ANN and RSM based predictive model development and EDM process parameters optimization on AISI 304 stainless steel

The current research was conducted in an Electrical Discharge Machining (EDM) on AISI 304 stainless steel. The experimental study was carried out using full factorial design methods, with three input parameters chosen for this experiment: pulse time (T), discharge current (C), and gap voltage (V), and the responses being material removal rate (MRR) and surface roughness (SR). Artificial neural network (ANN) and response surface methodology (RSM) models are the two different types of predicting models for MRR and SR that have been established. It has been found that both models have good accuracy, albeit ANN predicted model is more accurate. The maximum percentage of error in the MRR prediction was 6.16 for the ANN model and 10.45 for the RSM model, respectively Also, the maximum percentage of error in the SR prediction was 5.32 and 16.18 for the ANN and RSM models, respectively. The purpose of this paper is to increase the MRR as well as decrease the SR and equivalent optimum machining parameters which is a very important aspect of the present research. For this purpose, a multi objective function for MRR and SR has been developed based on the RSM method and finally, it is optimised. It has been found that 9 A, 200 s, and 45 V, respectively, are the optimal machining parameters for obtaining discharge current (C), pulse on time (T), and gap voltage (V). Future attempts may use more advanced artificial intelligence methods like ANN-GA, ANN-PSO, etc.