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

塑造超级合金和金属基复合材料等先进材料仍然是工程界的一项艰巨任务。人们正在开发创新的制造工艺 (MP) 来塑造这种先进材料。电弧加工 (EAM) 就是一种处于开发阶段的减材 MP。本文讨论了一种新开发的先进加工工艺，称为振动辅助 EAM (VEAM)。金属基复合材料的加工已使用 VEAM 进行。在使用 VEAM 进行加工过程中，已经探索了两个性能指标，即材料去除率 (MRR) 和刀具磨损率。实验结果表明，与传统 EDM 相比，VEAM 显着提高了 MRR。最后利用混合人工神经网络和自适应差分进化、混洗蛙跳算法、基于协调聚合的粒子群优化和正弦余弦算法（SCA）四种先进的优化算法进行单目标优化。据观察，SCA 与其同行相比表现出更好的表现。