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Direct comparison of microstructure-sensitive fatigue crack initiation via crystal plasticity simulations and in situ high-energy X-ray experiments
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Uncertainty Reduction With Multi-Model Monte Carlo for Crystal Plasticity Simulations of Additively Manufactured Metals
In this work, multi-model Monte Carlo estimators are developed to reduce uncertainty in quantities of interest (QoIs) extracted from crystal plasticity simulations of additively manufactured (AM) metals. A significant concern in AM parts is uncertainty in mechanical properties caused in part by complex microstructures that arise from the AM process. Quantifying uncertainty in microstructure-sensitive behavior using experiments alone is costly, especially when mechanical allowables must be established. Quantitative relationships among microstructure, micromechanical metrics like slip accumulation, crack initiation, and failure are also difficult to capture with limited experiments. Crystal plasticity material models instead enable computational prediction of micromechanical stress and strain fields given a discretized microstructure. However, high-fidelity finely discretized crystal plasticity simulations are computationally expensive, while lower-fidelity models are less accurate and generally biased, making uncertainty quantification and reduction computationally difficult as well. Multi-model Monte Carlo methods leverage correlations between high- and low-fidelity models to produce unbiased estimators for QoIs with reduced uncertainty relative to standard Monte Carlo. Crystal plasticity QoIs considered in this work include yield strength and the mean and extreme values of micromechanical fields that are relevant to crack initiation. Multi-model Monte Carlo estimators are developed for each individual QoI and several groups of QoIs. The results of this work establish relationships among model correlations, sample allocation, and uncertainty reduction for different combinations of QoIs and demonstrate a trend of less uncertainty reduction as QoIs become more sensitive to local microstructure. Limitations from using pilot samples to estimate model covariances and train low-fidelity models are also addressed. The uncertainty reduction achieved by multi-model Monte Carlo is an important step toward using computational mechanics models to predict microstructure-sensitive crack initiation and failure in AM parts.
Investigating the Interaction between Persistent Slip Bands and Surface Hard Coatings via Crystal Plasticity Simulations
Fatigue cracks often initiate from the surface extrusion/intrusions formed due to the operation of persistent slip bands (PSBs). Suppression of these surface topographical features by hard surface coatings can significantly extend fatigue lives under lower stress amplitudes (i.e., high cycle fatigue), while cracks initiate early in the coating or in the coating–substrate interface under higher stress amplitudes (i.e., low cycle fatigue), deteriorating the fatigue performance. However, both beneficial and detrimental effects of the coatings appear to be affected by the coating–substrate material combination and coating thickness. A quantitative understanding of the role of these factors in the fatigue performance of materials is still lacking. In this study, crystal plasticity simulations were employed to elucidate the dependence of the coating’s effects on two factors—i.e., the coating thickness and loading amplitudes. The results revealed that the thicker coatings more effectively suppress the operation of the PSBs, but generate higher tensile and shear stresses, normal and parallel to the interfaces, respectively, promoting interfacial delamination. The tensile stresses parallel to the interface within the coating, which favors coating fracture, are not sensitive to the coating thickness.
A cycle-jump acceleration method for the crystal plasticity simulation of high cycle fatigue of the metallic microstructure
For metallic materials, high-cycle fatigue life is sensitive to underlying microstructure features including secondary phases, textures, grains morphology, etc. The traditional, data-based safe-life approaches for modeling fatigue don’t explicitly consider the microstructure and can’t guide study in microstructure modification for improved fatigue property. Crystal plasticity-based simulation provides increased model fidelity at the expense of immense computation time, making it inapplicable for high cycle fatigue. In this work, an acceleration method based on cycle-jump approach (Lesne and Savalle, 1989) was developed for microstructure-based high-cycle FE simulation using crystal plasticity constitutive-model. We report this method demonstrated high efficiency in benchmark tests of various conditions.
Deformation twinning and detwinning in extruded Mg-4Al: In-situ experiment and crystal plasticity simulation
Deformation twinning and detwinning in extruded Mg-4Al were investigated using in-situ SEM-DIC experiments and crystal plasticity finite element (CPFE) simulation. In this study, the in-situ SEM-DIC method was used to provide a unique set of data including twin/detwin characteristics and twin area fraction in addition to strain maps. A statistical analysis of the activation of twin variants and twin area fraction during both twinning and detwinning was conducted. A strong correlation was found between twin growth/shrinkage and the Schmid Factor (SF) for individual twin variants Higher twin SF during loading and unloading led to higher twin growth and shrinkage, respectively. However, after the applied compressive strain was removed, the pattern of the twin area fractions of the residual twin variants versus their nominal SFs did not follow the trend observed at the maximum compressive strain. Using a systematic methodology and an advanced twin/detwin model, the PRISMS-Plasticity CPFE simulation was calibrated using experimentally determined stress versus strain and twin area fraction versus strain information. A comprehensive evaluation of the CPFE model was conducted to determine its ability to capture the statistics of twin variants activation and twin area fraction. CPFE accurately captured the statistical aspects of both twinning and detwinning. Finally, it also predicted the first dominant twin variant for 47.5% of the grains and at least one of the two dominant twin variants for 80% of the grains at maximum compressive strain.
A Parametric, Data-Driven, Non-Intrusive Reduced-Order Model Framework for Crystal Plasticity Simulations of Voids
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).
Crystal Plasticity Simulation of Magnesium and Its Alloys: A Review of Recent Advances
Slip and extension twinning are the dominant deformation mechanisms in Magnesium (Mg) and its alloys. Crystal plasticity is a powerful tool to study these deformation mechanisms. Different schemes have incorporated crystal plasticity models to capture different properties, which vary from the simple homogenization Taylor model to the full-scale crystal plasticity finite element model. In the current study, a review of works available in the literature that addresses different properties of Mg and its alloys using crystal plasticity modes is presented. In addition to slip and twinning, detwinning is another deformation mechanism that is activated in Mg and its alloys. The different models that capture detwinning will also be addressed here. Finally, the recent experimental frameworks, such as in-situ neutron diffraction, 3D high energy synchrotron X-ray techniques, and digital image correlation under scanning electron microscopy (SEM-DIC), which are incorporated along crystal plasticity models to investigate the properties of Mg and its alloys, are addressed. Future research directions towards improving the deformation response of Mg and its alloys are identified, which can lead to increased deployment of the lightest structural metal in engineering applications.
Validation of Crystal Plasticity Simulations Using High Energy X-ray Diffraction Microscopy Measurements
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Scaling ductility from microscale to bulk by coupling crystal plasticity simulations with 3D convolutional neural networks
3D convolutional neural network approach to multi-scale stress/strain relationships in crystallized materials
Investigating the orientation dependence of local fields around spherical defects using crystal plasticity simulations
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Machine learning-enabled identification of micromechanical stress and strain hotspots predicted via dislocation density-based crystal plasticity simulations
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Initializing intragranular residual stresses within statistically equivalent microstructures for crystal plasticity simulations
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Investigating plastic anisotropy of Al7079 using crystal plasticity simulations.
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Investigating evolution of voids in Al2219 using 3D characterization and crystal plasticity simulations
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Quantitative Comparison between Experiment and Crystal Plasticity Simulations using Microstructure Clones
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Validation and Application of Crystal Plasticity Simulations to Study Effect of Process-specific Defects in Additively Manufactured Polycrystalline Materials
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