Электронный научный журнал
 
Diagnostics, Resource and Mechanics 
         of materials and structures
ВыпускиО журналеАвторуРецензентуКонтактыНовостиРегистрация

2020 Выпуск 6

Все выпуски
 
2024 Выпуск 1
 
2023 Выпуск 6
 
2023 Выпуск 5
 
2023 Выпуск 4
 
2023 Выпуск 3
 
2023 Выпуск 2
 
2023 Выпуск 1
 
2022 Выпуск 6
 
2022 Выпуск 5
 
2022 Выпуск 4
 
2022 Выпуск 3
 
2022 Выпуск 2
 
2022 Выпуск 1
 
2021 Выпуск 6
 
2021 Выпуск 5
 
2021 Выпуск 4
 
2021 Выпуск 3
 
2021 Выпуск 2
 
2021 Выпуск 1
 
2020 Выпуск 6
 
2020 Выпуск 5
 
2020 Выпуск 4
 
2020 Выпуск 3
 
2020 Выпуск 2
 
2020 Выпуск 1
 
2019 Выпуск 6
 
2019 Выпуск 5
 
2019 Выпуск 4
 
2019 Выпуск 3
 
2019 Выпуск 2
 
2019 Выпуск 1
 
2018 Выпуск 6
 
2018 Выпуск 5
 
2018 Выпуск 4
 
2018 Выпуск 3
 
2018 Выпуск 2
 
2018 Выпуск 1
 
2017 Выпуск 6
 
2017 Выпуск 5
 
2017 Выпуск 4
 
2017 Выпуск 3
 
2017 Выпуск 2
 
2017 Выпуск 1
 
2016 Выпуск 6
 
2016 Выпуск 5
 
2016 Выпуск 4
 
2016 Выпуск 3
 
2016 Выпуск 2
 
2016 Выпуск 1
 
2015 Выпуск 6
 
2015 Выпуск 5
 
2015 Выпуск 4
 
2015 Выпуск 3
 
2015 Выпуск 2
 
2015 Выпуск 1

 

 

 

 

 

A. S. Smirnov, A. V. Konovalov, V. S. Kanakin

NEURAL NETWORK MODELING OF THE RHEOLOGY OF THE AlMg6 ALLOY UNDER THE DISPERSOID BARRIER EFFECT AND THE INHIBITION OF DYNAMIC RELAXATION PROCESSES

DOI: 10.17804/2410-9908.2020.6.010-026

The paper deals with a neural network to model the flow stress of the AlMg6 alloy at temperatures ranging between 300 and 500 °C and strain rates from 1 to 25 s−1. In this temperature–strain-rate range, the movement of free dislocations is blocked and dynamic relaxation processes are inhibited. The results of training the neural network and its verification at a temperature not used in the training show that neural networks with a single hidden layer can correctly approximate and predict the rheological behavior of the AlMg6 alloy for the studied temperature–strain-rate range of deformation.

Acknowledgements: The work was financially supported by the RFBR, grant 19-08-00765 (modeling the rheological behavior of materials); it was also performed as part of the research program of the Institute of Engineering Science, UB RAS, project AAAA-A18-118020790140-5, (studying the rheological behavior of the alMg6 alloy).

Keywords: neural network, flow stress, high temperature, aluminum alloy, AlMg6, barrier effect

Bibliography:

  1. Vichuzhanin D.I., Khotinov V.A., Smirnov S. V. The Effect of the Stress State on the Ultimate Plasticity of Steel X80. Diagnostics, Resource and Mechanics of materials and structures, 2015, iss. 1, pp. 73–89. DOI: 10.17804/2410-9908.2015.1.073-089. Available at: http://dream-journal.org/issues/2015-1/2015-1_21.html
  2. Smirnov S.V, Vichuzhanin D.I, Nesterenko A.V., Smirnov A.S., Pugacheva N.B., Konovalov A.V. A fracture locus for a 50 volume-percent Al/SiC metal matrix composite at high temperature. Int. J. Mater. Form., 2017, vol. 10, no. 5, pp. 831–843. DOI: 10.1007/s12289-016-1323-6.
  3. Smirnov S.V., Vichuzhanin D.I., Nesterenko A.V., Igumnov A.S. A fracture locus for commercially pure aluminum at 300°C. AIP Conf. Proc., 2016, vol. 1785, pp. 1–5. DOI: 10.1063/1.4967124.
  4. Smirnov S.V. Accumulation and Healing of Damage during Plastic Metal Forming Simulation and Experiment. Key Eng. Mater., 2012, vol. 528, pp. 61–69. DOI: 10.4028/www.scientific.net/KEM.528.61.
  5. Rollett A., Humphreys F., Rohrer G.S., Hatherly M. Recrystallization and Related Annealing Phenomena, Elsevier Ltd., 2004, 628 p.
  6. Doherty R.D., Hughes D.A., Humphreys F.J., Jonas J.J., Juul Jensen D., Kassner M.E., King W.E., McNelley T.R., McQueen H.J., Rollett A.D. Current issues in recrystallization: A review. Mater. Sci. Eng. A, 1997, vol. 238, no. 2, pp. 219–274. DOI: 10.1016/S0921-5093(97)00424-3.
  7. Polukhin P.I., Gorelik S.S., Vorontsov V.K. Fizicheskie osnovy plasticheskoi deformatsii [Basic Pysics of Plastic Deformation]. Moscow, Metallurgiya Publ., 1982, 584 p. (In Russian).
  8. Gorelik S.S., Dobatkin S.V., Kaputkina L.M. Rekristallizatsiya metallov i splavov [Recrystallization of Metals and Alloys, 3 ed.]. Moscow, MISSIS Publ., 2005, 432 p. (In Russian).
  9. Shibkov A.A., Mazilkin A.A., Protasova S.G., Mikhlik D.V., Zolotov A.E., Zheltov M.A., Shuklinov A.V. The influence of impurities on discontinuous deformation of the AMg6 alloy. Deformatsiya i Razrusheniye Materialov, 2008, no. 5, pp. 24–32. (In Russian).
  10. Chen S., Xie X., Chen B., Qiao J., Zhang Y., Ren Y., Dahmen K.A., Liaw P.K. Effects of Temperature on Serrated Flows of Al0.5CoCrCuFeNi High-Entropy Alloy. JOM, 2015, vol. 67 (10), pp. 2314–2320. DOI: 10.1007/s11837-015-1580-8.
  11. Belyaev A.I., Bochvar O.S., Buynov N.N. Metallovedenie alyuminiya i ego splavov [Physical Metallurgy of Aluminium and its Alloys]. Moscow, Metallurgiya Publ., 1983, 280 p. (In Russian).
  12. Anjabin N., Karimi Taheri A., Kim H.S. Simulation and experimental analyses of dynamic strain aging of a supersaturated age hardenable aluminum alloy. Mater. Sci. Eng. A, 2013, vol. 585, pp. 165–173.
  13. Wang C., Xu Y., Han E. Serrated flow and abnormal strain rate sensitivity of a magnesium–lithium alloy. Mater. Lett., 2006, vol. 60, no. 24, pp. 2941–2944.
  14. Hähner P., Rizzi E. On the kinematics of portevin-le chatelier bands: Theoretical and numerical modelling. Acta Mater., 2003, vol. 51, no. 12, pp. 3385–3397. DOI: 10.1016/S1359-6454(03)00122-8.
  15. Krishtal M.M. Discontinuous fluidity in aluminium-magnesium alloys. Fizika Metallov i Metallovedenie, 1990, no. 12, pp. 140–143. (In Russian).
  16. Rizzi E., Hähner P. On the Portevin-Le Chatelier effect: Theoretical modeling- and numerical results. Int. J. Plast., 2004, vol. 20, no. 1, pp. 121–165. DOI: 10.1016/S0749-6419(03)00035-4.
  17. Smirnov S.V., Veretennikova I.A., Vichuzhanin D.I. Modeling of delamination in multilayer metals produced by explosive welding under plastic deformation. Comput. Contin. Mech., 2014, vol. 7, no. 4, pp. 398–411. DOI: 10.7242/1999-6691/2014.7.4.38.
  18. Xu W., Jin X., Xiong W., Zeng X., Shan D. Study on hot deformation behavior and workability of squeeze-cast 20 vol%SiCw/6061Al composites using processing map. Mater. Charact., 2018, vol. 135, pp. 154–166. DOI: 10.1016/j.matchar.2017.11.026.
  19. Jang D.H., Kim W.J. Warm Temperature Deformation Behavior and Processing Maps of 5182 and 7075 Aluminum Alloy Sheets with Fine Grains. Met. Mater. Int., 2018, vol. 24, no. 3, pp. 455–463. DOI: 10.1007/s12540-018-0061-3.
  20. Lu J., Song Y., Hua L., Zheng K., Dai D. Thermal deformation behavior and processing maps of 7075 aluminum alloy sheet based on isothermal uniaxial tensile tests. J. Alloys Compd., 2018, vol. 767, pp. 856–869. DOI: 10.1016/j.jallcom.2018.07.173.
  21. Chen G., Chen L., Zhao G., Lu B. Investigation on longitudinal weld seams during porthole die extrusion process of high strength 7075 aluminum alloy. Int. J. Adv. Manuf. Technol., 2017, vol. 91, nos. 5–8, pp. 1897–1907. DOI: 10.1007/s00170-016-9902-8.
  22. Abo-Elkhier M. Modeling of High-Temperature Deformation of Commercial Pure Aluminum (1050). J. Mater. Eng. Perform., 2004, vol. 13, no. 2, pp. 241–247. DOI: 10.1361/10599490418280.
  23. Kodzhaspirov G.E., Physical modeling of thermomechanical processing processes and structure control of structural steel. Voprosy Materialovedeniya, 2009, no. 3, pp. 65–84. (In Russian).
  24. Polukhin P.I., Gun G.YA., Galkin A.M. Soprotivleniye plasticheskoy deformatsii metallov i splavov: spravochnik [Flow Stress of Metals and Alloys: handbook, 2 ed.]. Moscow, Metallurgiya Publ., 1983, 352 p. (In Russian).
  25. Lin Y.C., Chen X.-M. A critical review of experimental results and constitutive descriptions for metals and alloys in hot working. Mater. Des., 2011, vol. 32, no. 4, pp. 1733–1759. DOI: 10.1016/j.matdes.2010.11.048.
  26. Mochalov N.A., Galkin A.M., Mochalov S.N., Parfenov D.Yu. Plastometricheskie issledovaniya metallov [Plastometric Studies of Metals]. Moscow, Intermet Inzhiniring Publ., 2003, 318 p. (In Russian).
  27. Gourdet S., Montheillet F. A model of continuous dynamic recrystallization. Acta Mater., 2003, vol. 51, no. 9, pp. 2685–2699. DOI: 10.1016/S1359-6454(03)00078-8.
  28. Konovalov A.V. Viscoplastic model for the resistance of metals to high-temperature deformation. Metally, 2005, no. 5, pp. 94–98. (In Russian).
  29. Maizza G., Pero R., Richetta M., Montanari R., Mater J. Continuous dynamic recrystallization (CDRX) model for aluminum alloys. J. Mater. Sci., 2018, vol. 53, no. 6, pp. 4563–4573. DOI: 10.1007/s10853-017-1845-4.
  30. Sun Z.C., Wu H.L., Cao J., Yin Z.K. Modeling of continuous dynamic recrystallization of Al-Zn-Cu-Mg alloy during hot deformation based on the internal-state-variable (ISV) method. Int. J. Plast., 2018, vol. 106, pp. 73–87. DOI: 10.1016/j.ijplas.2018.03.002.
  31. Smirnov A.S., Konovalov A.V., Muizemnek O.Yu. Modelling and Simulation of Strain Resistance of Alloys Taking into Account Barrier Effects. Diagnostics, Resource and Mechanics of materials and structures, 2015, iss. 1, pp. 61–72. DOI: 10.17804/2410-9908.2015.1.061-072. URL: http://dream-journal.org/issues/2015-1/2015-1_18.html
  32. Kondratev N.S., Trusov P.V. Calculation of the intergranular energy in two-level physical models for describing thermomechanical processing of polycrystals with account for discontinuous dynamic recrystallization. Int. J. Nanomechanics Sci. Technol., 2016, vol. 7, no. 2, pp. 107–122. DOI: 10.1615/NanomechanicsSciTechnolIntJ.v7.i2.20.
  33. Zhang C., Zhang L.-W., Shen W.-F., Xia Y.-N., Yan Y.-T. 3D Crystal Plasticity Finite Element Modeling of the Tensile Deformation of Polycrystalline Ferritic Stainless Steel. Acta Metall. Sin. (English Lett.), 2017, vol. 30, no. 1, pp. 79–88. DOI: 10.1007/s40195-016-0488-9.
  34. Opĕla P., Kawulok P., Schindler I., Kawulok R., Rusz S., Navrátil H. On the zener-hollomon parameter, multi-layer perceptron and multivariate polynomials in the struggle for the peak and steady-state description. Metals, 2020, vol. 10, no. 11, pp. 1–20. DOI: 10.3390/met10111413.
  35. Panicker S.S., Prasad K.S., Basak S. Panda S.K., Constitutive Behavior and Deep Drawability of Three Aluminum Alloys Under Different Temperatures and Deformation Speeds. J. Mater. Eng. Perform., 2017, vol. 26, no. 8, pp. 3954–3969. DOI: 10.1007/s11665-017-2837-x.
  36. Lin Y.C., Zhang J., Zhong J. Application of neural networks to predict the elevated temperature flow behavior of a low alloy steel. Comput. Mater. Sci., 2008, vol. 43, no. 4, pp. 752–758. DOI: 10.1016/j.commatsci.2008.01.039.
  37. Zhu Y., Cao Y., Liu C., Luo R., Li N. Shu G., Huang G., Liu Q., Dynamic behavior and modified artificial neural network model for predicting flow stress during hot deformation of Alloy 925. Mater. Today Commun., 2020, vol. 25. DOI: 10.1016/j.mtcomm.2020.101329.
  38. Yuan Z., Li F., Ji G., Qiao H., Li J. Flow stress prediction of SiCp/Al composites at varying strain rates and elevated temperatures. J. Mater. Eng. Perform., 2014, vol. 23, no. 3, pp. 1016–1027. DOI: 10.1007/s11665-013-0838-y.
  39. Lin Y.C., Fang X., Wang Y.P. Prediction of metadynamic softening in a multi-pass hot deformed low alloy steel using artificial neural network. J. Mater. Sci., 2008, vol. 43, no. 16, pp. 5508–5515. DOI: 10.1007/s10853-008-2832-6.
  40. Singh K., Rajput S.K., Mehta Y. Modeling of the hot deformation behavior of a high phosphorus steel using artificial neural networks. Materials Discovery, 2016, vol. 6, pp. 1–8. DOI: 10.1016/j.md.2017.03.001.
  41. Bahrami A., Anijdan S.H.M. Hosseini H.R.M., Shafyei A., Narimani R., Effective parameters modeling in compression of an austenitic stainless steel using artificial neural network. Comput. Mater. Sci., 2005, vol. 34, no. 4, pp. 335–341. DOI: 10.1016/j.commatsci.2005.01.006.
  42. Li H.Y., Wei D.D., Li Y.H., Wang X.F., Application of artificial neural network and constitutive equations to describe the hot compressive behavior of 28CrMnMoV steel. Mater. Des., 2012, vol. 35, pp. 557–562. DOI: 10.1016/j.matdes.2011.08.049.
  43. Ji G., Li F., Li Q., Li H., Li Z. A comparative study on Arrhenius-type constitutive model and artificial neural network model to predict high-temperature deformation behaviour in Aermet100 steel. Mater. Sci. Eng. A., 2011, vol. 528, no. 13–14, pp. 4774–4782. DOI: 10.1016/J.MSEA.2011.03.017.
  44. Reddy N.S., Lee Y.H., Park C.H., Lee C.S. Prediction of flow stress in Ti-6Al-4V alloy with an equiaxed α + β microstructure by artificial neural networks. Mater. Sci. Eng. A, 2008, vol. 492, no. 1–2, pp. 276–282.
  45. Guo L.F., Li B.C., Zhang Z.M. Constitutive relationship model of TC21 alloy based on artificial neural network. Trans. Nonferrous Met. Soc. China (English Ed. The Nonferrous Metals Society of China), 2013, vol. 23, no. 6, pp. 1761–1765. DOI: 10.1016/S1003-6326(13)62658-8.
  46. Sabokpa O., Zarei-Hanzaki A., Abedi H.R., Haghdadi N. Artificial neural network modeling to predict the high temperature flow behavior of an AZ81 magnesium alloy. Mater. Des., 2012, vol. 39, pp. 390–396. DOI: 10.1016/j.matdes.2012.03.002.
  47. Sani S.A., Ebrahimi G.R., Vafaeenezhad H., Kiani-Rashid A.R. Modeling of hot deformation behavior and prediction of flow stress in a magnesium alloy using constitutive equation and artificial neural network (ANN) model. J. Magnes. Alloy, 2018, vol. 6, no. 2, pp. 134–144. DOI: 10.1016/j.jma.2018.05.002.
  48. Anaraki M.T., Sanjari M., Akbarzadeh A. Modeling of high temperature rheological behavior of AZ61 Mg-alloy using inverse method and ANN. Mater. Des., 2008, vol. 29, no. 9, pp. 1701–1706. DOI: 10.1016/j.matdes.2008.03.027.
  49. Mehtedi M. El, Forcellese A., Greco L., Pieralisi M., Simoncini M. Flow curve prediction of ZAM100 magnesium alloy sheets using artificial neural network-based models. Procedia CIRP, 2019, vol. 79, p. 661–666. DOI: 10.1016/j.procir.2019.02.050.
  50. Song R.G., Zhang Q.Z., Tseng M.K., Zhang B.J. The application of artificial neural networks to the investigation of aging dynamics in 7175 aluminium alloys. Mater. Sci. Eng. C, 1995, vol. 3, no. 1, pp. 39–41.
  51. Bruni C., Forcellese A., Gabrielli F., Simoncini M. Modelling of the rheological behaviour of aluminium alloys in multistep hot deformation using the multiple regression analysis and artificial neural network techniques. J. Mater. Process. Technol., 2006, vol. 177, no. 1–3, pp. 323–326. DOI: 10.1016/j.jmatprotec.2006.03.230.
  52. Dixit M.C., Srivastava N., Rajput S.K. Modeling of flow stress of AA6061 under hot compression using artificial neural network. Mater. Today Proc., 2017, vol. 4, no. 2, pp. 1964–1971. DOI: 10.1016/j.matpr.2017.02.042.
  53. Haghdadi N., Zarei-Hanzaki A., Khalesian A.R., Abedi H.R. Artificial neural network modeling to predict the hot deformation behavior of an A356 aluminum alloy. Mater. Des., 2013, vol. 49, pp. 386–391. DOI: 10.1016/j.matdes.2012.12.082.
  54. Lu Z., Pan Q., Liu X., Qin Y., He Y., Cao S. Artificial neural network prediction to the hot compressive deformation behavior of Al-Cu-Mg-Ag heat-resistant aluminum alloy. Mech. Res. Commun., 2011, vol. 38, no. 3, pp. 192–197. DOI: 10.1016/j.mechrescom.2011.02.015.
  55. Yang Y., Li F., Yuan Z., Qiao H. A modified constitutive equation for aluminum alloy reinforced by silicon carbide particles at elevated temperature. J. Mater. Eng. Perform., 2013, vol. 22, no. 9, pp. 2641–2655. DOI: 10.1007/s11665-013-0550-y.
  56. Jalham I.S. Modeling capability of the artificial neural network (ANN) to predict the effect of the hot deformation parameters on the strength of Al-base metal matrix composites. Compos. Sci. Technol., 2003, vol. 63, no. 1, pp. 63–67. DOI: 10.1016/S0266-3538(02)00176-8.
  57. Konovalov A.V., Smirnov A.S. Influence of dynamic strain aging of AMg6 alloy on strain resistance. Fiziko-khimicheskaya Kinetika v Gazovoy Dinamike, 2011, vol. 11, no. 1, pp. 12–16. (In Russian).
  58. Konovalov A.V., Smirnov A.S. Identification of a strain resistance model of metals according to the results of compression tests of specimens. Zavodskaya laboratoriya. Diagnostika materialov, 2010, vol. 76, no 1, pp. 53–56. (In Russian).
  59. Li H., Phung D. Journal of Machine Learning Research: Preface. J. Mach. Learn. Res., 2014, vol. 39, no. 2014, pp. 1–2.
  60. Konovalov A.V., Smirnov A.S. Simulation of strain resistance of AMg6 alloy under hot tem perature deformation. Deformatsiya i Razrusheniye Materialov, 2008, no 5, pp. 33–36. (In Russian).

А. С. Смирнов, А. В. Коновалов, В. С. Канакин

НЕЙРОСЕТЕВОЕ МОДЕЛИРОВАНИЕ РЕОЛОГИИ СПЛАВА АМг6 В УСЛОВИЯХ ПРОЯВЛЕНИЯ БАРЬЕРНОГО ЭФФЕКТА ДИСПЕРСОИДАМИ И ЗАМЕДЛЕНИЯ ДИНАМИЧЕСКИХ РЕЛАКСАЦИОННЫХ ПРОЦЕССОВ

В статье с помощью нейронной сети моделируется сопротивление деформации сплава АМг6 в диапазоне температур 300–500 °С и скоростей деформаций 1–25 с-1, в котором происходит блокирование движения свободных дислокаций и замедление динамических релаксационных процессов. Результаты обучения нейронной сети и ее верификации при температуре, не применявшейся при обучении, показали, что нейронные сети с одним скрытым слоем могут корректно аппроксимировать и прогнозировать реологическое поведение сплава АМг6 в исследуемом температурно-скоростном диапазоне деформаций.

Благодарности: Работа выполнена при финансовой поддержке гранта РФФИ 19-08-00765 в части моделирования реологического поведения материалов, а также в рамках программы исследований Института машиноведения УрО РАН (проект № AAAA-A18-118020790140-5) в части изучения реологического поведения сплава АМг6.

Ключевые слова: нейронная сеть, сопротивление деформации, высокая температура, алюминиевый сплав, АМг6, барьерный эффект

Библиография:

  1. Vichuzhanin D. I., Khotinov V. A., Smirnov S. V. The Effect of the Stress State on the Ultimate Plasticity of Steel X80 // Diagnostics, Resource and Mechanics of materials and structures. – 2015. – Iss. 1. – P. 73–89. – DOI: 10.17804/2410-9908.2015.1.073-089. – URL: http://dream-journal.org/issues/2015-1/2015-1_21.html
  2. A fracture locus for a 50 volume-percent Al/SiC metal matrix composite at high temperature / S. V. Smirnov, D. I. Vichuzhanin, A. V. Nesterenko, A. S. Smirnov, N. B. Pugacheva, A. V. Konovalov // Int. J. Mater. Form.– 2017. – Vol. 10, no. 5. – P. 831–843. – DOI: 10.1007/s12289-016-1323-6.
  3. A fracture locus for commercially pure aluminum at 300°C / S. V. Smirnov, D. I. Vichuzhanin, A. V. Nesterenko, A. S. Igumnov // AIP Conf. Proc. – 2016. – Vol. 1785. – P. 1–5. – DOI: 10.1063/1.4967124.
  4. Smirnov S. V. Accumulation and Healing of Damage during Plastic Metal Forming Simulation and Experiment // Key Eng. Mater. Trans Tech Publications. – 2012. – Vol. 528. – P. 61–69. – DOI: 10.4028/www.scientific.net/KEM.528.61.
  5. Recrystallization and Related Annealing Phenomena / A. Rollett, F. Humphreys, G. S. Rohrer, M. Hatherly. – Elsevier Ltd., 2004. – 628 p.
  6. Current issues in recrystallization: A review / R. D. Doherty, D. A. Hughes, F. J. Humphreys, J. J. Jonas, Jensen D. Juul, M. E. Kassner, W. E. King, T. R. McNelley, H. J. McQueen, A. D. Rollett // Mater. Sci. Eng. A. – 1997. – Vol. 238, no. 2. – P. 219–274. – DOI: 10.1016/S0921-5093(97)00424-3.
  7. Полухин П. И., Горелик С. С., Воронцов В. К. Физические основы пластической деформации. – М. : Металлургия, 1982. – 584 c.
  8. Горелик С. С., Добаткин С. В., Капуткина Л. М. Рекристаллизация металлов и сплавов. – 3-е изд. – М. : МИССИС, 2005. – 432 c.
  9. Влияние состояния примесей на скачкообразную деформациюсплава АМГ6 / А. А. Шибков, А. А. Мазилкин, С. Г. Протасова, Д. В. Михлик, А. Е. Золотов, М. А. Желтов, А. В. Шуклинов // Деформация и разрушение материалов. – 2008. – № 5. – C. 24–32.
  10. Effects of Temperature on Serrated Flows of Al0.5CoCrCuFeNi High-Entropy Alloy / S. Chen, X. Xie, B. Chen, J. Qiao, Y. Zhang, Y. Ren, K. A. Dahmen, P. K. Liaw // JOM. – 2015. – Vol. 67 (10). – P. 2314–2320. – DOI: 10.1007/s11837-015-1580-8.
  11. Беляев А. И., Бочвар О. С., Буйнов Н. Н. Металловедение алюминия и его сплавов. – М. : Металлургия, 1983. – 280 c.
  12. Anjabin N., Karimi Taheri A., Kim H. S. Simulation and experimental analyses of dynamic strain aging of a supersaturated age hardenable aluminum alloy // Mater. Sci. Eng. A. – 2013. – Vol. 585. – P. 165–173.
  13. Wang C., Xu Y., Han E. Serrated flow and abnormal strain rate sensitivity of a magnesium–lithium alloy // Mater. Lett. – 2006. – Vol. 60, no. 24. – P. 2941–2944.
  14. Hähner P., Rizzi E. On the kinematics of portevin-le chatelier bands: Theoretical and numerical modelling // Acta Mater. – 2003. – Vol. 51, no. 12. – P. 3385–3397. – DOI: 10.1016/S1359-6454(03)00122-8.
  15. Криштал М. М. Прерывистая текучесть в алюминиево-магниевых сплавах // Физика металлов и металловедение. – 1990.– № 12. – C. 140–143.
  16. Rizzi E., Hähner P. On the Portevin-Le Chatelier effect: Theoretical modeling- and numerical results // Int. J. Plast. – 2004. – Vol. 20, no. 1. – P. 121–165. – DOI: 10.1016/S0749-6419(03)00035-4.
  17. Smirnov S. V., Veretennikova I. A., Vichuzhanin D. I. Modeling of delamination in multilayer metals produced by explosive welding under plastic deformation // Comput. Contin. Mech. – 2014. – Vol. 7, no. 4. – P. 398–411. – DOI: 10.7242/1999-6691/2014.7.4.38.
  18. Study on hot deformation behavior and workability of squeeze-cast 20 vol%SiCw/6061Al composites using processing map / W. Xu, X. Jin, W. Xiong, X. Zeng, D. Shan // Mater. Charact. – 2018. – Vol. 135. – P. 154–166. – DOI: 10.1016/j.matchar.2017.11.026.
  19. Jang D. H., Kim W. J. Warm Temperature Deformation Behavior and Processing Maps of 5182 and 7075 Aluminum Alloy Sheets with Fine Grains // Met. Mater. Int. – 2018. – Vol. 24, no. 3. – P. 455–463. – DOI: 10.1007/s12540-018-0061-3.
  20. Thermal deformation behavior and processing maps of 7075 aluminum alloy sheet based on isothermal uniaxial tensile tests / J. Lu, Y. Song, L. Hua, K. Zheng, D. Dai // J. Alloys Compd. – 2018. – Vol. 767. – P. 856–869. – DOI: 10.1016/j.jallcom.2018.07.173.
  21. Investigation on longitudinal weld seams during porthole die extrusion process of high strength 7075 aluminum alloy / G. Chen, L. Chen, G. Zhao, B. Lu // Int. J. Adv. Manuf. Technol. – 2017. – Vol. 91, nos. 5–8. – P. 1897–1907. – DOI: 10.1007/s00170-016-9902-8.
  22. Abo-Elkhier M. Modeling of High-Temperature Deformation of Commercial Pure Aluminum (1050) // J. Mater. Eng. Perform. – 2004. – Vol. 13, no. 2. – P. 241–247. – DOI: 10.1361/10599490418280.
  23. Коджаспиров Г. Е. Физическое моделирование процессов термомеханической обработки и управление структурой конструкционной стали // Вопросы материаловедения. – 2009. – № 3. – C. 65–84.
  24. Полухин П. И., Гун Г. Я., Галкин А. М. Сопротивление пластической деформации металлов и сплавов : справочник. – 2-е изд.. – М. : Металлургия, 1983. –352 c.
  25. Lin Y. C., Chen X.-M. A critical review of experimental results and constitutive descriptions for metals and alloys in hot working // Mater. Des. – 2011. – Vol. 32, no. 4. – P. 1733–1759. – DOI: 10.1016/j.matdes.2010.11.048.
  26. Пластометрические исследования металлов / Н. А. Мочалов, А. М. Галкин, С. Н. Мочалов, Д. Ю. Парфенов. – М. : Интермет инжиниринг, 2003. – 318 c.
  27. Gourdet S., Montheillet F. A model of continuous dynamic recrystallization // Acta Mater. – 2003. – Vol. 51, no. 9. – P. 2685–2699. – DOI: 10.1016/S1359-6454(03)00078-8.
  28. Коновалов А. В. Вязкопластическая модель сопротивления металла высокотемпературной деформации // Металлы. – 2005. – № 5. – C. 94–98.
  29. Continuous dynamic recrystallization (CDRX) model for aluminum alloys / G. Maizza, R. Pero, M. Richetta, R. Montanari // J. Mater. Sci. – 2018. – Vol. 53, no. 6. – P. 4563–4573. – DOI: 10.1007/s10853-017-1845-4.
  30. Modeling of continuous dynamic recrystallization of Al-Zn-Cu-Mg alloy during hot deformation based on the internal-state-variable (ISV) method / Z. C. Sun, H. L. Wu, J. Cao, Z. K. Yin // Int. J. Plast. – 2018. – Vol. 106. – P. 73–87. – DOI: 10.1016/j.ijplas.2018.03.002.
  31. Smirnov A. S., Konovalov A. V., Muizemnek O. Yu. Modelling and Simulation of Strain Resistance of Alloys Taking into Account Barrier Effects // Diagnostics, Resource and Mechanics of materials and structures. – 2015. – Iss. 1. – P. 61–72. – DOI: 10.17804/2410-9908.2015.1.061-072. – URL: http://dream-journal.org/issues/2015-1/2015-1_18.html
  32. Kondratev N. S., Trusov P. V. Calculation of the intergranular energy in two-level physical models for describing thermomechanical processing of polycrystals with account for discontinuous dynamic recrystallization // Int. J. Nanomechanics Sci. Technol. – 2016. – Vol. 7, no. 2. – P. 107–122. – DOI: 10.1615/NanomechanicsSciTechnolIntJ.v7.i2.20.
  33. 3D Crystal Plasticity Finite Element Modeling of the Tensile Deformation of Polycrystalline Ferritic Stainless Steel / C. Zhang, L.-W. Zhang, W.-F. Shen, Y.-N. Xia, Y.-T. Yan // Acta Metall. Sin. (English Lett.). – 2017. – Vol. 30, no. 1. – P. 79–88. – DOI: 10.1007/s40195-016-0488-9.
  34. On the zener-hollomon parameter, multi-layer perceptron and multivariate polynomials in the struggle for the peak and steady-state description / P. Opĕla, P. Kawulok, I. Schindler, R. Kawulok, S. Rusz, H. Navrátil // Metals. – 2020. – Vol. 10, no. 11. – P. 1–20. – DOI: 10.3390/met10111413.
  35. Constitutive Behavior and Deep Drawability of Three Aluminum Alloys Under Different Temperatures and Deformation Speeds / S. S. Panicker, K. S. Prasad, S. Basak, S. K. Panda // J. Mater. Eng. Perform. – 2017. – Vol. 26, no. 8. – P. 3954–3969. – DOI: 10.1007/s11665-017-2837-x.
  36. Lin Y. C., Zhang J., Zhong J. Application of neural networks to predict the elevated temperature flow behavior of a low alloy steel // Comput. Mater. Sci. – 2008. – Vol. 43, no. 4. – P. 752–758. – DOI: 10.1016/j.commatsci.2008.01.039.
  37. Dynamic behavior and modified artificial neural network model for predicting flow stress during hot deformation of Alloy 925 / Y. Zhu, Y. Cao, C. Liu, R. Luo, N. Li, G. Shu, G. Huang, Q. Liu // Mater. Today Commun. – 2020. – Vol. 25. – P. 101329. – DOI: 10.1016/j.mtcomm.2020.101329.
  38. Flow stress prediction of SiCp/Al composites at varying strain rates and elevated temperatures / Z. Yuan, F. Li, G. Ji, H. Qiao, J. Li // J. Mater. Eng. Perform. – 2014. – Vol. 23, no. 3. – P. 1016–1027. – DOI: 10.1007/s11665-013-0838-y.
  39. Lin Y. C., Fang X., Wang Y. P. Prediction of metadynamic softening in a multi-pass hot deformed low alloy steel using artificial neural network // J. Mater. Sci. – 2008. – Vol. 43, no. 16. – P. 5508–5515. – DOI: 10.1007/s10853-008-2832-6.
  40. Singh K., Rajput S. K., Mehta Y. Modeling of the hot deformation behavior of a high phosphorus steel using artificial neural networks // Mater. Discov. – 2016. – Vol. 6. – P. 1–8. – DOI: 10.1016/j.md.2017.03.001
  41. Effective parameters modeling in compression of an austenitic stainless steel using artificial neural network / A. Bahrami, S. H. M. Anijdan, H. R. M. Hosseini, A. Shafyei, R. Narimani // Comput. Mater. Sci. – 2005. – Vol. 34, no. 4. – P. 335–341. – DOI: 10.1016/j.commatsci.2005.01.006.
  42. Application of artificial neural network and constitutive equations to describe the hot compressive behavior of 28CrMnMoV steel / H. Y. Li, D. D. Wei, Y. H. Li, X.F. Wang // Mater. Des. – 2012. – Vol. 35. – P. 557–562. – DOI: 10.1016/j.matdes.2011.08.049.
  43. A comparative study on Arrhenius-type constitutive model and artificial neural network model to predict high-temperature deformation behaviour in Aermet100 steel / G. Ji, F. Li, Q. Li, H. Li, Z. Li // Mater. Sci. Eng. A. – 2011. – Vol. 528, nos. 13–14. – P. 4774–4782. – DOI: 10.1016/J.MSEA.2011.03.017.
  44. Prediction of flow stress in Ti-6Al-4V alloy with an equiaxed α + β microstructure by artificial neural networks / N. S. Reddy, Y. H. Lee, C. H. Park, C. S. Lee // Mater. Sci. Eng. A. – 2008. – Vol. 492, nos. 1–2. – P. 276–282.
  45. Guo L.F., Li B.C., Zhang Z.M. Constitutive relationship model of TC21 alloy based on artificial neural network // Trans. Nonferrous Met. Soc. China (English Ed. The Nonferrous Metals Society of China, – 2013. – Vol. 23, no. 6. – P. 1761–1765. – DOI: 10.1016/S1003-6326(13)62658-8.
  46. Artificial neural network modeling to predict the high temperature flow behavior of an AZ81 magnesium alloy / O. Sabokpa, A. Zarei-Hanzaki, H. R. Abedi, N. Haghdadi // Mater. Des. – 2012. – Vol. 39. – P. 390–396. – DOI: 10.1016/j.matdes.2012.03.002.
  47. Modeling of hot deformation behavior and prediction of flow stress in a magnesium alloy using constitutive equation and artificial neural network (ANN) model / S. A. Sani, G. R. Ebrahimi, H. Vafaeenezhad, A. R. Kiani-Rashid // J. Magnes. Alloy. Elsevier B.V. – 2018. – Vol. 6, no. 2. – P. 134–144. – DOI: 10.1016/j.jma.2018.05.002.
  48. Anaraki M. T., Sanjari M., Akbarzadeh A. Modeling of high temperature rheological behavior of AZ61 Mg-alloy using inverse method and ANN // Mater. Des. – 2008. – Vol. 29, no. 9. – P. 1701–1706. – DOI: 10.1016/j.matdes.2008.03.027.
  49. Flow curve prediction of ZAM100 magnesium alloy sheets using artificial neural network-based models / M. El Mehtedi, A. Forcellese, L. Greco, M. Pieralisi, M. Simoncini // Procedia CIRP. – 2019. – Vol. 79. – P. 661–666. – DOI: 10.1016/j.procir.2019.02.050.
  50. The application of artificial neural networks to the investigation of aging dynamics in 7175 aluminium alloys / R. G. Song, Q. Z. Zhang, M. K. Tseng, B. J. Zhang // Mater. Sci. Eng. C. – 1995. – Vol. 3, no. 1. – P. 39–41.
  51. Modelling of the rheological behaviour of aluminium alloys in multistep hot deformation using the multiple regression analysis and artificial neural network techniques / C. Bruni, A. Forcellese, F. Gabrielli, M. Simoncini // J. Mater. Process. Technol. – 2006. – Vol. 177, nos. 1–3. – P. 323–326. – DOI: 10.1016/j.jmatprotec.2006.03.230.
  52. Dixit M. C., Srivastava N., Rajput S. K. Modeling of flow stress of AA6061 under hot compression using artificial neural network // Mater. Today Proc. – 2017. – Vol. 4, no. 2. – P. 1964–1971. – DOI: 10.1016/j.matpr.2017.02.042.
  53. Artificial neural network modeling to predict the hot deformation behavior of an A356 aluminum alloy / N. Haghdadi, A. Zarei-Hanzaki, A. R. Khalesian, H. R. Abedi // Mater. Des. Elsevier Ltd, – 2013. – Vol. 49. – P. 386–391. – DOI: 10.1016/j.matdes.2012.12.082.
  54. Artificial neural network prediction to the hot compressive deformation behavior of Al-Cu-Mg-Ag heat-resistant aluminum alloy / Z. Lu, Q. Pan, X. Liu, Y. Qin, Y. He, S. Cao // Mech. Res. Commun. – 2011. – Vol. 38, no. 3. – P. 192–197. – DOI: 10.1016/j.mechrescom.2011.02.015.
  55. A modified constitutive equation for aluminum alloy reinforced by silicon carbide particles at elevated temperature / Y. Yang, F. Li, Z. Yuan, H. Qiao // J. Mater. Eng. Perform. – 2013. – Vol. 22, no. 9. – P. 2641–2655. – DOI: 10.1007/s11665-013-0550-y.
  56. Jalham I. S. Modeling capability of the artificial neural network (ANN) to predict the effect of the hot deformation parameters on the strength of Al-base metal matrix composites // Compos. Sci. Technol. – 2003. – Vol. 63, no. 1. – P. 63–67. – DOI: 10.1016/S0266-3538(02)00176-8.
  57. Коновалов А. В., Смирнов А. С. Влияние динамического деформационного старения сплава АМг6 на сопротивление деформации // Физико-химическая кинетика в газовой динамике. – 2011. – Т. 11, № 1. – C. 12–16.
  58. Коновалов А. В., Смирнов А. С. Идентификация модели сопротивления деформации металлов по результатам испытаний на сжатие образцов // Заводская лаборатория. Диагностика материалов. – 2010. – Т. 76, № 1. – C. 53–56.
  59. Li H., Phung D. Journal of Machine Learning Research: Preface // J. Mach. Learn. Res. – 2014. – Vol. 39, no. 2014. – P. 1–2.
  60. Коновалов А. В., Смирнов А. С. Моделирование сопротивления деформации сплава АМг6 при температуре горячей деформации // Деформация и разрушение материалов. – 2008. – № 5. – C. 33–36.

PDF      

Библиографическая ссылка на статью

Smirnov A. S., Konovalov A. V., Kanakin V. S. Neural Network Modeling of the Rheology of the Almg6 Alloy under the Dispersoid Barrier Effect and the Inhibition of Dynamic Relaxation Processes // Diagnostics, Resource and Mechanics of materials and structures. - 2020. - Iss. 6. - P. 10-26. -
DOI: 10.17804/2410-9908.2020.6.010-026. -
URL: http://dream-journal.org/issues/2020-6/2020-6_309.html
(accessed: 16.04.2024).

 

импакт-фактор
РИНЦ 0.42

категория К2
в перечне ВАК

МРДМК 2024
ЦКП Пластометрия
НЭБ РИНЦ
Google Scholar


РНБ
Лань

 

Учредитель:  Федеральное государственное бюджетное учреждение науки Институт машиноведения имени Э.С. Горкунова Уральского отделения Российской академии наук
Главный редактор:  С.В.Смирнов
При цитировании ссылка на Электронный научно-технический журнал "Diagnostics, Resource and Mechanics of materials and structures" обязательна. Воспроизведение материалов в электронных или иных изданиях без письменного разрешения редакции запрещено. Опубликованные в журнале материалы могут использоваться только в некоммерческих целях.
Контакты  
 
Главная E-mail 0+
 

ISSN 2410-9908 Регистрация СМИ в Роскомнадзоре Эл № ФС77-57355 от 24 марта 2014 г. © ИМАШ УрО РАН 2014-2024, www.imach.uran.ru