Modeling and optimization of VEAM of 10% B4C/Al metal matrix composite using ANN-SCA approach

Shaping advanced materials such as superalloys and metal matrix composites is still a herculean task for the engineering fraternity. People are developing innovative manufacturing processes (MPs) to shape kind of materials. Electrical arc machining (EAM) one subtractive MP which under development stage. The present paper discusses newly developed process named vibration assisted EAM (VEAM). on composite has been performed using VEAM. Two performance indicators that material removal rate (MRR) tool wear explored during Experimental findings indicate VEAM results in significantly enhanced MRR compared conventional EDM. At last single objective optimization done by hybrid artificial neural network & four algorithms self-adaptive differential evolution, shuffled frog leaping algorithm, coordinated aggregation based particle swarm sine cosine algorithm (SCA). It observed SCA demonstrate better its peers.