Abstract
Electric discharge machining (EDM) process is one of the earliest and most extensively used unconventional machining processes. It is a noncontact machining process that uses a series of electric discharges to remove material from an electrically conductive workpiece. This article is aimed to do a comprehensive experimental and thermal investigation of the EDM, which can predict the machining characteristic and then optimize the output parameters with a newly integrated neural network‐based methodology for modelling and optimal selection of process variables involved in powder mixed EDM (PMEDM) process. To compare and investigate the effects caused by powder of differently thermo physical properties on the EDM process performance with each other as well as the pure case, a series of experiments were conducted on a specially designed experimental setup developed in the laboratory. Peak current, pulse period, and source voltage are selected as the independent input parameters to evaluate the process performance in terms of material removal rate (MRR) and surface roughness (Ra). In addition, finite element method (FEM) is utilized for thermal analysis on EDM of stainless‐steel 630 (SS630) grade. Further, back propagated neural network (BPNN) with feed forward architecture with analysis of variance (ANOVA) is used to find the best fit and approximate solutions to optimization and search problems. Finally, confirmation test results of experimental MRR are compared using the values of MRR obtained using FEM and ANN. Similarly, the test results of experimental Ra also compared with obtained Ra using ANN.
Highlights
Introduction e process of powder mixed EDM (PMEDM) is known as the appropriate abrasive material mixing with the powder formatted metallic material into the dielectric fluid, where the powder particles are amalgamated in the dielectric fluid that mitigates the insulating strength of particles and enhances the distance of spark gap between the workpiece and tool for uniform distribution of electric discharge in all the directions. is is referred as new advancement to get better innovations and enhancement in the potentialities of Electric discharge machining (EDM) process [1]. is process is more static, i.e., highly stable, and results in enhanced material removal rate (MRR) and smoothened surface [2, 3]
The properties of component are influenced by thermal residual stresses with higher magnitude, which are formed on the surface of workpiece upper layer because of speedy curing of EDM process [5, 6]
Surekha et al [15] have studied aluminium powder added EDM for EN-19 alloy steel machining based on a brass electrode and disclosed that peak current (Ip) and gap voltage (VG) parameters have impacted the values of MRR
Summary
MRR and surface durability improves, and instrument wear rate reduces by adding the dielectric powder. E higher interelectrode gap is resulted for larger particles based on the analyzation of experimental results It leads to the lower deionization between tool and workpiece and greater contamination. E gap increases by the larger powder size, but MRR is reduced, and Ra is increased [8]. E discharge gap increases by adding the conductive fine powder electrically in dielectric that enhances spark frequency and improves debris flushing [7]. Due to the enhancement of number of discharges by powder concentration, which improves MRR [11], surface roughness is decreased while lowering the energy per spark [12]. E surface forces affect the powder particle density that has allowed the particle distribution in the dielectric uniformly. Circulating pump reduced due to the reduction of settling down of powder amount at the tank bottom [14]
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