Teaching-Learning-Based Parametric Optimization of an Electrical Discharge Machining Process

Abstract

Due to several unique features, electrical discharge machining (EDM) has proved itself as one of the efficient non-traditional machining processes for generating intricate shape geometries on various advanced engineering materials in order to fulfill the requirement of the present day manufacturing industries. In this paper, the machining capability of an EDM process is studied during standard hole making operation on pearlitic SG iron 450/12 grade material, while considering gap voltage, peak current, cycle time and tool rotation as input parameters. On the other hand, material removal rate, surface roughness, tool wear rate, overcut and circularity error are treated as responses. Based on single- and multi-objective optimization models, this process is optimized using the teaching-learning-based optimization (TLBO) algorithm, and its performance is contrasted against firefly algorithm, differential evolution algorithm and cuckoo search algorithm. It is revealed that the TLBO algorithm supersedes the others with respect to accuracy and consistency of the derived optimal solutions, and computational efforts.

Enhancing Corrosion and Wear Resistance of Ti6Al4V Alloy Using CNTs Mixed Electro-Discharge Process

“…As a progressive technique in modifying substrates, EDT widely used to improve the surface characteristics, chemical, mechanical, corrosion, and tribological behavior of the base material [8]. In this process, the train of sparks within the electrodes resulted in the modification of substrate surface due to the material transfer mechanism [9][10][11]. During the EDT process, the particles of desired coating powder, added in the dielectric medium suspended around the discharge column, accelerated and gained sufficient velocity to penetrate to the molten pool before solidification by means of electrophoresis and negative pressure.…”

Experimental Investigation and Optimization of Electric Discharge Machining Process Parameters Using Grey-Fuzzy-Based Hybrid Techniques

“…The available discharge energy increases as the T on increases, resulting in greater melting and vaporization of the workpiece. The impulsive force in the spark gap rises as well, resulting in a larger MRR [ 25 , 49 ]. Figure 4 d,f show a similar pattern of behavior.…”

“…When the temperature difference between the machining zones is raised, more material melts [ 51 ]. Furthermore, when the distance between the workpiece and the tool widens, the debris gathered during machining has more room to be flushed out of the machining zone, causing more material to be errored and ultimately leading to MRR [ 25 ], [ 51 ]. Figure 4 d,e shows a similar pattern of behavior.…”

“…To ensure sustainability while optimizing the conflicting responses, multi-objective optimization was used as well. Kumar et al [ 25 ] suggested teaching learning-based multi-objective optimization (TLBO) for optimizing MRR, SR, TWR, ROC, and circularity error during an EDM operation, with the results compared to existing metaheuristic algorithms. Pradhan [ 26 ] examined the machining behavior of AISI D2 tool steel using commercial grade EDM oil and optimized the MRR, TWR, and radial overcut using an RSM-based GRA method.…”

Sustainable Eco-friendly Wire-cut Electrical Discharge Machining: Gas Emission Analysis

“…While increasing the mixing water flow rate, the MRR is increased due to effective cooling and an increase in dielectric strength in the cutting zone (Boopathi and Sivakumar 2012). The spark current and pulse width are significantly contributing to improving the MRR due to high thermal energy, high melting, and vaporization of metal in the cutting zone (Abbas et al 2012;Kumar et al 2020).…”