Modeling and Application of Deformation Resistance Model for Medium and Heavy Plate Based on Machine Learning

doi:10.11900/0412.1961.2021.00283

Acta Metall Sin

2023, Vol. 59

Issue (3): 435-446 DOI: 10.11900/0412.1961.2021.00283

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Modeling and Application of Deformation Resistance Model for Medium and Heavy Plate Based on Machine Learning

JI Xiumei¹^,², HOU Meiling², WANG Long¹(

), LIU Jie¹, GAO Kewei²

1 School of Materials Science and Engineering, Shanghai University, Shanghai 200444, China
2 Jiangyin Xingcheng Special Steel Co. Ltd., Jiangyin 214400, China

Cite this article:

JI Xiumei, HOU Meiling, WANG Long, LIU Jie, GAO Kewei. Modeling and Application of Deformation Resistance Model for Medium and Heavy Plate Based on Machine Learning. Acta Metall Sin, 2023, 59(3): 435-446.

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Abstract

Based on the actual production data of the plate mill of Xingcheng Special Steel, two machine learning methods for predicting deformation resistance are proposed to improve prediction accuracy. The first is a multi-steel deformation resistance model and modeling method that is combined with an extreme learning machine (ELM) and a traditional mathematical model, and the second is a deformation resistance model and modeling method that is based on the TensorFlow deep learning framework. Method one: The structural form of the original deformation resistance model was improved by referring to the Zhou Jihua-Guan Kezhi deformation resistance model, and the reference deformation resistances of representative steel grades of low alloy steel, alloy steel, and high alloy steel were calculated. The influence coefficient of deformation parameters independent of steel grade was calculated using nonlinear regression. The ELM neural network algorithm was presented, and neural network parameters were optimized using grey correlation analysis and cross-validation. To reduce the residual error of ELM prediction, the prediction results were smoothed using linear interpolation and then combined with the traditional mathematical model to obtain the deformation resistance. Method two: Based on deep learning technology, two types of deep neural networks with different structures were built and combined with the mechanism. To improve the generalizability and stability of the model, the mini-batch and RMSprop optimization algorithms were used in conjunction with batch normalization (BN) and early stopping regularization strategies. Finally, deformation resistance prediction models for roughing mill (RM) and finishing mill (FM) were developed respectively in conjunction with the process characteristics to improve model accuracy. The results showed that the deformation resistance prediction using deep learning has high prediction accuracy. Offline analysis indicated that the mean absolute percentage error decreased from 9.27% of the original model to an average of 2.59%. The online application demonstrated that the ratio of rolling force prediction accuracy within 10% relative error increased from 72.31% to an average of 90.24%, raising the technological level of onsite production.

Key words: machine learning prediction of deformation resistance ELM neural network deep learning reference deformation resistance grey relational analysis

Received: 12 July 2021

ZTFLH:

TG335.5

About author: WANG Long, senior engineer, Tel: 18616377700, E-mail: longwang@shu.edu.cn

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	Xiumei JI
	Meiling HOU
	Long WANG
	Jie LIU
	Kewei GAO

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https://www.ams.org.cn/EN/10.11900/0412.1961.2021.00283 OR https://www.ams.org.cn/EN/Y2023/V59/I3/435

Table 1 Chemical composition of samples

Fig.1 Stress-strain curves of samples under deformation temperatures of t = 900^oC (a), t = 1000^oC (b), and t = 1100^oC (c) and strain rates of $ε ˙$ = 0.1 s^-1 and $ε ˙$ = 10 s^-1

Fig.2 Relationship between deformation resistance and deformation temperature (a), strain rate (b), and strain (c)

Process parameter/composition	Relational degree	Process parameter/composition	Relational degree
Al	0.9437	Ti	0.9033
C	0.9435	Ni	0.9017
Cu	0.9428	V	0.8589
Mn	0.9345	Mo	0.8544
P	0.9328	Cr	0.8517
Si	0.9324	N	0.8171
$ε$	0.9309	Sn	0.7974
t	0.9291	W	0.7832
$ε ˙$	0.9288	As	0.6871
B	0.9133	Bi	0.6453
S	0.9084	Sn	0.6098
Nb	0.9059	Ca	0.5074

Table 2 Grey correlation degrees of influencing factors of deformation resistance

Fig.3 Calculation diagram of extreme learning machine (ELM) neural network and mathematical model (W(C), W(Al), $?$ , W(Cr)—mass fractions of C, Al, …, and Cr, respectively; $K t K ε K ε ˙$ —product of the influence coefficients of temperature, strain, and strain rate on deformation resistance)

Fig.4 Network structure of reference deformation resistance model (σ_{0, chem}—reference deformation resistance relative to chemical composition)

Fig.5 Change curves of loss function under different strategies (MSE—mean square error)

Table 3 Data distributions of deformation resistance and its influencing factors

Table 4 Prediction accuracies of the original model and four groups of schemes

Fig.6 Comparisons of the measured value and the predicted value of deformation resistance for scheme 1 (a), scheme 2 (b), scheme 3 (c), scheme 4 (RM) (d1), and scheme 4 (FM) (d2)

Fig.7 Comparisons of the distribution of the absolute value of the deviation between the true value of RM deformation resistance and the predicted value obtained by Deep_RM (a) and Deep_All (b) ( $Δ y$ —absolute value of the deviation between the true value and predicted value)

Fig.8 Comparisons of the distribution of the absolute value of the deviation between the true value of FM deformation resistance and the predicted value obtained by Deep_FM (a) and Deep_All (b)

Fig.9 Relative error distributions of rolling force prediction
(a) before model modification
(b) the first month after model modification
(c) the second month after model modification
(d) the third month after model modification

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