Application of Machine Learning to predict the mechanical properties of high strength steel at elevated temperatures based on the chemical composition

Shaheen, Mohamed A.; Presswood, Rebecca; Afshan, Sheida

doi:10.1016/j.istruc.2023.03.085

Cited by 9 publications

(2 citation statements)

References 21 publications

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“…In the process of establishing empirical or semi-empirical constitutive models for materials using the Arrhenius equation, numerical errors are introduced by numerical methods such as approximate solutions and linear regression fitting [31]. Therefore, deep learning algorithms have been increasingly utilized in constitutive modeling due to their non-linear modeling capabilities, adaptability, and generalization abilities [32,33]. The BP neural network algorithm, proposed by Rumelhart and McClelland [34], is widely recognized and extensively used.…”

Section: Bp Neural Network Algorithmmentioning

confidence: 99%

Study on the Constitutive Modeling of (2.5 vol%TiB + 2.5 vol%TiC)/TC4 Composites under Hot Compression Conditions

Qiang,

Wang,

Wang

et al. 2024

Materials

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The hot deformation behavior of titanium matrix composites plays a crucial role in determining the performance of the formed components. Therefore, it is significant to establish an accurate constitutive relationship between material deformation parameters and flow stress. In this study, hot compression experiments were conducted on a (2.5 vol%TiB + 2.5 vol%TiC)/TC4. The experiments were performed under temperatures ranging from 1013.15 to 1133.15 K and strain rates ranging from 0.001 to 0.1 s−1. Based on the stress–strain data obtained from the experiment, the constitutive models were established by using the Arrhenius model and the BP neural network algorithm, respectively. Considering the relationship between strain rate, hot working temperature, and flow stress, a comparative analysis was conducted to evaluate the prediction accuracy of two different constitutive models. The research results indicate that the flow stress of (2.5 vol%TiB + 2.5 vol%TiC)/TC4 increases with decreasing temperature and increasing strain rate, and the stress–strain curve shows obvious work hardening and softening behaviors. Both the Arrhenius model and the BP neural network algorithm are effective in predicting the hot compression flow stress of (2.5 vol%TiB + 2.5 vol%TiC)/TC4, but the average relative error and root mean square error of the BP neural network algorithm are smaller and the correlation coefficient is higher, thus possessing higher accuracy and reliability.

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Section: Bp Neural Network Algorithmmentioning

confidence: 99%

Study on the Constitutive Modeling of (2.5 vol%TiB + 2.5 vol%TiC)/TC4 Composites under Hot Compression Conditions

Qiang,

Wang,

Wang

et al. 2024

Materials

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“…The results showed that the ANN can accurately predict the flow stress of steel in a large range of alloy-element concentrations. Mohamed A. Shaheen et al [12] developed a method based on an ANN to predict the mechanical properties of high-strength steel (HSS) at high temperature with chemical composition and temperature as the input parameters. The results showed that the model has good predictive ability.…”

Section: Introductionmentioning

confidence: 99%

Analysis and Predictive Evaluation of Mechanical Properties of Steel Anchor Box for High-Speed-Railway Cable-Stayed Bridge

Wang

et al. 2023

Applied Sciences

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As the stress amplitude in the anchorage zone of cable-stayed bridges vary considerably, it is essential to study the fatigue-load-bearing capacity of the anchorage structures of cable-stayed-bridge girders. In this study, the mechanical properties and force-transmission mechanism of a built-in double-fixed steel anchor box, as well as the influence of the geometric design parameters of the main pressure plates and support plates on its stress performance, were studied. A PSO-BP built-in double-fixed steel anchor box mechanical-performance-prediction-and-evaluation system was established, with the geometric design parameters of the important plates of the main body of the anchor box as the input variables and the mechanical properties of the main stress-transmitting welds of the steel anchor box as the output-evaluation variables. The results were as follows: the cable force was mainly transmitted in the form of shear force through the welds between the support plate and the web of the main beam; the transmission ratio of the four main welds was generally maintained at about 23%, and the stress distribution of each plate was relatively uniform, with most of the stresses in the range of 10~50 MPa. The shear-stress-inhomogeneity coefficient of the transmission weld was sensitive to the changes in the thickness of the support plate and the pressure plate, and the transmission ratio of the main weld was sensitive to the changes in the thickness and length of the support plate. The PSO-BP-based mechanical-performance-prediction-and-evaluation system can be used to explore the intrinsic relationship between the designed cable force, important plate-geometry parameters, and the force performance of steel anchor boxes during the iterative process, and to generate more accurate prediction and evaluation values.

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Prediction Model of Material Dynamic Mechanical Properties Embedded with Physical Mechanism Neural Network

Wang,

Zhao,

Zhan

et al. 2024

JOM

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Application of Machine Learning to predict the mechanical properties of high strength steel at elevated temperatures based on the chemical composition

Cited by 9 publications

References 21 publications

Study on the Constitutive Modeling of (2.5 vol%TiB + 2.5 vol%TiC)/TC4 Composites under Hot Compression Conditions

Study on the Constitutive Modeling of (2.5 vol%TiB + 2.5 vol%TiC)/TC4 Composites under Hot Compression Conditions

Analysis and Predictive Evaluation of Mechanical Properties of Steel Anchor Box for High-Speed-Railway Cable-Stayed Bridge

Prediction Model of Material Dynamic Mechanical Properties Embedded with Physical Mechanism Neural Network

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