Hybrid Learning for Tool Wear Monitoring |
| |
Authors: | X Li S Dong PK Venuvinod |
| |
Affiliation: | (1) Department of Manufacturing Engineering, City University of Hong Kong, Hong Kong, CN;(2) Department of Mechanical Engineering, Harbin Institute of Technology, Harbin, China, CN |
| |
Abstract: | 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. |
| |
Keywords: | : Fuzzy neural network Vibration Wear |
本文献已被 SpringerLink 等数据库收录! |
|