Application Research of Neural Network in the Tensile Constitutive Relationship of Carbon Steel
DOI:
https://doi.org/10.54097/5y4vz311Keywords:
Neural Network, Generalization Ability, Carbon Steel, Constitutive RelationAbstract
Most carbon steels are multi-phase alloys with high specific strength, good mid-temperature properties and corrosion resistance, which lead to great differences in mechanical properties. The traditional model has some limitations and only reflects the mechanical behavior of the forming process in a certain range of temperature and strain rate. In this paper, a constitutive model of carbon steel based on neural network is established by using the high precision nonlinear fitting ability and strong generalization ability of neural network, reflect its different stages of mechanical properties. The model is trained, studied and simulated, and the results of the model are compared and analyzed. It is found that the model has high fitting precision and practical application value.
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[1] LEE, Sang-In; SHIN, Seung-Hyeok; HWANG, Byoungchul. Application of artificial neural network to the prediction of tensile properties in high-strength low-carbon bainitic steels. Metals, 2021, 11.8: 1314.
[2] ISHIHARA, Kenichi, et al. Application of an artificial neural network to develop fracture toughness predictor of ferritic steels based on tensile test results. Metals, 2021, 11.11: 1740.
[3] SHOKRY, Abdallah, et al. Constitutive models for the prediction of the hot deformation behavior of the 10% Cr steel alloy. Materials, 2019, 12.18: 2873.
[4] MURUGESAN, Mohanraj; JUNG, Dong Won. Back propagation artificial neural network approach to predict the flow stress in isothermal tensile test of medium carbon steel material. In: Materials Science Forum. Trans Tech Publications Ltd, 2020. p. 163-168.
[5] CHURYUMOV, Alexander; KAZAKOVA, Alena; CHURYUMOVA, Tatiana. Modelling of the steel high-temperature deformation behaviour using artificial neural network. Metals, 2022, 12.3: 447.
[6] LI, Xiaowei, et al. Thermal deformation behavior of Mg–3Sn–1Mn alloy based on constitutive relation model and artificial neural network. Journal of Materials Research and Technology, 2023, 24: 1802-1815.
[7] JENÍK, Ivan, et al. Sequential simulation and neural network in the stress–strain curve identification over the large strains using tensile test. Archive of Applied Mechanics, 2017, 87: 1077-1093.
[8] MURUGESAN, Mohanraj, et al. Supervised machine learning approach for modeling hot deformation behavior of medium carbon steel. steel research international, 2023, 94.2: 2200188.
[9] Lv, Junya, Huiyu Ren, and Kai Gao. "Artificial neural network-based constitutive relationship of inconel 718 superalloy construction and its application in accuracy improvement of numerical simulation." Applied Sciences 7.2 (2017): 124.
[10] SONG, Shin-Hyung. A comparison study of constitutive equation, neural networks, and support vector regression for modeling hot deformation of 316L stainless steel. Materials, 2020, 13.17: 3766.
[11] DAS, Suchandan K. Neural network modelling of flow stress and mechanical properties for hot strip rolling of TRIP steel using efficient learning algorithm. Ironmaking & Steelmaking, 2013, 40.4: 298-304.
[12] SONG, Ming, et al. Determination of elastoplastic properties of in-service pipeline steel based on backpropagation neural network and small punch test. International Journal of Pressure Vessels and Piping, 2021, 190: 104316.
[13] AHMADI, H.; ASHTIANI, HR Rezaei; HEIDARI, M. A comparative study of phenomenological, physically-based and artificial neural network models to predict the Hot flow behavior of API 5CT-L80 steel. Materials Today Communications, 2020, 25: 101528.
[14] XU, Gening; LIU, Ying; XU, Ning. Constitutive relation research of Q235 Steel based on support vector machine. In: 2015 International Conference on Architectural, Civil and Hydraulics Engineering. Atlantis Press, 2015. p. 165-170.
[15] LIN Y C, TNG Y, ZHANG X Y, et al. Effects of solution temperature and cooling rate on microstructure and micro-hard-ness of a hot compressed Ti-6Al-4V alloy[J]. Vacuum,2019,159:191-199.
[16] AI Y, ZHUS P, LIAO D, et al. Probabilistic modelling of notch fatigue and size effect of components using highly stressed volume approach[J]. International Journal of Fatigue, 2019, 127:110-119.
[17] WU Y, KOU H C, WU Z H, et al. Dynamic recrystallization and texture evolution of Ti-22Al-25Nb alloy during plane-strain compression [J].Journal of Alloys and Compounds, 2018, 749: 844- 852.
[18] WEN, DongXu, et al. High-temperature tensile characteristics and constitutive models of ultrahigh strength steel. Materials Science and Engineering: A, 2021, 803: 140491..
[19] Singh P, Pungotra H, Kalsi NS. On the characteristics of titanium alloys for the aircraft applications [J]. Materials Today: Proceedings. 2017, 4(8): 8971-8982.
[20] Peng X H, Luo Y S, Zhou J Y, etc. Research on creep constitutive model of TC11 titanium alloy based on RBFNN [J]. Materials Science Forum, 2008, 575-578: s1050-1055.
[21] Luo Y S, Dohda K, Wang Z. Experimental Solution for Viscosity Coefficient of Solid Alloy Material, Int. J. of Applied Mechanics and Engineering, 2003, 8: 271-276.
[22] Yu M, Peng X H, Luo Y S, etc. Applying Research on Creep Constitutive Model of LC4 Aluminium Alloy Based on BC-RBFNN [C]. Proceedings of the International Conference on Multimedia Technology, ICMT2010,2010: 2013-2016.
[23] TIZE MHA, Pierre, et al. Artificial neural network-based critical conditions for the dynamic recrystallization of medium carbon steel and application. Metals, 2023, 13.10: 1746.
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