Prediction of surface roughness in Electrical Discharge Machining of SKD 11 TOOL steel using Recurrent Elman Networks

Elman Networks is a one of the dynamic recurrent neural networks. In this research it is used for the prediction of surface roughness in Electrical Discharge Machining (EDM). Training of the models was performed with data from series of EDM experiments on SKD 11 (AISI D2) Tool steel; in the development of predictive models, machining parameters of discharge current, pulse duration and duty cycle were considered as model variables with a constant voltage 50 volt. For this reason, extensive experiments were carried out in order to collect surface roughness dataset. The developed model is validated with a new set of experimental data, and predictive behavior of models is analyzed. The reported results indicate that the proposed model can satisfactorily predict the surface roughness in EDM. And can be considered as valuable tools for the process planning for EDMachining. © 2013 Jordan Journal of Mechanical and Industrial Engineering. All rights reserved