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

Si-wei Wu; Jian Yang; Guang-ming Cao

doi:10.1007/s12613-020-2168-z

Volume 28 Issue 8

Aug. 2021

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Article Navigation > International Journal of Minerals, Metallurgy and Materials > 2021 > 28(8): 1309-1320

Si-wei Wu, Jian Yang, and Guang-ming Cao, 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(2021), No. 8, pp. 1309-1320. https://doi.org/10.1007/s12613-020-2168-z

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Si-wei Wu, Jian Yang, and Guang-ming Cao, 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(2021), No. 8, pp. 1309-1320. https://doi.org/10.1007/s12613-020-2168-z

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Research Article

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

+ Author Affiliations

Corresponding author:
Jian Yang E-mail: yang_jian@t.shu.edu.cn
Received: 17 July 2020; Revised: 6 August 2020; Accepted: 10 August 2020; Available online: 14 August 2020

Abstract

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.
- prediction;
- shallow neural network;
- deep neural network;
- impact energy;
- low carbon steel

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