Digital twins are emerging as powerful tools for supporting innovation as well as optimizing the in-service performance of a broad range of complex physical machines, devices, and components. A digital twin is generally designed to provide accurate in-silico representation of the form (i.e., appearance) and the functional response of a specified (unique) physical twin. This paper offers a new perspective on how the emerging concept of digital twins could be applied to accelerate materials innovation efforts. Specifically, it is argued that the material itself can be considered as a highly complex multiscale physical system whose form (i.e., details of the material structure over a hierarchy of material length) and function (i.e., response to external stimuli typically characterized through suitably defined material properties) can be captured suitably in a digital twin. Accordingly, the digital twin can represent the evolution of structure, process, and performance of the material over time, with regard to both process history and in-service environment. This paper establishes the foundational concepts and frameworks needed to formulate and continuously update both the form and function of the digital twin of a selected material physical twin. The form of the proposed material digital twin can be captured effectively using the broadly applicable framework of n-point spatial correlations, while its function at the different length scales can be captured using homogenization and localization process-structure-property surrogate models calibrated to collections of available experimental and physics-based simulation data.
With the proliferation of additive manufacturing and 3D printing technologies, a broader palette of material properties can be elicited from cellular solids, also known as metamaterials, architected foams, programmable materials, or lattice structures. Metamaterials are designed and optimized under the assumption of perfect geometry and a homogeneous underlying base material. Yet in practice real lattices contain thousands or even millions of complex features, each with imperfections in shape and material constituency. While the role of these defects on the mean properties of metamaterials has been well studied, little attention has been paid to the stochastic properties of metamaterials, a crucial next step for high reliability aerospace or biomedical applications. In this work we show that it is precisely the large quantity of features that serves to homogenize the heterogeneities of the individual features, thereby reducing the variability of the collective structure and achieving effective properties that can be even more consistent than the monolithic base material. In this first statistical study of additive lattice variability, a total of 239 strut-based lattices were mechanically tested for two pedagogical lattice topologies (body centered cubic and face centered cubic) at three different relative densities. The variability in yield strength and modulus was observed to exponentially decrease with feature count (to the power −0.5), a scaling trend that we show can be predicted using an analytic model or a finite element beam model. The latter provides an efficient pathway to extend the current concepts to arbitrary/complex geometries and loading scenarios. These results not only illustrate the homogenizing benefit of lattices, but also provide governing design principles that can be used to mitigate manufacturing inconsistencies via topological design.
Alloying is often employed to stabilize nanocrystalline materials against microstructural coarsening. The stabilization process results from the combined effects of thermodynamically reducing the curvature-dominated driving force of grain-boundary motion via solute segregation and kinetically pinning these same grain boundaries by solute drag and Zener pinning. The competition between these stabilization mechanisms depends not only on the grain-boundary character but can also be affected by imposed compositional and thermal fields that further promote or inhibit grain growth. In this work, we study the origin of the stability of immiscible nanocrystalline alloys in both homogeneous and heterogeneous compositional and thermal fields by using a multi-phase-field formulation for anisotropic grain growth with grain-boundary character-dependent segregation properties. This generalized formulation allows us to model the distribution of mobilities of segregated grain boundaries and the role of grain-boundary heterogeneity on solute-induced stabilization. As an illustration, we compare our model predictions to experimental results of microstructures in platinum-gold nanocrystalline alloys. Our results reveal that increasing the initial concentration of available solute progressively slows the rate of grain growth via both heterogeneous grain-boundary segregation and Zener pinning, while increasing the temperature generally weakens thermodynamic stabilization effects due to entropic contributions. Finally, we demonstrate as a proof-of-concept that spatially-varying compositional and thermal fields can be used to construct dynamically-stable, graded, nanostructured materials. We discuss the implications of using such concepts as alternatives to conventional plastic deformation methods.
With the rapid proliferation of additive manufacturing and 3D printing technologies, architected cellular solids including truss-like 3D lattice topologies offer the opportunity to program the effective material response through topological design at the mesoscale. The present report summarizes several of the key findings from a 3-year Laboratory Directed Research and Development Program. The program set out to explore novel lattice topologies that can be designed to control, redirect, or dissipate energy from one or multiple insult environments relevant to Sandia missions, including crush, shock/impact, vibration, thermal, etc. In the first 4 sections, we document four novel lattice topologies stemming from this study: coulombic lattices, multi-morphology lattices, interpenetrating lattices, and pore-modified gyroid cellular solids, each with unique properties that had not been achieved by existing cellular/lattice metamaterials. The fifth section explores how unintentional lattice imperfections stemming from the manufacturing process, primarily sur face roughness in the case of laser powder bed fusion, serve to cause stochastic response but that in some cases such as elastic response the stochastic behavior is homogenized through the adoption of lattices. In the sixth section we explore a novel neural network screening process that allows such stocastic variability to be predicted. In the last three sections, we explore considerations of computational design of lattices. Specifically, in section 7 using a novel generative optimization scheme to design novel pareto-optimal lattices for multi-objective environments. In section 8, we use computational design to optimize a metallic lattice structure to absorb impact energy for a 1000 ft/s impact. And in section 9, we develop a modified micromorphic continuum model to solve wave propagation problems in lattices efficiently.
The advanced materials team investigated the use of additively manufactured metallic lattice structures for mitigating impact response in a Davis gun earth penetrator impact experiment. High-fidelity finite element models were developed and validated with quasistatic experiments. These models were then used to simulate the response of such lattices when subjected to the acceleration loads expected in the Davis gun experiment. Results reveal how the impact mitigation performance of lattices can change drastically at a certain relative density. Based on these observations, an experiment deck was designed to probe the response of lattices with different relative densities during the Davis gun phase 2 shots. The expected performance of these lattices is predicted before testing based on simulation results. The results of the Davis gun phase 2 shots are expected to provide data which will be used to assess the predictive capability of the finite element simulations in such a complex impact environment.
Lithium-metal anodes can theoretically enable 10× higher gravimetric capacity than conventional graphite anodes. However, Li-metal anode cycling has proven difficult due to porous and dendritic morphologies, extensive parasitic solid electrolyte interphase reactions, and formation of dead Li. We systematically investigate the effects of applied interfacial pressure on Li-metal anode cycling performance and morphology in the recently developed and highly efficient 4 M lithium bis(fluorosulfonyl)imide in 1,2-dimethoxyethane electrolyte. We present cycling, morphology, and impedance data at a current density of 0.5 mA/cm2 and a capacity of 2 mAh/cm2 at applied interfacial pressures of 0, 0.01, 0.1, 1, and 10 MPa. Cryo-focused ion beam milling and cryo-scanning electron microscopy imaging in cross section reveal that increasing the applied pressure during Li deposition from 0 to 10 MPa leads to greater than a fivefold reduction in thickness (and therefore volume) of the deposited Li. This suggests that pressure during cycling can have a profound impact on the practical volumetric energy density for Li-metal anodes. A "goldilocks zone"of cell performance is observed at intermediate pressures of 0.1-1 MPa. Increasing pressure from 0 to 1 MPa generally improves cell-to-cell reproducibility, cycling stability, and Coulombic efficiency. However, the highest pressure (10 MPa) results in high cell overpotential and evidence of soft short circuits, which likely result from transport limitations associated with increased pressure causing local pore closure in the separator. All cells exhibit at least some signs of cycling instability after 50 cycles when cycled to 2 mAh/cm2 with thin 50 μm Li counter electrodes, though instability decreases with increasing pressure. In contrast, cells cycled to only 1 mAh/cm2 perform well for 50 cycles, indicating that capacity plays an important role in cycling stability.