ANN-Driven modeling of high-temperature flow behavior in P650 for nonmagnetic drilling collars

WANG Yinghu; CHENG Limei; WANG Jianqiang; WANG E'nuo; SONG Lingxi; SHENG Zhendong

doi:10.7513/j.issn.1004-7638.2025.05.008

Volume 46 Issue 5

Oct. 2025

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WANG Yinghu, CHENG Limei, WANG Jianqiang, WANG E'nuo, SONG Lingxi, SHENG Zhendong. ANN-Driven modeling of high-temperature flow behavior in P650 for nonmagnetic drilling collars[J]. IRON STEEL VANADIUM TITANIUM, 2025, 46(5): 75-84. doi: 10.7513/j.issn.1004-7638.2025.05.008

Citation:

WANG Yinghu, CHENG Limei, WANG Jianqiang, WANG E'nuo, SONG Lingxi, SHENG Zhendong. ANN-Driven modeling of high-temperature flow behavior in P650 for nonmagnetic drilling collars[J]. IRON STEEL VANADIUM TITANIUM, 2025, 46(5): 75-84. doi: 10.7513/j.issn.1004-7638.2025.05.008

Citation:

WANG Yinghu, CHENG Limei, WANG Jianqiang, WANG E'nuo, SONG Lingxi, SHENG Zhendong. ANN-Driven modeling of high-temperature flow behavior in P650 for nonmagnetic drilling collars[J]. IRON STEEL VANADIUM TITANIUM, 2025, 46(5): 75-84. doi: 10.7513/j.issn.1004-7638.2025.05.008

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ANN-Driven modeling of high-temperature flow behavior in P650 for nonmagnetic drilling collars

doi: 10.7513/j.issn.1004-7638.2025.05.008

1.
Chengdu Advanced Metal Material Industry Technology Research Institute Co., Ltd., Chengdu 610000, Sichuan, China
2.
National Center for Materials Service Safety, University of Science and Technology Beijing, Beijing 100083, China
3.
Pangang Group Jiangyou Changcheng Special Steel Co., Ltd., Jiangyou 621704, Sichuan, China

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Received Date: 2025-04-27
Accepted Date: 2025-05-15
Rev Recd Date: 2025-05-07
Publish Date: 2025-10-30

Abstract

Abstract

High-temperature tensile tests on P650 high-nitrogen steel had been conducted under 1000-1150 ℃ and strain rates of 0.01-10 s⁻¹, using a Gleeble-3500 thermomechanical simulator. Based on the obtained stress-strain data, a strain-compensated Arrhenius constitutive model and an artificial neural network (ANN) model were developed, with prediction accuracy evaluated by average absolute relative error, root mean square error, and correlation coefficient. Results demonstrated that the prediction by ANN model with a single hidden layer (17 neurons) achieved high-precision nonlinear mapping between input parameters (temperature, strain rate, strain) and flow stress. Besides, the ANN predictions exhibited good agreement with experimental data (r=0.996, E_AARE=4.63%, E_RMSE=6.721 MPa) compared to the Arrhenius model (r=0.975, E_AARE=7.94%, E_RMSE=16.032 MPa). This study reveals that artificial neural networks can effectively capture constitutive relationship characteristics of complex thermal deformation behaviors, providing an improved strategy for establishing high-accuracy flow stress prediction models and optimizing material processing technologies.

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References(21)

References

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