Optimization of electrochemical machining process based on artificial neural network technique

Abstract EN

Electrochemical machining (ECM) is one of nonconventional machining process used to operation the most harsh materials that difficult to operate in conventional machining.

This search has been used to study impact of different parameters on material removal rate (MRR) and to improve the MRR.

The workpiece material in this search is stainless steel 316L, tool material from copper and NaCl (10, 25, 50) g/l was used as electrolyte.

Through the experiments noted that the MRR increasing at increased current from (50 to 200) the increasing in MRR reach to 57.60%, also MRR increasing at increasing electrolyte concentration from (10 to 50) g/l increasing in MRR (reach) to 75.17 % and MRR decreasing at increased gap size from (0.5 to 1.5) mm the decreasing in MRR reach to 39.2 %.

To predict the values of MRR and to get optimization, artificial neural network was used to get minimum mean squared error (MSE) and minimum average percentage error.

In network, separated some values to training set and the remaining for testing set and it was noted that the predicated and experimental values are very close to each other.