A comparison of neural network architectures for the prediction of MRR in EDM

[摘要] The aim of the research work is to predict the material removal rate of a work-piece in electrical discharge machining (EDM). Here, an effort has been made to predict the material removal rate through back-propagation neural network (BPN) and radial basis function neural network (RBFN) for a work-piece of AISI D2 steel. The input parameters for the architecture are discharge-current (Ip), pulse-duration (Ton), and duty-cycle (τ) taken for consideration to obtained the output for material removal rate of the work-piece. In the architecture, it has been observed that radial basis function neural network is comparatively faster than back-propagation neural network but logically back-propagation neural network results more real value. Therefore BPN may consider as a better process in this architecture for consistent prediction to save time and money for conducting experiments.

[发布机构] School of Computing Science and Engineering, VIT University, Vellore; 632014, India^1

[效力级别] 工业技术

[关键词] Back propagation neural networks;Discharge currents;Electrical discharge machining;Input parameter;Material removal rate;Pulse durations;Radial basis function neural networks;Real values