Predicting fatigue limits of defective A356-T6 and A357-T6 cast aluminum alloys using a hybrid empirical–machine learning approach
##plugins.themes.bootstrap3.article.sidebar##
##plugins.themes.bootstrap3.article.main##
https://orcid.org/0009-0009-9157-8003
Anouar Nasr
LGM, ENIM, University of Monastir, Avenue Ibn El Jazzar 5019, Monastir, Tunisia; IPEIM, University of Monastir, Avenue Ibn El Jazzar 5019, Monastir, Tunisia.
https://orcid.org/0000-0002-2152-9910
Wided Bel Haj Sghaier
Sup’Com, Université El Manar, Route de Raoued Km 3,5-2083, Ariana, Tunisia
Marwa Youssef
LGM, ENIM, University of Monastir, Avenue Ibn El Jazzar 5019, Monastir, Tunisia
https://orcid.org/0000-0002-4902-2097
Abstract
The present study develops a knowledge-based machine learning framework for estimating the fatigue life of Al alloy using a combination of data-driven models and an empirical equation. Three different ML models were developed and tested, including Support Vector Regression, Random Forest and Gaussian Process Regression, to predict the fatigue limit for the spherical defective cast A356-T aluminum alloy, considering different Second Dendrite Army spacing (SDAS) values and different defect sizes. The effectiveness of the models is assessed using standard analytical metrics, like Root Mean Squared Error (RMSE) and R-squared ( ). With R² value 0.99, the SVR model outperformed the others.
##plugins.themes.bootstrap3.article.details##
How to Cite
This work is licensed under a Creative Commons Attribution 4.0 International License.
Copyright
Authors are allowed to retain both the copyright and the publishing rights of their articles without restrictions.
Open Access Statement
Fracture and Structural Integrity (F&SI) is an open-access journal which means that all content is freely available without charge to the user or his/her institution. Users are allowed to read, download, copy, distribute, print, search, or link to the full texts of the articles in this journal without asking prior permission from the publisher or the author. This is in accordance with the DOAI definition of open access.
F&SI operates under the Creative Commons Licence Attribution 4.0 International (CC-BY 4.0). This allows to copy and redistribute the material in any medium or format, to remix, transform and build upon the material for any purpose, even commercially, but giving appropriate credit, providing a link to the license, and indicating if changes were made.