Predicting the Process Performance of Electrical Discharged Machined D3 Steel using Artificial Neural Network
Résumé
This paper investigates the measures of Electrical Discharge Machining (EDM) process of D3
steel in which the material removal rate (MRR) and surface roughness (Ra) are given the prime
importance. An Artificial Neural Network (ANN) model with feed forward network is proposed for
predicting the MRR and Ra as outputs depending on the process parameters such as voltage (Vs),
current (Cs), Pulse on time (Ton) and Pulse frequency (fp). The multi-layer neural network model has
been developed with 125 experimental data sets, 4 different process parameters were defined as input
parameters and MRR and Ra output values were obtained. The accuracy of the model was assessed
using five known statistical metrics like mean square error (MSE), mean absolute error (MAE), sum
of squares error (SSE), the coefficient of determination (R
2
), and correlation coefficient (R). For the
proposed ANN model, was found with the performance of R values of 0.992, 0.944, 0.949 for training,
validation and testing data sets in case of Ra. With respect to MRR the correlation coefficient R
values of 0.997, 0.995 and 0.981 for training, validation and testing data sets are obtained. The
results indicate that the proposed artificial neural network model can accurately predict both MRR
and Ra, depending on the process parameters.
Téléchargements
Références
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