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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 1,585 records · Page 88
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

The origin and formation of oxygen inclusions in austenitic stainless steels manufactured by laser powder bed fusion

The origins of nano-scale oxide inclusions in 316L austenitic stainless steel (SS) manufactured by laser powder bed fusion (L-PBF) was investigated by quantifying the possible intrusion pathways of oxygen contained in the precursor powder, extraneous oxygen from the process environment during laser processing, and moisture contamination during powder handling and storage. When processing the fresh, as-received powder in a wellcontrolled environment, the oxide inclusions contained in the precursor powder were the primary contributors to the formation of nano-scale oxides in the final additive manufactured (AM) product. These oxide inclusions were found to be enriched with oxygen getter elements like Si and Mn. By controlling the extraneous oxygen level in the process environment, the oxygen level in AM produced parts was found to increase with the extraneous oxygen level. The intrusion pathway of this extra oxygen was found to be dominated by the incorporation of spatter particles into the build during processing. Moisture induced oxidation during powder storage was also found to result in a higher oxide density in the AM produced parts. SS 316L powder free of Si and Mn oxygen getters was processed in a well-controlled environment and resulted in a similar level of oxygen intrusion. Finally, microhardness testing indicated that the oxide volume fraction increase from extraneous oxygen did not influence hardness values. However, a marked decrease in hardness was found for the humidified and Si-Mn free AM processed parts.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Towards developing multiscale-multiphysics models and their surrogates for digital twins of metal additive manufacturing

Artificial intelligence (AI) embedded within digital models of manufacturing processes can be used to improve process productivity and product quality significantly. The application of such advanced capabilities particularly to highly digitalized processes such as metal additive manufacturing (AM) is likely to make those processes commercially more attractive. AI capabilities will reside within Digital Twins (DTs) which are living virtual replicas of the physical processes. DTs will be empowered to operate autonomously in a diagnostic control capacity to supervise processes and can be interrogated by the practitioner to inform the optimal processing route for any given product. The utility of the information gained from the DTs would depend on the quality of the digital models and, more importantly, their faster-solving surrogates which dwell within DTs for consultation during rapid decision-making. In this article, we point out the exceptional value of DTs in AM and focus on the need to create high-fidelity multiscale-multiphysics models for AM processes to feed the AI capabilities. We identify technical hurdles for their development, including those arising from the multiscale and multiphysics characteristics of the models, the difficulties in linking models of the subprocesses across scales and physics, and the scarcity of experimental data. We discuss the need for creating surrogate models using machine learning approaches for real-time problem-solving. We further identify non-technical barriers, such as the need for standardization and difficulties in collaborating across different types of institutions. We offer potential solutions for all these challenges, after reflecting on and researching discussions held at an international symposium on the subject in 2019. Here, we argue that a collaborative approach can not only help accelerate their development compared with disparate efforts, but also enhance the quality of the models by allowing modular development and linkages that account for interactions between the various sub-processes in AM. A high-level roadmap is suggested for starting such a collaboration.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Material properties and mechanical behaviour of functionally graded steel produced by wire-arc additive manufacturing

Metal Big Area Additive Manufacturing is an additive manufacturing technique based on Gas Metal Arc Welding (GMAW) with the option to use many shielding gases, and materials. The system is equipped with a dual torch design allowing for printing different materials; in our study, AISI 410 stainless steel and AWS ER70S-6 mild steel are both printed in the same component. Different print strategies were designed to highlight changes in material and mechanical properties. Deformation behaviour of a materials’ interface was analysed by two-dimensional digital image correlation of uniaxial tensile specimens in displacement-controlled tests. Instances of non-homogeneous local strains adjacent to the interface are observed, as well as variability in mechanical behaviour and microstructure based on location within the print. Optical and electron microscopy are used to evaluate three microstructural zones in a 5 mm range of the interface between mild steel and stainless steel. Areas far from the interface produced polygonal ferrite and pearlite, while areas close to the interface produced acicular ferrite and bainite. Chromium redistribution profiles are dependent on the print strategy used, as shown by scanning electron microscopy with Energy dispersive spectroscopy. Evidence produced via electron backscatter diffraction is shown to support the argument that transformation induced plasticity is not the cause for the non-homogeneous deformation.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Predicting phase transformation kinetics during metal additive manufacturing using non-isothermal Johnson-Mehl-Avrami models: Application to Inconel 718 and Ti-6Al-4V

Here, a computational model was developed to predict solid-state phase transformation kinetics within mechanical parts during metal additive manufacturing processes. This model is a modified version of the Johnson-Mehl-Avrami model for non-isothermal phase transformations that can be applied to various material systems undergoing solid-state phase transformations. Using the thermal history of an additive manufacturing fabricated mechanical part, along with the necessary thermodynamic data and kinetic information as inputs, the model outputs the history of phase fraction evolution during the build process. The model was applied to an Inconel 718 part built by powder bed fusion and a Ti-6Al-4V part built by directed energy deposition. Microstructure characterization and mechanical testing were performed for the validation of the model.

36 MATERIALS SCIENCE↗
DOE OSTI2022

The Trajectory of Additively Manufactured Titanium Alloys with Superior Mechanical Properties and Engineered Microstructures

Additive manufacturing (AM) plays a crucial role in the manufacturing and development of advanced materials including titanium alloys for engineering applications. During the last two decades, a great deal of effort has been devoted to print titanium parts with desired properties. However, due to presence of non-equilibrium/metastable phases, anisotropic microstructure, and various porosities in the printed titanium alloys, achieving an excellent combination of mechanical properties has been challenging. Here this review paper aims to provide a comprehensive overview of multiple post-process and in-process approaches to achieve enhanced strength-ductility combinations by microstructure engineering. Approaches such as addition of alloying elements/nucleants, multi-step post heat treatment, in-situ heat treatment, forced inter-pass cooling, in situ peening/rolling, hybridizing microstructure, high-intensity acoustic vibration, thermo-hydrogen refinement of microstructure, deliberately introducing lack of fusion defects with subsequent hot isostatic pressing are discussed in detail. Finally, we conclude by highlighting the challenges associated with each method and potential areas of improvement that can pave the way to develop reliable components with enhanced performance.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Improving fatigue performance of additively manufactured Ti-6Al-4V using sulfur-based self-terminating etching processes

The adoption of Additively Manufactured (AM) Ti-6Al-4V (Ti64) is continually increasing in the industry due to its benefits over the conventional manufacturing techniques. However, AM Ti64 parts are prone to poor surface finish, which significantly affects their fatigue performance. Therefore, AM Ti64 parts need post-processing to enhance surface finish and fatigue performance. Prior work with a sulfur-based self-terminating etching process for AM Ti64 demonstrated support removal and surface roughness reduction. To further this work, this manuscript details the impact of this sulfur-based post-processing technique on the mechanical properties and the fatigue life of AM Ti64 by characterizing the surface roughness, microstructural evolution, and fracture surfaces. Finally, this post-processing technique decreased the surface roughness parameter - S v by 55 % from 84 ± 11 µm to 38 ± 19 µm and increased the average fatigue life by 340 % from about 7000 cycles to 30,000 cycles.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Additively manufactured Al-Ce-Ni-Mn alloy with improved elevated-temperature fatigue resistance

Here, the high cycle fatigue behavior of an additively manufactured (AM) Al-10.5Ce-3.1Ni-1.2Mn wt% alloy was evaluated at 350 °C. The measured fatigue strength of 50 MPa at this temperature is comparable to the most fatigue resistant wrought Al alloys (2618-T6 and 7079-T6) at 315 °C. Refinement of pore populations through advanced AM processing in the examined alloy led to oxide inclusions becoming dominant fatigue crack initiation sites. The distribution of microstructural features that determine crack initiation was measured by X-ray computed tomography and served as input to make fatigue strength model predictions with various methodologies. A Monte-Carlo based approach that was previously applied to predict the fatigue strength of cast aluminum alloys through pore size distributions yielded accurate predictions for the fatigue strength of this AM alloy. Given the tunability of defect distributions in AM alloys, the sensitivity of the Al-Ce-Ni-Mn alloy fatigue behavior to defect distributions and outstanding elevated-temperature fatigue resistance of this alloy; the results together suggest the possibility of a new generation of fatigue-resistant alloys produced by the additive manufacturing route.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Additive manufacturing enabled synergetic strengthening of bimodal reinforcing particles for aluminum matrix composites

An additive manufactured TiB 2 /Al-Cu-Mg-Ni composite with a minor amount of Sc was fabricated by laser powder bed fusion (LPBF). The composite shows a yield strength of ~370 MPa, almost doubling the strength of its wrought matrix counterpart, and an elongation of ~7 %. The superior mechanical properties are attributed to a unique micro-nano hierarchical microstructure, consisting of nanoscale and microscale TiB 2 particles dispersed in a matrix of fine aluminum grains (3 µm) together with intragranular intermetallic nanoparticles and nano-cellular networks (cell size 30 nm). The formation of the TiB 2 nanoparticles is the consequence of partial dissolution of the TiB 2 particulates and the enrichment of Sc in the newly formed nanoparticles. Remarkable strengthening effects are achieved by the bimodal TiB 2 particles, intermetallic nanoparticles and intragranular nano-cellular networks. Finally, this study provides new insights into the role of additive manufacturing in tailoring the microstructure of particulate reinforced metal matrix composites (MMCs) with advanced properties.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Scalable in situ non-destructive evaluation of additively manufactured components using process monitoring, sensor fusion, and machine learning

Laser Powder Bed Fusion (L-PBF) Additive Manufacturing (AM) is among the metal 3D printing technologies most broadly adopted by the manufacturing industry. However, the current industry qualification paradigm for critical-application L-PBF parts relies heavily on expensive non-destructive inspection techniques, which significantly limits the use-cases of L-PBF. In situ monitoring of the process promises a less expensive alternative to ex situ testing, but existing sensor technologies and data analysis techniques struggle to detect sub-surface flaws (e.g., porosity and cracking) on production-scale L-PBF printers. In this work, an in situ NDE (INDE) system was engineered to detect subsurface flaws detected in X-Ray Computed Tomography (XCT) directly from process monitoring data. A multilayer, multimodal data input allowed the INDE system to detect numerous subsurface flaws in the size range of 200–1000µm using a novel human-in-the-loop annotation procedure. Furthermore, a framework was established for generating probability-of-detection (POD) and probability-of-false-alarm (PFA) curves compliant with NDE standards by systematically comparing instances of detected subsurface flaws to post-build XCT data. Here, we also introduce for the first time in the AM in situ sensing literature the a 90/95 – the flaw size corresponding to a 90% detection rate on the lower 95% confidence interval of the POD curve. The INDE system successfully demonstrated POD capabilities commensurate with traditional NDE methods. Traditional ML performance metrics were also shown to be inadequate for assessing the ability of the INDE system’s flaw detection performance. It is the hope of the authors that future studies will adopt the POD and PFA approach outlined here to provide better insight into the utility of process monitoring for AM.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Leveraging the digital thread for physics-based prediction of microstructure heterogeneity in additively manufactured parts

A major limitation of additive manufacturing (AM) processes is that local conditions of material deposition frequently lead to unintentional heterogeneities in microstructure and properties within a single component, despite nominally uniform process conditions. Up to now, there has been no way to a priori determine the distribution of these heterogeneities, requiring expensive trial-and-error approaches to fabrication, testing, and characterization. Here, a physics-based framework for creating a digital representation of the laser powder bed fusion (PBF) process is proposed to predict the variation in solidification behavior that leads to heterogeneous microstructures in an as-built part. By leveraging in situ process data stored in the part’s digital thread, the scan path and process parameters were input into a heat transfer model which predicted solidification data at the melt pool scale. A two-step unsupervised clustering algorithm was used to first cluster the local solidification conditions (12.5µm 3 voxels) and then to cluster the regional behavior on the scale of multiple scan passes and print layers (250µm 3 super-voxels). This process was used to identify regions with similar solidification characteristics for multiple locations in a Stainless Steel 316-L component. The corresponding as-built part was sectioned and characterized using electron backscatter diffraction (EBSD). Quantitative analysis of the pole figures confirmed that the predicted regions of heterogeneity in the solidification conditions corresponded with differences in the observed microstructure. In conclusion, this work shows a viable path for estimating the microstructural heterogeneity for additively manufactured parts to either limit microstructural variation throughout a part or to enable functionality-based variation of the microstructure.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Predictive binder jet additive manufacturing enabled by clean burn-off binder design

Binder jet additive manufacturing (BJAM) presents an avenue for advanced manufacturing of various high-value materials due to high deposition rates, scalability, and geometric flexibility. However, conventional organic binders in BJAM introduce residual carbon upon pyrolysis, often leading to imprecise alloy composition in the final sintered part. The undesirable residual carbon from binder burn-off limits the application of BJAM for high-performance alloys due to their high sensitivity to carbon addition. In this study, we have designed poly(vinylpyrrolidone-co-vinyl acetate) (PVP-VAc) as a clean burn-off binder for BJAM, where excess oxygen groups in VAc enable cleaner burn-off and reduce residual carbon retention. Compared to a widely used commercial binder, the optimized PVP-VAc binder reduced residual carbon retention by over 90% in H13 tool steel. The significant reduction in residual carbon enables predictable printing and subsequent sintering of complex H13 tool steel geometries, an alloy known to have substantial challenges around distortion due to the sintering window shifting from carbon addition. The design of a clean burnout binder provides a major path forward for BJAM by enabling new AM designs and applications for composition-sensitive high-performance alloys, such as nickel-based superalloys, titanium alloys, and high alloyed steels.

36 MATERIALS SCIENCE↗
DOE OSTI2024

New insights into the mechanism of ultrasonic atomization for the production of metal powders in additive manufacturing

Ultrasonic atomization is one of the promising technologies for producing metal powders for additive manufacturing, where precise control of particle size and morphology is essential. In this study, we coupled an ultrasonic transducer with a carbon fiber plate and atomized liquid droplets and films under different vibration amplitudes. Water, glycerol, and pure aluminum melt were used to study the atomization mechanism and the resulting droplet/powder characteristics, respectively. High-speed optical and ultrafast synchrotron X-ray imaging were used to study in situ the ultrasonic atomization dynamics, including pulsation and clustering of cavities inside the liquid layer/films, development of capillary waves, and formation of liquid droplets. For the first time, we observed and captured the occurrence of cavitation during the atomization of resting drops, films and impact droplets. The inertial cavitation events interfered with the capillary waves across the interphase boundary, puncturing and breaking the boundary to produce atomized mist. The in situ observation revealed the intricate dynamics of ultrasonic atomization and underscored the pivotal role of cavitation events throughout the entire atomization process. We also conducted experiments on ultrasonic atomization of liquid aluminum, producing particles of perfectly spherical shape. The particle size tended to decrease with reduced vibration amplitude Our work has demonstrated the important processing strategies on how to tailor the particle size while ensuring consistent particle shape and morphology, which is the key processing capability for producing high quality powders for additive manufacturing applications.

36 MATERIALS SCIENCE↗
DOE OSTI2024

A dynamic volumetric heat source model for laser additive manufacturing

Melt pool scale models of laser powder bed fusion (LPBF) offer insights into the process-structure-property relationships in additive manufacturing (AM). These models often neglect physical phenomena such as vapor cavity formation and fluid mechanics to reduce computational demands. Instead, volumetric heat source models are used to represent the effects that these phenomena have on the predicted melt pool dimensions. Generally, the dimensions and effective absorption of the volumetric heat source are calibrated to reproduce melt pool dimensions observed in metallographic cross sections taken from single-track experiments on bare plate. However, the transient nature of LPBF often deviates the melt pool dimensions from the assumed steady-state conditions of single-track experiments, motivating the need for a volumetric heat source model that more generally considers the dynamic relationship between melt pool shape and laser-material interactions. Here, we introduce a two-parameter volumetric heat source model that integrates several existing models into a generalized mathematical expression, providing independent control over the radial heat distribution via the parameter k and the volumetric shape of the heat source via the parameter m. This parameterization enables the calibration of melt pool shape predictions through simultaneous adjustment of these parameters, while keeping the radial heat source dimensions consistent with the experimental spot size (D4σ) and constraining the heat source depth and absorption to physically derived expressions for cavities. Consequently, the proposed volumetric heat source model adapts to changes in the local melt pool conditions due to scanning strategy and part geometry by dynamically adjusting the heat source depth and absorption. We demonstrate the capabilities of the proposed model through comparisons with a collection of experiments from the Additive Manufacturing Benchmark (AMBench).

36 MATERIALS SCIENCE↗
DOE OSTI2026

A time-parallel method for scalable heat transfer simulations of additive manufacturing

Here, a major challenge in simulating the thermal behavior in additive manufacturing processes is the disparate length and time scales between transport phenomena occurring in the melt pool and the component. A common simulation approach relies on spatial decomposition for parallel computing, but due to the nature of heat transfer in AM, where most of the computational expenditure is localized near the melt pool, the computational speedup from spatial parallelization saturates quickly. Therefore, additional parallelism by means of time-domain decomposition is needed to fully take advantage of high-performance computing (HPC) resources. This work introduces a time-parallel method to improve the computational scalability of additive manufacturing simulations on HPC systems, while maintaining high temporal resolution of heat transfer near the melt pool. The method, inspired by the nonlinear paraexp formalism, performs an iterative superposition of nonlinear solutions to the initial value problem, integrating the heat equation across overlapping time-parallel intervals. For a single layer of the NIST AMB2018–01 L7 benchmark problem, the method achieves a 38.51x speedup in wall-clock time with a maximum error in the global temperature solution of 0.99%. This reduces the total solution time from 196.72 min to 5.11 min on 128 nodes of the ORNL Frontier supercomputer. The tradeoff between accuracy and total wall-clock time is investigated and recommendations for time-parallel deployment for AM problems are made.

Additive manufacturing↗
DOE OSTI2026

Exploring the tempering behaviour of additively manufactured high-alloy tool steel using synchrotron X-ray and neutron techniques

Post-processing heat treatment provides a critical pathway toward the commercialisation of additively manufactured (AM) S390 high-speed steel, which is a representative high-alloy tool steel employed in precision manufacturing, offering up to 1.5 times longer tool life and over 20% higher cutting speeds compared to conventional grades. In this study, the phase evolution of AM S390 steel during heat treatment, with particular emphasis on carbide precipitation behaviour, was systematically investigated using a combination of synchrotron X-ray and neutron techniques. The metastable M2C carbides were found to dissolve during austenitisation, while the stable primary carbides MC and M6C experienced coarsening with an average size increase of about 60 nm after just 2 min of tempering. Moreover, the austenite lattice parameters reduced from 3.618 to 3.608 Å within the first 10 min of tempering, suggesting carbon depletion in the steel matrix was likely associated with the formation of secondary carbides. This interpretation was substantiated by small-angle scattering results, which revealed the presence of nanoscale precipitates with a volume fraction of 3.1% after 60 min of tempering. These microstructural evolutions collectively accounted for the observed peak hardness of 921 HV. Furthermore, a comparative analysis of synchrotron and neutron small-angle scattering data highlighted the complementary strengths of each technique, offering critical insight into their suitability for characterising nanoscale features in AM high-alloy steels.

Synchrotron X-ray diffraction↗
DOE OSTI2018

Fully printed and integrated electrolyzer cells with additive manufacturing for high-efficiency water splitting

Using additive manufacturing (AM) technology, a fundamental material and structure innovation was proposed to significantly increase the energy efficiency, and to reduce the weight, volume and component quantity of proton exchange membrane electrolyzer cells (PEMECs). Four conventional parts (liquid/gas diffusion layer, bipolar plate, gasket, and current distributor) in a PEMEC were integrated into one multifunctional AM plate without committing to tools or molds for the first time. In addition, since the interfacial contact resistances between those parts were eliminated, the comprehensive in-situ characterizations of AM cells showed that an excellent energy efficiency of up to 86.48% was achieved at 2 A/cm2 and 80 degrees C, and the hydrogen generation rate was increased by 61.81% compared to the conventional cell. More importantly, the highly complex inner structures of the AM integrated multifunctional plates also exhibit the potential to break limitations of conventional manufacture methods for hydrogen generation and to open a door for the development of other energy conversion devices, including fuel cells, solar cells and batteries.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Innovative accident tolerant fuel concept enabled through direct manufacturing technology

Nuclear energy is one of the largest sources of carbon-free electricity in the world. Some countries are looking at new ways to support and revitalize the nuclear sector since the Fukushima disaster. The accident tolerant fuel program is geared toward improving the safety of nuclear energy by investigating materials that can replace or modify the current uranium-dioxide nuclear fuel and zirconium-based cladding. This research program is being supported by all major nuclear countries since 2011. The practical limitations on allowable uranium enrichment has taken the focus away from the most promising fuels with high radioactivity retention such as tristructural isotropic particle fuel. To overcome such enrichment limitation, a new fuel concept is proposed using advanced ceramic direct manufacturing with laser-induced chemical vapor deposition. The fuel-as-fiber concept is an accident-tolerant fuel design that features high thermal conductivity, strong capability of radioactivity retention and most importantly requires reasonable enrichment levels with uranium nitride as the fuel. In this work, the initial fabrication of uranium-based fuel with laser-induced chemical vapor deposition technology is demonstrated. Then an advanced multi-physics guided modeling approach based on finite element analysis codes and informed by the manufacturing capabilities is developed to accelerate the advancement of fuel-as-fiber concept for use in current light-water reactor technology. The detailed thermomechanical analysis showed promising results for viability of the innovative fuel-as-fiber concept. The predicted stresses in the fuel structural materials were similar to the case of tristructural isotropic particle fuel experience base that has shown excellent reliability in retention of fuel radioactivity at high temperatures.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

Extreme hardness at high temperature with a lightweight additively manufactured multi-principal element superalloy

Materials are needed that can tolerate increasingly harsh environments, especially ones that retain high strength at extreme temperatures. Higher melting temperature alloys, like those consisting primarily of refractory elements, can greatly increase the efficiency of turbomachinery used in grid electricity production worldwide. Existing alloys, including Ni- and Co-based superalloys, used in components like turbine blades, bearings, and seals, remain a performance limiting factor due to their propensity, despite extensive optimization efforts, for softening and diffusion-driven elongation at temperatures often well above half their melting point. To address this critical materials challenge, we present results from integrating additive manufacturing and alloy design to guide significant improvements in performance via traditionally difficult-to-manufacture refractory alloys. We present an example of a multi-principal element alloy (MPEA), consisting of five refractory elements and aluminum, that exhibited high hardness and specific strength surpassing other known alloys, including superalloys. The alloy shows negligible softening up to 800°C and consists of four compositionally distinct phases, in distinction to previous work on MPEAs. Density functional theory calculations reveal a thermodynamic explanation for the observed temperature-independent hardness and favorability for the formation of this multiplicity of phases.

36 MATERIALS SCIENCE↗