[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124414-en":3,"doc-seo-124414-105":29,"detail-sidebar-cat-0-en-105":90},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":11,"language":21,"language_code":22,"site_id":23,"html_lang":22,"table_of_contents":24,"faqs":25,"seo_title":26,"seo_description":14,"update_tm":27,"read_time":28},124414,1374391974585,"Genevieve","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Research on predicting flow stress of 7075 aluminum alloy using machine learning models","Accurate prediction of flow stress during the hot deformation of 7075 aluminum alloy is vital for reliable processing, yet conventional constitutive models often lack sufficient accuracy and ANN approaches can be computationally complex. Hot compression experiments on as-rolled 7075 aluminum alloy were conducted across 573–733 K and strain rates of 0.001–1.0 s−1, then used to train decision tree, random forest, support vector machine, and XGBoost models to predict flow stress of annealed 7075 alloy. Model accuracy was assessed via residual analysis and metrics including MAE, MSE, AARE, correlation coefficient (R), and R². Results show performance comparable to prior ANN models, reaching up to 99.9%.","TYPE Original Research PUBLISHED 23 September 2025 DOI 10.3389/fmats.2025.1671753  \nOPEN ACCESS  \nEDITED BY  \nShahed Rezaei,  \nAccess e.V., Germany  \nREVIEWED BY  \nSathickbasha K,  \nB. S. Abdur Rahman Crescent Institute of Science and Technology, India Hariharasakthisudhan P,  \nDr. Mahalingam College of Engineering and Technology, India  \n*CORRESPONDENCE  \nZhuo Qian,  \n [20210067@kust.edu.cn](20210067@kust.edu.cn)  \n†These authors have contributed equally to this work and share first authorship  \nRECEIVED 23 July 2025  \nACCEPTED 02 September 2025  \nPUBLISHED 23 September 2025  \nCITATION  \nWen Q, Cao Z, Yang S, Tan H, Zhou F, Yin J, Wang T, Qian Z and Gan G (2025) Research on predicting flow stress of 7075 aluminum alloy using machine learning models.  \nFront. Mater. 12:1671753 .  \ndoi: 10.3389/fmats.2025.1671753  \nCOPYRIGHT  \n© 2025 Wen, Cao, Yang, Tan, Zhou, Yin, Wang, Qian and Gan. This is an open-access article distributed under the terms of the  \nCreative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) 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.  \nResearch on predicting flow stress of 7075 aluminum alloy using machine learning models  \nQiang Wen†, Zishen Cao†, Sida Yang†, Haoyu Tan , Fengzhan Zhou , Jiantao Yin , Tianhao Wang , Zhuo Qian* and Guoyou Gan  \nFaculty of Materials Science and Engineering, Kunming University of Science and Technology, Kunming, China  \nIntroduction: Accurate 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.  \nMethods: Hot 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) . Results: 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%) .  \nDiscussion: While offering a simpler and more efficient model construction process.  \nKEYWORDS  \n7075 aluminum alloy, decision tree, random forest, support vector machine, XG boost  \n1 Introduction  \n7,075 aluminum alloy is renowned for its high specific strength, excellent fracture toughness, and good corrosion resistance, making it one of the most widely used alloys in the 7,000 series 00 . These properties render it critical for manufacturing structural components in aerospace and automotive applications00 . Hot deformation is essential for producing high-quality parts, with temperature, strain, and strain rate significantly influencing the process 00 . Under various hot deformation conditions, micro structural mechanisms such as dynamic recovery, dynamic recrystallization, precipitation, and dissolution occur, which directly affect flow stress behavior00 . Thus, understanding the hot deformation behavior of 7,075 aluminum alloy is crucial for optimizing processing parameters to achieve improved micro structure and mechanical properties.  \nConventional constitutive models, such as the strain-compensated Arrhenius model (SCAM) (Chen et a","cbCaiehgt5GS4twI","https://ap.wps.com/l/cbCaiehgt5GS4twI","pdf",27246466,1,"English","en",105,"# Introduction\n# Methods\n# Results\n# Discussion","[{\"question\":\"Why is predicting flow stress of 7075 aluminum alloy during hot deformation challenging?\",\"answer\":\"Conventional constitutive models often provide limited accuracy, while traditional ANN-based approaches require extensive tuning and computational resources.\"},{\"question\":\"What experiments and data were used to train the machine learning models?\",\"answer\":\"Hot compression experiments on as-rolled 7075 aluminum alloy were performed using a TA DIL805D thermal simulator over 573–733 K and strain rates from 0.001 to 1.0 s−1.\"},{\"question\":\"Which machine learning models were evaluated for flow stress prediction?\",\"answer\":\"Decision tree, random forest, support vector machine, and XGBoost were trained to predict the flow stress of annealed 7075 aluminum alloy.\"}]","Research on predicting flow stress of 7075 aluminum alloy using machine learning models | 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is predicting flow stress of 7075 aluminum alloy during hot deformation challenging?","Question",{"text":74,"@type":75},"Conventional constitutive models often provide limited accuracy, while traditional ANN-based approaches require extensive tuning and computational resources.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"What experiments and data were used to train the machine learning models?",{"text":79,"@type":75},"Hot compression experiments on as-rolled 7075 aluminum alloy were performed using a TA DIL805D thermal simulator over 573–733 K and strain rates from 0.001 to 1.0 s−1.",{"name":81,"@type":72,"acceptedAnswer":82},"Which machine learning models were evaluated for flow stress prediction?",{"text":83,"@type":75},"Decision tree, random forest, support vector machine, and XGBoost were trained to predict the flow stress of annealed 7075 aluminum 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