caa a22 4500 467936951 CHVBK 20180406153000.0 cr unu---uuuuu 170328e20061001xx s 000 0 eng 10.1007/s00138-006-0038-y doi (NATIONALLICENCE)springer-10.1007/s00138-006-0038-y Tool condition classification using Hidden Markov Model based on fractal analysis of machined surface textures [Elektronische Daten] [A. Kassim, Zhu Mian, M. Mannan] 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. Springer-Verlag, 2006 Tool wear monitoring nationallicence End-milling nationallicence Surface texture analysis nationallicence Fractal analysis nationallicence Hidden Markov Model nationallicence Kassim A. Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore aut Mian Zhu Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore aut Mannan M. Department of Mechanical Engineering, National University of Singapore, Singapore, Singapore aut Machine Vision and Applications Springer-Verlag 17/5(2006-10-01), 327-336 0932-8092 17:5<327 2006 17 138 https://doi.org/10.1007/s00138-006-0038-y text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/s00138-006-0038-y text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Kassim A. Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore aut NATIONALLICENCE 700 1- Mian Zhu Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore aut NATIONALLICENCE 700 1- Mannan M. Department of Mechanical Engineering, National University of Singapore, Singapore, Singapore aut NATIONALLICENCE 773 0- Machine Vision and Applications Springer-Verlag 17/5(2006-10-01), 327-336 0932-8092 17:5<327 2006 17 138 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer