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Research on constitutive model of aluminum alloy 7075 thermal deformation based on deep neural network

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Abstract

The hot deformation behavior of the Al-Zn-Mg-Cu alloy was studied by isothermal tensile tests in the temperature range of 200–350 °C and the strain rate range of 0.001–0.1 s−1. A data-driven deep neural network (DNN) constitutive model and a phenomenological Arrhenius constitutive model were developed for the studied alloy model. The parameters of the DNN model were optimized to improve the prediction accuracy of flow stress. The results show that the accuracy of predictions of the DNN model is better than the Arrhenius model for the hot deformation behavior of 7075 aluminum alloy. The average absolute relative error and correlation coefficient of the DNN model is 1.70 % and 0.9996, respectively. The accuracy of the constitutive model of Arrhenius is relatively low for 7075 aluminum alloy in the range 200–350 °C, 0.001–0.1 s−1. The optimal network depth and the number of neurons per layer for the analytically optimized DNN constitutive model are 6 and 28, respectively. In addition, the developed DNN model can be effectively applied in intelligent manufacturing, such as short-process high-efficiency hot stamping and other plastic-forming technologies.

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Abbreviations

σ :

True stress

Z :

Zener-holloman parameter

ε :

True strain

̇ε :

Strain rate

Q h :

Activation energy

T :

Absolute temperature

R I :

Universal gas constant (8.31 Jmol−1·K−1)

N :

Number of samples

x input :

Input data

x input :

Output data

W :

Weighting matrix

b :

Offset vector

E :

Loss function

e :

Error signal

f :

Activation function

θ :

Parameters matrix

X :

Input characteristic matrix

R :

Output characteristic matrix

σ E :

Experimental stress

σ P :

Predicted stress

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Acknowledgments

This work was supported by Research Programs: (1) Project (52165020) supported by the National Natural Science Foundation of China; (2) Project (52005244) supported by the National Natural Science Foundation of China; (3) Youth Top Talent Project of Ningxia (2020011); (4) Project (2018YFB 20004000) supported by the Key Research and development Program of China.

Author information

Authors and Affiliations

  1. School of Mechanical Engineering, Ningxia University, Yinchuan, 750000, China

    Guan Wang, Pei Zhang, Linyuan Kou, Yan Wu, Tianxiang Wen & Xin Shang

  2. Ningxia Key Laboratory of Computer Aided Engineering Technology for Intelligent Equipment, Ningxia University, Yinchuan, 750000, China

    Guan Wang, Pei Zhang, Linyuan Kou, Yan Wu & Xin Shang

  3. Zoomlion Heavy Industry Science & Technology Co., Ltd., Changsha, 410131, China

    Tianxiang Wen

  4. School of Mechanical Engineering, University of South China, Hengyang, 421001, China

    Zhiwen Liu

Authors
  1. Guan Wang
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  2. Pei Zhang
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  3. Linyuan Kou
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  4. Yan Wu
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  5. Tianxiang Wen
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  6. Xin Shang
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  7. Zhiwen Liu
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Corresponding author

Correspondence to Guan Wang.

Additional information

Guan Wang is currently an Associate Professor in the School of Mechanical Engineering, Ningxia University. He received his Ph.D. degree from Hunan University in 2013. His main research interests include optimization design, numerical simulation of multiphase flow, metal plastic deformation mechanism and multiscale intrinsic structure model.

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Wang, G., Zhang, P., Kou, L. et al. Research on constitutive model of aluminum alloy 7075 thermal deformation based on deep neural network. J Mech Sci Technol 37, 707–717 (2023). https://doi.org/10.1007/s12206-023-0114-5

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  • DOI: https://doi.org/10.1007/s12206-023-0114-5

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