Parametric optimization for electrical discharge diamond grinding (EDDG) system using dual approach.

Abstract

Generally, electrically conductive materials are extremely sturdy and stiff, electric discharge milling (EDM) is a broadly utilized method. The usage of diamond grinding together with EDM in a machine is called the " and Electrical Discharge Diamond Grinding " (EDDG) gadget is an extensively used method for producing strong, long-lasting electrically conductive substances. The Modified Ant Lion Optimization- Artificial Neural Network (MALO-ANN) technique is recommended to boost the performance of EDDG machine. The MALO technique improves the overall performance of ANN by optimizing hidden layers and weights, which are regularly the cause of issues in traditional models. Input factors, along with grit size, pulse-on/off duration, height modern and pulse-off duration, are analysed to see if they affect Material Removal Rate (MRR) along with Surface Roughness (SR). The findings suggest that the MALO-ANN method greatly enhances the parametric optimization of EDDG gadget. The result indicates tremendous ability in improving the efficiency of EDDG systems, because conventional ANN models regularly struggle because of insifficient hidden layers and weights. The best MRR and SR were obtained with an absolute error interval ranging from 1.03% to 4.49%, achieving a convergence rate of 89%, performing enhanced accuracy in EDDG processes.