Multi‐Objective Optimization During Magnetic Field Assisted ECDM of Alumina‐Zirconium Dioxide Ceramic Composite using Battle Royale Optimization

Abstract

Alumina‐Zirconium dioxide (Al2O3‐ZrO2) Ceramic Composite Material (CCM) is specifically known for its enhanced mechanical and corrosion resistance properties and is widely used as raw material for mechanical parts like, pump components, die inserts, bearings, etc. As a result, industrialists are searching for an efficient method for machining this Al2O3‐ZrO2 material. In this regard, a hybrid unconventional machining process called Electrochemical Discharge Machining (ECDM) is adapted to analyze the machinability of Al2O3‐ZrO2 CCM. Besides, to ensure the efficiency of the ECDM process, a magnetic field is also given to the tool electrode during this study to improve the Material Removal Rate (MRR) of the ECDM machine. The experiment is designed using Response Surface Methodology (RSM) by changing the magnitudes of input controls, namely Electrolytic Concentration (EC), Inter‐electrode Gap (IEG) and Applied Voltage (AV). Moreover, a novel hybrid machine learning optimization strategy called Deep Belief Network based Battle Royal Optimization (DBN‐BRO) algorithm is developed to predict and optimize the ECDM process. Finally, the optimum results are perceived from 55 V AV, 22.727% EC and 40.909 mm IEG input levels. The proposed method shows less than 0.6207 Root Mean Square Error (RMSE) and tools nearly 80 iterations for optimizing the results.