The flow stress prediction of TiB2/2024 aluminum matrix composites based on modified Arrhenius model and gene expression programming model

The high temperature flow data of TiB 2 /2024 aluminum matrix composites (referred to as TiB 2 /2024 alloy) was investigated using a Gleeble-3500 thermal simulation testing machine. The experiments were conducted at various deformation temperatures (573 K, 623 K, 673 K, and 723 K), strain rates (0.01s −1 , 0.1s −1 , 1s −1 , and 10s −1 ), and a maximum deformation of 60%. By comprehensively accounting for the deformation conditions, the relationships between the material parameters α , n , S , f of TiB 2 /2024 alloy and the deformation temperature, strain, and strain rate were determined, leading to the modification of the Arrhenius model. A constitutive model for TiB 2 /2024 alloy was constructed using the Gene expression programming (GEP) approach. The flow stress of TiB 2 /2024 alloy during the compression process was predicted using both the modified Arrhenius model and the GEP model. The statistical analysis was performed to evaluate the prediction accuracy of the two models, and the extended stress-strain data was implemented in finite element simulations of the hot compression process. The results indicate that the flow stress of TiB 2 /2024 alloy is significantly affected by the strain rate and temperature during the deformation process. The flow stress decreases with increasing temperature and increases with increasing strain rate. Both the modified Arrhenius model and the GEP model can effectively predict the alloy's flow stress. However, the modified Arrhenius model exhibits greater prediction accuracy than the GEP model.

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Publication Details

Journal
Journal of Science Advanced Materials and Devices
Published
2024-09-14
DOI
https://doi.org/10.1016/j.jsamd.2024.100777
Citations
3
Primary Topic
Metallurgy and Material Forming
Type
article
Field-Weighted Citation Impact
0.81
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article

The flow stress prediction of TiB2/2024 aluminum matrix composites based on modified Arrhenius model and gene expression programming model

Jing Wang, Yan Li, Qiang Liang
3 citations
Journal of Science Advanced Materials and Devices
Metallurgy and Material Forming
0.81
article

The flow stress prediction of TiB2/2024 aluminum matrix composites based on modified Arrhenius model and gene expression programming model

Jing Wang, Yan Li, Qiang Liang
article en
3 citations

Abstract

The high temperature flow data of TiB 2 /2024 aluminum matrix composites (referred to as TiB 2 /2024 alloy) was investigated using a Gleeble-3500 thermal simulation testing machine. The experiments were conducted at various deformation temperatures (573 K, 623 K, 673 K, and 723 K), strain rates (0.01s −1 , 0.1s −1 , 1s −1 , and 10s −1 ), and a maximum deformation of 60%. By comprehensively accounting for the deformation conditions, the relationships between the material parameters α , n , S , f of TiB 2 /2024 alloy and the deformation temperature, strain, and strain rate were determined, leading to the modification of the Arrhenius model. A constitutive model for TiB 2 /2024 alloy was constructed using the Gene expression programming (GEP) approach. The flow stress of TiB 2 /2024 alloy during the compression process was predicted using both the modified Arrhenius model and the GEP model. The statistical analysis was performed to evaluate the prediction accuracy of the two models, and the extended stress-strain data was implemented in finite element simulations of the hot compression process. The results indicate that the flow stress of TiB 2 /2024 alloy is significantly affected by the strain rate and temperature during the deformation process. The flow stress decreases with increasing temperature and increases with increasing strain rate. Both the modified Arrhenius model and the GEP model can effectively predict the alloy's flow stress. However, the modified Arrhenius model exhibits greater prediction accuracy than the GEP model.

Journal of Science Advanced Materials and DevicesVol. 9(4)
Chongqing Technology and Business University (CN)
Openalex Percentile: Top 33%
Metallurgy and Material Forming
0.81
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