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材料导报  2024, Vol. 38 Issue (17): 23060023-7    https://doi.org/10.11896/cldb.23060023
  金属与金属基复合材料 |
基于反向传播神经网络预测7Mo 超级奥氏体不锈钢的热变形行为
王帆1, 王西涛1,2, 徐世光1, 何金珊1,*
1 北京科技大学钢铁共性技术协同创新中心,北京 100083
2 齐鲁工业大学先进材料研究所(山东省科学院),山东省轻质高强金属材料省级重点实验室,济南 250353
Prediction of Hot Deformation Behavior of 7Mo Super Austenitic Stainless Steel Based on Back Propagation Neural Network
WANG Fan1, WANG Xitao1,2, XU Shiguang1, HE Jinshan1,*
1 Collaborative Innovation Center of Steel Technology, University of Science and Technology Beijing, Beijing 100083, China
2 Shandong Provincial Key Laboratory for High Strength Lightweight Metallic Materials, Advanced Materials Institute, Qilu University of Technology (Shandong Academy of Science), Jinan 250353, China
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摘要 对7Mo超级奥氏体不锈钢(SASS)进行热压缩试验,获得其在变形温度为1 000～1 200 ℃、应变速率为0.001～1 s-1条件下的流变曲线。为了预测该钢种的非线性热变形行为,提出了具有16×8×8隐层神经元的反向传播人工神经网络(BP-ANN)。根据平均绝对误差(MAE)和相对误差的分布来评估ANN模型的可预测性。BP-ANN模型85%的数据相对误差在±5%之间,而Arrhenius本构方程预测的只有42.5%的数据在此范围内。特别是在高应变速率和低温下,ANN模型的MAE为2.49%,比传统Arrhenius本构方程低18.78%。
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王帆
王西涛
徐世光
何金珊
关键词:  7Mo超级奥氏体不锈钢  热变形行为  流变应力  反向传播人工神经网络  Arrhenius本构方程    
Abstract: The hot compression tests of 7Mo super austenitic stainless (SASS) were conducted to obtain flow curves at the temperature of 1 000—1 200 ℃ and strain rate of 0.001 s-1 to 1 s-1. To predict the non-linear hot deformation behaviors of the steel, back propagation-artificial neural network (BP-ANN) with 16×8×8 hidden layer neurons was proposed. The predictability of the ANN model is evaluated according to the distribution of mean absolute error (MAE) and relative error. The relative error of 85% data for the BP-ANN model is among ±5% while only 42.5% data predicted by the Arrhenius constitutive equation is in this range. Especially, at high strain rate and low temperature, the MAE of the ANN model is 2.49%, which has decreases for 18.78%, compared with conventional Arrhenius constitutive equation.
Key words:  7Mo super austenitic stainless steel    hot deformation behavior    flow stress    BP-ANN    Arrhenius constitutive equation
出版日期:  2024-09-10      发布日期:  2024-09-30
ZTFLH:  TG142  
基金资助: 国家自然科学基金(U1810207);齐鲁理工大学“科教产融合(国际合作)”创新先导项目(2020KJC-GH03)
引用本文:    
王帆, 王西涛, 徐世光, 何金珊. 基于反向传播神经网络预测7Mo 超级奥氏体不锈钢的热变形行为[J]. 材料导报, 2024, 38(17): 23060023-7.
WANG Fan, WANG Xitao, XU Shiguang, HE Jinshan. Prediction of Hot Deformation Behavior of 7Mo Super Austenitic Stainless Steel Based on Back Propagation Neural Network. Materials Reports, 2024, 38(17): 23060023-7.
链接本文:  
http://www.mater-rep.com/CN/10.11896/cldb.23060023  或          http://www.mater-rep.com/CN/Y2024/V38/I17/23060023
1 Pu E X, Zheng W J, Song Z G, et al. Journal of Iron and Steel Research International, 2014, 21(10), 975.
2 Zhang R, He J, Xu S, et al. Journal of Materials Research and Technology, 2022, 21, 1248.
3 Han Y, Wu H, Zhang W, et al. Materials & Design, 2015, 69, 230.
4 Phaniraj M P, Lahiri A K, Journal of Materials Processing Technology, 2003, 141(2), 219.
5 Zener C, Hollomon J H, Journal of Applied Physics, 1944, 15(1), 22.
6 Liu J, Zeng W, Lai Y, et al. Materials Science and Engineering:A, 2014, 597, 387.
7 Liu Shaofei, Qu Yinhu, Wang Chonglou, et al. Materials Reports 2018, 32 (13), 2241.
8 Han Y, Qiao G, Sun J, et al. Computational Materials Science, 2013, 67, 93.
9 Bhattacharya R, Lan Y J, Wynne B P, et al. Journal of Materials Processing Technology, 2014, 214(7), 1408.
10 Han Y, Qiao G, Sun Y, et al. Materials Science and Engineering:A, 2012, 539, 61.
11 Lin Y C, Chen M S, Zhong J. Mechanics Research Communications, 2008, 35(3), 142.
12 Ren J S, Li X L, Xiao S T, et al. Materials Reports, 2020, 34(Z1), 283.
13 Mirzadeh H, Cabrera J M, Najafizadeh A. Acta Materialia, 2011, 59(16), 6441.
14 Bock F E, Aydin R C, Cyron C J, et al. Frontiers in Materials, 2019, 6, 110.
15 Kang Jing, Mi Xiaoxi, Wang Hailian, et al. Materials Reports, 2020, 34(21), 21172.
16 Cheng P, Wang D, Zhou J, et al. Metals, 2022, 12(9), 1429.
17 Qiao L, Deng Y, Liao M, et al. Materials Today Communications, 2020, 25, 101134.
18 Gupta A K, Singh S K, Reddy S, et al. Materials & Design, 2012, 35, 589.
19 Quan G Z, Pan J, Wang X. Applied Sciences, 2016, 6(3), 66.
20 Deb S, Muraleedharan A, Immanuel R J, et al. Theoretical and Applied Fracture Mechanics, 2022, 119, 103338.
21 Xu S, He J, Zhang R, et al. Journal of Materials Research and Technology, 2023, 23, 1738.
22 Huang K, Logé R E, Materials & Design, 2016, 111, 548.
23 Li H, Yang S, Zhang S, et al. Materials & Design, 2017, 118, 207.
24 Churyumov A, Kazakova A, Churyumova T. Metals, 2022, 12(3), 447.
25 Li B, Pan Q, Yin Z. Journal of Alloys and Compounds, 2014, 584, 406.
26 Zhu Y, Cao Y, Liu C, et al. Materials Today Communications, 2020, 25, 101329.
27 Quan G Z, Lv W Q, Mao Y P, et al. Materials & Design, 2013, 50, 51.
28 Sabokpa O, Zarei Hanzaki A, Abedi H R, et al. Materials & Design, 2012, 39, 390.
29 Dixit M C, Srivastava N, Rajput S K. Materials Today:Proceedings, 2017, 4(2), 1964.
30 Yan J, Pan Q L, Li A D, et al. Transactions of Nonferrous Metals Society of China, 2017, 27(3), 638.
31 Zhao J, Ding H, Zhao W, et al. Computational Materials Science, 2014, 92, 47.
32 Zhang C, Li X Q, Li D S, et al. Transactions of Nonferrous Metals Society of China, 2012, 22, s457.
33 Lin Y C, Chen X M. Materials & Design, 2011, 32(4), 1733.
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