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 manufacturing process. The process requires a tool electrode but the selection of the right material for preparing the tool continues to remain an engineering puzzle. This work makes use of a hybrid intelligent algorithm for selecting the right electrode out of three tool electrodes such as 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 simultaneous multi-objective optimization was performed to maximise the workpiece material removal rate while minimizing the remaining variables. For this purpose, a hybrid grey-TOPSIS based quantum-behaved particle swarm optimization was chosen and additional data gathered from scanning electron microscopy and energy dispersive spectroscopy techniques revealed 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.