SpringerLink
Log in

Microstructure simulation of AA2219 alloy in hot/warm forming and heat treatment using cellular automata methods

  • Computation & theory
  • Published:
Journal of Materials Science Aims and scope Submit manuscript

Abstract

The preparation of component with 2219 aluminum alloy consists of hot/warm forming and the subsequent heat treatment, inducing complex microstructure evolution. The microstructure simulation in forming and solution treatments is investigated using cellular automata (CA) models, consisting of initial microstructure generation model, static recrystallization (SRX) model and thermal–mechanical treatment model. The effect of parameters (temperature, strain, strain rate and pass interval time) on the microstructure characteristic is analyzed. SRX occurs during the pass interval time by the transformation from low-angle grain boundaries into high-angle grain boundaries. Low temperature, large strain and strain rate during the hot compression contribute to the SRX process during pass interval time due to the resulted higher dislocation. Warm forming is beneficial for the increased dislocation density and sub-grain structures, which change to equiaxed grains after solution treatment, indicating the total recrystallization. The increased strain and strain rate, and lower temperature in the warm forming process contribute to the grain refinement and acceleration of the velocity of the SRX process after solution treatment. Experiments of hot compression, warm compression followed by solution treatment are conducted. The CA simulated flow stress and microstructure agree with the experimental results. The CA models established can provide guidance for the microstructure evolution for the hot/warm forming and the subsequent heat treatment of aluminum alloy.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9
Figure 10
Figure 11
Figure 12

Similar content being viewed by others

Data availability

Due to the nature of this research, participants of this study did not agree for their data to be shared publicly, so supporting data are not available.

References

  1. Cheng M, Luo G, **ao X, Song L (2022) Microstructure evolution of pulsed laser melting 2219 aluminum alloy: solidification modes and inter-pulse thermal cycles. Mater Lett 313:131768

    Article  CAS  Google Scholar 

  2. Wan Z, Wang Q, Zhao Y, Zhao T, Shan J, Meng D, Song J, Wu A, Wang G (2022) Improvement in tensile properties of 2219–T8 aluminum alloy TIG welding joint by PMZ local properties and stress distribution. Mat Sci Eng A-Struct 839:142863

    Article  CAS  Google Scholar 

  3. Zhu H, Huang L, Wang Z, Li J, Ma H, Su H (2019) Fracture behaviour of laser-welded 2219–T6 aluminium alloy under pulsed Lorentz force. J Mater Sci 54:9857–9874. https://doi.org/10.1007/s10853-019-03588-4

    Article  CAS  Google Scholar 

  4. Roh A, Um HY, Kim D, Nam S, Kim HS (2017) Influence of high-pressure torsion and hot rolling on the microstructure and mechanical properties of aluminum–fullerene composites. J Mater Sci 52:11988–12000. https://doi.org/10.1007/s10853-017-1230-3

    Article  CAS  Google Scholar 

  5. Wei L, Han B, Ye F, Ditta A, Wu S (2020) Influencing mechanisms of heat treatments on microstructure and comprehensive properties of Al–Zn–Mg–Cu alloy formed by spray forming. J Mater Res Technol 9(3):6850–6858

    Article  CAS  Google Scholar 

  6. Zhang C, Yadav V, Moelans N, Jensen D, Yu T (2022) The effect of voids on boundary migration during recrystallization in additive manufactured samples-a phase field study. Scr Mater 214:114675

    Article  CAS  Google Scholar 

  7. Liang S, Wei C, Ke C (2021) Effect of anisotropy in thermal conductivity on grain boundary migration under temperature gradient - a phase field study. Mater Lett 303:130517

    Article  CAS  Google Scholar 

  8. ** Y, Bozzolo N, Rollett AD, Bernacki M (2015) 2D finite element modeling of misorientation dependent anisotropic grain growth in polycrystalline materials: level set versus multi-phase-field method. Comput Mater Sci 104:108–123

    Article  Google Scholar 

  9. Steiner MA, Mccabe RJ, Garlea E, Steiner M (2017) Monte Carlo modeling of recrystallization processes in alpha-uranium. J Nucl Mater 492:74–87

    Article  CAS  Google Scholar 

  10. Schwen D, Schunert S, Jokisaari A (2021) Evolution of microstructures in radiation fields using a coupled binary-collision Monte Carlo phase field approach. Comput Mater Sci 192:110321

    Article  CAS  Google Scholar 

  11. Salehi MS, Serajzadeh S (2012) Simulation of static recrystallization in non-isothermal annealing using a coupled cellular automata and finite element model. Comput Mater Sci 53:145–152

    Article  Google Scholar 

  12. Ach U (2021) Modeling of microstructure evolution during deformation processes by cellular automata-boundary conditions and space reorganization aspects. Materials 14:1377

    Article  Google Scholar 

  13. Teferra K, Rowenhorst DJ (2021) Optimizing the cellular automata finite element model for additive manufacturing to simulate large microstructures. Acta Mater 213:116930

    Article  CAS  Google Scholar 

  14. Li X, Ma W (2021) Molecular dynamics simulation and theoretical modeling of free surface effect on nanocrack initiation induced by grain boundary sliding in nanocrystalline materials. Mater Lett 304:130647

    Article  CAS  Google Scholar 

  15. Yang Q, Xue C, Chu Z, Li Y, Ma L, Gao H (2021) Molecular dynamics study on the relationship between phase transition mechanism and loading direction of AZ31. Sci Rep 11:17229

    Article  CAS  Google Scholar 

  16. Zhang H, Wang J, Chen Q, Shu D, Wang C, Chen G, Zhao Z (2019) Study of dynamic recrystallization behavior of T2 copper in hot working conditions by experiments and cellular automaton method. J Alloy Compd 784:1071–1083

    Article  CAS  Google Scholar 

  17. Zhang F, Liu D, Yang Y, Liu C, Zhang Z, Wang H, Wang J (2020) Investigation on the meta-dynamic recrystallization behavior of inconel 718 superalloy in the presence of δ phase through a modified cellular automaton model. J Alloy Compd 817:152773

    Article  CAS  Google Scholar 

  18. Lin YC, Liu Y, Chen M, Huang M, Ma X, Long Z (2016) Study of static recrystallization behavior in hot deformed Ni-based superalloy using cellular automaton model. Mater Des 99:107–114

    Article  CAS  Google Scholar 

  19. Madej L, Sitko M, Radwanski K, Kuziak R (2016) Validation and predictions of coupled finite element and cellular automata model: influence of the degree of deformation on static recrystallization kinetics case study. Mater Chem Phys 179:282–294

    Article  CAS  Google Scholar 

  20. Chen F, Qi K, Cui Z, Lai X (2014) Modeling the dynamic recrystallization in austenitic stainless steel using cellular automaton method. Comput Mater Sci 83:331–340

    Article  CAS  Google Scholar 

  21. Bakhtiari M, Salehi MS (2018) Reconstruction of deformed microstructure using cellular automata method. Comput Mater Sci 149:1–13

    Article  Google Scholar 

  22. Timoshenkov A, Warczok P, Albu M, Klarner J, Kozeschink E, Bureau R, Sommitsch C (2014) Modelling the dynamic recrystallization in C-Mn micro-alloyed steel during thermo-mechanical treatment using cellular automata. Comput Mater Sci 94:85–94

    Article  CAS  Google Scholar 

  23. ** Z, Liu J, Cui Z, Wei D (2010) Identification of nucleation parameter for cellular automaton model of dynamic recrystallization. Trans Nonferrous Met Soc 20:458–464

    Article  CAS  Google Scholar 

  24. ** Z, Cui Z (2012) Investigation on dynamic recrystallization using a modified cellular automaton. Comput Mater Sci 63:249–255

    Article  CAS  Google Scholar 

  25. Madej L, Sitko M, Legwand A, Perzynski K, Michalik K (2018) Development and evaluation of data transfer protocols in the fully coupled random cellular automata finite element model of dynamic recrystallization. J Comput Sci 26:66–77

    Article  Google Scholar 

  26. Chen K, Tang J, Jiang F, Teng J, Zhang H (2019) The role of various Zr additions in static softening behavior of Al–Zn–Mg–Cu alloys during interval holding of double-stage hot deformation. J Alloy Compd 792:1112–1121

    Article  CAS  Google Scholar 

  27. Jiang F, Zurob HS, Purdy GR, Hui Z (2015) Static softening following multistage hot deformation of 7150 aluminum alloy: experiment and modeling. Mat Sci Eng A-Struct 648:164–177

    Article  CAS  Google Scholar 

  28. Pouraliakbar H, Pakbaz M, Firooz S, Jandaghi MR, Khalaj G (2016) Study on the dynamic and static softening phenomena in Al–6Mg alloy during two-stage deformation through interrupted hot compression test. Measurement 77:50–53

    Article  Google Scholar 

  29. Bo G, Jiang F, Su H, Wu L, Zhang H (2020) Static softening behavior and modeling of an Al–Cu–Mg–Zr alloy with various pre-precipitation microstructures during multistage hot deformation. Mat Sci Eng A-Struct 778:139094

    Article  CAS  Google Scholar 

  30. Zhang F, Liu D, Yang Y, Wang J, Zheng Y (2017) Study of factors affecting simulation of static recrystallization of Ni-based superalloy through cellular automaton model. Procedia Eng 207:2131–2136

    Article  CAS  Google Scholar 

  31. Sitko M, Chao Q, Wang J, Perzynski K, Madej L (2020) A parallel version of the cellular automata static recrystallization model dedicated for high performance computing platforms - development and verification. Comp Mater Sci 172:109283

    Article  CAS  Google Scholar 

  32. Zheng C, **ao N, Li D (2008) Microstructure prediction of the austenite recrystallization during multi-pass steel strip hot rolling: a cellular automaton modeling. Comput Mater Sci 44:507–514

    Article  CAS  Google Scholar 

  33. Zhang T, Li L, Lu S, Zhang J, Gong H (2018) Comparisons of flow behavior characteristics and microstructure between asymmetrical shear rolling and symmetrical rolling by macro/micro coupling simulation. J Comput Sci 29:142–152

    Article  Google Scholar 

  34. Liu W, Chen X, Ahmad T, Zhou C, **ao X, Wang H, Yang B (2022) Microstructures and mechanical properties of Cu–Ti alloys with ultrahigh strength and high ductility by thermo-mechanical treatment. Mat Sci Eng A-Struct 835:142672

    Article  CAS  Google Scholar 

  35. Yu X, Chen S, Wang L (2009) Simulation of recrystallization in cold worked stainless steel and its effect on chromium depletion by cellular automaton. Comput Mater Sci 46:66–72

    Article  CAS  Google Scholar 

  36. Zheng C, Raabe D (2013) Interaction between recrystallization and phase transformation during intercritical annealing in a cold-rolled dual-phase steel: a cellular automaton model. Acta Mater 61:5504–5517

    Article  CAS  Google Scholar 

  37. Madej L, Sieradzki L, Sitko M, Perzynski K, Radwanski K, Kuziak R (2013) Multi scale cellular automata and finite element based model for cold deformation and annealing of a ferritic–pearlitic microstructure. Comput Mater Sci 77:172–181

    Article  CAS  Google Scholar 

  38. He H, Yi Y, Huang S, Zhang Y (2019) An improved process for grain refinement of large 2219 Al alloy rings and its influence on mechanical properties. J Mater Sci Technol 35:55–63. https://doi.org/10.1016/j.jmst.2018.09.007

    Article  CAS  Google Scholar 

  39. Guo W, Yi Y, Huang S, He H, Fang J (2020) Effects of warm rolling deformation on the microstructure and ductility of large 2219 Al–Cu alloy rings. Met Mater Int 26:56–68

    Article  CAS  Google Scholar 

  40. Liu L, Wu Y, Ahmad S (2021) A novel simulation of continuous dynamic recrystallization process for 2219 aluminium alloy using cellular automata technique. Mat Sci Eng A-Struct 815:141256

    Article  CAS  Google Scholar 

  41. Oudin A, Barnett MR, Hodgson PD (2004) Grain size effect on the warm deformation behaviour of a Ti-IF steel. Mat Sci Eng A-Struct 367:282–294

    Article  Google Scholar 

  42. Yoshie A, Fujita T, Fujioka M (1996) Formulation of the decrease in dislocation density of deformed austenite due to static recovery and recrystallization. ISIJ Int 36:474–480

    Article  Google Scholar 

  43. Ding R, Guo ZX (2001) Coupled quantitative simulation of microstructural evolution and plastic flow during dynamic recrystallization. Acta Mater 49:3163–3175

    Article  CAS  Google Scholar 

  44. Ding R, Guo ZX (2002) Microstructural modelling of dynamic recrystallisation using an extended cellular automaton approach. Comput Mater Sci 23:209–218

    Article  CAS  Google Scholar 

Download references

Acknowledgements

The research was funded by the State Key Laboratory of Precision Manufacturing for Extreme Service Performance, Central South University (ZZYJKT2021-05), key research and development plan of Heilongjiang Province (GA21D003), selection of the best candidates to undertake key research projects by Dalian City, science and technology planning project of **yun County.

Author information

Authors and Affiliations

  1. Light Alloy Research Institute, Central South University, Changsha, 410083, China

    Tao Zhang, Junwen Chen, Hai Gong, Yunxin Wu & Tiewen Hao

  2. State Key Laboratory of Precision Manufacturing for Extreme Service Performance, Central South University, Changsha, 410083, China

    Tao Zhang, Junwen Chen, Hai Gong, Yunxin Wu & Tiewen Hao

  3. Dalian Engineering Technology Co., LTD, China First Heavy Industries, Dalian, 116600, China

    Tiewen Hao

  4. Zhejiang Junhong Machinery Co., LTD, Lishui, 323000, China

    **aofeng He

Authors
  1. Tao Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Junwen Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hai Gong
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yunxin Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Tiewen Hao
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. **aofeng He
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

TZ involved in conceptualization, writing—original draft, resources, data curation. JC took part in writing, methodology, and editing. HG involved in supervision and project administration. YW involved in funding acquisition. TH involved in visualization, validation, formal analysis.

Corresponding author

Correspondence to Hai Gong.

Ethics declarations

Conflict of interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Ethical approval

Ethics Committee approval was obtained from the Institutional Ethics Committee of Central South University to the commencement of the study. This manuscript did not involve human tissue.

Additional information

Handling Editor: Ghanshyam Pilania.

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhang, T., Chen, J., Gong, H. et al. Microstructure simulation of AA2219 alloy in hot/warm forming and heat treatment using cellular automata methods. J Mater Sci 58, 7968–7985 (2023). https://doi.org/10.1007/s10853-022-08123-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10853-022-08123-6

Search

Navigation