Data-Driven Texture Design for Reducing Elastic and Plastic Anisotropy in Titanium Alloys

Ahmadikia, Behnam; Paraskevas, Orestis; Hyning, William Van; Hestroffer, Jonathan M.; Beyerlein, Irene J.; Thrampoulidis, Christos

doi:10.2139/ssrn.4486851

Cited by 1 publication

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“…SB-FFT is a full-field, crystal plasticity model that extends the elasto-viscoplastic fast Fourier transform (EVP-FFT) technique [36], to permit the development of discrete, crystallographic slip bands in grains. These models have been extensively used in simulations of slip bands [11,12,37,38,39,40,41] and deformation twinning [42,43,44,45,46]. We review them briefly here and refer the reader to the original articles, [36] and [11], for more detailed information about these computational schemes.…”

Section: Explicit Slip Band Modelingmentioning

confidence: 99%

Designing Ti-6Al-4V microstructure for strain delocalization using neural networks

Ahmadikia,

Beyerlein,

Hestroffer

et al. 2023

Preprint

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The deformation behavior of Ti-6Al-4V titanium alloy is significantly influenced by slip localized within crystallographic slip bands. Experimental observations reveal that intense slip bands in Ti-6Al-4V form at strains well below the macroscopic yield strain and may serially propagate across grain boundaries, resulting in long-range localization that percolates through the microstructure. These connected, localized slip bands serve as potential sites for crack initiation. Although slip localization in Ti-6Al-4V is known to be influenced by various factors, an investigation of optimal microstructures that limit localization remains lacking. In this work, we develop a novel strategy that integrates an explicit slip band crystal plasticity technique, graph networks, and neural network models to identify Ti-6Al-4V microstructures that reduce the propensity for strain localization. Simulations are conducted on a dataset of 3D polycrystals, each represented as a graph to account for grain neighborhood and connectivity. The results are then used to train neural network surrogate models that accurately predict localization-based properties of a polycrystal, given its microstructure. These properties include the ratio of slip accumulated in the band to that in the matrix, fraction of total applied strain accommodated by slip bands, and spatial connectivity of slip bands throughout the microstructure. The initial dataset is enriched by synthetic data generated by the surrogate models, and a grid search optimization is subsequently performed to find optimal microstructures. We show that while each material property is optimized through a unique microstructure solution, elongated grain shape emerges as a recurring feature among all optimal microstructures. This finding suggests that designing microstructures with elongated grains could potentially mitigate strain localization without compromising strength.

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