Comparative optimization of wire-cut EDM parameter for enhancing surface finish and machining time on stainless steel: a machine learning, genetic algorithms, teaching–learning-based optimization, and multi-objective Jaya approach

This study focuses on optimizing wire electric discharge machining (WEDM) parameters to enhance surface finish (Ra) and reduce machining time (Mt) for stainless steel. The performance of three algorithms, including the genetic algorithm (GA), teaching–learning-based optimization (TLBO), and multi-ob...

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Vydané v:International journal of advanced manufacturing technology Ročník 137; číslo 9; s. 5339 - 5362
Hlavní autori: Siyoum, Yitayal Belew, Kindie, Fikir Gashaw, Gebeyehu, Mebratu Assefa, Chanie, Sewale Enyew, Yeshiwas, Teshager Awoke, Zelalem, Yilkal Azene
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: London Springer London 01.04.2025
Springer Nature B.V
Predmet:
Advanced manufacturing technologies
Algorithms
Application
Artificial neural networks
CAE) and Design
Computer-Aided Engineering (CAD
Electric discharge machining
Engineering
Feed rate
Genetic algorithms
Industrial and Production Engineering
Machine learning
Mechanical Engineering
Media Management
Multiple objective analysis
Optimization
Parameter identification
Parameter robustness
Stainless steel
Stainless steels
Surface finish
Surface properties
Wire
TLBO
Surface roughness
Artificial neural network
Parameter optimization
Genetic algorithm
MO-Jaya algorithm
ISSN:0268-3768, 1433-3015
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Shrnutí:This study focuses on optimizing wire electric discharge machining (WEDM) parameters to enhance surface finish (Ra) and reduce machining time (Mt) for stainless steel. The performance of three algorithms, including the genetic algorithm (GA), teaching–learning-based optimization (TLBO), and multi-objective Jaya (MO-Jaya) algorithms, was compared to identify the most efficient parameter tuning methods. The experimental array was designed using a Taguchi L9 matrix, utilizing controlled parameters including pulse-off time (Toff), peak current (Ip), pulse-on time (Ton), and wire feed rate (WFR). The experimental results were validated by artificial neural network modeling and found a 1.97% error and a mean squared error of 0.113. The optimal parameters determined by MO-Jaya were Toff of 14.29 µs, Ip of 2.438 A, Ton of 8.391 µs, and WFR of 21.274 mm/s, achieving a Ra of 2.193 µm and Mt of 183.469 s. MO-Jaya confirmed greater performance over TLBO, reducing Ra by 1.65% and Mt by 1.69%. Confirmatory tests further improved, enhancing Ra by 1.57% and Mt by 1.17%. The integration of machine learning models provided accurate predictions of surface quality and machining efficiency, while GA, TLBO, and MO-Jaya offered robust frameworks for parameter optimization. Comparative analysis revealed significant improvements in surface finish and reduced machining time using these approaches. These findings contribute to the precision manufacturing of stainless-steel parts and establish a methodological framework for multi-objective optimization in advanced machining processes.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
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content type line 14
ISSN:0268-3768
1433-3015
DOI:10.1007/s00170-025-15450-w