[1]贺彤, 王海建, 李花兵, 等.含氮热作模具钢的研究现状与发展[J]. 钢铁研究学报, 2022, 34(12): 1334-1344.
He T, Wang H J, Li H B, et al. Current research status and development of nitrogen-containing hot-work die steel [J].Journal of Iron and Steel Research, 2022, 34(12): 1334-1344.
[2]Lin Y C, Chen M S, Zhong J. Constitutive modeling for elevated temperature flow behavior of 42CrMo steel [J]. Computational Materials Science, 2008, 42(3): 470-477.
[3]Lin Y C, Chen X M. A critical review of experimental results and constitutive descriptions for metals and alloys in hot working [J]. Materials & Design, 2011, 32(4): 1733-1759.
[4]周靖, 王宝雨, 徐伟力, 等.耦合损伤的22MnB5热变形本构模型[J]. 北京科技大学学报, 2013, 35(11): 1450-1457.
Zhou J, Wang B Y, Xu W L, et al. Damage-coupled constitutive model of 22MnB5 steel in hot deformation [J]. Journal of University of Science and Technology Beijing, 2013, 35(11): 1450-1457.
[5]傅垒, 王宝雨, 林建国, 等.耦合位错密度的6111铝合金热变形本构模型[J]. 北京科技大学学报, 2013, 35(10): 1333-1339.
Fu L, Wang B Y, Lin J G, et al. Constitutive model coupled with dislocation density for hot deformation of 6111 aluminum alloy [J]. Journal of University of Science and Technology Beijing, 2013, 35(10): 1333-1339.
[6]袁康博, 姚小虎, 王瑞丰, 等.金属材料的率-温耦合响应与动态本构关系综述[J]. 爆炸与冲击, 2022, 42(9): 4-37.
Yuan K B, Yao X H, Wang R F, et al. A review on rate-temperature coupling response and dynamic constitutive relation of metallic materials [J].Explosion and Shock Waves, 2022, 42(9): 4-37.
[7]邱宇, 袁飞, 曾元松, 等.4Cr5MoSiV1热作模具钢的热变形行为与热加工图[J]. 机械工程材料, 2021, 45(2): 71-77.
Qiu Y, Yuan F, Zeng Y S, et al. Hot deformation behavior and hot processing map of 4Cr5MoSiV1 hot working die steel [J].Materials for Mechanical Engineering, 2021, 45(2): 71-77.
[8]陈国鑫, 桑宝光, 刘宏伟, 等.H13钢高温热变形特征与动态再结晶行为[J]. 塑性工程学报, 2022, 29(6): 193-202.
Chen G X, Sang B G, Liu H W, et al. Hot deformation characteristics and dynamic recrystallization behavior of H13 steel at high temperature [J].Journal of Plasticity Engineering, 2022, 29(6): 193-202.
[9]毛欢, 韩莹莹.基于应变补偿Arrhenius模型的TC20钛合金本构方程研究[J]. 铸造技术, 2018, 39(9): 1939-1942,1947.
Mao H, Han Y Y. Study on constitutive equations of TC20 alloy based on strain-compensated arrhenius model [J]. Foundry Technology, 2018, 39(9): 1939-1942,1947.
[10]曹建国, 王天聪, 李洪波, 等.基于Arrhenius改进模型的无取向电工钢高温变形本构关系[J]. 机械工程学报, 2016, 52(4): 90-96,102.
Cao J G, Wang T C, Li H B, et al. High-temperature constitutive relationship of non-oriented electrical steel based on modified Arrhenius model [J]. Journal of Mechanical Engineering, 2016, 52(4): 90-96,102.
[11]丁慧莹, 管延锦, 李玉琦, 等.GGG70L球墨铸铁的高温变形行为及其本构模型建立[J]. 锻压技术, 2022, 47(12): 249-255.
Ding H Y, Guan Y J, Li Y Q, et al. Deformation behavior at high temperature and establishment of constitutive model of GGG70L ductile iron [J].Forging & Stamping Technology, 2022, 47(12): 249-255.
[12]杨合, 詹梅.材料加工过程实验建模方法[M]. 西安: 西北工业大学出版社, 2008.
Yang H, Zhan M. Material Processing Experimental Modeling [M]. Xi′an: Northwestern Polytechnical University Press, 2008.
[13]刘雪峰, 马胜军, 刘锦平, 等.Cu-12%Al合金高温压缩变形过程本构关系的BP神经网络模型[J]. 材料工程, 2009(1): 10-14.
Liu X F, Ma S J, Liu J P, et al. BP neural networks models for constitutive relationship during high temperature deformation process of Cu-12%Al alloy [J]. Journal of Materials Engineering, 2009(1): 10-14.
[14]邓肖峰, 王凯, 石伟.ZL205A铝合金淬火过程本构模型及数值模拟[J]. 材料热处理学报, 2021, 42(8): 125-136.
Deng X F, Wang K, Shi W. Constitutive model and numerical simulation of ZL205A aluminum alloy during quenching [J].Transactions of Materials and Heat Treatment, 2021, 42(8): 125-136.
[15]Pasco J, McCarthy T, Parlee J, et al. Constitutive modeling of modified-H13 steel [J]. MRS Communications, 2022, 12(3): 343-351.
[16]Zamani M R, Mirzadeh H, Malekan M. Artificial neural network applicability in studying hot deformation behaviour of high-entropy alloys [J]. Materials Science and Technology, 2023,39(18):1-9.
[17]Ji G L, Li F G, Li Q H, et al. A comparative study on Arrhenius-type constitutive model and artificial neural network model to predict high-temperature deformation behaviour in Aermet100 steel [J]. Materials Science and Engineering: A, 2011, 528(13-14): 4774-4782.
[18]ASTM E209-18, Standard practice for compression tests of metallic materials at elevated temperatures with conventional or rapid heating rates and strain rates [S].
[19]Sarkar A, Kapoor R, Verma A, et al. Hot deformation behavior of Nb-1Zr-0.1C alloy in the temperature range 700-1700 ℃ [J]. Journal of Nuclear Materials, 2012, 422(1-3): 1-7.
[20]Song C N, Cao J G, Xiao J, et al. High-temperature constitutive relationship involving phase transformation for non-oriented electrical steel based on PSO-DNN approach[J]. Materials Today Communications,2023,34: 105210.
[21]Lin Y C, Zhang J, Zhong J. Application of neural networks to predict the elevated temperature flow behavior of a low alloy steel [J]. Computational Materials Science, 2008, 43(4): 752-758.
[22]汪雅婷, 黎俊良, 袁楷峰, 等. 基于GA改进BP神经网络预测热变形流变应力模型的建立[J]. 材料工程, 2022,50(6):170-177.
Wang Y T, Li J L, Yuan K F, et al. Establishment of hot deformation flow stress prediction model based on GA improved BP neural network [J]. Journal of Materials Engineering, 2022,50(6): 170-177.
|