Multiobjective optimization of electric discharge machining of an Al–SiCp composite using the Taguchi–PCA method as well as the firefly and cuckoo search algorithms

Abstract

Electric discharge machining (EDM) processes are extensively used in industries to machine materials and geometries that are complex and are not machinable by conventional methods. In our study, we focused on identifying the optimal process parameters for EDM during the machining of an aluminum alloy 6061 (matrix) –10% silicon carbide (particle) composite. The novel aspect of this work is the use of a copper electrode with different geometries (circular, triangular, square) for machining, together with input variables such as discharge current density (A) as well as pulse on- and off-timing (Ton and Toff). We used the L27 (313) Taguchi orthogonal array for our experimental layout and the responses we measured were recast layer thickness (RCT), electrode tool wear rate (TWR), and material removal rate (MRR). Taguchi’s approach of signal-to-noise (S:N) ratio was integrated with principal component analysis (PCA) for multicriterion optimization. Also, the nature inspired cuckoo search (CS) and firefly (FA) algorithms were used to identify the optimal conditions and to predict the outputs for maximum MRR and minimum TWR and RCT. From S:N + PC analyses, the optimal conditions we identified were: circle (12 A, 65 μs, 2 μs); triangle (12 A, 95 μs, 6 μs); and square (12 A, 65 μs, 8 μs). Under all of the conditions, the influence of discharge current was the most significant. Metallurgical examination conducted through micrographs of the machined surface clearly supported the predicted results. The optimized conditions we identified are appropriate for use in the automobile and aerospace industries to obtain holes of specific geometries with good surface integrity and reduced wear of tools.