Please use this identifier to cite or link to this item: https://doi.org/10.1007/s00138-006-0038-y
Title: Tool condition classification using Hidden Markov model based on fractal analysis of machined surface textures
Authors: Kassim, A.A. 
Mian, Z.
Mannan, M.A. 
Keywords: End-milling
Fractal analysis
Hidden Markov Model
Surface texture analysis
Tool wear monitoring
Issue Date: Oct-2006
Source: Kassim, A.A., Mian, Z., Mannan, M.A. (2006-10). Tool condition classification using Hidden Markov model based on fractal analysis of machined surface textures. Machine Vision and Applications 17 (5) : 327-336. ScholarBank@NUS Repository. https://doi.org/10.1007/s00138-006-0038-y
Abstract: The texture of a machined surface generated by a cutting tool, with geometrically well-defined cutting edges, carries essential information regarding the extent of tool wear. There is a strong relationship between the degree of wear of the cutting tool and the geometry imparted by the tool on to the workpiece surface. The monitoring of a tool's condition in production environments can easily be accomplished by analyzing the surface texture and how it is altered by a cutting edge experiencing progressive wear and micro-fractures. This paper discusses our work which involves fractal analysis of the texture of surfaces that have been subjected to machining operations. Two characteristics of the texture, high directionality and self-affinity, are dealt with by extracting the fractal features from images of surfaces machined with tools with different levels of tool wear. The Hidden Markov Model is used to classify the various states of tool wear. In this paper, we show that fractal features are closely related to tool condition and HMM-based analysis provides reliable means of tool condition prediction.
Source Title: Machine Vision and Applications
URI: http://scholarbank.nus.edu.sg/handle/10635/57679
ISSN: 09328092
DOI: 10.1007/s00138-006-0038-y
Appears in Collections:Staff Publications

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

SCOPUSTM   
Citations

19
checked on Dec 5, 2017

WEB OF SCIENCETM
Citations

19
checked on Nov 15, 2017

Page view(s)

35
checked on Dec 9, 2017

Google ScholarTM

Check

Altmetric


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