TSK fuzzy modeling for tool wear condition in turning processes: An experimental study

Authors:
Qun Ren;Marek Balazinski;Luc Baron;Krzysztof Jemielniak
Affiliations:
Mechanical Engineering Department, ícole Polytechnique de Montréal, C.P. 6079, succ. Centre-Ville, Montréal, Québec, Canada H3C 3A7;Mechanical Engineering Department, ícole Polytechnique de Montréal, C.P. 6079, succ. Centre-Ville, Montréal, Québec, Canada H3C 3A7;Mechanical Engineering Department, ícole Polytechnique de Montréal, C.P. 6079, succ. Centre-Ville, Montréal, Québec, Canada H3C 3A7;Faculty of Production Engineering, Warsaw University of Technology, Narbutta 86, 02-524 Warsaw, Poland
Venue:
Engineering Applications of Artificial Intelligence
Year:
2011

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Abstract

This paper presents an experimental study for turning process in machining by using Takagi-Sugeno-Kang (TSK) fuzzy modeling to accomplish the integration of multi-sensor information and tool wear information. It generates fuzzy rules directly from the input-output data acquired from sensors, and provides high accuracy and high reliability of the tool wear prediction over a wide range of cutting conditions. The experimental results show its effectiveness and satisfactory comparisons relative to other artificial intelligence methods.