ORIGINAL RESEARCH article
Front. Mater.
Sec. Computational Materials Science
Volume 12 - 2025 | doi: 10.3389/fmats.2025.1671753
This article is part of the Research TopicAdvancing Computational Material Science and Mechanics through Integrated Deep Learning ModelsView all 7 articles
Research on Predicting Flow Stress of 7075 Aluminum Alloy Using Machine Learning Models
Provisionally accepted
- Faculty of Science, Kunming University of Science and Technology, Kunming, China
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Hot compression experiments on as-rolled 7075 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 7075 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). The 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%), while offering a simpler and more efficient model construction process.
Keywords: 7075 aluminum alloy, decision tree, random forest, Support vector machine, XG Boost
Received: 23 Jul 2025; Accepted: 02 Sep 2025.
Copyright: © 2025 Qian, Cao, Gan, Yang, Tan, Zhou, Yin and Wang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Zhuo Qian, Faculty of Science, Kunming University of Science and Technology, Kunming, China
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