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Modeling and analysis between texture evolution and mechanical properties of ZK60 magnesium alloy based on artificial neural network

Research output: Contribution to journal › Article › peer-review

Standard Standard

Modeling and analysis between texture evolution and mechanical properties of ZK60 magnesium alloy based on artificial neural network. / Yan, Hongwei; Yang, Qingshan; Zhang, Dan et al.
In: Materials Today Communications, Vol. 44, 06.03.2025, p. 112150.

Research output: Contribution to journal › Article › peer-review

HarvardHarvard

Yan, H, Yang, Q, Zhang, D, Liu, G, Li, X, Ren, F, Yue, L & Jiang, B 2025, 'Modeling and analysis between texture evolution and mechanical properties of ZK60 magnesium alloy based on artificial neural network', Materials Today Communications, vol. 44, pp. 112150. https://doi.org/10.1016/j.mtcomm.2025.112150

APA

Yan, H., Yang, Q., Zhang, D., Liu, G., Li, X., Ren, F., Yue, L., & Jiang, B. (2025). Modeling and analysis between texture evolution and mechanical properties of ZK60 magnesium alloy based on artificial neural network. Materials Today Communications, 44, 112150. Advance online publication. https://doi.org/10.1016/j.mtcomm.2025.112150

CBE

Yan H, Yang Q, Zhang D, Liu G, Li X, Ren F, Yue L, Jiang B. 2025. Modeling and analysis between texture evolution and mechanical properties of ZK60 magnesium alloy based on artificial neural network. Materials Today Communications. 44:112150. https://doi.org/10.1016/j.mtcomm.2025.112150

MLA

Yan, Hongwei et al. "Modeling and analysis between texture evolution and mechanical properties of ZK60 magnesium alloy based on artificial neural network". Materials Today Communications. 2025, 44. 112150. https://doi.org/10.1016/j.mtcomm.2025.112150

VancouverVancouver

Yan H, Yang Q, Zhang D, Liu G, Li X, Ren F et al. Modeling and analysis between texture evolution and mechanical properties of ZK60 magnesium alloy based on artificial neural network. Materials Today Communications. 2025 Mar 6;44:112150. Epub 2025 Mar 6. doi: 10.1016/j.mtcomm.2025.112150

Author

Yan, Hongwei ; Yang, Qingshan ; Zhang, Dan et al. / Modeling and analysis between texture evolution and mechanical properties of ZK60 magnesium alloy based on artificial neural network. In: Materials Today Communications. 2025 ; Vol. 44. pp. 112150.

RIS

TY - JOUR

T1 - Modeling and analysis between texture evolution and mechanical properties of ZK60 magnesium alloy based on artificial neural network

AU - Yan, Hongwei

AU - Yang, Qingshan

AU - Zhang, Dan

AU - Liu, Guanglin

AU - Li, Xianzheng

AU - Ren, Fei

AU - Yue, Liyang

AU - Jiang, Bin

PY - 2025/3/6

Y1 - 2025/3/6

N2 - This work presents a method of artificial neural network (ANN) to predict the stretch formability of ZK60 alloy associated with crystal deflection angle, tensile mechanical properties. To discuss the regulation of texture, the balance of strength and plasticity, an ANN model was constructed with datasets of texture and mechanical properties parameters that were prepared from published literature. The results of model training and prediction based on 7 predictors show that the model has good generalization ability and the prediction accuracy is over than 0.93. The quantitative evolution of microstructure was analyzed and calculated. Correlation analysis of the predicted results showed that the tensile mechanical properties and the texture distribution play a nonlinear role in regulating formability. Experimental results validate the reliability of the ANN model. Furthermore, high formability conditions were analyzed and discussed.

AB - This work presents a method of artificial neural network (ANN) to predict the stretch formability of ZK60 alloy associated with crystal deflection angle, tensile mechanical properties. To discuss the regulation of texture, the balance of strength and plasticity, an ANN model was constructed with datasets of texture and mechanical properties parameters that were prepared from published literature. The results of model training and prediction based on 7 predictors show that the model has good generalization ability and the prediction accuracy is over than 0.93. The quantitative evolution of microstructure was analyzed and calculated. Correlation analysis of the predicted results showed that the tensile mechanical properties and the texture distribution play a nonlinear role in regulating formability. Experimental results validate the reliability of the ANN model. Furthermore, high formability conditions were analyzed and discussed.

KW - Magnesium alloy

KW - artificial neural network

KW - Texture evolution

KW - Stretch formability

U2 - 10.1016/j.mtcomm.2025.112150

DO - 10.1016/j.mtcomm.2025.112150

M3 - Article

VL - 44

SP - 112150

JO - Materials Today Communications

JF - Materials Today Communications

SN - 2352-4928

ER -