Al-Zn-Mg-Cu合金热变形过程应力预测及显微组织表征

IF 4 3区 材料科学 Q2 MATERIALS SCIENCE, MULTIDISCIPLINARY
Min Bai, Xiaodong Wu, Lingfei Cao, Songbai Tang, Youcai Qiu, Ying Zhou, Xiaomin Lin, Zhenghao Zhang
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引用次数: 0

摘要

建立了热变形Al-Zn-Mg-Cu合金应力预测数据驱动模型。该模型利用9397个数据集,包含合金成分、均匀化处理参数和热变形参数等22个特征。机器学习方法,包括线性回归、随机森林回归、决策树(DT)、人工神经网络、支持向量机和高斯过程回归,用于开发预测流动应力的模型。通过数据预处理和特征选择,识别出19个关键特征,发现训练集与测试集的数据分割比为8:2时,模型性能最优,调整后的决定系数(R2)为0.93329,离群-偏倚-偏倚误差率为0.00851。采用插值和外推的方法对Al-6.3Zn-2.5Mg-2.6 Cu-0.11Zr合金的热压缩流动应力进行了预测。结果表明,RF和DT模型在预测合金流动行为方面具有良好的稳定性和通用性。表征了不同变形温度和应变速率下的微观组织变化,分析了流变应力、变形参数和微观组织之间的相关性,为进一步了解Al-Zn-Mg-Cu合金的热变形行为提供了依据,并有可能指导工业应用的工艺优化。图形抽象
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Data–Driven Stress Prediction and Microstructure Characterization During Hot Deformation of Al–Zn–Mg–Cu Alloys

A data-driven model for stress prediction of hot-deformed Al–Zn–Mg–Cu alloys was developed. The model utilized 9397 datasets, encompassing 22 features including alloy composition, homogenization treatment parameters and hot deformation parameters. Machine learning methods, including Linear Regression, Random Forest Regression, Decision Tree (DT), Artificial Neural Network, Support Vector Machine, and Gaussian Process Regression, are used to develop models to predict flow stress. Through data preprocessing and feature selection, 19 key features were identified, and a data partition ratio of 8:2 for training-to-test sets was found to yield optimal model performance, with an adjusted coefficient of determination (R2) of 0.93329 and an outlier-bias-bias error ratio of 0.00851. The developed models were used to predict the flow stress of the Al–6.3Zn–2.5Mg–2.6 Cu–0.11Zr alloy upon hot compression with interpolation and extrapolation strategies. The results indicated that the RF and DT models demonstrated excellent stability and generalization capability in predicting the alloy’s flow behavior. Microstructure changes at various deformation temperatures and strain rates were characterized, and the correlation among flow stresses, deformation parameters and microstructure was analyzed, providing deeper insights into the hot deformation behavior of Al–Zn–Mg–Cu alloys and potentially guiding the process optimization for industrial applications.

Graphical Abstract

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来源期刊
Metals and Materials International
Metals and Materials International 工程技术-材料科学:综合
CiteScore
7.10
自引率
8.60%
发文量
197
审稿时长
3.7 months
期刊介绍: Metals and Materials International publishes original papers and occasional critical reviews on all aspects of research and technology in materials engineering: physical metallurgy, materials science, and processing of metals and other materials. Emphasis is placed on those aspects of the science of materials that are concerned with the relationships among the processing, structure and properties (mechanical, chemical, electrical, electrochemical, magnetic and optical) of materials. Aspects of processing include the melting, casting, and fabrication with the thermodynamics, kinetics and modeling.
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