Name
Abstract | ||
|---|---|---|
An important element of the automatic machining process control function is the online monitoring of cutting tool wear and fracture mechanisms. The paper presents an intelligent tool condition monitoring system. Cutting tool condition monitoring is a very complex process hence sensor fusion techniques and artificial intelligence based signal processing algorithms are employed. The tool condition monitoring system is equipped with four kinds of sensors, signal transformation and collection apparatus and a microcomputer. Multi-sensor signals reflect the tool condition comprehensively. Redundant signal features are removed by using a fuzzy clustering feature filter. A unique neurofuzzy classification network has been developed to carry out the fusion of multi-sensor information and tool wear classification. It combines the transparent representation of fuzzy systems with the learning ability of neural networks hence the algorithm has strong modelling and noise suppression ability. Successful tool wear classification can be realized under a range of machining conditions. A large number of monitoring experiments suggests that the proposed intelligent data processing algorithm is practical and reliable |
Year | DOI | Venue |
|---|---|---|
1998 | 10.1109/ICSMC.1998.727510 | Systems, Man, and Cybernetics, 1998. 1998 IEEE International Conference |
Keywords | Field | DocType |
condition monitoring,cutting,feature extraction,filtering theory,fuzzy systems,learning (artificial intelligence),machine tools,machining,neural nets,pattern clustering,process control,process monitoring,automatic machining process control function,collection apparatus,cutting tool wear,fracture mechanisms,fuzzy clustering feature filter,intelligent tool condition monitoring system,learning ability,neurofuzzy classification network,online monitoring,signal transformation,tool wear classification | Fuzzy clustering,Computer science,Sensor fusion,Tool wear,Artificial intelligence,Condition monitoring,Fuzzy control system,Artificial neural network,Machine learning,Machine tool,Cutting tool | Conference |
Volume | ISSN | ISBN |
5 | 1062-922X | 0-7803-4778-1 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| F. Pan | 1 | 1 | 0.97 |
| A. Hope | 2 | 0 | 0.34 |
| G. King | 3 | 0 | 0.68 |
| Pan Fu | 4 | 0 | 0.34 |
| Hope, A.D. | 5 | 0 | 0.34 |
| King, G.A. | 6 | 0 | 0.34 |