AUTHOR=Wen Qiang , Cao Zishen , Yang Sida , Tan Haoyu , Zhou Fengzhan , Yin Jiantao , Wang Tianhao , Qian Zhuo , Gan Guoyou 

TITLE=Research on predicting flow stress of 7075 aluminum alloy using machine learning models

JOURNAL=Frontiers in Materials

VOLUME=Volume 12 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/materials/articles/10.3389/fmats.2025.1671753

DOI=10.3389/fmats.2025.1671753

ISSN=2296-8016

ABSTRACT=IntroductionAccurate prediction of flow stress during the hot deformation of 7075 aluminum alloy is essential yet challenging, as conventional constitutive models are often inaccurate and artificial neural network (ANN) approaches are computationally complex.MethodsHot compression experiments on as-rolled 7,075 aluminum alloy were carried out using a TA DIL805D thermal simulator over a temperature range of 573–733 K and strain rates between 0.001 and 1.0 s-1. The resulting experimental data were subsequently used to train four machine learning models—decision tree, random forest, support vector machine, and XGBoost—for predicting the flow stress of annealed 7,075 aluminum alloy. Model performance was evaluated through residual analysis and several statistical indicators, including mean absolute error (MAE), mean squared error (MSE), average absolute relative error (AARE), correlation coefficient (R), and coefficient of determination (R2).ResultsThe results demonstrate that, compared with previously reported artificial neural network (ANN) models, these four machine learning approaches achieve comparable predictive accuracy (up to 99.9%).DiscussionWhile offering a simpler and more efficient model construction process.