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An Algorithm to Generate Synthetic 3D Microstructures from 2D Exemplars
The inverse problem of constructing 3D microstructures from 2D data is an area of active research within the materials science community. This paper presents the implementation of a robust, computationally efficient algorithm, Hierarchical Algorithm for the Reconstruction of Exemplars (HARE), written in Python to reconstruct 3D features in a given microstructure from up to three orthogonal 2D exemplars using nearest neighbor matching to reproduce feature qualities, such as shape, size, and distribution. HARE’s feature sampling implements histogram reweighting to avoid both over- and undersampling. A neighborhood voting scheme allows each pixel to provisionally affect its neighbors according to its weight. Finally, the algorithm is presently configured for two-phase materials and is being extended to accommodate multiple phases. HARE is a convenient and robust base from which to generate statistically representative synthetic microstructures for use in multi-scale modeling or machine learning applications to support advanced manufacturing and materials discovery.
3D microstructure design of lithium-ion battery electrodes assisted by X-ray nano-computed tomography and modelling
Driving range and fast charge capability of electric vehicles are heavily dependent on the 3D microstructure of lithium-ion batteries (LiBs) and substantial fundamental research is required to optimise electrode design for specific operating conditions. Here we have developed a full microstructure-resolved 3D model using a novel X-ray nano-computed tomography (CT) dual-scan superimposition technique that captures features of the carbon-binder domain. This elucidates how LiB performance is markedly affected by microstructural heterogeneities, particularly under high rate conditions. The elongated shape and wide size distribution of the active particles not only affect the lithium-ion transport but also lead to a heterogeneous current distribution and non-uniform lithiation between particles and along the through-thickness direction. Building on these insights, we propose and compare potential graded-microstructure designs for next-generation battery electrodes. To guide manufacturing of electrode architectures, in-situ X-ray CT is shown to reliably reveal the porosity and tortuosity changes with incremental calendering steps.
Scan strategies in EBM-printed IN718 and the physics of bulk 3D microstructure development
Three-dimensional (3D) characterization provides opportunities for understanding processing-structure relationships in additively manufactured (AM) materials. Bulk samples of Inconel 718 were fabricated via electron beam melting (EBM) in order to study microstructural development as a function of energy input and beam scan strategy. TriBeam tomography of bulk Inconel 718 microstructures built under steady-state growth conditions reveals the sensitivity of microstructure formation and evolution to machine process parameters. Here, samples manufactured using a narrow range of energy input per unit build area result in varied grain morphologies and crystallographic textures. Using TRUCHAS, a thermal simulation software, the thermal history of bulk scan strategies was predicted, and combined with a calibrated microstructure-processing map to accurately predict bulk grain morphologies. The solidification parameters and the 3D measured nucleation density are used to predict the transition between columnar and equiaxed grain morphologies, providing a process map to guide AM parameter choices to locally control as-printed microstructure. A two-dimensional metric for characterizing bulk grain morphology was also found to agree well with predictions from the process map calibrated by 3D data. Combined with 3D tomography and thermal modelling, the physics of structure development were understood at a new level of detail with respect to the competing processes of grain nucleation and epitaxial growth.
Computational Characterization and Model Verification for 3D Microstructure Reconstruction of Additively-Manufactured Materials
The goal of this study is to characterize and validate the texture and grain topology of additively-manufactured anisotropic three-dimensional (3D) polycrystalline microstructures. The special focus is on developing methodologies to compare the grain shapes and orientations of two-dimensional (2D) and 3D microstructure representations using the same metric. To generate statistical data, synthetic microstructures are reconstructed from experimental data using Markov random field (MRF). The statistical similarity between the experimental and synthetic microstructures is verified by comparing their grain topologies. A universal measure to compare 2D and 3D grains is portrayed through the concept of image moments that are invariant to shape transformations. The graphical plots developed based on moment invariants to compare the 2D and 3D grains are used to verify the synthetic model
Multi-modal Dataset of a Polycrystalline Metallic Material: 3D Microstructure and Deformation Fields
The development of high-fidelity mechanical property prediction models for the design of polycrystalline materials relies on large volumes of microstructural feature data. Concurrently, at these same scales, the deformation fields that develop during mechanical loading can be highly heterogeneous. Spatially correlated measurements of 3D microstructure and the ensuing deformation fields at the micro-scale would provide highly valuable insight into the relationship between microstructure and macroscopic mechanical response. They would also provide direct validation for numerical simulations that can guide and speed up the design of new materials and microstructures. However, to date, such data have been rare. Here, a one-of-a-kind, multi-modal dataset is presented that combines recent state-of-the-art experimental developments in 3D tomography and high-resolution deformation field measurements.
High-energy ion beams generated with high efficiency using laser-driven 3D microstructures
Laser-driven ion acceleration in plasma is being proposed as a source of ion beams with a high peak current that can be useful in many fields of science and medicine. Using this method, high proton energies have been achieved by increasing the laser power and by using ultrathin (≤ 200 nm) foils. However, this approach is limited by survivability of the nanotargets to laser prepulses and by difficulty in controlling the plasma acceleration properties. Here, we introduce a new target platform using two-photon polymerization, 3D laser-printed “clone” microstructures with average densities lower than solid that are relatively insensitive to the laser prepulse. Two types of microstructured targets consisting of either a multilayered log-pile or a stochastic arrangement of one micron diameter wires are used. Both demonstrate a higher energy and higher yield proton acceleration compared to thin solid-density foil targets by the robust target normal sheath acceleration (TNSA) mechanism. We find that when such 10–20 μm thick structures are irradiated with a petawatt laser, protons with energies up to 110 MeV and a laser-to-proton conversion efficiency of ~ 10% are obtained. Our work suggests that such microstructures optimized for 60–200 MeV compact proton accelerators are promising for future radiotherapy and other applications.
Programming Deformations of 3D Microstructures: Opportunities Enabled by Magnetic Alignment of Liquid Crystalline Elastomers
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Simulating additive manufacturing with SPPARKS - process modeling for realistic 3D microstructures.
Abstract not provided.
Algorithm to Generate Synthetic 3D Microstructures from 2D Exemplars
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Generation of 3D Microstructures From 2D Surface Images Using Markov Random Fields
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Generation of Large-Scale 3D Microstructures from Surface Images: Application to Additive Manufacturing
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Computational Characterization and Model Verification for 3D Microstructure Reconstruction of Additively Manufactured Materials
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3D microstructures for rapid absorption/desorption in mechanically constrained liquid absorbents
An absorber or desorber contains one or more micro-channels that have a 3-D structured heat-exchanging surface and a membrane on the microchannel situated distal to the 3-D structured heat-exchanging surface, where the membrane is permeable to a solvent of a solution employed in the absorber or desorber. The 3-D structured surface promotes mixing of hot and cold solution between the 3-D structured heat-exchanging surface and a vapor-exchanging surface proximal to the membrane. The mixing reduces the differences in concentration and temperature of the bulk solution and the solution at the vapor-exchanging surface to enhance the efficiency and rate of absorption or desorption of the solvent.
Relating 2D Experimental Information to 3D Simulations using Surface Structure Conserving 3D Microstructure Generation.
Abstract not provided.
The TriBeam: Combining Electron Ion and Laser Beams for 3D Microstructural Characterization.
Abstract not provided.
Improving Microstructural Quantification in 3D FIB-SEM Tomography
A detailed understanding of 3D microstructural features is crucial during postprocessing of the acquired data, while determining microstructural properties. Focused ion beam and scanning electron microscopy (fib-sem) is an ideal technique for the acquisition and quantification of 3D microstructures in subsurface features. In this work, we evaluated and implemented approaches to quantify 3D morphological features of plutonium oxalate and oxide particles. Image analysis, as well as statistical analysis were utilized to study the effect of process variables on the pore morphology of plutonium oxalate and oxide particles. The primary results were the creation of morphological maps of particles and identify indicators that correlated parameters allowing for discrimination between direct and reverse strike processes. This enabled us to properly quantify and map particles with their images obtained via sem. For this project, the highest correlation was found to be between Volume and Sphericity (R 2 = 0.7432) for the Revere Strike process, and Volume % and Sphericity (R 2 = 0.7450) for the Direct Strike process.