| Article ID | Journal | Published Year | Pages | File Type |
|---|---|---|---|---|
| 381161 | Engineering Applications of Artificial Intelligence | 2011 | 6 Pages |
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.
Keywords
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Qun Ren, Marek Balazinski, Luc Baron, Krzysztof Jemielniak,