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Organisationseinheit der BAM
Electron Beam Melting (EBM) as a means of Additive Manufacturing (AM), is of interest for the fabrication of intricate geometries for cellular materials in areas where complex architectures are needed, e.g. biomedical implants. Most studies have focused on specific geometries and so the effect of the structure on mechanical performance is not well understood. Many kinds of micro- and macro-scale defects can arise in additively manufactured components, so assessment of their influence on properties is needed. In this work, lattices of Ti-6Al-4V having a cubic structure have been manufactured by EBM, and the effect of heat treatments above and below the β-transus temperature on microstructure and compression response have been investigated. The former modifies only slightly the α + β structure and mechanical performance whereas the latter leads to coarse alternating α and β lamellae packets and α at the prior grain boundaries with a 10% loss in yield strength. The variation in the compressive yield stress with strut diameter is in good accord with simple models based on compressive deformation rather than shearing or buckling. Internal pores for struts aligned with the build direction are found around the edges of the solid form, in regions which seem to be associated with the EB scan pattern. Struts normal to the build direction show more significant defects but their redundancy means that they do not compromise the compressive performance in the build direction. Using a particle size in the range 45–100 μm minimum weld-track sizes were experimentally and numerically identified to be 176 and 148 μm in depth respectively with a depth-to-width ratio of 0.55. This produced a beam pass of the order of 300 μm oversizing small features (struts of 0.4 and 0.6 mm nominal diameter) when a contour around the strut periphery was applied.
Simple models describing the relationship between basic mechanical properties and the relative density of various types of porous metals (such as foams, sponges and lattice structures) are well established. Carefully evaluating these relationships experimentally is challenging, however, because of the stochastic structure of foams and the fact that it is difficult to systematically isolate density changes from variations in other factors, such as pore size and pore distribution. Here a new method for producing systematic sets of stochastic foams is employed based on electron beam melting (EBM) additive manufacturing (AM). To create idealised structures, structural blueprints were reverse-engineered by inverting X-ray computed tomographs of a randomly packed bed of glass beads. This three-dimensional structure was then modified by computer to create five foams of different relative density ρr, but otherwise consistent structure. Yield strength and Young’s modulus have been evaluated in compression tests and compared to existing models for foams. A power of 3 rather than a squared dependence of stiffness on relative density is found, which agrees with a recent model derived for replicated foams. A similar power of 3 relation was found for yield strength. Further analysis of the strength of nominally fully dense rods of different diameters built by EBM AM suggest that surface defects mean that the minimum size of features that can be created by EBM with similar strengths to machined samples is ∼1 mm.
X-ray tomography characterisation of lattice structures processed by selective electron beam melting
(2017)
Metallic lattice structures intentionally contain open porosity; however, they can also contain unwanted closed porosity within the structural members. The entrained porosity and defects within three different geometries of Ti-6Al-4V lattices, fabricated by Selective Electron Beam Melting (SEBM), is assessed from X-ray computed tomography (CT) scans. The results suggest that horizontal struts that are built upon loose powder show particularly high (~20 x 10⁻³ vol %) levels of pores, as do nodes at which many (in our case 24) struts meet. On the other hand, for struts more closely aligned (0° to 54°) to the build direction, the fraction of porosity appears to be much lower (~0.17 x 10⁻³%) arising mainly from pores contained within the original atomised powder particles.
High-throughput density functional theory (DFT) calculations have become a vital element of computational materials science, enabling materials screening, property database generation, and training of “universal” machine learning models. While several software frameworks have emerged to support these computational efforts, new developments such as machine learned force fields have increased demands for more flexible and programmable workflow solutions. This manuscript introduces atomate2, a comprehensive evolution of our original atomate framework, designed to address existing limitations in computational materials research infrastructure. Key features include the support for multiple electronic structure packages and interoperability between them, along with generalizable workflows that can be written in an abstract form irrespective of the DFT package or machine learning force field used within them. Our hope is that atomate2's improved usability and extensibility can reduce technical barriers for high-throughput research workflows and facilitate the rapid adoption of emerging methods in computational material science.
Atomistic simulations of matter, especially those that leverage first-principles (ab initio) electronic structure theory, provide a microscopic view of the world, underpinning much of our understanding of chemistry and materials science. Over the last decade or so, machine-learned force fields have transformed atomistic modeling by enabling simulations of ab initio quality over unprecedented time and length scales. However, early machine-learning (ML) force fields have largely been limited by (i) the substantial computational and human effort required to develop and validate potentials for each particular system of interest and (ii) a general lack of transferability from one chemical system to the next. Here, we show that it is possible to create a general-purpose atomistic ML model, trained on a public dataset of moderate size, that is capable of running stable molecular dynamics for a wide range of molecules and materials. We demonstrate the power of the MACE-MP-0 model—and its qualitative and at times quantitative accuracy—on a diverse set of problems in the physical sciences, including properties of solids, liquids, gases, chemical reactions, interfaces, and even the dynamics of a small protein. The model can be applied out of the box as a starting or “foundation” model for any atomistic system of interest and, when desired, can be fine-tuned on just a handful of application-specific data points to reach ab initio accuracy. Establishing that a stable force-field model can cover almost all materials changes atomistic modeling in a fundamental way: experienced users obtain reliable results much faster, and beginners face a lower barrier to entry. Foundation models thus represent a step toward democratizing the revolution in atomic-scale modeling that has been brought about by ML force fields.
