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Machining of present days superior engineering materials is still a challenging task before industries, as conventional machining processes have proven to be inefficient to process these materials. In order to meet challenges, numerous processes with innovative mechanism of material removal have come into existence. Electrical discharge machining (EDM) is one among many such processes that has got wide attention. However, EDM results in very poor material removal and requires very high specific energy as compared to conventional machining processes. Electrical arc machining (EAM) is a process, which is very similar to EDM but results in very high material removal rate (MRR). In the present research, an innovative process known as vibration-assisted electrical arc machining has been developed. The process has been used to machine aluminium-boron carbide metal matrix composite. Peak current, frequency of vibration and dielectric flushing velocity has been considered as input control factors to evaluate MRR and surface roughness (SR). An artificial intelligence (AI)-based approach has been applied for single objective optimisation for MRR and SR. The AI-based approach results in an improvement of approximately 230 and 50% in MRR and SR, respectively.

Experimental modelling and optimisation of electrical arc machining of Al-B 4 C metal matrix composite