MicroProcSim: A Software for Simulation of Microstructure Evolution
Source abstract
Abstract
Abstract Understanding the large deformation behavior of materials under external forces is crucial for reliable engineering applications. The mechanical properties of materials depend on their underlying microstructures, which change over time during deformation. Experimental observation of these processes is time-consuming and influenced by various conditions. Therefore, we developed , a physics-based simulation tool to replicate the deformation process of cubic microstructures. can predict the evolution of texture, represented by the orientation distribution function (ODF), over time under various loads and strain rates. This software package can be run on both Windows and Linux operating systems. Unlike conventional crystal plasticity finite element software, offers a distinct advantage by rapidly generating deformed textures, as it bypasses incorporating grain morphology. Furthermore, comparisons with existing experimental and computational studies on texture evolution have demonstrated that this software seamlessly replicates real-world material processing conditions through a simple modification of a single input matrix.
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Research context
OpenAlex topics: Microstructure and mechanical properties · High-Velocity Impact and Material Behavior · Nonlocal and gradient elasticity in micro/nano structures
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (80)
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1. Li H, Chen T, Li W, Zhang H, Han S, Zhou C, Chen Z, Flores-Johnson EA, Shen L, Lian J et al (2021) Grain size dependent microstructure and texture evolution during dynamic deformation of nanocrystalline face-centered cubic materials. Acta Mater 216:117088
2. Fujii H, Cui L, Maeda M, Nogi K (2006) Effect of tool shape on mechanical properties and microstructure of friction stir welded aluminum alloys. Mater Sci Eng A 419(1–2):25–3110.1016/j.msea.2005.11.045 ↗
3. Mao Y, Hasan M, Paul A, Gupta V, Choudhary K, Tavazza F, Liao WK, Choudhary A, Acar P, Agrawal A (2023) An ai-driven microstructure optimization framework for elastic properties of titanium beyond cubic crystal systems. NPJ Comput Mater 9(1):11110.1038/s41524-023-01067-8 ↗
4. Sundararaghavan V, Zabaras N (2008) A multi-length scale sensitivity analysis for the control of texture-dependent properties in deformation processing. Int J Plast 24(9):1581–160510.1016/j.ijplas.2007.12.005 ↗
5. Agius D, Kourousis KI, Wallbrink C, Song T (2017) Cyclic plasticity and microstructure of as-built SLM Ti-6Al-4V: the effect of build orientation. Mater Sci Eng A 701:85–10010.1016/j.msea.2017.06.069 ↗
6. Kim DK, Kim JM, Park WW, Lee HW, Im YT, Lee YS (2015) Three-dimensional crystal plasticity finite element analysis of microstructure and texture evolution during channel die compression of if steel. Comput Mater Sci 100:52–6010.1016/j.commatsci.2014.09.032 ↗
7. Hirsch J, Lücke K (1988) Overview no. 76: Mechanism of deformation and development of rolling textures in polycrystalline fcc metals-i. description of rolling texture development in homogeneous cuzn alloys. Acta Metallurgica 36(11):2863–288210.1016/0001-6160(88)90172-1 ↗
8. Cho CH, Kim DO, Son K, Park HS (2023) Relationship between hot workability and texture evolution in an Al-Zn-Mg-Cu alloy under hot compressive stress mode. J Mater Sci 58(42):16537–1654910.1007/s10853-023-09081-3 ↗
9. Sundararaghavan V, Zabaras N (2005) On the synergy between texture classification and deformation process sequence selection for the control of texture-dependent properties. Acta Mater 53(4):1015–102710.1016/j.actamat.2004.11.001 ↗
10. Lin J, Hasan M, Acar P, Blanchet J, Tarokh V (2023) Neural network accelerated process design of polycrystalline microstructures. Mater Today Commun 36:106884
11. Hasan M, Ender Eger Z, Senthilnathan A, Acar P (2024) Microstructure-sensitive deformation modeling and materials design with physics-informed neural networks. AIAA J 62(5):1864–187410.2514/1.J062708 ↗
12. Kumar A, Dawson P (2000) Computational modeling of FCC deformation textures over Rodrigues’ space. Acta Mater 48(10):2719–273610.1016/S1359-6454(00)00044-6 ↗
13. Hingole RS (2016) Advances in metal forming. Springer, New York10.1007/978-3-662-44497-9 ↗
14. Blakey-Milner B, Gradl P, Snedden G, Brooks M, Pitot J, Lopez E, Leary M, Berto F, Du Plessis A (2021) Metal additive manufacturing in aerospace: a review. Mater Des 209:11000810.1016/j.matdes.2021.110008 ↗
15. Taylor GI (1938) Plastic strain in metals. J Inst Metals 62:307–324
16. Van Houtte P (1982) On the equivalence of the relaxed Taylor theory and the bishop-hill theory for partially constrained plastic deformation of crystals. Mater Sci Eng 55(1):69–7710.1016/0025-5416(82)90085-4 ↗
17. Kocks U, Chandra H (1982) Slip geometry in partially constrained deformation. Acta Metall 30(3):695–70910.1016/0001-6160(82)90119-5 ↗
18. Van Houtte P, Delannay L, Samajdar I (1999) Quantitative prediction of cold rolling textures in low-carbon steel by means of the LAMEL model. Texture Stress Microstruct 31(3):109–14910.1155/TSM.31.109 ↗
19. Liu Y, Delannay L, Van Houtte P (2002) Application of the LAMEL model for simulating cold rolling texture in molybdenum sheet. Acta Mater 50(7):1849–185610.1016/S1359-6454(02)00037-X ↗
20. Van Houtte P, Kanjarla AK, Van Bael A, Seefeldt M, Delannay L (2006) Multiscale modelling of the plastic anisotropy and deformation texture of polycrystalline materials. Eur J Mech A/Solids 25(4):634–64810.1016/j.euromechsol.2006.05.003 ↗
21. Crumbach M, Goerdeler M, Gottstein G, Neumann L, Aretz H, Kopp R (2003) Through-process texture modelling of aluminium alloys. Modell Simul Mater Sci Eng 12(1):110.1088/0965-0393/12/1/S01 ↗
22. Engler O, Crumbach M, Li S (2005) Alloy-dependent rolling texture simulation of aluminium alloys with a grain-interaction model. Acta Mater 53(8):2241–225710.1016/j.actamat.2005.01.032 ↗
23. Mu S, Al-Samman T, Mohles V, Gottstein G (2011) Cluster type grain interaction model including twinning for texture prediction: application to magnesium alloys. Acta Mater 59(18):6938–694810.1016/j.actamat.2011.07.045 ↗
24. Tjahjanto D, Eisenlohr P, Roters F (2009) A novel grain cluster-based homogenization scheme. Modell Simul Mater Sci Eng 18(1):01500610.1088/0965-0393/18/1/015006 ↗
25. Eisenlohr P, Tjahjanto D, Hochrainer T, Roters F, Raabe D (2009) Texture prediction from a novel grain cluster-based homogenization scheme. Springer, New York10.1007/s12289-009-0561-2 ↗
26. Lebensohn RA, Tomé C (1993) A self-consistent anisotropic approach for the simulation of plastic deformation and texture development of polycrystals: application to zirconium alloys. Acta Metall Mater 41(9):2611–262410.1016/0956-7151(93)90130-K ↗
27. Tomé C, Lebensohn R (2007) Visco-plastic self-consistent (vpsc). Los Alamos National Laboratory (USA) and Universidad Nacional de Rosario (Argentina) vol 6
28. Turner P, Tomé C (1994) A study of residual stresses in zircaloy-2 with rod texture. Acta Metall Mater 42(12):4143–415310.1016/0956-7151(94)90191-0 ↗
29. Shao Y, Tang T, Li D, Tang W, Peng Y (2015) Crystal plasticity finite element modelling of the extrusion texture of a magnesium alloy. Modell Simul Mater Sci Eng 23(5):05501110.1088/0965-0393/23/5/055011 ↗
30. Wang H, Wu P, Tomé C, Huang Y (2010) A finite strain elastic-viscoplastic self-consistent model for polycrystalline materials. J Mech Phys Solids 58(4):594–61210.1016/j.jmps.2010.01.004 ↗
31. Kronsteiner J, Theil E, Ott AC, Arnoldt AR, Papenberg NP (2024) Modeling of texture development during metal forming using finite element visco-plastic self-consistent model. Curr Comput-Aided Drug Des 14(6):533
32. Eghtesad A, Germaschewski K, Knezevic M (2022) Coupling of a multi-GPU accelerated elasto-visco-plastic fast Fourier transform constitutive model with the implicit finite element method. Comput Mater Sci 208:11134810.1016/j.commatsci.2022.111348 ↗
33. Adams BL, Kalidindi SR, Fullwood DT (2012) Microstructure sensitive design for performance optimization. Butterworth-Heinemann10.1016/B978-0-12-396989-7.00010-1 ↗
34. Kumar A, Dawson PR (2009) Dynamics of texture evolution in face-centered cubic polycrystals. J Mech Phys Solids 57(3):422–44510.1016/j.jmps.2008.11.013 ↗
35. Sundararaghavan V (2007) Multi-scale computational techniques for design of polycrystalline materials
36. Acar P (2020) Machine learning reinforced crystal plasticity modeling under experimental uncertainty. AIAA J 58(8):3569–357610.2514/1.J059233 ↗
37. Acar P, Sundararaghavan V (2016) Linear solution scheme for microstructure design with process constraints. AIAA J 54(12):4022–403110.2514/1.J055247 ↗
38. Acar P, Sundararaghavan V, De Graef M (2018) Computational modeling of crystallographic texture evolution over cubochoric space. Modell Simul Mater Sci Eng 26(6):06501210.1088/1361-651X/aad20b ↗
39. Kalidindi SR, Knezevic M, Niezgoda S, Shaffer J (2009) Representation of the orientation distribution function and computation of first-order elastic properties closures using discrete fourier transforms. Acta Mater 57(13):3916–392310.1016/j.actamat.2009.04.055 ↗
40. Suwas S, Ray RK, Suwas S, Ray RK (2014) Representation of texture. Crystallographic texture of materials, 11–38.10.1007/978-1-4471-6314-5_2 ↗
41. Inoue H (2015) Prediction of in-plane anisotropy of bendability based on orientation distribution function for polycrystalline face-centered cubic metal sheets with various textures. Mater Trans 56(1):61–6910.2320/matertrans.ME201405 ↗
42. Sundararaghavan V, Zabaras N (2004) A dynamic material library for the representation of single-phase polyhedral microstructures. Acta Mater 52(14):4111–411910.1016/j.actamat.2004.05.024 ↗
43. Acar P, Sundararaghavan V (2016) Utilization of a linear solver for multiscale design and optimization of microstructures in an airframe panel buckling problem. In: 57th AIAA/ASCE/AHS/ASC structures, structural dynamics, and materials conference, p 015610.2514/6.2016-0156 ↗
44. Acar P, Srivastava S, Sundararaghavan V (2017) Stochastic design optimization of microstructures with utilization of a linear solver. AIAA J 55(9):3161–316810.2514/1.J056000 ↗
45. Acar P, Ramazani A, Sundararaghavan V (2017) Crystal plasticity modeling and experimental validation with an orientation distribution function for Ti-7Al alloy. Metals 7(11):45910.3390/met7110459 ↗
46. Cho JH (2007) Determination of volume fraction of the texture components in the Rodrigues fundamental region. Mater Sci Eng A 465(1–2):228–23710.1016/j.msea.2007.03.030 ↗
47. Raabe D, Sachtleber M, Zhao Z, Roters F, Zaefferer S (2001) Micromechanical and macromechanical effects in grain scale polycrystal plasticity experimentation and simulation. Acta Mater 49(17):3433–344110.1016/S1359-6454(01)00242-7 ↗
48. Raabe D, Klose P, Engl B, Imlau KP, Friedel F, Roters F (2002) Concepts for integrating plastic anisotropy into metal forming simulations. Adv Eng Mater 4(4):169–18010.1002/1527-2648(200204)4:4<169::AID-ADEM169>3.0.CO;2-G ↗
49. Zhao Z, Ramesh M, Raabe D, Cuitino A, Radovitzky R (2008) Investigation of three-dimensional aspects of grain-scale plastic surface deformation of an aluminum oligocrystal. Int J Plast 24(12):2278–229710.1016/j.ijplas.2008.01.002 ↗
50. Roters F, Eisenlohr P, Hantcherli L, Tjahjanto DD, Bieler TR, Raabe D (2010) Overview of constitutive laws, kinematics, homogenization and multiscale methods in crystal plasticity finite-element modeling: theory, experiments, applications. Acta Mater 58(4):1152–121110.1016/j.actamat.2009.10.058 ↗
51. Acar P, Sundararaghavan V (2017) Uncertainty quantification of microstructural properties due to variability in measured pole figures. Acta Mater 124:100–10810.1016/j.actamat.2016.10.070 ↗
52. Billah MM, Acar P (2024) Design of polycrystalline metallic alloys under multi-scale uncertainty by connecting atomistic to meso-scale properties. Acta Mater 270:11987910.1016/j.actamat.2024.119879 ↗
53. Acar P (2019) Machine learning approach for identification of microstructure-process linkages. AIAA J 57(8):3608–361410.2514/1.J058244 ↗
54. Wang K, Mao Y, Hasan M, Billah MM, Kilic MNT, Gupta V, Liao WK, Choudhary A, Acar P, Agrawal A (2014) Deep learning based inverse modeling for materials design: From microstructure and property to processing. In: 2024 international conference on machine learning and applications (ICMLA), pp 236–241. IEEE10.1109/ICMLA61862.2024.00038 ↗
55. Billah MM, Acar P Process-informed property evolutions of microstructures. Available at SSRN 5198175
56. Frank C (1988) Orientation mapping: 1987 MRS fall meeting von hippel award lecture. MRS Bull 13(3):24–3110.1557/S0883769400066112 ↗
57. Neumann P (1991) Representation of orientations of symmetrical objects by Rodrigues vectors. Texture Stress Microstruct 14(1):53–5810.1155/TSM.14-18.53 ↗
58. Neumann P (1991) Graphical representations of orientations and ODFS by Rodrigues vectors. Steel research 62(12):560–56610.1002/srin.199100450 ↗
59. Schwartz A (2009) Electron backscatter diffraction in materials science. Springer, New York10.1007/978-0-387-88136-2 ↗
60. Sundararaghavan V, Zabaras N (2007) Linear analysis of texture-property relationships using process-based representations of Rodrigues space. Acta Mater 55(5):1573–158710.1016/j.actamat.2006.10.019 ↗
61. Acar P (2017) Multi-scale design and optimization of microstructures under uncertainties. Ph.D. dissertation, University of Michigan. https://deepblue.lib.umich.edu/handle/2027.42/138534
62. Billah MM, Acar P (2025) Design of functionally graded inconel 718 alloy structures by developing material property closures. In: AIAA SCITECH 2025 Forum, p 174610.2514/6.2025-1746 ↗
63. Ganapathysubramanian S, Zabaras N (2004) Design across length scales: a reduced-order model of polycrystal plasticity for the control of microstructure-sensitive material properties. Comput Methods Appl Mech Eng 193(45–47):5017–503410.1016/j.cma.2004.04.004 ↗
64. Hansen PB (1973) Operating system principles. Prentice-Hall, Inc
65. Tanenbaum A (2009) Modern operating systems. Pearson Education, Inc
66. Yuan P, Guo Y, Zhang L, Chen X, Mei H (2018) Building application-specific operating systems: a profile-guided approach. Sci China Inf Sci 61:1–17
67. Fink J (2014) Docker: a software as a service, operating system-level virtualization framework. Code4Lib J (25)
68. Microsoft: Windows API Index (2023) https://learn.microsoft.com/en-us/windows/win32/apiindex/windows-api-list. Accessed: 2024-06-19
69. Koopman P, DeVale J (1999) Comparing the robustness of posix operating systems. In: Digest of papers. Twenty-ninth annual international symposium on fault-tolerant computing (Cat. No. 99CB36352), pp 30–37. IEEE10.1109/FTCS.1999.781031 ↗
70. WineHQ: About Wine (2023). https://www.winehq.org/about. Accessed: 2024-06-19
71. Barton NR, Boyce DE, Dawson PR (2002) Pole figure inversion using finite elements over Rodrigues space. Textures Microstruct 35(2):113–14410.1080/073033002100000182 ↗
72. Bachmann F, Hielscher R, Schaeben H (2010) Texture analysis with MTEX-free and open source software toolbox. Solid State Phenom 160:63–6810.4028/www.scientific.net/SSP.160.63 ↗
73. Bronkhorst CA, Kalidindi SR (1992) Anand L (1992) Polycrystalline plasticity and the evolution of crystallographic texture in FCC metals. Philos Trans R Soc London. Series A Phys Eng Sci 341(1662):443–477
74. Yaghoobi M, Allison JE, Sundararaghavan V (2022) Prisms-plasticity TM: an open-source rapid texture evolution analysis pipeline. Integr Mater Manuf Innov 11(4):479–49610.1007/s40192-022-00275-2 ↗
75. Duan J, Wen H, Zhou C, Islamgaliev R, Li X (2019) Evolution of microstructure and texture during annealing in a high-pressure torsion processed Fe-9cr alloy. Materialia 6:10034910.1016/j.mtla.2019.100349 ↗
76. Griffiths RJ, Garcia D, Song J, Vasudevan VK, Steiner MA, Cai W, Hang ZY (2021) Solid-state additive manufacturing of aluminum and copper using additive friction stir deposition: process-microstructure linkages. Materialia 15:10096710.1016/j.mtla.2020.100967 ↗
77. Gazder AA, Dalla Torre F, Gu C, Davies CH, Pereloma EV (2006) Microstructure and texture evolution of BCC and FCC metals subjected to equal channel angular extrusion. Mater Sci Eng A 415(1–2):126–13910.1016/j.msea.2005.09.065 ↗
78. Tome CN, Lebensohn RA (2023) Material modeling with the visco-plastic self-consistent (VPSC) approach: theory and practical applications. Elsevier
79. Kestens L, Pirgazi H (2016) Texture formation in metal alloys with cubic crystal structures. SAGE Publications, London
80. Zecevic M, Lebensohn RA, Rogers M, Moore J, Chiravalle V, Lieberman E, Dunning D, Shipman G, Knezevic M, Morgan N (2021) Viscoplastic self-consistent formulation as generalized material model for solid mechanics applications. Appl Eng Sci 6:100040