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

14th International Conference on Science, Engineering and Technology

自然科学;工业技术

Jena, A.R.^1 ; Das, Raja^1

School of Computing Science and Engineering, VIT University, Vellore

632014, India^1

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;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/263/4/042151/pdf
DOI  :  10.1088/1757-899X/263/4/042151

来源: IOP

【 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.