Please use this identifier to cite or link to this item: https://doi.org/10.1016/j.imavis.2006.05.024
Title: Texture analysis methods for tool condition monitoring
Authors: Kassim, A.A. 
Mannan, M.A. 
Mian, Z.
Keywords: Machine vision
Texture analysis
Tool wear monitoring
Issue Date: 1-Jul-2007
Source: Kassim, A.A., Mannan, M.A., Mian, Z. (2007-07-01). Texture analysis methods for tool condition monitoring. Image and Vision Computing 25 (7) : 1080-1090. ScholarBank@NUS Repository. https://doi.org/10.1016/j.imavis.2006.05.024
Abstract: Tool wear dramatically affects the texture of the machined surface. Analyzing the texture of machined surfaces has been shown to be promising for tool wear monitoring. However, most methods have their limitations when applied to real environments, where the geometric features of machined surface depend on the machining operation, and where image quality is affected by illumination and other factors. Problems of non-uniform illumination and image noise can be reduced by applying image segmentation and image enhancement techniques. This paper discusses our work on statistical and structural approaches for analyzing machined surfaces and investigates the correlation between tool wear and quantities characterizing machined surfaces. The column projection method can be used for machined surfaces with highly repetitive and regular textures while the connectivity oriented fast Hough transform based method is able to characterize surfaces produced by various machining processes and conditions. Our results clearly indicate that tool condition monitoring which is defined as the ability to distinguish between a sharp, a semi-dull, or a dull tool can be successfully accomplished by analysis of statistical and structural information extracted from the machined surface. © 2006 Elsevier B.V. All rights reserved.
Source Title: Image and Vision Computing
URI: http://scholarbank.nus.edu.sg/handle/10635/57608
ISSN: 02628856
DOI: 10.1016/j.imavis.2006.05.024
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