Predictive modelling and optimization of WEDM parameter for Mg–Li alloy using ANN integrated CRITIC-WASPAS approach
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21220%2F24%3A00376389" target="_blank" >RIV/68407700:21220/24:00376389 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1016/j.heliyon.2024.e35194" target="_blank" >https://doi.org/10.1016/j.heliyon.2024.e35194</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.heliyon.2024.e35194" target="_blank" >10.1016/j.heliyon.2024.e35194</a>
Alternative languages
Result language
angličtina
Original language name
Predictive modelling and optimization of WEDM parameter for Mg–Li alloy using ANN integrated CRITIC-WASPAS approach
Original language description
This work intended to improve the precision and machining efficiency of Magnesium alloy (Mg-Li-Sr) using Wire electrical discharge machining (WEDM). Mg-Li-Sr alloy is prepared through inert gas assisted stir casting route. Taguchi approach is used for experimental design for WEDM parameter such as pulse OFF time, pulse ON time, wire feed rate, servo voltage and current. L27 orthogonal array is considered to understand the influence of control parameter such as Kerf Width (KW), Roughness of the surface (Ra), Material Removal Rate (MRR). Integration of the CRITIC (Criteria Importance Through Intercriteria Correlation) -WASPAS (Weighted Aggregated Sum Product Assessment) multi-objective optimization method with Artificial Neural Network (ANN) modelling with different network structure for prediction and optimization is a novel approach that significantly improves prediction accuracy and machining outcomes. The developed ANN model with better R2 value of 99.9 % has better ability for prediction while correlated with formulated conventional regression equation. The error percentages identified through confirmation tests for regression and ANN models are Ra - 8.5 % and 3.4 %, MRR - 5.9 % and 2.8 %, KW - 6.7 % and 2.2 % respectively. Optimal output response attained by CRITICWASPAS approach yields surface roughness of 4.62 mu m, material removal rate of 0.073 g/min and kerf width of 0.388 mu m.
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
20501 - Materials engineering
Result continuities
Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
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
Heliyon
ISSN
2405-8440
e-ISSN
2405-8440
Volume of the periodical
10
Issue of the periodical within the volume
15
Country of publishing house
GB - UNITED KINGDOM
Number of pages
15
Pages from-to
1-15
UT code for WoS article
001285965600001
EID of the result in the Scopus database
2-s2.0-85199925406