A hybrid Grey-TOPSIS based quantum behaved particle swarm optimization for selection of electrode material to machine Ti6Al4V by electro-discharge machining

Electro-discharge machining is an extensively used process for machining of hard-to-cut materials. The process necessitates a conducting tool electrode; however, selection of right material for preparing the tool continues to remain an engineering challenge. This work makes use of a hybrid intelligent algorithm for selecting the right electrode out of three tool electrodes such as composite tool manufactured by laser sintering process (AlSi10Mg), copper and graphite for efficient electro-discharge machining of Ti6Al4V. The work began by constructing a Taguchi’s L₂₇ experimental design and then collecting the output data such as the material removal rate, tool wear rate, surface roughness, surface crack density, white layer thickness and micro-hardness. A multi-objective optimization is proposed to maximise the work piece material removal rate while minimize the remaining output responses. For this purpose, a hybrid grey-TOPSIS based quantum-behaved particle swarm optimization is chosen. Additional data gathered from scanning electron microscopy and energy dispersive spectroscopy techniques reveal new insights into the post-machining material behaviour such as the use of graphite electrode makes the machined surface far harder due to the dissociated carbon.