Please use this identifier to cite or link to this item: https://doi.org/10.1007/s00138-004-0137-6
Title: Tool wear monitoring using a fast Hough transform of images of machined surfaces
Authors: Mannan, M.A. 
Mian, Z.
Kassim, A.A. 
Keywords: Hough transform
Texture analysis
Tool wear monitoring
Issue Date: Jul-2004
Source: Mannan, M.A., Mian, Z., Kassim, A.A. (2004-07). Tool wear monitoring using a fast Hough transform of images of machined surfaces. Machine Vision and Applications 15 (3) : 156-163. ScholarBank@NUS Repository. https://doi.org/10.1007/s00138-004-0137-6
Abstract: The texture of machined surfaces provides reliable information regarding the extent of tool wear. In this paper, we propose a structure-based approach to analyzing machined surfaces. The original surface images are first preprocessed by a Canny edge detector. A new connectivity-oriented fast Hough transform is then applied to the edge image to detect all the line segments. The distributions of the orientations and lengths of the line segments are used to determine tool wear. Through our experiments, we found a strong correlation between tool wear and features. The computational complexity of the fast Hough transform is also analyzed. © Springer-Verlag 2004.
Source Title: Machine Vision and Applications
URI: http://scholarbank.nus.edu.sg/handle/10635/57681
ISSN: 09328092
DOI: 10.1007/s00138-004-0137-6
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