Driven by the exceedingly high computational demands of simulating mechanical response in complex engineered systems with finely resolved finite element models, there is a critical need to optimally reduce the fidelity of such simulations. The minimum required fidelity is constrained by error tolerances on the simulation results, but error bounds are often impossible to obtain a priori. One such source of error is the variability of material properties within a body due to spatially non-uniform processing conditions and inherent stochasticity in material microstructure. This study seeks to quantify the effects of microstructural heterogeneity on component- and system-scale performance to aid in the choice of an appropriate material model and spatial resolution for finite element analysis.
Herein, the formulation, parameter sensitivities, and usage methods for the Microstructure-Aware Plasticity (MAP) model are presented. This document is intend to serve as a reference for the underlying theory that constitutes the MAP model and as a practical guide for analysts and future developers on how aspects of this material model influence generalized mechanical behavior.
To model and quantify the variability in plasticity and failure of additively manufactured metals due to imperfections in their microstructure, we have developed uncertainty quantification methodology based on pseudo marginal likelihood and embedded variability techniques. We account for both the porosity resolvable in computed tomography scans of the initial material and the sub-threshold distribution of voids through a physically motivated model. Calibration of the model indicates that the sub-threshold population of defects dominates the yield and failure response. The technique also allows us to quantify the distribution of material parameters connected to microstructural variability created by the manufacturing process, and, thereby, make assessments of material quality and process control.
To develop a fundamental understanding of dynamic strain aging, discovery experiments were designed and completed to inform the development of a dislocation based micromechanical constitutive model that will ultimately tie to continuum level plasticity and failure models. Dynamic strain aging occurs when dislocation motion is hindered by the repetitive interaction of solute atoms, most frequently interstitials, with dislocation cores. Initially, the solute atmospheres pin the dislocation core until the virtual force on the dislocation is high enough to allow glissile motion. At temperatures where the interstitials are mobile enough, the atmospheres can repeatedly reform, lock, and release dislocations producing a characteristic serrated flow curve. This phenomenon can produce unusual mechanical behavior of materials and changes in the strain rate and temperature responses. Detrimental effects such as loss of ductility often accompany these altered responses.
In this work we employ data-driven homogenization approaches to predict the particular mechanical evolution of polycrystalline aggregates with tens of individual crystals. In these oligocrystals the differences in stress response due to microstructural variation is pronounced. Shell-like structures produced by metal-based additive manufacturing and the like make the prediction of the behavior of oligocrystals technologically relevant. The predictions of traditional homogenization theories based on grain volumes are not sensitive to variations in local grain neighborhoods. Direct simulation of the local response with crystal plasticity finite element methods is more detailed, but the computations are expensive. To represent the stress-strain response of a polycrystalline sample given its initial grain texture and morphology we have designed a novel neural network that incorporates a convolution component to observe and reduce the information in the crystal texture field and a recursive component to represent the causal nature of the history information. This model exhibits accuracy on par with crystal plasticity simulations at minimal computational cost per prediction.
Karlson, Kyle K.; Alleman, Coleman A.; Battaile, Corbett B.; Bergel, Guy L.; Boyce, Brad L.; Emery, John M.; Foulk, James W.; Hanson, Alexander H.; Jin, Huiqing J.; Kramer, Sharlotte L.; Madison, Jonathan M.; Skulborstad, Alyssa S.
The third Sandia Fracture Challenge highlighted the geometric and material uncertainties introduced by modern additive manufacturing techniques. Tasked with the challenge of predicting failure of a complex additively-manufactured geometry made of 316L stainless steel, we combined a rigorous material calibration scheme with a number of statistical assessments of problem uncertainties. Specifically, we used optimization techniques to calibrate a rate-dependent and anisotropic Hill plasticity model to represent material deformation coupled with a damage model driven by void growth and nucleation. Through targeted simulation studies we assessed the influence of internal voids and surface flaws on the specimens of interest in the challenge which guided our material modeling choices. Employing the Kolmogorov–Smirnov test statistic, we developed a representative suite of simulations to account for the geometric variability of test specimens and the variability introduced by material parameter uncertainty. This approach allowed the team to successfully predict the failure mode of the experimental test population as well as the global response with a high degree of accuracy.