Optimum surface roughness in end milling Inconel 718 by coupling neural network model and genetic algorithm
- 19 January 2005
- journal article
- research article
- Published by Springer Science and Business Media LLC in The International Journal of Advanced Manufacturing Technology
- Vol. 27 (3-4), 234-241
- https://doi.org/10.1007/s00170-004-2175-7
Abstract
No abstract availableKeywords
This publication has 12 references indexed in Scilit:
- Optimization of cutting conditions during cutting by using neural networksRobotics and Computer-Integrated Manufacturing, 2003
- Optimization of cutting process by GA approachRobotics and Computer-Integrated Manufacturing, 2003
- A study of tool life in hot machining using artificial neural networks and regression analysis methodJournal of the American Academy of Dermatology, 2002
- A genetic algorithmic approach for optimization of surface roughness prediction modelInternational Journal of Machine Tools and Manufacture, 2002
- Prediction of surface roughness in CNC face milling using neural networks and Taguchi's design of experimentsRobotics and Computer-Integrated Manufacturing, 2002
- The predictive model for machinability of 304 stainless steelJournal of the American Academy of Dermatology, 2001
- Cutting performance and wear characteristics of PVD coated and uncoated carbide tools in face milling Inconel 718 aerospace alloyJournal of the American Academy of Dermatology, 2001
- An in-process surface recognition system based on neural networks in end milling cutting operationsInternational Journal of Machine Tools and Manufacture, 1999
- End-Milling Machinability of Inconel 718Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 1996
- Surface abuse when machining cast iron (G-17) and nickel-base superalloy (Inconel 718) with ceramic toolsJournal of the American Academy of Dermatology, 1995