A comprehensive review of parametric optimization of electrical discharge machining processes using multi-criteria decision-making techniques
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27230%2F24%3A10254942" target="_blank" >RIV/61989100:27230/24:10254942 - isvavai.cz</a>
Result on the web
<a href="https://www.webofscience.com/wos/woscc/full-record/WOS:001229291100001" target="_blank" >https://www.webofscience.com/wos/woscc/full-record/WOS:001229291100001</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.3389/fmech.2024.1404116" target="_blank" >10.3389/fmech.2024.1404116</a>
Alternative languages
Result language
angličtina
Original language name
A comprehensive review of parametric optimization of electrical discharge machining processes using multi-criteria decision-making techniques
Original language description
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 L 9 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.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
20300 - Mechanical engineering
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Frontiers in Mechanical Engineering - Switzerland
ISSN
2297-3079
e-ISSN
2297-3079
Volume of the periodical
10
Issue of the periodical within the volume
MAY 9 2024
Country of publishing house
CH - SWITZERLAND
Number of pages
26
Pages from-to
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UT code for WoS article
001229291100001
EID of the result in the Scopus database
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