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Prediction of the Charpy V-notch impact energy of low carbon steel using a shallow neural network and deep learning

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Abstract

The impact energy prediction model of low carbon steel was investigated based on industrial data. A three-layer neural network, extreme learning machine, and deep neural network were compared with different activation functions, structure parameters, and training functions. Bayesian optimization was used to determine the optimal hyper-parameters of the deep neural network. The model with the best performance was applied to investigate the importance of process parameter variables on the impact energy of low carbon steel. The results show that the deep neural network obtains better prediction results than those of a shallow neural network because of the multiple hidden layers improving the learning ability of the model. Among the models, the Bayesian optimization deep neural network achieves the highest correlation coefficient of 0.9536, the lowest mean absolute relative error of 0.0843, and the lowest root mean square error of 17.34 J for predicting the impact energy of low carbon steel. Among the variables, the main factors affecting the impact energy of low carbon steel with a final thickness of 7.5 mm are the thickness of the original slab, the thickness of intermediate slab, and the rough rolling exit temperature from the specific hot rolling production line.

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Acknowledgements

This work was financially supported by the National Natural Science Foundation of China (No. U1960202), the China Post-doctoral Science Foundation funded Projects (No. 2019M651467), and the Natural Science Foundation Joint Fund Project of Liaoning Province, China (No. 2019-KF-25-06).

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Authors and Affiliations

  1. State Key Laboratory of Advanced Special Steel, School of Materials Science and Engineering, Shanghai University, Shanghai, 200444, China

    Si-wei Wu & Jian Yang

  2. State Key Laboratory of Rolling and Automation, Northeastern University, Shenyang, 110819, China

    Guang-ming Cao

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  1. Si-wei Wu
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  2. Jian Yang
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Correspondence to Jian Yang.

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Wu, Sw., Yang, J. & Cao, Gm. Prediction of the Charpy V-notch impact energy of low carbon steel using a shallow neural network and deep learning. Int J Miner Metall Mater 28, 1309–1320 (2021). https://doi.org/10.1007/s12613-020-2168-z

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  • DOI: https://doi.org/10.1007/s12613-020-2168-z

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