caa a22 4500 475798961 CHVBK 20180406123732.0 cr unu---uuuuu 170329e20000401xx s 000 0 eng 10.1007/s001700050161 doi (NATIONALLICENCE)springer-10.1007/s001700050161 Hybrid Learning for Tool Wear Monitoring [Elektronische Daten] [X. Li, S. Dong, P.K. Venuvinod] In automated manufacturing systems such as flexible manufacturing systems (FMSs), one of the most important issues is the detection of tool wear during the cutting process. This paper presents a hybrid learning method to map the relationship between the features of cutting vibration and the tool wear condition. The experimental results show that it can be used effectively to monitor the tool wear in drilling. First, a neural network model with fuzzy logic (FNN), responding to learning algorithms, is presented. It has many advantageous features, compared to a backpropagation neural network, such as less computation. Secondly, the experimental results show that the frequency distribution of vibration changes as the tool wears, so the r.m.s. of the different frequency bands measured indicates the tool wear condition. Finally, FNN is used to describe the relationship between the characteristics of vibration and the tool wear condition. The experimental results demonstrate the feasibility of using vibration signals to monitor the drill wear condition.: Springer-Verlag London Limited, 2000 Keywords: Fuzzy neural network nationallicence Vibration nationallicence Wear nationallicence Li X. Department of Mechanical Engineering, Harbin Institute of Technology, Harbin, China, CN aut Dong S. Department of Mechanical Engineering, Harbin Institute of Technology, Harbin, China, CN aut Venuvinod P.K. Department of Manufacturing Engineering, City University of Hong Kong, Hong Kong, CN aut https://doi.org/10.1007/s001700050161 text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/s001700050161 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Li X. Department of Mechanical Engineering, Harbin Institute of Technology, Harbin, China, CN aut NATIONALLICENCE 700 1- Dong S. Department of Mechanical Engineering, Harbin Institute of Technology, Harbin, China, CN aut NATIONALLICENCE 700 1- Venuvinod P.K. Department of Manufacturing Engineering, City University of Hong Kong, Hong Kong, CN aut Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer