Research on prediction accuracy of the flow stress of 0Cr17Ni4Cu4Nb stainless steel based on machine learning

Zhao Lidong; Zhang Youming; Zhang Jilin; Dou Jianming; Yao Jiabao

doi:10.7513/j.issn.1004-7638.2023.04.028

Volume 44 Issue 4

Aug. 2023

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Article Navigation > IRON STEEL VANADIUM TITANIUM > 2023 > 44(4): 196-204

Zhao Lidong, Zhang Youming, Zhang Jilin, Dou Jianming, Yao Jiabao. Research on prediction accuracy of the flow stress of 0Cr17Ni4Cu4Nb stainless steel based on machine learning[J]. IRON STEEL VANADIUM TITANIUM, 2023, 44(4): 196-204. doi: 10.7513/j.issn.1004-7638.2023.04.028

Citation:

Zhao Lidong, Zhang Youming, Zhang Jilin, Dou Jianming, Yao Jiabao. Research on prediction accuracy of the flow stress of 0Cr17Ni4Cu4Nb stainless steel based on machine learning[J]. IRON STEEL VANADIUM TITANIUM, 2023, 44(4): 196-204. doi: 10.7513/j.issn.1004-7638.2023.04.028

Citation:

Zhao Lidong, Zhang Youming, Zhang Jilin, Dou Jianming, Yao Jiabao. Research on prediction accuracy of the flow stress of 0Cr17Ni4Cu4Nb stainless steel based on machine learning[J]. IRON STEEL VANADIUM TITANIUM, 2023, 44(4): 196-204. doi: 10.7513/j.issn.1004-7638.2023.04.028

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Research on prediction accuracy of the flow stress of 0Cr17Ni4Cu4Nb stainless steel based on machine learning

doi: 10.7513/j.issn.1004-7638.2023.04.028

1.
School of Computer and Artificial Intelligence, Lanzhou Institute of Technology, Lanzhou 730050, Gansu, China
2.
School of Locomotive and Vehicle Engineering, Dalian Jiaotong University, Dalian 116000, Liaoning, China
3.
School of Electrical and Mechanical Engineering, Lanzhou Institute of Technology, Lanzhou 730050, Gansu, China
4.
College of Electrical and Mechanical Engineering, Lanzhou Jiaotong University, Lanzhou 730070, Gansu, China

Received Date: 2022-11-16
Publish Date: 2023-08-30

Abstract

Abstract

A new model for predicting rheological stresses based on particle swarm optimization BP neural network is proposed for 0Cr17Ni4Cu4Nb stainless steel as an example. Based on the quasi-static (0.001 s⁻¹) compression testing data at room temperature and the impact testing data at four temperatures (25, 350, 500 and 300 ℃) and six strain rates (750, 1500, 2000, 2600, 3500 and 4500 s⁻¹), a random forest prediction model for rheological stress of 0Cr17Ni4Cu4Nb stainless steel, a Particle Swarm Optimized Random Forest prediction model, a Back Propagation (BP) neural network, and a Particle Swarm Optimized BP neural network are constructed. Four indicators including the statistical coefficient of determination (R²), mean absolute error (MAE), mean square error (MSE) and root mean square error (RMSE) are used to analyze and evaluate the four models mentioned above. The comprehensive performance of the prediction models is in sequence of particle swarm optimization BP neural network model, BP neural network model, particle swarm optimization random forest model, and the random forest model. The coefficient of determination R²=0.9997, mean absolute error MAE=1.5773, mean squared error MSE=5.5053 and root mean squared error RMSE=2.3463 are determined for the particle swarm optimized BP neural network model, which can predict the rheological stress of 0Cr17Ni4Cu4Nb stainless steel very well.
- 0Cr17Ni4Cu4Nb stainless steel,
- rheological stress,
- prediction model,
- machine learning,
- particle swarm optimization BP neural network

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References(38)

References

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