Void growth plays a central role in ductile fracture, yet the specific mechanisms that control this remain obscure. Classical models, such as those proposed by Rice and Tracey in 1969, are able to capture average rates of void growth, but cannot capture the heterogeneity of individual void growth. Building on recent work, the present study employs laboratory-based diffraction contrast tomography and in-situ x-ray computed tomography to investigate the effect of grain structure and other microstructural factors on void growth in an Al-2219 alloy. Crystal plasticity finite element (CP-FE) modeling is used alongside experimental data to evaluate the contributions of local mechanical states, grain orientation, grain size, and neighboring microstructural features. No strong linear relationships are found with any of the considered descriptors and void growth rate. Potential complex nonlinear relationships are explored with the use of a random forest regression model, which identifies initial void volume, void aspect ratio, local normal stress state, local shear stress state, and local equivalent plastic strain (EQPS) as features that most improve void growth rate predictions. The combination of these analyses suggests that these features should be prioritized to improve models of void growth.

This study proposes an experiment-efficient ANN-based framework for characterizing the anisotropy of AA6022-T4 and calibrating the Yld2004-18p anisotropic yield function from a single hole expansion (HE) test and evaluates its predictions against two texture-driven meso-scale plasticity approaches, namely crystal plasticity finite element method (CPFEM) and elasto-visco-plastic self-consistent (ΔEVPSC). The ANN framework exploits the heterogeneous deformation field generated during the HE test, with training data obtained from finite element (FE) simulations using randomly assigned Yld2004-18p parameters. Two distinct ANN frameworks are developed: one predicts anisotropic properties (stress ratios and r-values), while the other directly estimates Yld2004-18p parameters. Three ANN input selection strategies are explored, i.e., using all strain components from 21 extraction locations in the HE simulation, local stress-based selection, and a variance-based approach. In the crystal plasticity approaches, CPFEM and ΔEVPSC simulations based solely on the measured initial crystallographic texture provide virtual uniaxial and equibiaxial responses that are subsequently fitted to Yld2004-18p. Comparative assessment shows that the ANN framework, as well as the CPFEM- and ΔEVPSC-based approaches, reproduces the anisotropic response, yield locus, and HE thinning profile of the material with generally good agreement with experiments and with the conventional calibration, while substantially reducing experimental time, cost, and material consumption.

Amorphous silicon nitride (a-SiNX) is widely used in microelectronics and MEMS; however, the long-term structural stability under elevated temperatures and repeated thermal cycling remains an active field of study. This study employs in situ transmission electron microscopy (TEM) to investigate the crystallization mechanisms and kinetics of amorphous silicon nitride thin films. Real-time observation during localized laser-induced heating enables direct visualization of devitrification processes and phase evolution with nanometer-scale resolution. In situ TEM observations show that localized material defects can lower the stability of the amorphous phase and promote crystallization. Notably, the magnitude and distribution of thermal stresses appear to strongly influence crystallization dynamics: intense, localized heating leads to rapid nucleation and near-instantaneous growth, whereas broader, lower-intensity heating induces a slower, two-stage crystallization process. These results are consistent with a strong role of thermal stress in modulating crystallization behavior and offer insights into the thermal reliability of a-SiNX. Such insights contribute to a deeper understanding of the thermal stability and inform the design of robust thin-film components for high-performance microdevices.

In this study, we investigate the crystallographic orientation-dependent fracture behaviors exhibited by various Nickel (Ni) single crystals in sheet form subjected to uniaxial tension at quasi-static rates and room temperature. Our experiments reveal two distinct modes of failure: three of the single crystals experienced necking, while the remaining three fractured along a slanted failure surface. To elucidate the underlying mechanisms of these different failure behaviors, we conducted a comprehensive slip analysis coupled with crystal plasticity finite element simulations. These analyses indicate that the direction of the active slip systems, particularly in the lateral (transverse and thickness) directions, plays a critical role in influencing the failure behavior of the crystals. We demonstrate that a simple calculation of the stress projection factor, based on the initial crystal orientations, can provide efficient and accurate predictions of the failure modes in single crystals in sheet form. In contrast, other local fields, such as stress and strain-based indicators, showed minimal correlation with the observed failure behaviors. This study elucidates the intriguing fracture behavior observed in single crystals and introduces a predictive parameter for assessing failure modes.

This study presents an integrated experimental and computational approach to predict the mechanical properties of wire-arc directed energy deposited (DED) aluminum 4043 (Al4043) structure. Thermo-metallurgical simulation has been performed to identify a deposition strategy that reduces heat accumulation during the deposition process. Based on the simulation results, a raster deposition strategy and a lower interpass temperature (50 degrees C) have been maintained during the fabrication to minimize temperature buildup and promote microstructural homogeneity. Electron backscatter diffraction (EBSD) analysis and mechanical testing with digital image correlation reveal the presence of isotropic mechanical properties (difference is less than 4% across three independently tested samples per direction). Porosity, commonly present in the wire-arc DED Al4043 structures, has been quantified using X-ray micro-computed tomography (X-CT). Representative volume elements (RVEs) are generated utilizing EBSD data from two perpendicular planes and pore size distribution from X-CT. The phenomenological crystal plasticity (CP) framework has been utilized to simulate deformation behavior and study local stress-strain behavior considering the effect of grain statistics and porosity. The CP model parameters are calibrated and validated utilizing an iterative Bayesian optimization framework by matching simulated stress-strain curves with tensile test data along the build direction and deposition direction, respectively. Calibration result indicates discrepancy of 1.4 f 0.15% for yield strength and 1.1 f 0.12% for ultimate tensile strength, whereas model validation shows discrepancies of 2.9 f 0.2% and 1.5 f 0.2%, respectively, for the corresponding values. The CP simulations reveal a high level of consistency in the local stress-strain response across the RVEs, further supporting the presence of a homogenous structure.

Understanding the fracture behavior of single crystal metals is critical for predicting material performance under mechanical loading. In this study, we investigate the fracture characteristics of single crystal nickel tensile bars using a crystal plasticity coupled phase field damage (CP-PFD) model. Experimental tensile tests were conducted on 15 specimens spanning five crystallographic orientations and three thickness variants per orientation. The results revealed two distinct fracture modes: brittle fractures with 45-degree angled surfaces and ductile fractures characterized by significant necking. The CP-PFD model successfully replicated these fracture behaviors, demonstrating strong agreement with experimental observations. The model effectively predicted the strain at which necking and fracture occurred, as well as the orientation-dependent fracture mechanisms. By comparing experimental and simulated fracture surfaces, we establish the CP-PFD model as a robust tool for predicting single crystal behavior and damage evolution. This work provides insight into the microstructural dependence of fracture behavior and establishes a predictive framework for modeling orientation-dependent damage evolution in single-crystal nickel.

Crystal plasticity finite element (CP-FE) models are now extensively employed to investigate grain-scale deformation in crystalline materials. The fidelity of the model is derived from verification against experimental data; however, it is challenging to quantitatively compare regions of interest across different length scales using various experimental techniques. In this work, we compare CP-FE predictions of local and global mechanical responses to “Microstructural Clones” data, comprising multiple experimental datasets from microscopically identical quasi-2D crystal specimens. These multi-crystal specimens exhibit nearly identical grain morphologies, grain orientations, grain boundary characteristics, and similar dislocation arrangements. Such specimens enable multiple in-situ and ex-situ experiments on nominally identical samples, allowing for the control of several variables and the exploration of the impact of a single variable in a more scientifically rigorous manner. We use these clone experiments to compare texture evolution, surface strain fields, and failure behavior with CP-FE predictions. This procedure provides an objective and quantitative methodology to evaluate the agreement between the model and experimental data, and allows for the testing of various model parameters to improve the CP-FE model.

The strength of materials is influenced by a range of external conditions, such as temperature and deformation rate. Consequently, materials that demonstrate substantial variations in their mechanical behavior due to fluctuations in temperature and strain rate require complex strength models to accurately predict material performance in real-world applications. To predict such complex behavior, a robust and flexible strength model is necessary. In this work, we utilize genetic programming-based symbolic regression (GPSR) to develop data-driven strength models that accurately represent the measured stress-strain responses of tin across a wide range of strain, strain rate and temperature regimes. The GPSR models are constrained by physically-informed conditions, which leads to significant improvement in extrapolation. The best model is integrated into a multi-physics code to perform Taylor impact simulations, validating the model's accuracy and robustness. The model predictions showed excellent agreement with experimental results, particularly when compared to predictions using traditional strength models.

Refractory alloys (RAs) are promising materials due to their exceptional physicochemical properties, but most research remains at the laboratory scale. For broader adoption, advancements in manufacturing are essential. Because their high stability makes conventional methods like machining and casting difficult, additive manufacturing (AM) is emerging as an effective approach for fabricating refractory alloy components. However, AM's repeated non-equilibrium thermal cycles introduce undesired features (e.g. defects, anisotropic microstructures, and residual stresses), which are magnified due to RAs’ unique properties. This paper comprehensively reviews the state-of-the-art methods of AM for refractory alloys. It explores data analytics techniques to establish design rules based on multi-fidelity experimental and computational methods. Furthermore, it investigates integrated, collaborative efforts to harmonise standalone databases, information, knowledge, and predictive models at multi-physics, multi-stage, and multi-scale. Unlike the existing literature that focuses primarily on material systems or process fundamentals, this work provides an integrated perspective on AM of refractory alloys from a data analytics standpoint, highlighting the roles of integrated computational materials engineering (ICME), verification, validation, and uncertainty quantification (VV&UQ), and digital twin-driven qualification in overcoming data scarcity and accelerating rapid qualification.

Accurately characterizing plastic anisotropy in metals is crucial for modeling sheet metal forming processes. One effective approach for calibrating this complex behavior is to use the Virtual Fields Method (VFM), a full-field inverse method based on the principle of virtual work, with a small number of experiments with spatially-heterogeneous fields, and utilizing both experimentally measured loads and full-field displacements. These measurements are used in calculating the internal and external virtual work by assuming a constitutive model and iteratively updating the material model constants until a sufficiently small equilibrium-gap is achieved. The current standard for applying VFM to large-strain plasticity problems is sensitivity-based virtual fields; stress-fields are calculated with initial and slightly perturbed material constants, which are then converted back into virtual strain fields to assess the virtual work balance. Several critical decisions regarding various parameters for the analysis must be made, beginning with the experimental setup and post-processing regarding spatial and temporal data density and filtering. Additional parameters are introduced into the analysis, including perturbation size and scaling of sensitivity-based virtual fields, frequency of virtual field updating, virtual mesh size, and convergence tolerance. This work focuses on a parametric study of these parameters in the analysis of simulated plane-strain tension experiments for AA6061-T6 in three in-plane directions, to identify parameters for the Yld2000-2D yield function. This work provides a framework for assessing the sensitivity of parameter values used in VFM analyses for yield function identification, leading to improved material models and enhanced predictive capabilities in modeling sheet metal forming processes.

Voids have a significant impact on the structural safety and performance of polycrystalline metal alloys given their crucial role on the initiation and evolution of damage. Therefore, a fundamental understanding of the relationship between the internal crystalline structure of metal alloys and their corresponding damage behavior and properties is essential for the materials community. Crystal plasticity theories, in conjunction with finite element (CPFEM), are actively used to describe and characterize this behavior given the fact that they directly consider the orientation of the crystallographic plains, slip systems and other microstructural features. Nevertheless, despite its accuracy, CPFEM-based analysis protocols are often ill-suited for establishing a computationally efficient and accurate linkage between the microstructure and the resulting damage performance given their high computational cost and their need to iteratively solve complex, numerically stiff and highly non-linear equations. In this work, we address this challenge by establishing a machine learning (ML)-based linkage between the microstructure and the resulting damage performance. Specifically, we leverage AdaBoosted decision trees to connect crystal orientations, represented with Generalized Spherical Harmonics, to a measure of damage derived from the classical Lemaitre continuum damage model. The developed ML model accurately predicts the Lemaitre stress around a spherical void at a fraction of the computational cost compared to CPFEM simulations.

A material’s microstructure drives its material performance. Contemporary crystal plasticity experiments compare full-field strain measurements of polycrystal specimens to models. Because each specimen is unique, it is impossible to know which features of the observed deformation are deterministic vs statistical; thus, differences between model and experiment may or may not be significant. This paper introduces the invention of microstructure clones. Microstructure clones are 2D oligocrystal specimens that have nearly identical microstructures to remedy the aforementioned experimental limitations. Having specimens with nearly identical microstructures will allow for multiple destructive tests of a microstructure (either as repeats or intentionally different experiments), an ability to “see the future” by providing insight into how a specimen will deform, variability quantification, and experimental investigations of response to small microstructural changes. This work introduces microstructure clones. Repeatability of these clones is demonstrated in tensile bars of pure nickel. Local strain measurements from digital image correlation are compared between clone specimens and compared to results from a crystal plasticity finite element model. Two sets of microstructure clones were tested in this study and displayed very consistent deformation responses within each clone set. Small observed differences in deformation invite investigation into microstructure stochasticity and the effect of small microstructural and loading differences. Microstructure clones represent a significant shift in understanding structure–property relationships. This work reshapes experimental crystal plasticity to allow for experiments that control for specific variables, quantification of microstructural stochasticity (and other sources of stochasticity), and opportunities for replicating experiments.

Damage evolution in engineering metal alloys at the grain scale exhibits significant microstructural heterogeneity and anisotropy. These heterogeneities create local hotspots for stress and strain localization, leading to void nucleation. Crystal orientation influences the active slip systems around voids, affecting lattice rotation and potentially forming discontinuities. At low triaxiality, voids may change shape due to lower stress, rotation, elongation, and coalescence. At high triaxiality, the correlation between crystal orientation and void growth rate becomes stronger, resembling the behavior observed in isolated single crystals. Therefore, understanding the effects of crystal orientation, heterogeneous strain, and defect evolution is crucial for single crystal fracture characterization. In this work, a coupled phase-field damage (PFD) and crystal plasticity (CP) model is implemented within a finite element framework to analyze crystal deformation and failure. The CP method employs a dislocation density-based constitutive model, while intragranular failure is modeled using an anisotropic PFD method. The PFD model considers both the stored energy due to elastic stretching and the energy release due to defect formation and crack formation. A single crystal Al2219 with an intracrystalline spherical void is chosen to analyze fracture. The study finds that fracture propagation is strongly correlated with crystal orientations. This coupled CP-PFD model provides accurate failure prediction in crystalline materials by incorporating the effects of crystal orientations and existing voids. This study demonstrates how the local microstructure and defects influence plastic deformation and failure mechanisms in metal alloys.

The influence of the internal structure at micrometer length scales on the deformation of polycrystalline materials can be effectively captured using crystal plasticity finite element methods (CPFEM). However, the complexity and nonlinearity of the deformation equations CPFEM solves demand significant computational power and resources to achieve accurate predictions, limiting its broader application. To address this challenge, we have identified a reduced-order representation of the complex data in order to establish a computationally efficient reduced-order models (ROM) and drastically reduce the computational expense of CPFEM. Specifically, in this work, we developed a parametric, data-driven, and non-intrusive ROM framework for CPFEM using proper orthogonal decomposition (POD) and sparse variational Gaussian process (SVGP) regression for single-crystal microstructures under tensile loading conditions. The developed protocol enables one to compress field into a latent/low-dimensional space described by principal component analysis (PCA) via the singular value decomposition (SVD) algorithm. As a result, the high-dimensional data are reduced to a significantly smaller amount of dimensions with POD bases and POD coefficients. Furthermore, we deployed an ensemble of SVGPs—extended from the classical Gaussian process (GP) regression for scalability and handling big data—in a massively parallel manner to train and predict latent POD coefficients using known POD bases from a set of previously obtained simulations results. Lastly, using the predicted POD coefficients, we reconstructed the full-field results and showed reasonable agreement compared with the true values obtained from running CPFEM. The developed framework is validated with a set of CPFEM simulations of a single embedded void in single-crystal aluminum alloy. While the framework is broadly applicable, this work specifically focuses on single-crystal microstructures, a single load case (e.g., tensile), and a specific void geometry (spherical).

Amorphous silicon nitride is a common material in microelectronics devices, which acts as an insulating barrier. Extended annealing times at elevated temperature can initiate crystallization of alpha-Si3N4, which does not possess the same barrier properties. Molecular dynamics can resolve the fundamental mechanism for alpha-Si3N4 crystallization and the influence of local environments. We compare two interatomic potentials and conclude that these models predict structural features (e.g., angular distributions and densities) which span the range of experimental measurements. We confirmed these models reproduce experimental estimates of activation energy and leveraged these models to identify crystallization drivers. We conclude that near-Tg, facet-dependent silicon nitride crystal growth rates can be predicted directly by either bulk or interfacial diffusion properties.