复杂制造系统中的热轧带钢力学性能预测的深度学习模型

李飞飞; 何安瑞; 宋勇; 王正; 徐小青; 张世伟; 强毅; 刘超

doi:10.1007/s12613-022-2536-y

Volume 30 Issue 6

Jun. 2023

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文章导航 > 矿物冶金与材料学报（英文） > 2023 > 30(6): 1093-1103

Feifei Li, Anrui He, Yong Song, Zheng Wang, Xiaoqing Xu, Shiwei Zhang, Yi Qiang, and Chao Liu, Deep learning for predictive mechanical properties of hot-rolled strip in complex manufacturing systems, Int. J. Miner. Metall. Mater., 30(2023), No. 6, pp. 1093-1103. https://doi.org/10.1007/s12613-022-2536-y

Cite this article as:

Feifei Li, Anrui He, Yong Song, Zheng Wang, Xiaoqing Xu, Shiwei Zhang, Yi Qiang, and Chao Liu, Deep learning for predictive mechanical properties of hot-rolled strip in complex manufacturing systems, Int. J. Miner. Metall. Mater., 30(2023), No. 6, pp. 1093-1103. https://doi.org/10.1007/s12613-022-2536-y

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研究论文

复杂制造系统中的热轧带钢力学性能预测的深度学习模型

1)
高效轧制与智能制造国家工程研究中心, 北京 100083, 中国
2)
中国机械科学研究总院, 北京 100044, 中国

通讯作者:
宋勇 E-mail: songyong@ustb.edu.cn

文章亮点

(1) 研究了从复杂流程性制造数据中进行特征提取的方法。

(2) 在gcForest中添加了子采样环节，使其适用于不同长度的序列数据。

(3) 建立了小样本下的热轧带钢力学性能预测深度学习模型。

中文摘要

钢铁工业向智能制造转型升级过程中对相关模型的精度提出了更高的要求，传统的热轧板带力学性能预测模型已很难满足现场需要，基于多层网络的深度学习模型在实际应用中往往受到数据不足、调参困难等限制，而且选择的有限个离散工艺参数很难准确反映板带的实际加工过程。为了应对这些问题，本文提出了一种新的基于gcForest框架的板带力学性能输入数据采样方法，该方法根据热轧板带生产这类工序流程复杂且工艺路径及参数对产品质量异常敏感的特点，设计了一种基于时间-温度-形变的三维连续时序过程数据采样方式，并将多粒度扫描得到的局部历程信息与板坯的基础信息（化学成分和典型工艺参数）进行融合，使下一环节的输入同时具备局部特征和全局特征；此外，在多粒度扫描结构中设计了可变窗口的子采样方案，使具有不同维度的输入数据通过多粒度扫描结构后能够得到相同维度的输出特征，使级联森林结构能够的正常训练。最后，在3个钢种的实际生产数据上进行实验评估，结果表明，这种基于gcForest的力学性能预报模型综合性能更好，而且调参容易，在样本较少的情况下也能保持很高的预测精度。

关键词:

Research Article

Deep learning for predictive mechanical properties of hot-rolled strip in complex manufacturing systems

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Abstract

Abstract

Higher requirements for the accuracy of relevant models are put throughout the transformation and upgrade of the iron and steel sector to intelligent production. It has been difficult to meet the needs of the field with the usual prediction model of mechanical properties of hot-rolled strip. Insufficient data and difficult parameter adjustment limit deep learning models based on multi-layer networks in practical applications; besides, the limited discrete process parameters used make it impossible to effectively depict the actual strip processing process. In order to solve these problems, this research proposed a new sampling approach for mechanical characteristics input data of hot-rolled strip based on the multi-grained cascade forest (gcForest) framework. According to the characteristics of complex process flow and abnormal sensitivity of process path and parameters to product quality in the hot-rolled strip production, a three-dimensional continuous time series process data sampling method based on time–temperature–deformation was designed. The basic information of strip steel (chemical composition and typical process parameters) is fused with the local process information collected by multi-grained scanning, so that the next link’s input has both local and global features. Furthermore, in the multi-grained scanning structure, a sub sampling scheme with a variable window was designed, so that input data with different dimensions can get output characteristics of the same dimension after passing through the multi-grained scanning structure, allowing the cascade forest structure to be trained normally. Finally, actual production data of three steel grades was used to conduct the experimental evaluation. The results revealed that the gcForest-based mechanical property prediction model outperforms the competition in terms of comprehensive performance, ease of parameter adjustment, and ability to sustain high prediction accuracy with fewer samples.
- hot-rolled strip;
- prediction of mechanical properties;
- deep learning;
- multi-grained cascade forest;
- time series feature extraction;
- variable window subsampling

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[1]	B. Sun, J.T. Dai, K.K. Huang, C.H. Yang, and W.H. Gui, Smart manufacturing of nonferrous metallurgical processes: Review and perspectives, Int. J. Miner. Metall. Mater., 29(2022), No. 4, p. 611. doi: 10.1007/s12613-022-2448-x
[2]	C.C. Tasan, M. Diehl, D. Yan, et al., An overview of dual-phase steels: Advances in microstructure-oriented processing and picromechanically guided design, Annu. Rev. Mater. Res., 45(2015), No. 1, p. 391. doi: 10.1146/annurev-matsci-070214-021103
[3]	J.X. Li, W. Wang, Y. Zhou, et al., A review of research status of hydrogen embrittlement for automotive advanced high-strength steels, Acta Metall. Sin., 56(2020), No. 4, p. 444. doi: 10.11900/0412.1961.2019.00427
[4]	J. Kaczmarczyk and A. Grajcar, Numerical simulation and experimental investigation of cold-rolled steel cutting, Materials, 11(2018), No. 7, art. No. 1263. doi: 10.3390/ma11071263
[5]	H.D. Fu, H.T. Zhang, C.S. Wang, W. Yong, and J.X. Xie, Recent progress in the machine learning-assisted rational design of alloys, Int. J. Miner. Metall. Mater., 29(2022), No. 4, p. 635. doi: 10.1007/s12613-022-2458-8
[6]	T.Q. Cheng and G.C. Chen, Prediction of mechanical properties of hot-rolled strip steel based on PCA-GBDT method, J. Phys. Conf. Ser., 1774(2021), art. No. 012002. doi: 10.1088/1742-6596/1774/1/012002
[7]	W. Yang, W.G. Li, Y.T. Zhao, B.K. Yan, and W.B. Wang, Mechanical property prediction of steel and influence factors selection based on random forests, Iron Steel, 53(2018), No. 3, p. 44.
[8]	C.X. Zhang and L. Wu, SVM-based mechanical model used to predict mechanical properties of cold rolled stainless steel sheet, J. Donghua Univ. Nat. Sci., 36(2010), No. 5, p. 559.
[9]	F.F. Li, Y. Song, C. Liu, R.J. Jia, and B. Li, Research on error distribution modeling of mechanical performance prediction model for hot rolled strip, Metall. Ind. Autom., 43(2019), No. 6, p. 28.
[10]	J. Yang and S.W. Wu, Property prediction of steel rolling process based on machine learning, Iron Steel, 56(2021), No. 9, p. 1.
[11]	X.D. Wang and R.D. An, Mechanical properties prediction models based on machine learning for hot rolled strip and application, J. Plast. Eng., 28(2021), No. 10, p. 155. doi: 10.3969/j.issn.1007-2012.2021.10.020
[12]	I.J. Ding, N.W. Zheng, and M.C. Hsieh, Hand gesture intention-based identity recognition using various recognition strategies incorporated with VGG convolution neural network-extracted deep learning features, J. Intell. Fuzzy Syst., 40(2021), No. 4, p. 7775. doi: 10.3233/JIFS-189598
[13]	T. Mazda, Y. Kajita, T. Akedo, and T. Hazama, Recognition of nonlinear hysteretic behavior by neural network using deep learning, IOP Conf. Ser. Mater. Sci. Eng., 809(2020), art. No. 012010. doi: 10.1088/1757-899X/809/1/012010
[14]	G.E. Hinton, S. Osindero, and Y.W. Teh, A fast learning algorithm for deep belief nets, Neural Comput., 18(2006), No. 7, p. 1527. doi: 10.1162/neco.2006.18.7.1527
[15]	B. SravyaPranati, D. Suma, C. ManjuLatha, and S. Putheti, Large-scale video classification with convolutional neural networks, [in] T. Senjyu, P.N. Mahalle, T. Perumal, and A. Joshi, eds, International Conference on Transportation Information and Safety (ICTIS), Springer, Singapore, 2020, p. 689.
[16]	D. Liu, S. Li, and Z.D. Cao, State-of-the-art on deep learning and its application in image object classification and detection, Comput. Sci., 43(2016), No. 12, p. 13. doi: 10.11896/j.issn.1002-137X.2016.12.003
[17]	L.N. Zhang and X.L. Sun, Study on Speech Recognition Method of Artificial Intelligence Deep Learning, J. Phys. Conf. Ser., 1754(2021), art. No. 012183. doi: 10.1088/1742-6596/1754/1/012183
[18]	W.G. Li, L. Xie, Y.T. Zhao, Z.X. Li, and W.B. Wang, Prediction model for mechanical properties of hot-rolled strips by deep learning, J. Iron Steel Res. Int., 27(2020), No. 9, p. 1045. doi: 10.1007/s42243-020-00450-9
[19]	H. Xu, Z.W. Xu, and K. Zhang, Mechanical properties prediction for hot roll steel Using convolutional neural network, [in] L.Q. Pan, J. Liang, and B.Y. Ou, eds, Bio-inspired Computing: Theories and Applications. BIC-TA 2019. Communications in Computer and Information Science, Vol. 1160, Springer, Singapore, 2020, p. 56.
[20]	B.B. Wang, Y. Song, and J. Wang, A deep learning model for the accurate prediction of the microstructure performance of hot rolled steel, Int. J. Manuf. Res., 16(2021), No. 3, p. 262. doi: 10.1504/IJMR.2021.117918
[21]	J.X. Chen, F. Zhao, Y.G. Sun, L Zhang, and YL Yin, Prediction model based on XGBoost for mechanical properties of steel materials, Int. J. Model. Ident. Control, 33(2020), No. 4, p. 322. doi: 10.1504/IJMIC.2019.107482
[22]	Y. Song, B. Li, C. Liu, and F.F. Li, Prediction model of mechanical properties of hot rolled strip based on improved stacked self-encoder, Metall. Ind. Autom., 44(2020), No. 6, p. 2.
[23]	Z.H. Zhou and F. Ji, Deep forest: Towards an alternative to deep neural networks, [in] International Joint Conference on Artificial Intelligence (IJCAI), Melbourne, 2017, p. 3553.
[24]	Z.H. Zhou and F. Ji, Deep forest, Natl. Sci. Rev., 6(2019), No. 1, p. 74. doi: 10.1093/nsr/nwy108
[25]	J.M. Ding, Y.H. Wu, Q.B. Luo, and D. Yi, A fault diagnosis method of mechanical bearing based on the deep forest, J. Vib. Shock, 40(2021), No. 12, p. 107.
[26]	N. Yao and K. Cheng, Electric power equipment image recognition based on deep forest learning model with few samples, J. Phys. Conf. Ser., 1732(2021), art. No. 012025. doi: 10.1088/1742-6596/1732/1/012025
[27]	X.Z. Zhang, J.S. Liu, W.W. He, X.X. Yan, and C.P. Yang, Effect of heating parameters on grain growth behavior of 12%Cr steel, Heat Treat. Met., 45(2020), No. 9, p. 77.
[28]	D. Xu, C. Ji, H.Y. Zhao, D.Y. Ju, and M.Y. Zhu, A new study on the growth behavior of austenite grains during heating processes, Sci. Rep., 7(2017), art. No. 3968. doi: 10.1038/s41598-017-04371-8
[29]	K.M. Agarwal, R.K. Tyagi, V.K. Chaubey, and A. Dixit, Comparison of different methods of Severe Plastic Deformation for grain refinement, IOP Conf. Ser. Mater. Sci. Eng., 691(2019), art. No. 012074. doi: 10.1088/1757-899X/691/1/012074
[30]	B. Li, Q.Y. Liu, S.J. Jia, and J. Chen, Grain ultrafining of low-carbon microalloyed steel by intercritical deformation, Heat Treat. Met., 43(2018), No. 5, p. 133.
[31]	Y.J. Hui, Y. Yu, L. Wang, C. Wang, and J. Chen, Strain induced precipitation of second phase in titanium microalloyed IF steel, Iron Steel, 51(2016), No. 1, p. 82.
[32]	X.L. Wu, Z.Z. Zhao, Y. Tian, et al., Phase transformation and second-phase precipitation behavior of Nb–Ti microalloyed steel during cooling after deformation, Iron Steel Vanadium Titanium, 29(2008), No. 1, p. 66.
[33]	J.F. Cheng, Z.D. Liu, and G.B. Tang, Numerical simulation of phase transformation process for carbon steel during Run-out table cooling, Iron Steel, 47(2012), No. 1, p. 55.
[34]	M.J. Sun, X.M. Xia, and L.Z. Long, Finite element analysis of temperature field and phase transition in laminar-like cooling and coiling process of strip steel, Hot Work. Technol., 49(2020), No. 10, p. 131.
[35]	S.Z. Wang, Z.J. Gao, G.L. Wu, and X.P. Mao, Titanium microalloying of steel: A review of its effects on processing, microstructure and mechanical properties, Int. J. Miner. Metall. Mater., 29(2022), No. 4, p. 645. doi: 10.1007/s12613-021-2399-7