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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Bibliographic Details
Published in:	International journal of advanced manufacturing technology Vol. 137; no. 9; pp. 5339 - 5362
Main Authors:	Siyoum, Yitayal Belew, Kindie, Fikir Gashaw, Gebeyehu, Mebratu Assefa, Chanie, Sewale Enyew, Yeshiwas, Teshager Awoke, Zelalem, Yilkal Azene
Format:	Journal Article
Language:	English
Published:	London Springer London 01.04.2025 Springer Nature B.V
Subjects:	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
Online Access:	Get full text
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