Reduced-Order Representations of Crystallographic Texture for Application to Surrogate Models of Material Behaviour

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The mechanical behaviour of materials is influenced by processing and thermomechanical exposure. In safety-sensitive industries there is a need to make predictions on the envelope of safe use beyond proven constitutive equations. Microstructural simulations, such as crystal plasticity modelling, can model features like grain size, morphology and texture. However, they are computationally demanding and it can be hard to translate measured microstructures into meaningful or representative statistical distributions. Surrogate models incorporate machine learning regression and statistical methods to emulate the response of a complex model. As they are much faster, they can model the response over a wide range of material parameters, permitting sensitivity analysis and uncertainty quantification. Preferred orientation (texture) can be challenging to incorporate into surrogate models as accurate representations can require a lot of parameters. In this study, reduced-order representations of crystallographic texture are presented to represent the bulk response of a polycrystal volume element. These representations are used as inputs to a gaussian process regression (GPR) model that is used to predict the macroscopic stress-strain response of a polycrystal for different crystallographic textures. The GPR acts as a surrogate model of the underlying crystal plasticity model and allows an inherent quantification of the model epistemic uncertainty and the uncertainty related to unobserved effects not captured by the texture parameterization. Incorporation of the surrogate model into finite element coding will be used as an application of the method.

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January 2026

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[1] R. Bostanabad, Y. Zhang, X. Li, T. Kearney, L. C. Brinson, D. W. Apley, W. K. Liu, W. Chen, Computational microstructure characterization and reconstruction: Review of the state-of-the-art techniques, Prog. Mater. Sci. 95 (2018) 1-41.

DOI: 10.1016/j.pmatsci.2018.01.005

Google Scholar

[2] E. Demir, E. W. Horton, M. Mokhtarishirazabad, M. Mostafavi, D. Knowles, Grain size and shape dependent crystal plasticity finite element model and its application to electron beam welded SS316L, J. Mech. Phys. Solids. 178 (2023) 105331.

DOI: 10.2139/ssrn.4332086

Google Scholar

[3] F. Roters, P. Eisenlohr, L. Hantcherli, D. D. Tjahjanto, T. R. Bieler, D. Raabe, Overview of constitutive laws, kinematics, homogenization and multiscale methods in crystal plasticity finite-element modeling: Theory, experiments, applications, Acta Mater. 58, No. 4 (2010) 1152-1211.

DOI: 10.1016/j.actamat.2009.10.058

Google Scholar

[4] D. Bishara, Y. Xie, W. K. Liu, S. Li, A state-of-the-art review on machine learning-based multiscale modeling, simulation, homogenization and design of materials, Arch. Comp. Methods. Eng. 30, No. 1 (2023) 191-222.

DOI: 10.1007/s11831-022-09795-8

Google Scholar

[5] H. J. Bunge, Texture analysis in materials science: mathematical methods, Butterworths, 1982.

Google Scholar

[6] J. F. W. Bishop, R. Hill. CXXVIII. A theoretical derivation of the plastic properties of a polycrystalline face-centred metal, London Edinburgh Dublin Philos. Mag. & J. Sci. 42, No. 334 (1951) 1298-1307.

DOI: 10.1080/14786444108561385

Google Scholar

[7] M. Mokhtarishirazabad, M. McMillan, V. D. Vijayanand, C. Simpson, D. Agius, C. Truman, D. Knowles, M. Mostafavi, Predicting residual stress in a 316L electron beam weld joint incorporating plastic properties derived from a crystal plasticity finite element model, Int. J. Press. Ves. Pip. 201 (2023) 104868.

DOI: 10.1016/j.ijpvp.2022.104868

Google Scholar

[8] F. Bachmann, R. Hielscher, H. Schaeben, Texture analysis with MTEX–free and open source software toolbox, Solid State Phenom. 160 (2010) 63-68.

DOI: 10.4028/www.scientific.net/ssp.160.63

Google Scholar

[9] C. K. I. Williams, C. E. Rasmussen, Gaussian processes for machine learning, MIT press, 2006.

Google Scholar

[10] R. Saunders, C. Butler, J. Michopoulos, D. Lagoudas, A. Elwany, A. Bagchi, Mechanical behavior predictions of additively manufactured microstructures using functional Gaussian process surrogates, Npj Comput. Mater. 7, No. 1 (2021) 81.

DOI: 10.1038/s41524-021-00548-y

Google Scholar

[11] H. Dorward, D. Knowles, M. Mostafavi, M. Peel, Towards a Data-Driven Evolutionary Model of the Cyclic Behaviour of Austenitic Steels, In Pressure Vessels and Piping Conference (Vol. 88506, p. V004T06A045), ASME, 2024, July.

DOI: 10.1115/pvp2024-123322

Google Scholar

[12] H. Dorward, D. M. Knowles, E. Demir, M. Mostafavi, M. J. Peel, Calibration and surrogate model-based sensitivity analysis of crystal plasticity finite element models, Mater. Des. 247 (2024) 113409.

DOI: 10.1016/j.matdes.2024.113409

Google Scholar