Abstract
Tool wears directly affect the quality of product and service life of tool. This paper proposes a machine vision-based measurement method for chisel edge wear of drills. Firstly, the full contour of a drill is extracted by local variance threshold segmentation. Secondly, the image is enhanced by using an adaptive contrast enhancement algorithm based on bidimensional local mean decomposition (BLMD). A threshold segmentation method is proposed to extract contour of the non-worn area. After the above two contours are superimposed, the centroid of each region in the binary image is calculated as the starting point to fill in the overflow water for tool wear extraction. Finally, the chisel edge wear can be measured directly by counting the number of pixels. The experiment in the process of drilling is performed to verify the effectiveness of the proposed method. The experimental results show that the proposed method effectively implements the measurement of tool wear.
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