Vibration-assisted electrical arc machining of 10% B 4 C/Al metal matrix composite

Abstract

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.

Keywords

Artificial neural network electrical arc machining genetic algorithm optimization vibration

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