Experimental investigation on electrochemical discharge machining of ceramic composite

Abstract

The Zirconia-based ceramic composites are widely used in Automobile and Aerospace sectors because of their High-end mechanical properties such as improved hardness, durability, and strength characteristics. Many researches show that the machining of these composites is much harder than the traditional machining processes. Even though the machining was done but ended up with an unfinished surface finish, overcut, a high rate of tool wear, less material removal rate, huge time consumption, and damages. These difficulties become a great challenge to industrialists and researchers. As a part of this issue, more research was carried out to find the best machining process for the machining of these Zirconia-silicon-based composites and revealed that the perfect machining of such composites is still an incomplete task. However, some researches show that unconventional machining processes are giving a considerable improvement in the machining of Zirconia composites. Hence, this study investigates on the suitability of Electro-Chemical Discharge Machining process for the machining of Zirconia and Zirconia Silicon Nitride Composite. Subsequently, the major influencing parameters are studied. The results help to develop the prediction models using advanced machine learning algorithms. The obtained actual outcome from RSM is validated with advanced algorithms Artificial Neural network (ANN), hybrid Deep Neural Network-based Spotted Hyena optimization (DNN-SHO) using MATLAB platform version 2020 a. In which, Box Behnken Design of RSM is performed in the Design Expert Software newline