TSK fuzzy modeling for tool wear condition in turning processes: An experimental study |
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| Authors: | Qun Ren Marek Balazinski |
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| Affiliation: | a Mechanical Engineering Department, École Polytechnique de Montréal, C.P. 6079, succ. Centre-Ville, Montréal, Québec, Canada H3C 3A7
b Faculty of Production Engineering, Warsaw University of Technology, Narbutta 86, 02-524 Warsaw, Poland |
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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. |
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| Keywords: | TSK fuzzy modeling Tool wear condition Subtractive clustering |
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