Parametric Optimization for Quality of Electric Discharge Machined Profile by Using Multi-Shape Electrode

Abstract

The electrical discharge machining (EDM) process is one of the most efficient non-conventional precise material removal processes. It is a smart process used to intricately shape hard metals by creating spark erosion in electroconductive materials. Sparking occurs in the gap between the tool and workpiece. This erosion removes the material from the workpiece by melting and vaporizing the metal in the presence of dielectric fluid. In recent years, EDM has evolved widely on the basis of its electrical and non-electrical parameters. Recent research has sought to investigate the optimal machining parameters for EDM in order to make intricate shapes with greater accuracy and better finishes. Every method employed in the EDM process has intended to enhance the capability of machining performance by adopting better working conditions and developing techniques to machine new materials with more refinement. This new research aims to optimize EDM’s electrical parameters on the basis of multi-shaped electrodes in order to obtain a good surface finish and high dimensional accuracy and to improve the post-machining hardness in order to improve the overall quality of the machined profile. The optimization of electrical parameters, i.e., spark voltage, current, pulse-on time and depth of cut, has been achieved by conducting the experimentation on die steel D2 with a specifically designed multi-shaped copper electrode. An experimental design is generated using a statistical tool, and actual machining is performed to observe the surface roughness, variations on the surface hardness and dimensional stability. A full factorial design of experiment (DOE) approach has been followed (as there are more than two process parameters) to prepare the samples via EDM. Regression analysis and analysis of variance (ANOVA) for the interpretation and optimization of results has been carried out using Minitab as a statistical tool. The validation of experimental findings with statistical ones confirms the significance of each operating parameter on the output parameters. Hence, the most optimized relationships were found and presented in the current study.

Investigation of Hardness and Microanalysis of Sintered Aluminum-Based Supplemented Metal Matrix Machined Composites

“…EDM removes the material by erosion through melting and vaporizing in the presence of dielectric fluid [12][13][14]. Spark erosion occurs between the tool and the workpiece gap [15]. The machinability of MMCs is dependent upon the processes of machining being adopted, along with the proper selection of the process material and operating parameters, irrespective of the natures of the AMMCs [16].…”

Performance of an Adaptive Optimization Paradigm for Optimal Operation of a Mono-Switch Class E Induction Heating Application

“…Meta-heuristics are a type of optimization strategy that consists of a set of sophisticated algorithms that apply heuristics in an intelligent manner to identify reasonably close solutions to intricate optimization problems [22] rapidly and effectively [23,24]. Genetic algorithm [25], ant colony optimization [26], simulated annealing approach [27], black widow optimization approach [28], whale optimization approach [29,30], firefly algorithm [31,32], interior point method [33], evolutionary programming [34], bat search optimization strategy [35], ant-loin optimization approach [36], and Pareto-optimal front [37] are among the approaches growingly employed in a variety of complex practical engineering applications.…”