An Enhanced Adaptive Neural Fuzzy Tool Condition Monitoring for Turning Process
Qun Ren , Sofiane Achiche , Krzysztof Jemielniak , Pascal Bigras
AbstractThe research work carried out in this paper presents a novel intelligent tool condition monitoring solution for the turning process using an enhanced adaptive neural fuzzy inference system based on extended subtractive clustering. The hybrid system is constructed from training a takagi-sugeno-kang fuzzy logic system by integrating machining parameters- feed, cutting force, feed force and cutting tool wear dataset using the extended subtractive clustering. The parametric search for clustering parameters in extended subtractive clustering ensures the high precision of the fuzzy system identification. Further on, neural network automatically acquires the knowledge by the back propagation algorithm and trains the connectionist structure to refine the fuzzy logic rules and find optimal input/output membership functions for the enhanced adaptive neural fuzzy tool condition monitoring. The experimental results show its effectiveness and competitiveness in comparison with five other artificial intelligence methods applied on the same data sets.
|Publication size in sheets||0.5|
|Book||Ren Qun, Achiche Sofiane, Jemielniak Krzysztof, Bigras Pascal (eds.): Fuzzy Systems (FUZZ-IEEE), 2016 IEEE International Conference on Fuzzy Systems, 2016, ISBN 978-1-5090-0627-4, [978-1-5090-0626-7]|
|Keywords in English||tool condition monitoring, fuzzy logic, subtractive clustering, hybrid intelligent system|
|Score|| = 15.0, 31-01-2020, BookChapterMatConfByConferenceseries|
= 15.0, 31-01-2020, BookChapterMatConfByConferenceseries
|Publication indicators||= 2; = 1|
|Citation count*||3 (2020-09-02)|
* presented citation count is obtained through Internet information analysis and it is close to the number calculated by the Publish or Perish system.