Experimental Investigation and Estimation of Surface Roughness using ANN, GMDH & MRA models in High Speed Micro End Milling of Titanium Alloy (Grade-5)
- 1 January 2017
- journal article
- Published by Elsevier BV in Materials Today
- Vol. 4 (2), 1019-1028
- https://doi.org/10.1016/j.matpr.2017.01.115
Abstract
No abstract availableThis publication has 18 references indexed in Scilit:
- Titanium alloys: modelling of microstructure, properties and applicationsPublished by Elsevier BV ,2009
- Biomedical applications of titanium and its alloysJOM, 2008
- Prediction of minimum surface roughness in end milling mold parts using neural network and genetic algorithmMaterials & Design (1980-2015), 2006
- Predicting surface roughness in machining: a reviewInternational Journal of Machine Tools and Manufacture, 2003
- An In-Process Neural Network-Based Surface Roughness Prediction (INN-SRP) System Using a Dynamometer in End Milling OperationsThe International Journal of Advanced Manufacturing Technology, 2003
- Prediction of surface roughness in CNC face milling using neural networks and Taguchi's design of experimentsRobotics and Computer-Integrated Manufacturing, 2002
- Tool condition monitoring using artificial intelligence methodsEngineering Applications of Artificial Intelligence, 2002
- Prediction of flank wear by using back propagation neural network modeling when cutting hardened H-13 steel with chamfered and honed CBN toolsInternational Journal of Machine Tools and Manufacture, 2002
- Modeling micro-end-milling operations. Part III: influence of tool wearInternational Journal of Machine Tools and Manufacture, 2000
- Modeling micro-end-milling operations. Part II: tool run-outInternational Journal of Machine Tools and Manufacture, 2000