Cellular Automata-based computational library for development of digital material representation models of heterogenous microstructures

• Autorzy: Boguń Konrad , Sitko Mateusz , Mojżeszko Mateusz , Madej Łukasz

Identyfikatory

DOI

10.1007/s43452-021-00211-9

• Języki publikacji: EN

Abstrakty

EN

The development of an efficient numerical approach for the generation of a wide range of heterogeneous microstructures models with the application of the lean workflow concept is presented in the paper. First, the idea and implementation details of the developed cellular automata-based computational library allowing the development of digital material representation models within a workflow are presented in the paper. Such an approach provides the desired flexibility in the generation of various digital models of heterogenous microstructures. Therefore, the proposed library is mostly implemented within the object-oriented C + + programming language with the assumption of modularity. In this case, the main part of the application consists of classes and methods, which can be treated like base elements to be inherited and extended in other libraries. Each additional dynamic link library implements particular algorithms for the generation of specific microstructure features in the digital model within the unified data structures that allow the application of the workflow concept. The set of developed libraries and their assumptions are described as case studies to show the capabilities of the presented solution. Finally, examples of practical applications of the developed library in the full-field numerical simulations of complex material deformation are presented at the end of the paper.

Słowa kluczowe

EN

digital material representation; cellular automata; Monte Carlo

PL

metoda Monte Carlo; automat komórkowy; model cyfrowy

• Wydawca: Springer ; Wrocław University of Science and Technology
• Czasopismo: Archives of Civil and Mechanical Engineering
• Rocznik: 2021
• Tom: Vol. 21, no. 2
• Strony: 335--349
• Opis fizyczny: Bibliogr. 31 poz., rys., wykr.

Twórcy

autor

Boguń Konrad

• Faculty of Metals Engineering and Industrial Computer Science, AGH University of Science and Technology, Al. Mickiewicza 30, 30-059 Krakow, Poland

autor

Sitko Mateusz

• Faculty of Metals Engineering and Industrial Computer Science, AGH University of Science and Technology, Al. Mickiewicza 30, 30-059 Krakow, Poland

• https://orcid.org/0000-0003-0893-8784

autor

Mojżeszko Mateusz

• Faculty of Metals Engineering and Industrial Computer Science, AGH University of Science and Technology, Al. Mickiewicza 30, 30-059 Krakow, Poland

• https://orcid.org/0000-0001-7208-0482

autor

Madej Łukasz

• Faculty of Metals Engineering and Industrial Computer Science, AGH University of Science and Technology, Al. Mickiewicza 30, 30-059 Krakow, Poland

• https://orcid.org/0000-0003-1032-6963

Bibliografia

• [1] Pietrzyk M, Madej L, Rauch L, Szeliga D. Computational materials engineering achieving high accuracy and efficiency in metals, 2015.
• [2] Gawad J, Kuziak R, Madej L, Szeliga D, Pietrzyk M. Identification of rheological parameters on the basis of various types of compression and tension tests. Steel Res Int. 2005;76:131–7.
• [3] Szeliga D, Gawad J, Pietrzyk M. Inverse analysis for identification of rheological and friction models in metal forming. Comput Methods Appl Mech Eng. 2006;195:6778–98. https:// doi. org/ 10. 016/j. cma. 2005. 03. 015.
• [4] Lacroix G, Furnemont Q, Jacques PJ, Pardoen T. Mechanisms of damage and fracture in trip assisted multiphase steels. In: Fracture of nano and engineering materials and structures. Dordrecht: Springer; 2006. pp. 819–820.
• [5] Estrin Y, Vinogradov A. Extreme grain refinement by severe plastic deformation: a wealth of challenging science. Acta Mater. 2013;61:782–817. https:// doi. org/ 10. 1016/j. actam at. 2012. 10. 038.
• [6] Pereloma E, Beladi H, Zhang L, Timokhina I. Understanding the behavior of advanced high-strength steels using atom probe tomography. Metall Mater Trans A. 2012;43:3958–71. https:// doi. org/ 10. 1007/ s11661- 011- 0782-0.
• [7] Muszka K, Lopez-Pedrosa M, Raszka K, Thomas M, Rainforth WM, Wynne BP. The impact of strain reversal on microstructure evolution and orientation relationships in Ti-6Al-4V with an initial alpha colony microstructure. Metall Mater Trans A. 2014;45:5997–6007. https:// doi. org/ 10. 1007/ s11661- 014- 2590-9.
• [8] Madej L, Sitko M, Radwanski K, Kuziak R. 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. 2016;179:282–94. https:// doi. org/ 10. 1016/j. match emphys. 2016. 05. 040.
• [9] Scholtes B, Shakoor M, Settefrati A, Bouchard PO, Bozzolo N, Bernacki M. New finite element developments for the full field modeling of microstructural evolutions using the level-set method. Comput Mater Sci. 2015;109:388–98. https:// doi. org/ 10. 1016/j. comma tsci. 2015. 07. 042.
• [10] Vondrous A, Bienger P, Schreijäg S, Selzer M, Schneider D, Nestler B, Helm D, Mönig R. Combined crystal plasticity and phase-field method for recrystallization in a process chain of sheet metal production. Comput Mech. 2015;55:439–52. https:// doi. org/ 10. 1007/ s00466- 014- 1115-0.
• [11] Liu J, Dai Q, Chen J, Chen S, Ji H, Dua W, Deng X, Wang Z, Guo G, Luo H. The two dimensional microstructure characterization of cemented carbides with an automatic image analysis process. Ceram Int. 2017;43:14865–72. https:// doi. org/ 10. 1016/j. ceram int. 2017. 08. 002.
• [12] Falco S, Jiang J, De Cola F, Petrinic N. Generation of 3D polycrystalline microstructures with a conditioned Laguerre-Voronoi tessellation technique. Comput Mater Sci. 2017;136:20–8. https:// doi. org/ 10. 1016/j. comma tsci. 2017. 04. 018.
• [13] Lewandowska M, Wejrzanowski T, Kurzydłowski KJ. Grain growth in ultrafine grained aluminium processed by hydrostatic extrusion. J Mater Sci. 2008;43:7495–500. https:// doi. org/ 10. 1007/ s10853- 008- 2808-6.
• [14] Bakhtiari M, Seyed Salehi M. Reconstruction of deformed microstructure using cellular automata method. Comput Mater Sci. 2018;149:1–13. https:// doi. org/ 10. 1016/j. comma tsci. 2018. 02. 053.
• [15] Madej L, Legwand A, Mojzeszko M, Chraponski J, Roskosz S, Cwajna J. Experimental and numerical two- and three- dimensional investigation of porosity morphology of the sintered metallic material. Arch Civil Mech Eng. 2018;18:1520–34. https:// doi. org/ 10. 1016/j. acme. 2018. 06. 007.
• [16] Wang P, He W, Mauer G, Mücke R, Vaßen R. Monte Carlo simulation of column growth in plasma spray physical vapor deposition process. Surf Coat Technol. 2018;335:188–97. https:// doi. org/ 10. 1016/j. surfc oat. 2017. 12. 023.
• [17] Villaret F, Hary B, de Carlan Y, Baudin T, Loge R, Maire L, Bernacki M. Probabilistic and deterministic full field approaches to simulate recrystallization in ODS steels. Comput Mater Sci. 2020;179:109646.
• [18] Wejrzanowski T, Lewandowska M, Sikorski K, Kurzydlowski KJ. Effect of grain size on the melting point of confined thin aluminum films. J Appl Phys. 2014. https:// doi. org/ 10. 1063/1. 48992 40.
• [19] Burczyński T, Kuś W, Brodacka A. Multiscale modeling of osseous tissues. J Theor Appl Mech. 2010;48:855–70.
• [20] Makowski P, Kuś W. Optimization of bone scaffold structures using experimental and numerical data. Acta Mech. 2016;227:139–49. https:// doi. org/ 10. 1007/ s00707- 015- 1421-4.
• [21] Madej L. Digital/virtual microstructures in application to metals engineering-a review. Arch Civil Mech Eng. 2017;17:839–54. https:// doi. org/ 10. 1016/j. acme. 2017. 03. 002.
• [22] Groeber MA, Jackson MA. DREAM 3D: a digital representation environment for the analysis of microstructure in 3D. Integr Mater Manuf Innov. 2014;3:1–17. https:// doi. org/ 10. 1186/ 2193- 9772-3-5.
• [23] Cao J, Zhuang W, Wang S, Lin J. Development of a VGRAIN system for CPFE analysis in micro-forming applications. Int J Adv Manuf Technol. 2010;47:981–91. https:// doi. org/ 10. 1007/ s00170- 009- 2135-3.
• [24] Bernacki M, Digonnet H, Resk H, Coupez T, Logé R. Development of numerical tools for the multiscale modelling of recrystallization in metals, based on a digital material framework. Comput Methods Mater Sci. 2007;7:142–9. https:// doi. org/ 10. 1063/1. 27408 40.
• [25] TIOBE Index (2019). https:// www. tiobe. com/ tiobe- index// .Accessed May 7, 2019.
• [26] Madej L, Sitko M, Pietrzyk M. Perceptive comparison of mean and full field dynamic recrystallization models. Arch Civil Mech Eng. 2016;16:569–89. https:// doi. org/ 10. 1016/j. acme. 2016. 03. 010.
• [27] Sitko M, Dybich D, Szyndler J, Madej L. Parallelization of the Monte Carlo grain growth algorithm. Mater Sci Technol. 2013;3:1657–67.
• [28] Hajder L, Madej L. Sphere packing algorithm for the generation of digital models of polycrystalline microstructures with heterogeneous grain sizes. Comput Methods Mater Sci. 2020;20:22–30.
• [29] Lyu H, Hamid M, Ruimi A, Zbib HM. Stress/strain gradient plasticity model for size effects in heterogeneous nano-microstructures. Int J Plast. 2017;97:46–63. https:// doi. org/ 10. 1016/j. ijplas. 2017. 05. 009.
• [30] Madej L, Pasternak K, Szyndler J, Wajda W. Development of the modified cellular automata sphere growth model for creation of thedigital material representations. Key Eng Mater. 2014;611:489–96. https:// doi. org/ 10. 4028/ www. scien tific. net/ KEM. 611- 612. 489.
• [31] Sitko M, Mojzeszko M, Rychlowski L, Cios G, Bala P, Muszka K, Madej L. Numerical procedure of three-dimensional reconstruction of ferrite-pearlite microstructure data from SEM/EBSD serial sectioning. Proc Manuf. 2020;47:1217–22. https:// doi. org/ 10. 1016/j. promfg. 2020. 04. 183.