Please use this identifier to cite or link to this item: https://doi.org/10.1016/j.patcog.2004.01.014
Title: Connectivity oriented fast Hough transform for tool wear monitoring
Authors: Kassim, A.A. 
Mian, Z.
Mannan, M.A. 
Keywords: Canny edge detector
Connectivity oriented
Hough transform
Texture analysis
Tool wear monitoring
Issue Date: Sep-2004
Source: Kassim, A.A., Mian, Z., Mannan, M.A. (2004-09). Connectivity oriented fast Hough transform for tool wear monitoring. Pattern Recognition 37 (9) : 1925-1933. ScholarBank@NUS Repository. https://doi.org/10.1016/j.patcog.2004.01.014
Abstract: Tool wear monitoring can be achieved by analyzing the texture of machined surfaces. In this paper, we present the new connectivity oriented fast Hough transform, which easily detects all line segments in binary edge images of textures of machined surfaces. The features extracted from line segments are found to be highly correlated to the level of tool wear. A multilayer perceptron neural network is applied to estimate the flank wear in various machining processes. Our experiments show that this Hough transform based approach is effective in analyzing the quality of machined surfaces and could be used to monitor tool wear. A performance analysis of our Hough transform is also provided. © 2004 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.
Source Title: Pattern Recognition
URI: http://scholarbank.nus.edu.sg/handle/10635/55401
ISSN: 00313203
DOI: 10.1016/j.patcog.2004.01.014
Appears in Collections:Staff Publications

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