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

doi:10.11896/cldb.23060023

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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

Published: 10 September 2024 Online: 2024-09-30

CLC number:

TG142

Fund:National Natural Science Foundation of China (U1810207) and the Innovation Pilot Project for Fusion of Science, Education and Industry (International Cooperation) from Qilu University of Technology (2020KJC-GH03).

Corresponding Authors: ^*Jinshan He, correspondence author, associate professor of Collaborative Innovation Center of Steel Generic Technology, Beijing University of Science and Techno-logy, Chief Young Scientist of National Key Research Program, Beijing Science and Technology Young Scho-lar. At present, it is mainly engaged in the study of service damage behavior of advanced steel and superalloy. As the first author/corresponding author,She has published more than 30 top papers in SCI academic journals at home and abroad, including Materials Science & Engineering A, Journal of Materials Research & Technology, Materials Characterization, Engineering Fracture Mechanics and Journal of Metals, and applied for 6 national invention patents.She presided over the national key research and development plan, the national natural science foundation, major national defense projects and large and medium-sized enterprise cooperation projects.hejinshan@ustb.edu.cn

About author: Fan Wang obtained a bachelor's degree in engineering from Tianjin University of Technology in June 2017. He is now a graduate student of the Collaborative Innovation Center of Iron and Steel Generic Technology of Beijing University of Science and Technology, under the guidance of Professor Xitao Wang and Associate Professor Jinshan He. At present, the main research field is the research of anti-fatigue heterogeneous microstructure wind power steel.

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Cite this article:

WANG Fan,WANG Xitao,XU Shiguang, et al. Prediction of Hot Deformation Behavior of 7Mo Super Austenitic Stainless Steel Based on Back Propagation Neural Network[J]. Materials Reports, 2024, 38(17): 23060023-7.

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http://www.mater-rep.com/EN/10.11896/cldb.23060023 OR http://www.mater-rep.com/EN/Y2024/V38/I17/23060023

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