Prediction of material removal rate for Ti-5Al-2.5Sn in EDM using multi-layered perceptron neural network technique

Extensive work has been reported on modelling and optimization for different materials such as aluminum, iron, nickel-base alloy, C40 steel, mild steel, Ti6Al4V, HE15, 15CDV6, M-250, AISI D2 steel material etc. However model of process parameters for EDM technique on Ti-5Al-2.5Sn material has not been developed yet. Thus in the present work, an effort is made to develop model of material removal rate (MRR) for Ti-5Al-2.5Sn material using Artificial Neural Network (ANN). The electrical discharge machining is carried out on this new material employing positive polarity of copper electrode. Investigation has been focused using five levels of each parameter as peak current, pulse on time, pulse off time and servo voltage to correlate these parameters with EDM characteristics as MRR. The developed model is validated with some of the experimental data, which was not utilized for developing the model. It is observed that the developed model is within the limits of the agreeable error when experimental and network model results are compared. It is further observed that the error when the model is developed by artificial neural network has come down to 2.28%. Sensitivity analysis is also done to find the relative influence of factors on the performance measures. It is observed that peak current effectively influences the performance measures.

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