To shape advanced engineering materials, many unconventional machining processes have been developed. Electrical discharge machining is such an unconventional machining process which is very popular nowadays but it is limited by poor material removal efficiency. Electrical arc machining is another unconventional machining process which is quite similar to electrical discharge machining and is now gaining attention from research fraternity due to its high material removal efficiency. In the present research, an innovative unconventional machining process known as vibration-assisted electrical arc machining has been developed. The performance of vibration-assisted electrical arc machining has been evaluated during machining of Al–B4C metal matrix composite by considering peak current, flushing velocity of dielectric and tool vibrations as input control factors. The quality characteristics considered were material removal rate, tool wear rate, relative electrode wear rate and surface roughness. It has been observed that vibration-assisted electrical arc machining results in approximately 3000% more material removal rate as compared to conventional electrical discharge machining during machining of Al–B4C metal matrix composite. The effects of various input control factors on output parameters have also been discussed. Further modelling and optimization of the process parameters has also been done by artificial intelligence approach.

Electrical arc machining is the thermal energy-based unconventional machining process, which utilizes energy of arc to melt and vaporize workpiece material. Electrical arc machining has the capability to machine advanced materials such as metal matrix composites, superalloys, and conductive ceramics effectively. The process is considered to be efficient than most of the other unconventional machining processes in terms of the material removal rate. But it has got limitations because it results in a very poor surface finish. Tool wear rate, recast layer formation, surface and subsurface cracks, and geometrical inaccuracy are other limitations up to a certain extent. In this paper, the comprehensive review of research carried out so for in the area of electrical arc machining has been presented. The paper discusses the detailed experimental and theoretical studies done on electrical arc machining to elucidate the effects of various input control factors on different quality characteristics. The paper also contains modeling and optimization studies done so far in electrical arc machining and finally discusses the future research possibilities in the area.

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Many advanced materials have been developed in the recent past to meet the present day technological demands. Aluminum-boron-carbide (Al-B 4 C) metal matrix composite (MMCs) is such a material slowly gaining popularity among researchers. The advanced machining processes (AMPs) are best manufacturing method to shape these types of innovative materials. The experimental investigations on Al-B 4 C MMC using one such AMP known as electrical discharge machining (EDM) have been carried out in the present work. Important electrical parameters of EDM have been considered as input control factors to evaluate two of the most important responses. Four evolutionary optimization techniques; black hole, differential evolution, shuffled frog leaping algorithm and coordinated aggregation based particle swarm optimization is applied to get best out of the process. Finally all the evolutionary optimization techniques have been compared for their performances.