Page 52 - 《爆炸与冲击》2026年第3期

第 46 卷 康正东，等： 基于人工神经网络的金属材料本构模型在显式有限元中的实现 第 3 期

[5] ZHANG Y, ZUO T T, TANG Z, et al. Microstructures and properties of high-entropy alloys [J]. Progress in Materials Science,
2014, 61: 1–93. DOI: 10.1016/j.pmatsci.2013.10.001.
[6] MENG B, WAN M, WU X D, et al. Constitutive modeling for high-temperature tensile deformation behavior of pure
molybdenum considering strain effects [J]. International Journal of Refractory Metals and Hard Materials, 2014, 45: 41–47.
DOI: 10.1016/j.ijrmhm.2014.03.005.
[7] SHOGHI R, HARTMAIER A. A machine learning constitutive model for plasticity and strain hardening of polycrystalline
metals based on data from micromechanical simulations [J]. Machine Learning: Science and Technology, 2024, 5(2): 025008.
DOI: 10.1088/2632-2153/AD379E.
[8] LU Z L, PAN Q L, LIU X Y, et al. Artificial neural network prediction to the hot compressive deformation behavior of Al-Cu-
Mg-Ag heat-resistant aluminum alloy [J]. Mechanics Research Communications, 2011, 38(3): 192–197. DOI: 10.1016/
j.mechrescom.2011.02.015.
[9] HUANG X M, ZANG Y, GUAN B. Constitutive models and microstructure evolution of Ti-6Al-4V alloy during the hot
compressive process [J]. Materials Research Express, 2021, 8(1): 016534. DOI: 10.1088/2053-1591/ABDAF0.
[10] XU S C, YUAN L, SHAN D B. An artificial neural network constitutive model to predict high temperature flow behaviour in
18Ni(250) maraging steel [J]. Journal of Materials Research and Technology, 2025, 37: 157–172. DOI: 10.1016/J.JMRT.
2025.06.002.
[11] ALI U, MUHAMMAD W, BRAHME A, et al. Application of artificial neural networks in micromechanics for polycrystalline
metals [J]. International Journal of Plasticity, 2019, 120: 205–219. DOI: 10.1016/j.ijplas.2019.05.001.
[12] YASSAR S R, ABUOMAR O, HANSEN E, et al. On dislocation-based artificial neural network modeling of flow stress [J].
Materials & Design, 2010, 31(8): 3683–3689. DOI: 10.1016/j.matdes.2010.02.051.
[13] 陈梓薇, 王仲琦, 曾令辉. 基于 BP 神经网络的爆炸用激波管峰值压力预测方法 [J]. 爆炸与冲击, 2024, 44(5): 054101.
DOI: 10.11883/bzycj-2023-0187.
CHEN Z W, WANG Z Q, ZENG L H. A method for predicting peak pressure in an explosion shock tube based on BP neural
network [J]. Explosion and Shock Waves, 2024, 44(5): 054101. DOI: 10.11883/bzycj-2023-0187.
[14] LENG Y, TAC V, CALVE S, et al. Predicting the mechanical properties of biopolymer gels using neural networks trained on
discrete fiber network data [J]. Computer Methods in Applied Mechanics and Engineering, 2021, 387: 114160. DOI: 10.1016/
J.CMA.2021.114160.
[15] JAVADI A, REZANIA M. Intelligent finite element method: an evolutionary approach to constitutive modeling [J]. Advanced
Engineering Informatics, 2009, 23(4): 442–451. DOI: 10.1016/j.aei.2009.06.008.
[16] ASHTIANI R R, SHAHSAVARI P. A comparative study on the phenomenological and artificial neural network models to
predict hot deformation behavior of AlCuMgPb alloy [J]. Journal of Alloys and Compounds, 2016, 687: 263–273. DOI:
10.1016/j.jallcom.2016.04.300.
[17] 李秦超, 姚成宝, 程帅, 等. 神经网络状态方程在强爆炸冲击波数值模拟中的应用 [J]. 爆炸与冲击, 2023, 43(4): 044202.
DOI: 10.11883/bzycj-2022-0222.
LI Q C, YAO C B, CHENG S, et al. Application of the neural network equation of state in numerical simulation of intense
blast wave [J]. Explosion and Shock Waves, 2023, 43(4): 044202. DOI: 10.11883/bzycj-2022-0222.
[18] 何宇轩, 尹涛, 王曦. 有限元模型修正中的贝叶斯深度神经网络构架优化设计 [J]. 振动与冲击, 2025, 44(6): 184–190.
DOI: 10.13465/j.cnki.jvs.2025.06.020.
HE Y X, YIN T, WANG X. Architecture design of the Bayesian deep neural network in structural model updating [J]. Journal
of Vibration and Shock, 2025, 44(6): 184–190. DOI: 10.13465/j.cnki.jvs.2025.06.020.
[19] 敬霖, 冯超, 苏兴亚, 等. 高速动车组 D2 车轮钢的率温耦合变形机理与本构关系 [J]. 科学通报, 2022, 67(34): 4068–4079.
DOI: 10.1360/TB-2022-0437.
JING L, FENG C, SU X Y, et al. Strain rate-temperature coupling deformation mechanism and constitutive relationship of D2
wheel steel for high-speed EMUs [J]. Chinese Science Bulletin, 2022, 67(34): 4068–4079. DOI: 10.1360/TB-2022-0437.
[20] ZHANG T, LU S H, WU Y X, et al. Optimization of deformation parameters of dynamic recrystallization for 7055 aluminum
alloy by cellular automaton [J]. Transactions of Nonferrous Metals Society of China, 2017, 27(6): 1327–1337. DOI: 10.
1016/S1003-6326(17)60154-7.
[21] DING R, GUO Z X. Coupled quantitative simulation of microstructural evolution and plastic flow during dynamic

031403-13