Optimization of WEDM parameters for machining Mg-Li-RE alloy using CRITIC-COCOSO approach

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21220%2F25%3A00375796" target="_blank" >RIV/68407700:21220/25:00375796 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1007/s12008-024-01913-z" target="_blank" >https://doi.org/10.1007/s12008-024-01913-z</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s12008-024-01913-z" target="_blank" >10.1007/s12008-024-01913-z</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Optimization of WEDM parameters for machining Mg-Li-RE alloy using CRITIC-COCOSO approach

Popis výsledku v původním jazyce

This study focuses on neural network modelling and optimization of the machining parameters of Mg-Li-RE alloy through Wire Electrical Discharge Machining (WEDM). The Taguchi method is employed to plan experiments by varying WEDM parameters such as pulse ON time, servo voltage, wire feed rate, current, and pulse OFF time. An L27 array is designed to examine the effects of considered parameters on surface roughness, material removal rate and kerf width. Multi-objective optimization, specifically the CRITIC-COCOSO method, is applied to determine optimal parameters for improved output responses. Artificial neural network (ANN) model is employed to predict output responses, showing superior prediction compared to conventional linear regression models with an R value of 99.9%. The CRITIC-COCOSO approach suggested optimal output responses yields surface roughness of 3.671 mu m, material removal rate of 0.048 g/min, and kerf width of 0.322 mu m.

Název v anglickém jazyce

Optimization of WEDM parameters for machining Mg-Li-RE alloy using CRITIC-COCOSO approach

Popis výsledku anglicky

This study focuses on neural network modelling and optimization of the machining parameters of Mg-Li-RE alloy through Wire Electrical Discharge Machining (WEDM). The Taguchi method is employed to plan experiments by varying WEDM parameters such as pulse ON time, servo voltage, wire feed rate, current, and pulse OFF time. An L27 array is designed to examine the effects of considered parameters on surface roughness, material removal rate and kerf width. Multi-objective optimization, specifically the CRITIC-COCOSO method, is applied to determine optimal parameters for improved output responses. Artificial neural network (ANN) model is employed to predict output responses, showing superior prediction compared to conventional linear regression models with an R value of 99.9%. The CRITIC-COCOSO approach suggested optimal output responses yields surface roughness of 3.671 mu m, material removal rate of 0.048 g/min, and kerf width of 0.322 mu m.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20501 - Materials engineering

Projekt

—

Návaznosti

V - Vyzkumna aktivita podporovana z jinych verejnych zdroju

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

International Journal on Interactive Design and Manufacturing

ISSN

1955-2513

e-ISSN

1955-2505

Svazek periodika

19

Číslo periodika v rámci svazku

5

Stát vydavatele periodika

DE - Spolková republika Německo

Počet stran výsledku

14

Strana od-do

3335-3348

Kód UT WoS článku

001244623400003

EID výsledku v databázi Scopus

2-s2.0-105003101367