Please use this identifier to cite or link to this item:
Title: Machine tool condition monitoring using workpiece surface texture analysis
Authors: Kassim, A.A. 
Mannan, M.A. 
Jing, Ma.
Issue Date: Feb-2000
Citation: Kassim, A.A., Mannan, M.A., Jing, Ma. (2000-02). Machine tool condition monitoring using workpiece surface texture analysis. Machine Vision and Applications 11 (5) : 257-263. ScholarBank@NUS Repository.
Abstract: Tool wear affects the surface roughness dramatically. There is a very close correspondence between the geometrical features imposed on the tool by wear and microfracture and the geometry imparted by the tool on to the workpiece surface. Since a machined surface is the negative replica of the shape of the cutting tool, and reflects the volumetric changes in cutting-edge shape, it is more suitable to analyze the machined surface than look at a certain portion of the cutting tool. This paper discusses our work that analyzes images of workpiece surfaces that have been subjected to machining operations and investigates the correlation between tool wear and quantities characterizing machined surfaces. Our results clearly indicate that tool condition monitoring (the distinction between a sharp, semi-dull, or a dull tool) can be successfully accomplished by analyzing surface image data.
Source Title: Machine Vision and Applications
ISSN: 09328092
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.


checked on May 10, 2018


checked on Dec 31, 2018

Page view(s)

checked on Dec 1, 2019

Google ScholarTM


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.