Experimental Analysis and Hybrid-Optimization of Micro-ECDM Process Parameters to Enhance Micro-Machining Performances of Silica by Gaussian-Quantum-PSO

Nowadays, hybridization of different algorithms for the optimization of non-conventional machining processes tries to accomplish better results. The paper consists of experimental evolutionary-particle Swarm Optimization (PSO), Quantum-PSO and Gaussian Quantum Particle Swarm Optimization (G-QPSO)-based ANN modeling and comparative investigation on performances such as material removal rate (MRR), machining depth (MD), roughness of surface and overcut (OC) for machining of silica by ECDM process using mixed electrolyte. The paper also shows the co-efficient of NN models for different machining criteria and G-QPSO and also the comparative study of MD, roughness (SR), overcut (OC) as well as MRR using different algorithms and convergence test for fitness of experimental results also propounded to achieve cross-validation of models and multi-response optimal results for micro-machining of Silica by ECDM using PSO, QPSO and GQPSO. It is found that Gaussian Quantum Particle Swarm Optimization (G-QPSO)-ANN is more efficient for ECDM and achieves optimal results at 55-volt, pulse on time 52.3 s, inter-electrode gap (IEG) 30 mm, duty ratio 0.475 and electrolytic concentration 30 (wt.%).