Neural Network Prediction and Analysis of Material Removal Process during Wire Cut Electrical Discharge Machining
Journal Title: REST Journal on Emerging trends in Modelling and Manufacturing - Year 2017, Vol 3, Issue 1
Abstract
This article illustrates the response surface methodology and artificial neural network primarily based mathematical modelling for average machining parameters of powdered activated carbon and 7075 Al metal matrix composite throughout wire spark machining. Four WEDM parameters particularly Discharge current, pulse-on time, pulse-off time and servo speed were chosen as machining technique parameters. A back propagation neural network was developed to envision the maneuver model. The performance of the developed ANN models were connected with the RSM mathematical models of average machining parameters like material removal rate and surface roughness. The comparison clearly indicates that the ANN models offer much correct prediction compared to the RSM models. Combined impact of input methodology parameters on machining parameters shows that servo speed could be a heap of necessary parameter on input machining parameter than pulse-off time and discharge current.
Authors and Affiliations
Ramanan G
Keywords
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- EP ID EP241104
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