A Neural-fuzzy Pattern recognition Algorithm based Cutting Tool Condition Monitoring Procedure

Cutting tool condition monitoring is the key technique for realizing automatic and "un-manned" manufacturing processes. This project applies cutting force and acoustic emission transducers to monitor metal cutting processes. A B-spline neurofuzzy networks based tool wear state monitoring m...

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Veröffentlicht in:Third International Conference on Natural Computation (ICNC 2007) Jg. 2; S. 580 - 584
Hauptverfasser: Pan Fu, Hope, A.D., Hongli Gao
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 01.08.2007
Schlagworte:
Acoustic emission
Acoustic measurements
Condition monitoring
Cutting tools
Frequency measurement
Machining
Neural networks
Pattern recognition
Spline
Vibration measurement
ISBN:9780769528755, 0769528759
ISSN:2157-9555
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Zusammenfassung:Cutting tool condition monitoring is the key technique for realizing automatic and "un-manned" manufacturing processes. This project applies cutting force and acoustic emission transducers to monitor metal cutting processes. A B-spline neurofuzzy networks based tool wear state monitoring model has been presented. The model can accurately describe the nonlinear relation between the tool wear value and signal features. Compared with the normal neural networks, such as BP type ANNs, this model has the advantages of fast convergence and having local learning capabilities. Large amounts of monitoring experiments show that the application of B-spline neurofuzzy networks can improve the accuracy and reliability of the tool wear condition monitoring processes.
ISBN:9780769528755
0769528759
ISSN:2157-9555
DOI:10.1109/ICNC.2007.82