a method based on Min/Max curvature flow was developed to remove noises. In the end, an abrasive grain in the image of a grinding wheel was segmented by the Mumford-Shah model. The results could be further developed to evaluate the worn status of grinding wheels. Introduction The examination of the wear of abrasive grain in the grinding wheel is very important for evaluation of performance of diamond grinding wheel. The three-dimensional (3D) reconstruction of grinding wheel topography can provide more information about wear of abrasive grains than common ways such as observation by optical microscope. Nowadays, there have been many techniques to be considered to obtain 3D data, for example, profilometry, the scanning electron microscope (SEM), the scanning laser microscope (SLM), stereo vision and so on. SEM is a powerful measuring tool, but the time needed for sample coating process and chamber air pumping is considerable. SLM is promising tool for 3D shape modeling, but still expensive for most of users. Stereo vision is simple and quick method to obtain the height information, but only the height of points which match in two corresponding images could been obtained. In this paper, a new method based on depth from focus (DFF) [1] is presented for 3D modeling. Compared with SEM and SLM, it is easy and not very expensive equipments are needed. Meanwhile, it can provide more real 3D model than stereo vision method. In order to measure the abrasive grains, a segmentation Algorithms based on Mumford-Shah model [4] is introduced to divide the grains from image of grinding wheel.



Key Engineering Materials (Volumes 359-360)

Edited by:

Jiuhua Xu, Xipeng Xu, Guangqi Cai and Renke Kang




J. F. Gong and X. P. Xu, "A Fundamental Study on the Digital Recognition of Grinding Wheel", Key Engineering Materials, Vols. 359-360, pp. 504-508, 2008

Online since:

November 2007




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[5] T.F. Chan and L.A. Vese: IEEE Trans. on Image processing, Vol. 10, No. 2, pp.266-277.