Prediction of tool wear using regression and ANN models in end-milling operation
Authors:
- 16 February 2007
- journal article
- research article
- Published by Springer Nature in The International Journal of Advanced Manufacturing Technology
- Vol. 37 (1-2) , 29-41
Abstract
No abstract availableKeywords
This publication has 12 references indexed in Scilit:
- Tool breakage detection using support vector machine learning in a milling processInternational Journal of Machine Tools and Manufacture, 2005
- High-speed milling of titanium alloys using binderless CBN toolsInternational Journal of Machine Tools and Manufacture, 2005
- Novel methods for rapid assessment of tool performance in millingInternational Journal of Machine Tools and Manufacture, 2004
- A cutting power model for tool wear monitoring in millingInternational Journal of Machine Tools and Manufacture, 2004
- Tool life determination based on the measurement of wear and tool force ratio variationInternational Journal of Machine Tools and Manufacture, 2004
- Influence of the number of inserts for tool life evaluation in face milling of steelsInternational Journal of Machine Tools and Manufacture, 2004
- Role of temperature and surface finish in predicting tool wear using neural network and design of experimentsInternational Journal of Machine Tools and Manufacture, 2003
- A novel approach to quantifying tool wear and tool life measurements for optimal tool managementInternational Journal of Machine Tools and Manufacture, 2003
- Tool wear estimation using resource allocation networkInternational Journal of Machine Tools and Manufacture, 2001
- Multi-sensor integration for on-line tool wear estimation through artificial neural networks and fuzzy neural networkEngineering Applications of Artificial Intelligence, 2000