Steel and Composite Structures

  • 제28권5호
  • /
  • Pages.517-525
  • /
  • 2018
  • /
  • 1229-9367(pISSN)
  • /
  • 1598-6233(eISSN)
테크노프레스 (Techno-Press)
권호 표지
DOI QR코드

DOI QR Code

Constitutive model for ratcheting behavior of Z2CND18.12N austenitic stainless steel under non-symmetric cyclic stress based on BP neural network

  • Wang, Xingang (Mechanical Engineering and Automation, Center of Mechanical Reliability & Dynamics, Northeastern University) ;
  • Chen, Xiaohui (Mechanical Engineering and Automation, Center of Mechanical Reliability & Dynamics, Northeastern University) ;
  • Yan, Mingming (Mechanical Engineering and Automation, Center of Mechanical Reliability & Dynamics, Northeastern University) ;
  • Chang, Miaoxin (Mechanical Engineering and Automation, Center of Mechanical Reliability & Dynamics, Northeastern University)
  • 투고 : 2017.01.26
  • 심사 : 2018.07.04
  • 발행 : 2018.09.10
⟨ 이전 논문 다음 논문 ⟩

초록

The specimens made by Z2CND18.12N austenitic stainless steel were conducted on a 100 kN closed loop servo hydraulic tension-compression testing machine with a digital controller. Uniaxial tension and uniaxial ratcheting effect tests were carried out at $25^{\circ}C$. Moreover, Uniaxial tension tests were conducted at $150^{\circ}C$, $250^{\circ}C$ and $350^{\circ}C$. Based on these experimental data, the prediction models of stress-strain curve and the relationship of ratcheting strain and number of cycles were established by the algorithm principle of BP neural network. The results indicated that the predicted results of neural network model were in well agreement with experimental data. It was found that the BP neural network model had high validity and accuracy.

키워드

과제정보

연구 과제 주관 기관 : National Natural Science Foundation of China, Natural Science Foundation of Liaoning province of China, Natural Science Foundation of Hebei province of China, Central Universities

참고문헌

  1. Chen, X.H. (2014), "Study on ratcheting and shakedown of pressure pipes under cyclic loading", Ph.D. Dissertation; Tianjin University, Tianjin, China.
  2. Chen, X.H., Gao, B.J. and Chen, X. (2016), "Evaluation of AF type cyclic plasticity models in ratcheting simulation of elbow pipes under cyclic bending and steady internal pressure", Steel Compos. Struct., Int. J., 21(4), 703-753. https://doi.org/10.12989/scs.2016.21.4.703
  3. Ding, Y.R., Cai, Y.J., Sun P.D. and Chen, B. (2014), "The use of combined neural networks and genetic algorithms for prediction of river water quality", Ph.D. Dissertation; Jiangnan University, Tianjin, China.
  4. Fotovati, A., Kadkhodapour, J. and Schmauder, S. (2014), "Artificial Neural Networks Investigation of Indentation Force Effects on Nano- and Microhardness of Dual Phase Steels", J. Metall., 1-11.
  5. Guo, X.L., Shi, P.H. and Qiu, S.T. (2013), "Application of BP neural network for on-line diagnostics of continuous casting slab quality", J. Iron Steel Res. Int., 25(7), 58-62.
  6. Hakim, S.J.S. and Abdul Razak, H. (2013), "Structural damage detection of steel bridge girder using artificial neural networks and finite element models", Steel Compos. Struct., Int. J., 14(4), 367-377. https://doi.org/10.12989/scs.2013.14.4.367
  7. Jimenez-Come, M.J., Turias, I.J., Ruiz-Aguilar, J.J. and Trujillo, F.J. (2015), "Characterization of pitting corrosion of stainless steel using artificial neural networks", Mater. Corros., 66(10), 1084-1091. https://doi.org/10.1002/maco.201408173
  8. Khalaj, G. and Khalaj, M.J. (2013), "Modeling the correlation between yield strength, chemical composition and ultimate tensile strength of X70 pipeline steels by means of gene expression programming", Int. J. Mater. Res., 104(7), 697-702. https://doi.org/10.3139/146.110910
  9. Khalaj, G. and Pouraliakbar, H. (2014), "Computer-aided modeling for predicting layer thickness of a duplex treated ceramic coating on tool steels", Ceram. Int., 40(4), 5515-5522. https://doi.org/10.1016/j.ceramint.2013.10.141
  10. Khalaj, G., Azimzadegan, T., Khoeini, M. and Etaat, M. (2013), "Artificial neural networks application to predict the ultimate tensile strength of X70 pipeline steels", Neural. Comput. Applic., 23, 2301-2308. https://doi.org/10.1007/s00521-012-1182-0
  11. Khalaj, G., Nazari, A. and Yoozbashizadeh, H. (2014), "ANN model to predict the effects of composition and heat treatment parameters on transformation start temperature of microalloyed steels", Neural Comput. Appl., 24, 301-308. https://doi.org/10.1007/s00521-012-1233-6
  12. Li, P., Xue, K.M. and Lv, Y. (2000), "Effect of hot processing parameters on flow stress of Ti-15-3 alloy", Harbin Inst. Tech., 32(4), 28-32.
  13. Liang, T. (2014), "A study on the ratcheting effect and ratchetingfatigue behavior of Z2CND18.12N piping material after thermal aging process", Ph.D. Dissertation; Tianjin University, Tianjin, China.
  14. Lin, Y.C., Chen, D.D., Chen, M.S. and Li, J. (2016), "A precise BP neural network-based online model predictive control strategy for die forging hydraulic press machine", Neural Compos. Appl., 1-12.
  15. Liu, Y.L., Yin, J.H., Jiang, Y. and Zhong, Y. (2016), "Artificial neural network modeling to predict hot deformation behavior of Zn-Al alloy", TMS 2016 145th Annual Meeting & Exhibition, 417-424.
  16. Lu, H.Y., Sun, Y., Zhu, Y.C. and Zeng, W.D. (2010), "Constitutive Relationship Model of BT20 Titanium Alloy Based on the Artificial Neural Network", Titanium Alloy Prog., 27(4), 20-24.
  17. Pouraliakbar, H., Khalaj, M.J., Nazerfakhari, M. and Khalaj, G. (2015a), "Artificial neural networks for hardness prediction of HAZ with chemical composition and tensile test of X70 pipeline steels", J. Iron. Steel Res. Int., 22(5), 446-450. https://doi.org/10.1016/S1006-706X(15)30025-X
  18. Pouraliakbar, H., Khalaj, G., Jandaghi, M.R. and Khalaj, M.J. (2015b), "Study on the correlation of toughness with chemical composition and tensile test results in microalloyed API pipeline steels", J. Min. Metall. Sect. B Metall, 51(2), 173-178. https://doi.org/10.2298/JMMB140525025P
  19. Phaniraj, M.P. and Lahiri, A.K. (2003), "The applicability of neural network model to predict flow stress for carbon steels", Mater. Process. Tech., 141(2), 219-227. https://doi.org/10.1016/S0924-0136(02)01123-8
  20. Quan, G.Z., Liang, J.T., Lv, W.Q., Wu, D.S., Liu, Y.Y., Luo, G.C. and Zhou, J. (2016), "A characterization for the constitutive relationships of 42CrMo high strength steel by artificial neural network and its application in isothermal deformation", Mater. Res., 34, 53-59.
  21. Reddy, N.S., Lee, Y.H., Park, C.H. and Lee, C.S. (2008), "Prediction of flow stress in Ti-6Al-4V alloy with an equiaxed ${\alpha}$+${\beta}$ microstructure by artificial neural networks", Mater. Sci. Eng. A, 492(1-2), 276 -282. https://doi.org/10.1016/j.msea.2008.03.030
  22. Stefanos, D. and Gyan, P. (2015), "On neural network constitutive models for geomaterials", Int. J. Civil Eng. Res., 5(5), 106-113.
  23. Su, Y., Zeng, W.D., Zhao, Y.Q., Qi, Y.L., Ma, X. and Han, Y.F. (2010) "Development of constitutive relationship model of Ti600 alloy using artificial neural network", Compos. Mater. Sci., 48(3), 686-691. https://doi.org/10.1016/j.commatsci.2010.03.007
  24. Sumantra, M., Sivaprasad, P.V., Venugopal, S. and Murthy, K.P.N. (2009), "Artificial neural network modeling to evaluate and predict the deformation behavior of stainless steel type AISI 304L during hot forsion", Appl. Soft Comput., 9(1), 237-244. https://doi.org/10.1016/j.asoc.2008.03.016
  25. Yu, D.J. (2010), "A study on the uniaxial ratcheting behavior of austenitic stainless steel Z2CND18.12N", Mater Dissertation; Tianjin University, Tianjin; China.
  26. Yu, H.Q. and Chen, J.D. (2005), Principles of Metal Forming, China Machine PRESS, Beijing, China.
  27. Yu, D.J., Yu W.W., Chen, G. and Chen, X. (2012), "Role of dynamic strain aging in the tensile property, cyclic deformation and fatigue behavior of Z2CND18.12N stainless steel between 293 K and 723 K", Mat. Sci. Eng. A, 558, 730-736. https://doi.org/10.1016/j.msea.2012.08.088
  28. Zhang, Y.X., Gao, X.D. and Katayama, S. (2015), "Weld appearance prediction with BP neural network improved by genetic algorithm during disk laser welding", J. Manuf. Syst., 34, 53-59. https://doi.org/10.1016/j.jmsy.2014.10.005
  29. Zhou, J., Wan, X.M., Zhang, J.P., Yan, Z. and Li, Y. (2015), "Modeling of Constitutive Relationship of Aluminum Alloy Based on BP Neural Network Model", Mater. Today Proceedings, 2(10), 5023-5028. https://doi.org/10.1016/j.matpr.2015.10.092