ANN modelling for surface roughness in electrical discharge machining: a comparative study. (1st January 2013)

• Record Type: Journal Article
• Authors: Das, Raja; Pradhan, M.K.
• Is Part Of: International journal of service and computing oriented manufacturing. Volume 1:Number 2(2013)
• Page Start: 124
• Page End: 140
• Publication Date: 2013-01-01
• Subjects: back propagation neural network -- electrical discharge machining -- EDM -- radial basis function neural network -- recurrent neural network -- RNN -- surface roughness; Computer integrated manufacturing systems -- Periodicals; Manufacturing industries -- Information technology -- Periodicals; Manufacturing industries -- Computer networks -- Periodicals; Service-oriented architecture (Computer science) -- Periodicals; 670.285
• Languages: English
• ISSNs: 2045-175X

Abstract

This is an attempt to present three different classes of artificial neural network (ANN) models, namely back-propagation network (BPN), radial basis function network (RBFN) and recurrent neural network (RNN) for the prediction of surface roughness (Ra) in electrical discharge machining (EDM). Surface roughness is an important issue in the manufacturing. The input variable chosen was the pulse current (Ip), the pulse duration (Ton) and duty cycle ( τ ). A series of experiments was conducted AISI D2 to acquire the data for training and testing, and it was found that the ANN models could predict Ra with reasonable accuracy, under varying machining conditions. A close correlation between the model prediction and the experimental results was witnessed. Moreover, it was noticed that all three models are offering quite an agreeable prediction. The RBFN model is quite analogous with other models but demonstrated a slightly better performance than others.