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Collimators and other components for use in neutron scattering experiments or to provide neutron shielding in nuclear reactors or accelerator based neutron sources are produced by additive manufacturing from multiple different types of material, such as boron carbide (B4C), steel, isotopically enriched boron carbide (10B4C), and blends thereof.
A metal matrix composite powder of molybdenum (Mo) + TiC was produced by mechanical alloying (MA) and used in additive manufacturing by electron beam powder bed fusion along with pure Mo powder to form sandwich structures. The Mo + TiC solid layers formed mixed structures of Mo with discrete TiC particles, eutectic Mo + TiC, and Mo dendrites. Thermodynamic modeling showed that the system contained an invariant eutectic reaction in the composition range used and indicated that the system was highly sensitive to changes in composition and temperature.
Abstract While there exists in nature abundant examples of materials with site-specific gradients in microstructures and properties, engineers and designers have traditionally used monolithic materials with discrete properties. Now, however, additive manufacturing (AM) offers the possibility of creating structures that mimic some aspects of nature. One example that has attracted attention in the recent years is the hierarchical structure in bamboo. The hierarchical architecture in bamboo is characterized by spatial gradients in properties and microstructures and is well suited to accommodate and survive complex stress states, severe mechanical forces, and large deformations. While AM has been used routinely to fabricate functionally graded materials, this study distinguishes itself by leveraging AM and physical metallurgy concepts to trigger cascading deformation in a single sample. Specifically, we have been successful in using AM to fabricate steel with unique spatial hierarchies in structure and property to emulate the structure and deformation mechanisms in natural materials. This study shows an improvement in the strength and ductility of the nature-inspired “hierarchical steel” compared with conventional cast stainless steels. In situ characterization proves that this improvement is due to the sequential activation of multiple deformation mechanisms namely twinning, transformation-induced plasticity, and dislocation-based plasticity. While significantly higher strengths can be achieved by refining the chemical and processing technique, this study sets the stage to achieve the paradigm of using AM to fabricate structures which emulate the flexibility in mechanical properties of natural materials and are able to adapt to in-service conditions.
This research aims to develop a framework for establishing the correlation between in-situ monitoring data, process parameters, and microstructure evolution in blown-powder laser-directed energy deposition (DED) additive manufacturing (AM). To achieve this, a comprehensive manufacturing framework has been developed, spanning from in-situ data acquisition, melt-pool simulation, microstructure modeling, and statistical microstructure quantification. A machine learning-based surrogate model is constructed to predict melt pool geometry directly from in-situ coaxial camera data. The surrogate model is trained using outputs from a high-fidelity melt pool simulation, which provides accurate melt pool dimension data under varying process conditions. The predicted melt pool geometry is then used as input to a microstructure model to predict microstructural features. To rigorously compare and analyze microstructures, the project introduces statistical metrics that quantify differences based on key features such as morphology and texture. Microstructures are represented using advanced statistical descriptors including angular chord length distribution, two-point spatial statistics, orientation distribution function, and global spherical harmonic. These representations are used to compute four distinct “dissimilarity scores” that quantitatively capture differences in texture and morphology. This framework is demonstrated to enable automated calibration of simulation parameters by minimizing discrepancies between simulated and target microstructures. The technology developed in this project enables direct correlation between in-situ monitoring data and resulting microstructure, paving the way for adaptive microstructure control in metal AM. This capability strengthens the connection between process parameters and final material properties, facilitating more precise and reliable material design.
This CRADA between UT-Battelle, LLC (Contractor) and re:3D Inc. (Participant) aims to develop an affordable large-scale granulator system designed to process reclaimed plastic feedstock suitable for additive manufacturing applications. The 24-month project will focus on testing, prototyping, and optimizing a shredder/granulator system. This project also focuses on the processing steps (e.g. washing and sorting) that can convert waste plastics, like discarded water bottles, into flake/granules which can be 3D printed using re:3D’s Fused Granular Fabrication (FGF) 3D printers. If successful, this could enable more localized 3D printing using recycled plastics, reducing waste and feedstock costs.
Melt pool (MP) temperature is one of the determining factors and a key signature for evaluating the properties of printed components in metal additive manufacturing (AM). The state-of-the-art measurement systems are hindered, primarily by the large-scale data acquisition and processing demands. In this work, we introduce a novel coaxial, high-speed, single-camera two-wavelength imaging pyrometer (STWIP) system as opposed to the typical utilization of multiple cameras for measuring MP temperature profiles in laser powder bed fusion (LPBF) processes. Developed on a commercial LPBF machine (EOS M290), the STWIP system demonstrated its ability to quantitatively monitor the MP temperature and its variation for 50 layers at high framerates (>30,000 fps) for a real-world application (standard fatigue specimens) print. High performance computing is employed to analyze the acquired big data (MP images), for determining each MP's average temperature and 2D temperature profile. The MP temperature evolution in the gage section of a fatigue specimen is also examined at a temporal resolution of 1 ms, by evaluating the MP temperatures in the samples' first, middle, and last layers. This report is the first of its kind on monitoring MP temperature distribution and evolution at such a large, detailed scale for longer durations in practical applications.
Refractory alloys are known for their high temperature capabilities and are intended for use at ultra-high temperatures above even 1200°C, which exceeds the capabilities of conventional superalloys. Additive manufacturing (AM) is an attractive alternative processing route because refractory alloys are difficult to fabricate through traditional methods; AM can form a near net-shape part with a tailored microstructure. This work seeks to evaluate the solidification behavior of refractory alloys under AM conditions and establish corresponding solidification models. Two binary alloys, Mo30Nb and Nb7.5Ta, as well as the commercial Nb alloy, C103, and one refractory high entropy alloy (RHEA), MoNbTaTi, were subjected to single track melts in a laser powder bed fusion (LPBF) machine. Each melt track was evaluated in the scanning electron microscope (SEM), which determined that some AM conditions used led to non-ideal behavior. Electron backscatter diffraction (EBSD) revealed that the Nb7.5Ta, C103, and MoNbTaTi all exhibited new grain nucleation to various extents while the Mo30Nb exhibited primarily epitaxial growth. This was found to be inconsistent with solidification models that were developed for each alloy and each set of AM conditions to predict the columnar to equiaxed transition behavior (CET) of the experimental microstructures. This disparity was largely attributed to the input parameters for the solidification model, which were developed using Thermo-Calc and SYSWELD. These input parameters include the alloy specific Gibbs-Thomson coefficient and thermophysical properties, the solute specific liquidus slopes and partitioning coefficients, as well as the process specific thermal gradients. Each input parameter was evaluated to determine the likely changes required for the modeled values to generate solidification models that better correspond to what was observed experimentally.
This article highlights work at Oak Ridge National Laboratory’s Manufacturing Demonstration Facility to develop closed-loop, feedback control for laser-wire based Directed Energy Deposition, a form of metal Big Area Additive Manufacturing (m-BAAM), a process being developed in partnership with GKN Aerospace specifically for the production of Ti-6Al-4V pre-forms for aerospace components. A large-scale structural demonstrator component is presented as a case-study in which not just control, but the entire 3D printing workflow for m-BAAM is discussed in detail, including design principles for large-format metal AM, toolpath generation, parameter development, process control, and system operation, as well as post-print net-shape geometric analysis and finish machining. In terms of control, a multi-sensor approach has been utilized to measure both layer height and melt pool size, and multiple modes of closed-loop control have been developed to manipulate process parameters (laser power, print speed, deposition rate) to control these variables. Layer height control and melt pool size control have yielded excellent local (intralayer) and global (component-level) geometry control, and the impact of melt pool size control in particular on thermal gradients and material properties is the subject of continuing research. Further, these modes of control have allowed the process to advance to higher deposition rates (exceeding 7.5 lb/hr), larger parts (1-meter scale), shorter build times, and higher overall efficiency. The control modes are examined individually, highlighting their development, demonstration, and lessons learned, and it is shown how they operate concurrently to enable the printing of a large-scale, near net shape Ti-6Al-4V component.
During the first phase of this project, small and large Strangpresse extruders were assessed at Oak Ridge National Laboratory’s Manufacturing Demonstration Facility (MDF). This phase resulted in Strangpresse exclusively licensing some of ORNL’s previously developed extruder technology for high-volume thermoplastic additive manufacturing (AM). Phase II of this project will focus on further development and optimization of Strangpresse extruders for increased efficiency and increased throughput. The objective of this project is to develop a new extruder with a higher output and less energy usage than current AM thermoplastic extruders and to develop and evaluate a new tamper mechanism for increasing Z-strength resulting in an advanced and optimized extruder and tamper system.
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U-shape flexural plates (UFPs) have gained significant attention in seismic structural design due to their ability to provide stable hysteresis behaviour and serve as effective energy-dissipating devices. Concurrently, Additive Manufacturing (AM) has advanced significantly, enabling the production of metallic components with mechanical properties comparable to those of conventionally manufactured cold-rolled materials. This study aims to explore the seismic response of single and double UFP fabricated through Laser Powder Based Fusion (LPBF) and conventional cold rolled 316L stainless steel (SS). The experimental results are evaluated based on force-displacement hysteresis behaviour, energy dissipation capacity, load-carrying capacity, and stiffness degradation. The findings confirm the efficacy of AM-fabricated UFPs for seismic energy dissipation. Additionally, the results suggest the potential for further improvement through the optimization of AM process parameters, such as laser scan speed, hatch spacing, and layer thickness, to enhance hysteretic performance and energy dissipation characteristics.
In this work, the effects of surface roughness on the high-temperature corrosion of nickel-based superalloy X produced through electron-beam melting (EBM) additive manufacturing were studied. Surface area of samples in the rough, as-fabricated condition was measured using a 2D cross-section image analysis technique and a 3D image analysis technique. To verify the accuracy of surface area measurements, oxidation testing of samples in the as-fabricated and polished conditions was performed; polished sample oxidation curves were then compared to the as-fabricated sample oxidation curves before and after being adjusted using obtained surface area measurements. It was found that the 2D technique accurately measured surface area and accounted for the overlapping features present on EBM sample surfaces, whereas the 3D technique did not. Further, it was also observed that oxidation kinetics were moderately faster on the alloy X samples in the as-fabricated condition than for the polished condition.
Elementum 3D, in partnership with NASA-MSFC under an ACO, seeks to help further increase launch performance and reduce launch and mission cost by advancing novel additively manufactured (AM) aluminum materials for large-scale complex rocket components and launch vehicle structures. The proposed outcome of this ACO is to advance large-scale directed energy deposition (DED) of highstrength aluminum alloys through process development, characterization, and testing.