Efficient optimization of process parameters in 2.5 D end milling using neural network and genetic algorithm
- 17 July 2018
- journal article
- research article
- Published by Springer Nature in International Journal of System Assurance Engineering and Management
- Vol. 9 (5) , 1198-1205
Abstract
No abstract availableKeywords
This publication has 19 references indexed in Scilit:
- Optimization of neural network parameters using Grey–Taguchi methodology for manufacturing process applicationsProceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, 2014
- Thermal modelling of end millingCIRP Annals, 2014
- Determining the effect of process parameters on particle size in mechanical milling using the Taguchi method: Measurement and analysisMeasurement, 2013
- Recent advances in modelling of metal machining processesCIRP Annals, 2013
- Modeling the influence of a process control agent on the properties of metal matrix composite powders using artificial neural networksPowder Technology, 2012
- Design predictive tool and optimization of journal bearing using neural network model and multi-objective genetic algorithmScientia Iranica, 2011
- Heat generation and temperature prediction in metal cutting: A review and implications for high speed machiningInternational Journal of Machine Tools and Manufacture, 2006
- An integrated system for setting the optimal parameters in IC chip-package wire bonding processesThe International Journal of Advanced Manufacturing Technology, 2005
- Thermal modeling of the metal cutting process — Part III: temperature rise distribution due to the combined effects of shear plane heat source and the tool–chip interface frictional heat sourceInternational Journal of Mechanical Sciences, 2001
- The use of neural networks for the prediction of fatigue lives of composite materialsComposites Part A: Applied Science and Manufacturing, 1999