Name
Abstract | ||
|---|---|---|
In order to predict tool wear accurately and reliably under different cutting conditions, a new monitoring methodology for turning operations is proposed. Firstly, the correlation coefficients approaches were used to indicate the dependencies between the different sensed information features and tool wear amount, and the most appropriate features were selected. Secondly, B-spline neural networks were introduced to model the non-linear relationship between extracted features and tool wear amount, and multi-sensor information were fused by an integrated neural network. Lastly, the final result of tool wear was given through fuzzy modeling. Experimental results have proved that the monitoring system based on the methodology is reliable and practical. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1007/11427469_140 | ISNN (3) |
Keywords | Field | DocType |
different cutting condition,integrated neural network,tool wear amount,information feature,multi-sensor information,monitoring system,tool wear,intelligent tool condition monitoring,new monitoring methodology,b-spline neural network,appropriate feature,neural network | B-spline,Data mining,Computer science,Sensor array,Tool wear,Artificial intelligence,Artificial neural network,Correlation coefficient,Simulation,Fuzzy logic,Sensor fusion,Machine learning,Cutting tool | Conference |
Volume | ISSN | ISBN |
3498 | 0302-9743 | 3-540-25914-7 |
Citations | PageRank | References |
1 | 0.44 | 1 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hongli Gao | 1 | 4 | 9.32 |
| Mingheng Xu | 2 | 1 | 1.79 |