arXiv subjects

Matthew Kasemer

Publications and source records attributed to Matthew Kasemer.

6 recordsLinked to original sources

arXiv2026

Sensitivity of grain-averaged elastic strain and orientation predictions on the mesh density and boundary conditions in crystal plasticity finite element simulations

Combined high-energy X-ray diffraction microscopy (HEDM) and crystal plasticity finite element (CPFE) modeling studies have emerged as a preferred paradigm to shed insight into the evolution of elasticity and plasticity at the intragrain scale of polycrystals. In particular, far-field HEDM measures the deformation response of upwards of thousands of individual grains simultaneously in situ during mechanical loading, though measurements are primarily limited, however, to the average state of each grain -- i.e., the grain's full strain tensor, crystallographic orientation, spatial location and volume. CPFE is utilized to shed information on the intragrain deformation response, via the sub-discretization of each grain into many finite elements, though the direct point of comparison to HEDM remains the grain-averaged response. We thus seek to find the minimum simulation conditions necessary to provide consistent grain-averaged predictions in an attempt to limit computational cost. In this study, we perform a suite of simulations and systematically study the effects of mesh density and boundary conditions, and consider different materials. We discuss these results and show that accurate prediction of grain-averaged elastic strains in a given region of interest typically requires a mesh with 250 elements per grain on average and a buffer layer of at least three grains between the region of interest and the control surfaces.

cond-mat.mtrl-sci↗

arXiv2025

A generalized calculation of the rate independent single crystal yield surface

In this paper, we discuss a method to calculate the topology of the rate independent single crystal yield surface for materials with arbitrary slip systems and arbitrary slip strengths. We describe the general problem, as motivated by Schmid's law, and detail the calculation of hyperplanes in deviatoric stress space, $\mathbb{D}^5$, which describe the criteria for slip on individual slip systems. We focus on finding the intersection of five linearly independent hyperplanes which represent stresses necessary to satisfy the criteria for general plastic deformation. Finally, we describe a method for calculating the inner convex hull of these intersection points, which describe the vertices of the five dimensional polytope that represents the single crystal yield surface. Our method applies to arbitrary crystal structure, allowing for an arbitrary number and type of slip systems and families, considers plastic anisotropy via inter- and intra-family strength anisotropy, and further considers strength anisotropy between slip in the positive and negative direction. We discuss the calculation and possible applications, and share a computational implementation of the calculation of the single crystal yield surface.

cond-mat.mtrl-sci↗

arXiv2024

Orientation reconstruction of transformation $\alpha$ titanium alloys via polarized light microscopy: methodology and assessment

Emerging microstructural characterization methods have received increased attention owing to their promise of relatively inexpensive and rapid measurement of polycrystalline surface morphology and crystallographic orientations. Among these nascent methods, polarized light microscopy (PLM) is attractive for characterizing alloys comprised of hexagonal crystals, but is hindered by its inability to measure complete crystal orientations. In this study, we explore the potential to reconstruct quasi-deterministic orientations for titanium microstructures characterized via PLM by considering the Burgers orientation relationship between the room temperature $\alpha$ (HCP) phase fibers measured via PLM, and the $\beta$ (BCC) phase orientations of the parent grains present above the transus temperature. We describe this method -- which is capable of narrowing down the orientations to one of four possibilities -- and demonstrate its abilities on idealized computational samples in which the parent $\beta$ microstructure is fully, unambiguously known. We further utilize this method to inform the instantiation of samples for crystal plasticity simulations, and demonstrate the significant improvement in deformation field predictions when utilizing this reconstruction method compared to using results from traditional PLM.

cond-mat.mtrl-sci↗

arXiv2024

Establishing the relationship between generalized crystallographic texture and macroscopic yield surfaces using partial input convex neural networks

In this study, we present a methodology to predict the macroscopic yield surface of metals and metallic alloys with general crystallographic textures. In previous work, we have established the use of partially input convex neural networks (pICNN) as macroscopic yield functions of crystal plasticity simulations. However, this work was performed with an over-abundance of data, and on limited crystallographic textures. Here, we extend this study to approach more realistic material states (i.e., complex crystallographic textures), and consider data-availability as a major driver for our approach. We present our modified framework capable of handling generalized material states and demonstrate its effectiveness on samples with multi-modal textures deformed under plane stress conditions. We further describe an adaptive algorithm for the generation of training data as informed by the shape of yield surfaces to reduce the time for both the generation of training data as well as pICNN training. Finally, we will discuss errors in both training and test datasets, limitations, and future extensibility.

cond-mat.mtrl-sci↗

arXiv2022

Machine-learning convex and texture-dependent macroscopic yield from crystal plasticity simulations

The influence of the microstructure of a polycrystalline material on its macroscopic deformation response is still one of the major problems in materials engineering. For materials characterized by elastic-plastic deformation responses, predictive computational models to characterize crystal-plasticity (CP) have been developed. However, due to their large demand of computational resources, CP simulations cannot be straightforwardly implemented in hierarchical computational models such as FE$^{2}$. This bottleneck intensifies the need for the development of macroscopic simulation tools that can be directly informed by microstructural quantities. Using a 3D Finite-Element solver for CP, we generate a macroscopic yield function database based on general loading conditions and crystallographic texture. We furthermore assume an independence of the yield function to hydrostatic pressure of the yield function. Leveraging the advancement in statistical modeling we describe and apply a machine learning framework for predicting macroscopic yield as a function of crystallographic texture. The convexity of the data-driven yield function is guaranteed by using partially input convex neural networks as the predictive tool. Furthermore, in order to allow for the predicted yield function to be directly incorporated in time-integration schemes, as needed for the Finite Element method, the yield surfaces are interpreted as the boundaries of signed distance function level sets.

cond-mat.mtrl-sci↗

arXiv2020

On Open and Strong-Scaling Tools for Atom Probe Crystallography: High-Throughput Methods for Indexing Crystal Structure and Orientation

Volumetric crystal structure indexing and orientation mapping are key data processing steps for virtually any quantitative study of spatial correlations between the local chemistry and the microstructure of a material. For electron and X-ray diffraction methods it is possible to develop indexing tools which compare measured and analytically computed patterns to decode the structure and relative orientation within local regions of interest. Consequently, a number of numerically efficient and automated software tools exist to solve the above characterisation tasks. For atom probe tomography (APT) experiments, however, the strategy of making comparisons between measured and analytically computed patterns is less robust because many APT datasets may contain substantial noise. Given that general enough predictive models for such noise remain elusive, crystallography tools for APT face several limitations: Their robustness to noise, and therefore, their capability to identify and distinguish different crystal structures and orientation is limited. In addition, the tools are sequential and demand substantial manual interaction. In combination, this makes robust uncertainty quantifying with automated high-throughput studies of the latent crystallographic information a difficult task with APT data. To improve the situation, we review the existent methods and discuss how they link to those in the diffraction communities. With this we modify some of the APT methods to yield more robust descriptors of the atomic arrangement. We report how this enables the development of an open-source software tool for strong-scaling and automated identifying of crystal structure and mapping crystal orientation in nanocrystalline APT datasets with multiple phases.

physics.comp-ph↗