Enhancing Constitutive Description of 5A06 Aluminum Alloy During Warm Deformation Using Machine Learning-Assisted Johnson-Cook Model.

Zhao Liu, Lei Deng, Jinchuan Long, Chang Gao, Yi Hao, Pan Gong, Xuefeng Tang, Xinyun Wang

Abstract read

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Article in Materials (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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Authors and funding

8 authors.

Zhao LiuState Key Laboratory of Materials Processing and Die & Mould Technology, Huazhong University of Science and Technology, No. 1037, Luoyu Road, Hongshan District, Wuhan 430074, China.ORCID 0009-0007-4268-2731

Lei DengState Key Laboratory of Materials Processing and Die & Mould Technology, Huazhong University of Science and Technology, No. 1037, Luoyu Road, Hongshan District, Wuhan 430074, China.ORCID 0000-0002-8105-6166

Jinchuan LongState Key Laboratory of Precision Manufacturing for Extreme Service Performance, School of Mechanical and Electrical Engineering, Central South University, Changsha 410083, China.

Chang GaoState Key Laboratory of Materials Processing and Die & Mould Technology, Huazhong University of Science and Technology, No. 1037, Luoyu Road, Hongshan District, Wuhan 430074, China.

Yi HaoState Key Laboratory of Materials Processing and Die & Mould Technology, Huazhong University of Science and Technology, No. 1037, Luoyu Road, Hongshan District, Wuhan 430074, China.

Pan GongState Key Laboratory of Materials Processing and Die & Mould Technology, Huazhong University of Science and Technology, No. 1037, Luoyu Road, Hongshan District, Wuhan 430074, China.ORCID 0000-0002-3833-8440

Xuefeng TangState Key Laboratory of Materials Processing and Die & Mould Technology, Huazhong University of Science and Technology, No. 1037, Luoyu Road, Hongshan District, Wuhan 430074, China.

Xinyun WangState Key Laboratory of Materials Processing and Die & Mould Technology, Huazhong University of Science and Technology, No. 1037, Luoyu Road, Hongshan District, Wuhan 430074, China.

Funding

National Major Science and Technology Projects of China 2024ZD0608100National Natural Science Foundation of China 52575836

6 · The paper itself

Abstract

To accurately characterize the warm deformation behavior and workability of the 5A06 aluminum alloy, this study presents an innovative workflow that develops and systematically validates machine learning-assisted Johnson-Cook (ML-JC) frameworks based on artificial neural network (ANN) surrogate models. Two predictive frameworks-the parallel-decoupled PD-ANN-JC and the multi-objective integrated MOI-ANN-JC-were constructed. Quantitatively, both developed ML-JC frameworks achieve significantly higher stress prediction accuracy and superior generalization capability compared with the conventional JC model. Specifically, on the testing set, the MOI-ANN-JC framework yields an average absolute relative error (AARE) of 1.424% and an

Indexed as

5A06 aluminum alloyartificial neural networkconstitutive modelingthermal softeningwarm deformation behavior

Identifiers

PMID42513724

DOI10.3390/ma19142987

PMCPMC13412178

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.