Micro-ECDM performance analysis and hybrid-optimization during micro-channel generation on silica using HGAPSO-ANN

Abstract

Hybridizing non-traditional machining (NTM) processes, such as micro-ECDM, is a great challenge in measurement and microfabrication. To find out the solution to this problem, in the present research, the overall model is demonstrated in three steps, during the first stage, ANN is used to construct the linear model of width of cut (WOC), metal removal rate (MRR), and surface roughness (SR) from experimental data consisting of process parameters, that is, voltage (V) pulse frequency (PF), electrolyte concentration (EC), and duty ratio (DR). In the second phase, to get the best-fitted model, we applied both Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and a hybrid of these two algorithms for cross-validation and validation on the train and test datasets. Based upon root mean square error, accuracy, and computational time, the proposed algorithm is more efficient than PSO and GA. Furthermore, in the final phase, the ANN model was optimized using hybrid GAPSO, which helped to determine the optimal process parameters responsible for maximum MRR, minimum WOC, and SR formation. The result shows that maximum MRR, minimum WOC, and SR were formed for the optimized value of voltage 45 volts, electrolytic concentration 30 wt%, DR 0.45, and PF 75 Hz. Moreover, hybrid GAPSO-ANN shows better convergence, accuracy, and computational time (seconds) for micro-machining characteristics analysis.