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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 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Sintering-based Metal Additive Manufacturing Methods for Magnetic Materials

In this chapter, we review the relationship between solid-state sintering and additive manufacturing (AM) methods for fabricating soft and hard magnetic materials. Heat treatment as a post-processing method has been done in conjunction with AM as a method to improve density and/or mechanical properties. In binder jetting (BJT) of metal powders, sintering is a crucial post-processing step and is necessary for obtaining the final product. This is because the parts are only in the “green” state when they leave the BJT printer, with poor mechanical properties and high porosity. Sintering densifies and strengthens the green part into the final product. A potential industrial application for sintering-based AM is the fabrication of magnetic materials. The motivation for doing so, the process and properties of the AM magnets, and the potential applications of post-sintered AM magnets are explored.

Wang, Haobo↗
DOE OSTI2026

Identifying the Accuracy of Surface Roughness for Metal Additive Manufacturing Parts Captured with Computed Tomography Scanning

Conventional surface roughness measurement techniques require direct access to internal surfaces, often necessitating destructive sectioning of test articles. Computed tomography (CT) offers a non-destructive alternative for internal surface characterization, but its application in metrology remains unstandardized and sensitive to machine resolution and operator technique. This study investigates the feasibility of using CT scanning to quantify areal surface roughness in metal AM heat exchanger tubes with three distinct internal geometries: ribbed, discrete W, and featureless. CT-derived surface parameters were extracted using custom Python scripts and compared to measurements obtained from a focus variation microscope calibrated against a known standard. Results show that CT-based roughness measurements closely matched microscope values for the discrete W specimen, deviations between CT and microscope measurements were minimal—less than 1 µm—indicating reliable reconstruction. In contrast, the ribbed and featureless specimens, with lower roughness values showed greater discrepancies. The findings suggest that CT scanning can be a viable non-destructive metrology tool for AM parts with surface roughness above approximately 8 µm. For smoother surfaces, current CT capabilities may not provide sufficient accuracy, highlighting the need for resolution-aware workflows and further standardization in CT-based surface metrology.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Investigating relationship between surface topography and emissivity of metallic additively manufactured parts

Due to the direct relationship between thermal history and mechanical behavior, in situ thermal monitoring is key in gauging quality of parts produced with additive manufacturing (AM). Accurate monitoring of temperatures in an AM process requires knowledge of environment and object parameters including object emissivity. The emissivity is dependent on several variables, including: wavelength, material composition, temperature, and surface topography. Researchers have been concerned with the thermal emissivity dependence on temperature since large ranges are seen in metal powder bed processes, but there is also an extensive range of surfaces produced by AM. This work focused on discovering what roughness characteristics control thermal emissivity through investigation of prototypic 316 stainless steel AM samples produced with a range of build conditions on a laser powder bed fusion machine. Through experimental measurements of emissivity using hemispherical directional reflectance (HDR), guided by simulations using a finite-difference time-domain (FDTD) Maxwell solver, it was found that combinations of existing roughness parameters describing both height and slope of the surface correlate well with emissivity changes. Furthermore, these parameters work well due to their apt description of surface features encouraging internal reflection, which is the phenomenon that increases emissivity when a surface falls under the geometric optical region conditions.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Understanding Flow Characteristics in Metal Additive Manufacturing

In fluid flow, the frictional effects from interior surfaces of flow components create pressure loss across the surface. Understanding this inherent pressure loss due to friction and other phenomena is important when designing a flow system. For conventional manufacturing, friction loss has been empirically studied and is well known in the engineering community. However, newer manufacturing techniques, such as laser powder bed fusion (LPBF), an additive manufacturing technique, need to be understood. The inherent flow conditions resulting from the additive processes were studied. Test samples were printed with the flow path oriented in the vertical direction with different channel diameters to characterize the inherent fundamental flow properties of the process. Moody diagrams were replicated for a greater understanding of possibilities and shortfalls when implementing LPBF into design applications. Experimental flow testing revealed that greater effective sand grain roughness resulted from this process when compared to conventional machining methods. This roughness did not correlate well to the average roughness measured with a laser scanning microscope but was consistent with the average particle size of the powder metal feedstock.

42 ENGINEERING↗
DOE OSTI2023

Temporally continuous thermofluidic–thermomechanical modeling framework for metal additive manufacturing

Additive manufacturing (AM) is known to generate large magnitudes of residual stresses (RS) within builds due to steep and localized thermal gradients. In the current state of commercial AM technology, manufacturers generally perform heat treatments in effort to reduce the generated RS and its detrimental effects on part distortion and in-service failure. Computational models that effectively simulate the deposition process can provide valuable insights to improve RS distributions. Accordingly, it is common to employ Computational fluid dynamics (CFD) models or finite element (FE) models. While CFD can predict geometric and thermal-fluid behavior, it cannot predict the structural response (e.g., stress–strain) behavior. On the other hand, an FE model can predict mechanical behavior, but it lacks the ability to predict geometric and fluid behavior. Thus, an effectively integrated thermofluidic–thermomechanical modeling framework that exploits the benefits of both techniques while avoiding their respective limitations can offer valuable predictive capability for AM processes. In contrast to previously published efforts, the work herein describes a one-way coupled CFD-FEA framework that abandons major simplifying assumptions, such as geometric steady-state conditions, the absence of material plasticity, and the lack of detailed RS evolution/accumulation during deposition, as well as insufficient validation of results. Here, the presented framework is demonstrated for a directed energy deposition (DED) process, and experiments are performed to validate the predicted geometry and RS profile. Both single- and double-layer stainless steel 316L builds are considered. Geometric data is acquired via 3D optical surface scans and X-ray micro-computed tomography, and residual stress is measured using neutron diffraction (ND). Comparisons between the simulations and measurements reveal that the described CFD-FEA framework is effective in capturing the coupled thermomechanical and thermofluidic behaviors of the DED process. The methodology presented is extensible to other metal AM processes, including power bed fusion and wire-feed-based AM.

42 ENGINEERING↗
DOE OSTI2024

Assessments of advanced reactor heat supply to high temperature industrial unit operations: Heat Engines and Heat Pumps

In this report, the feasibility of employing Advanced Nuclear Reactors to supply the necessary high-quality heat to Industrial Processes is investigated. As a first step, the most relevant industrial applications that could benefit from this coupling were identified. Four industries including petroleum refining (distillation, catalyst regeneration, hydro-steam cracking), chemical polymers, steel manufacturing (metals annealing/soaking) and cement manufacturing (calcination of limestone and dolomite industries) were considered. An overview of the heat duties, i.e., the temperatures and thermal powers required by representative plants of the different processes, was provided. Secondly, the thermodynamic conditions of the steam produced by representative designs of Advanced Reactor concepts (Liquid Metal Fast Breeder Reactors, Molten Salt Reactors, and High Temperature Gas-cooled Reactors) were summarized. The comparison between the requirements of the industrial processes and the capabilities of the candidate nuclear power plants showed that only a few processes could benefit from the coupling. Although Advanced Reactors operate at higher temperatures than Light Water Reactors, the thermodynamic conditions of the generated steam flow rates are generally not suitable for the selected applications. At the same time, data indicate that most of the processes can be suitably supplied if the temperature of steam is elevated to 900 °C. One possible solution to boost the quality of the steam from the nuclear island involves the use of a heat pump. Based on a technology similar to that found in refrigerators and air conditioners, heat pumps extract heat from a source, elevate its temperature and transfer it to where it is needed. A simple numerical example illustrating the viability of this method is described. Specifically, the energy conversion cycle of a conventional Pressurized Water Reactor was extended to incorporate a compressor to raise the heating value of the steam produced by the reactor. Two reference configurations, i.e., one where the industrial process coupled to the nuclear power plant exhausts saturated liquid and the other where it exhausts saturated steam, were considered. The thermal efficiencies achievable by increasing the heating value of the reactor steam in this way are significantly greater than by direct electric heating.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2021

A Bayesian Approach to the Eagar–Tsai Model for Melt Pool Geometry Prediction with Implications in Additive Manufacturing of Metals

Here, this paper focuses on improving the melt pool geometry predictions and quantifying uncertainties using an adapted version of the Eagar–Tsai (E–T) model that incorporates temperature-dependent properties of the material as well as powder conditions. Additionally, Bayesian inference is employed to predict distributions for the E–T model input parameters of laser absorptivity and powder bed porosity by incorporating experimental results into the analysis. Monte Carlo uncertainty propagation is then used with these parameter distributions to estimate the melt pool depth and associated uncertainty. Our results for the 316L stainless steel suggest that both the absorptivity and powder bed porosity are strongly influenced by the laser power. In contrast, the scanning speed has only a marginal effect on both the absorptivity and powder bed porosity. We constructed a printability map using the Bayesian E–T model based on power-dependent input parameter values to demonstrate the merit of the approach. The Bayesian approach improved the accuracy in predicting the keyhole regions in the laser power-scan speed parameter space for the 316L stainless steel. Although applied to a specific adaptation of the E–T model, the method put forth can be extended to quantify uncertainties in other numerical models as well as in the estimation of unknown parameters.

316L Stainless Steel (SS).↗
DOE OSTI2020

Effect of Particle Spreading Dynamics on Powder Bed Quality in Metal Additive Manufacturing

Powder spreading precedes creation of every new layer in powder bed additive manufacturing (AM). The powder spreading process can lead to powder layer defects such as porosity, poor surface roughness and particle segregation. Therefore, the creation of homogeneous layers is the first task for optimal part printing. Discrete element methods (DEM) powder spreading simulations are typically limited to a single layer and/or small number of particles. Therefore, results from such model configurations may not be generalized to multiple layer processes. In this study, a computationally efficient multi-layer powder spreading DEM simulation model is proposed. The model is calibrated experimentally using static Angle of Repose measurements. The adhesion model parameter, cohesive energy density is related to adhesive surface energy and strain energy release rate parameters. The model results show that interaction between particle and the powder spreading rake leads to noticeable variation in packing density, surface roughness, dynamic angle of repose (AOR), particle size distribution, and particle segregation. Finally, the powder model is experimentally validated using a recoater spreading rig to measure the dynamic AOR at spreading speeds consistent with recoating speeds and layer heights used in AM processes.

36 MATERIALS SCIENCE↗
DOE OSTI2025

A quality-agnostic combinatoric cost estimation model for large-format directed energy deposition metal additive manufacturing

Directed energy deposition (DED) additive manufacturing (AM) processes are amenable to synergistic combination into multi-process AM systems due to similar requirements for automation and energy sources. This work analyzes the economic performance of such DED AM systems from a quality-agnostic combinatoric standpoint with a model that calculates lowest-cost system combinations based on part geometry and process performance metrics. Common DED AM systems research focuses on a single process and does not consider the process, system, and application in the context of all possible system combinations (e.g., the combined set of process selection(s), motion system(s), and process hardware), leading to limited applicability of the resulting DED AM systems to cost-sensitive components such as those found in energy generation applications. The model developed herein incorporates the capital, material, and energy costs associated with DED AM system combinations into a predictive tool for estimating part and system cost, the output of which is intended to guide deployment of finite research and development resources towards DED AM system combinations with the lowest costs and greatest likelihood of economic impact. The DED AM systems identified by this framework may enable domestic production of the large conventionally cast and forged components necessary for energy generation.

Shanafield, Alexandra [ORNL]↗
DOE OSTI2021

Statistical analysis of spatial distribution of pores in metal additive manufacturing

We report mechanical properties of additively manufactured parts are sensitive to the presence of pores form during the manufacturing process. The impact of pores on the mechanical performance has been investigated extensively with respect to different parameters, such as pore volume fraction, shape, and size. However, statistical investigations focusing on the relationships between the spatial distribution of pores and process parameters; and consequently, the performance of the manufactured parts are scattered and limited. Also, these sparse investigations usually suffer from an ambiguous definition of terminologies. For instance, the required criteria to consider a point pattern as complete spatial randomness (CSR) are generally not clarified. Moreover, no numerical formalism is yet developed to show how much the observed spatial results are statistically significant. To address these shortcomings, the statistical definition of CSR in a point pattern and the procedure to quantitatively determine the deviation of a pattern from CSR were discussed. The explained statistical approach was used to investigate the effect of scanning speed parameter on the spatial distribution of gas pores in laser powder bed fusion manufactured stainless steel parts. Furthermore, and to highlight the impact of the spatial distribution of pores on mechanical properties, fatigue performances of parts with clustered and randomly distributed pores were simulated by finite element analysis. It is shown that by reducing the scanning speed, the spatial distribution of gas pores deviated more from CSR, and correspondingly fatigue performance deteriorated.

36 MATERIALS SCIENCE↗