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

作　　者：Si-wei Wu Jian Yang Guang-ming Cao 

机构地区：[1]State Key Laboratory of Advanced Special Steel,School of Materials Science and Engineering,Shanghai University,Shanghai 200444,China [2]State Key Laboratory of Rolling and Automation,Northeastern University,Shenyang 110819,China

出　　处：《International Journal of Minerals,Metallurgy and Materials》2021年第8期1309-1320,共12页矿物冶金与材料学报（英文版）

基　　金：financially supported by the National Natural Science Foundation of China (No.U1960202);the China Post-doctoral Science Foundation funded Projects (No.2019M651467);the Natural Science Foundation Joint Fund Project of Liaoning Province, China (No.2019-KF-2506)。

摘　　要：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 of7.5 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.

关 键 词：PREDICTION shallow neural network deep neural network impact energy low carbon steel 

分 类 号：TG142.1[一般工业技术—材料科学与工程] TP18[金属学及工艺—金属材料]