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.
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.
Metamaterials, otherwise known as architected or programmable materials, enable designers to tailor mesoscale topology and shape to achieve unique material properties that are not present in nature. Additionally, with the recent proliferation of additive manufacturing tools across industrial sectors, the ability to readily fabricate geometrically complex metamaterials is now possible. However, in many high-performance applications involving complex multi-physics interactions, design of novel lattice metamaterials is still difficult. Design is primarily guided by human intuition or gradient optimization for simple problems. In this work, we show how machine learning guides discovery of new unit cells that are Pareto optimal for multiple competing objectives; specifically, maximizing elastic stiffness during static loading and minimizing wave speed through the metamaterial during an impact event. Additionally, we show that our artificial intelligence approach works with relatively few (3500) simulation calls.
Metamaterials derive their unusual properties from their architected structure, which generally consists of a repeating unit cell designed to perform a particular function. However, existing metamaterials are, with few exceptions, physically continuous throughout their volume, and thus cannot take advantage of multi-body behavior or contact interactions. Here we introduce the concept of multi-body interpenetrating lattices, where two or more lattices interlace through the same volume without any direct connection to each other. This new design freedom allows us to create architected interpenetrating structures where energy transfer is controlled by surface interactions. As a result, multifunctional or composite-like responses can be achieved even with only a single print material. While the geometry defining interpenetrating lattices has been studied since the days of Euclid, additive manufacturing allows us to turn these mathematical concepts into physical objects with programmable interface-dominated properties. In this first study on interpenetrating lattices, we reveal remarkable mechanical properties including improved toughness, multi-stable/negative stiffness behavior, and electromechanical coupling.
Advances in printed electronics are predicated on the integration of sophisticated printing technologies with functional materials. Although scalable manufacturing methods, such as letterpress and flexographic printing, have significant history in graphic arts printing, functional applications require sophisticated control and understanding of nanoscale transfer of fluid inks. Herein, a versatile platform is introduced to study and engineer printing forms, exploiting a microscale additive manufacturing process to design micro-architected materials with controllable porosity and deformation. Building on this technology, controlled ink transfer for submicron functional films is demonstrated. The design freedom and high-resolution 3D control afforded by this method provide a rich framework for studying mechanics of fluid transfer for advanced manufacturing processes.
Product designs from a wide range of industries such as aerospace, automotive, biomedical, and others can benefit from new metamaterials for mechanical energy dissipation. In this study, we explore a novel new class of metamaterials with unit cells that absorb energy via sliding Coulombic friction. Remarkably, even materials such as metals and ceramics, which typically have no intrinsic reversible energy dissipation, can be architected to provide dissipation akin to elastomers. The concept is demonstrated at different scales (centimeter to micrometer), with different materials (metal and polymer), and in different operating environments (high and low temperatures), all showing substantial dissipative improvements over conventional non-contacting lattice unit cells. Further, as with other ‘programmable’ metamaterials, the degree of Coulombic absorption can be tailored for a given application. An analytic expression is derived to allow rapid first-order optimization. This new class of Coulombic friction energy absorbers can apply broadly to many industrial sectors such as transportation (e.g. monolithic shock absorbers), biomedical (e.g. prosthetics), athletic equipment (e.g. skis, bicycles, etc.), defense (e.g. vibration tolerant structures), and energy (e.g. survivable electrical grid components).
Particle characteristics can drastically influence the process-structure-property-performance aspects of granular materials in compression. We aim to computationally simulate the mechanical processes of stress redistribution in compacts including the kinematics of particle rearrangement during densification and particle deformation leading to fragmentation. Confined compression experiments are conducted with three sets of commercial microcrystalline cellulose particles nearly spherical in shape with different mean particle size. Experimentally measured compression curves from tall powder columns are fitted with the Kenkre et al. (J. of American Chemical Society, Vol. 79, No. 12) model. This model provides a basis to derive several common two-parameter literature models and as a framework to incorporate statistical representations of critical particle behaviors. We focus on the low-stress compression data and the model comparisons typically not discussed in the literature. Additional single particle compressions report fracture strength with particle size for comparison to the apparent particle strength extracted from bulk compression data.
Recent work in metal additive manufacturing (AM) suggests that mechanical properties may vary with feature size; however, these studies do not provide a statistically robust description of this phenomenon, nor do they provide a clear causal mechanism. Because of the huge design freedom afforded by 3D printing, AM parts typically contain a range of feature sizes, with particular interest in smaller features, so the size effect must be well understood in order to make informed design decisions. This work investigates the effect of feature size on the stochastic mechanical performance of laser powder bed fusion tensile specimens. A high-throughput tensile testing method was used to characterize the effect of specimen size on strength, elastic modulus and elongation in a statistically meaningful way. The effective yield strength, ultimate tensile strength and modulus decreased strongly with decreasing specimen size: all three properties were reduced by nearly a factor of two as feature dimensions were scaled down from 6.25 mm to 0.4 mm. Hardness and microstructural observations indicate that this size dependence was not due to an intrinsic change in material properties, but instead the effects of surface roughness on the geometry of the specimens. Finite element analysis using explicit representations of surface topography shows the critical role surface features play in creating stress concentrations that trigger deformation and subsequent fracture. The experimental and finite element results provide the tools needed to make corrections in the design process to more accurately predict the performance of AM components.