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Tool wear monitoring using a fast Hough transform of images of machined surfaces

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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.

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Authors and Affiliations

  1. Mechanical Engineering Department, National University of Singapore, 10 Kent Ridge Crescent, 119260, Singapore, Singapore

    M. A. Mannan

  2. Electrical and Computer Engineering Department, National University of Singapore, 10 Kent Ridge Crescent, 119260, Singapore, Singapore

    Zhu Mian & A. A. Kassim

Authors
  1. M. A. Mannan
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  2. Zhu Mian
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  3. A. A. Kassim
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Corresponding author

Correspondence to A. A. Kassim.

Additional information

Received: 6 November 2002, Accepted: 18 December 2003, Published online: 13 May 2004

Correspondence to: A.A. Kassim

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Mannan, M.A., Mian, Z. & Kassim, A.A. Tool wear monitoring using a fast Hough transform of images of machined surfaces. Machine Vision and Applications 15, 156–163 (2004). https://doi.org/10.1007/s00138-004-0137-6

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  • DOI: https://doi.org/10.1007/s00138-004-0137-6

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