Evaluation of Machinability and Recast Layer Analysis of Ferrous Clay Composite through Electric Discharge Machining Process

Bhosale, Shrikrushna B.; Bhowmik, Sumit; Ray, Amitava

doi:10.1007/s13369-021-06337-5

Cited by 3 publications

(1 citation statement)

References 33 publications

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“…Table 8 shows applications of multiple MCDM methods considered for optimizing the EDM processes. From this table, it can be unveiled that the researchers (Das et al, 2018;Pradhan, 2018;Yuvaraj and Suresh, 2019;Kumar and Mondal, 2020; Rai, 2020; Sahu and Mahapatra, 2020;Bhosale et al, 2021;Srinivasan et al, 2021b;Jana et al, 2021;Patnaik et al, 2022) have maximally preferred to employ both GRA and TOPSIS in identifying the optimal combinations of the EDM parameters. During machining of Si 3 N 4 -TiN ceramic material using an EDM process, Srinivasan et al (2021b) applied both GRA and TOPSIS to achieve the same optimal intermix of EDM parameters as Ip = 10 A, Ton = 8 μs, Toff = 4 μs, PF = 20 kg/cm 2 and Vs. = 32 V to achieve the measured response values as MRR = 0.00584 g/min, SR = 1.41 μm, TWR = 0.00118 g/min, PER = 0.0321 mm and PAR = 0.0411 mm.…”

Section: Multiple Mcdm Methodsmentioning

confidence: 99%

A comprehensive review of parametric optimization of electrical discharge machining processes using multi-criteria decision-making techniques

Pendokhare,

Kalita,

Chakraborty

et al. 2024

Front. Mech. Eng.

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Optimization of electrical discharge machining (EDM) processes is a critical issue due to complex material removal mechanism, presence of multiple input parameters and responses (outputs) and interactions among them and varying interest of different stakeholders with respect to relative importance assigned to the considered responses. Multi-criteria decision making (MCDM) techniques have become potent tools in solving parametric optimization problems of the EDM processes. In this paper, more than 130 research articles from SCOPUS database published during 2013–22 are reviewed extracting information with respect to experimental design plans employed, materials machined, dielectrics used, process parameters and responses considered and MCDM tools applied along with their integration with other mathematical techniques. A detailed analysis of those reviewed articles reveals that the past researchers have mostly preferred Taguchi’s L9 orthogonal array as the experimental design plan; EDM oil as the dielectric fluid; medium and high carbon steels as the work materials; peak current and pulse-on time as the input parameters; material removal rate, tool wear rate and surface roughness as the responses; and grey relational analysis as the MCDM tool during conducting and optimizing EDM operations. This review paper would act as a data repository to the future researchers in understanding the stochastic behaviour of EDM processes and providing guidance in setting the tentative operating levels of varying input parameters along with achievable response values. The extracted dataset can be treated as an input to any of the machine learning algorithms for subsequent development of appropriate prediction models. This review also outlines potential future research avenues, emphasizing advancements in EDM technology and the integration of innovative multi-criteria decision-making tools.

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