Multi-objective Optimization of Electric Discharge Machining of Al-SiCp Composite by Taguchi-PCA, Firefly and Cuckoo Search Algorithm

Abstract

Electric Discharge Machining (EDM) processes are extensively utilized in industries for cutting hard to machine materials and geometries that are complex which are not possible with conventional machining. In this research study, efforts are made to identify optimal process parameters of EDM during machining of AA6061-10%SiCp composite material. The novelty of the present work is copper electrode with different geometries such as circular, triangular and square are considered for machining along with input variables discharge current density (A), pulse on and off timing (Ton and Toff) which are varied through three values. The L27 (313) orthogonal array of Taguchi is used for experimental layout and responses measured are recast layer thickness (RCT), electrode tool wear rate (TWR) and material removal rate (MRR). Taguchi’s approach of signal-to-noise (S/N) ratio is integrated with principal component analysis (PCA) for multi-criteria optimization. Also, nature inspired cuckoo search (CS) and firefly algorithm (FA) is employed for identifying the optimal conditions and to predict the outputs for maximum MRR and minimum TWR and RCT. From S/N+PCA analysis the optimal conditions identified are: Circle (12A, 65µs, 2µs), Triangle (12A, 95µs, 6µs) and Square (12A, 65µs, 8µs) was obtained. In all the conditions, discharge current influences higher than the other inputs. Metallurgical examination conducted through micrographs on the machined surface clearly supports the predicted result.