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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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All sourcesNASA NTRSDOE OSTI
NASA NTRS2017

Leveraging the Usage of GPUs in SAR Processing for the NISAR Mission

The NASA ISRO Synthetic Aperture Radar (NISAR) mission will redefine the future of earth science in terms of both the quality as well as the quantity of data that will be downlinked daily. The current software architecture used to process this data is the InSAR Scientific Computing Environment (ISCE), a powerful and modular platform that applies a combination of novel and legacy processing modules to many sources of SAR data. Until recently, this architecture could process most images in a reasonable amount of time; however in the case of the NISAR mission (where the daily influx as well as the size of the images themselves are significantly larger) the current architecture can take hours to process even a single image. This paper explores new efforts to use a Graphics Processing Unit (GPU) to accelerate one of the processing modules to achieve unprecedented runtimes with no loss in precision, potentially setting a new standard in radar processing in the world of “Big Data”.

Cohen, Joshua↗
NASA NTRS2019

Simplifying NASA Earth Science Data and Information Access Through Natural Language Processing Based Data Analysis and Visualization

NASA Earth science data collected from satellites, model assimilation, airborne missions, and field campaigns, are large, complex and evolving. Such characteristics pose great challenges for end users (e.g., Earth science and applied science users, students, citizen scientists), particularly for those who are unfamiliar with NASA's EOSDIS and thus unable to access and utilize datasets effectively. For example, a novice user may simply ask: what is the total rainfall for a flooding event in my county yesterday? For an experienced user (e.g., algorithm developer), a question can be: how did my rainfall product perform, compared to ground observations, during a flooding event? Nonetheless, with rapid information technology development such as natural language processing, it is possible to develop simplified Web interfaces and back-end processing components to handle such questions and deliver answers in terms of text, data, or graphic results directly to users.In this presentation, we describe the main challenges for end users with different levels of expertise in accessing and utilizing NASA Earth science data. Surveys reveal that most non-professional users normally do not want to download and handle raw data as well as conduct heavy-duty data processing tasks. Often they just want some simple graphics or data for various purposes. To them, simple and intuitive user interfaces are sufficient because complicated ones can be difficult and time-consuming to learn. Professionals also want such interfaces to answer many questions from datasets. One solution is to develop a natural language based search box like Google and the search results can be text, data, graphics and more. Now the challenge is, with natural language processing, can we design a system to process a scientific question typed in by a user? In this presentation, we describe our plan for such a prototype. The workflow is: 1) extract needed information (e.g., variables, spatial and temporal information, processing methods, etc.) from the input, 2) process the data in the backend, and 3) deliver the results (data or graphics) to the user.

Liu, Zhong↗
NASA NTRSDate not supplied

Automation of the Uncertainty Quantification Process Based on Probability Boxes with DAKOTA

To date, while the use of CFD is prevalent, very few efforts have been undertaken that truly attempt to document all (or even most) of the sources of uncertainty in the simulations. Instead, the current state-of-the-art relies heavily on the experience of the CFD practitioner to estimate the uncertainty associated with their simulations through simple sensitivity studies or subject matter expertise. This practice will have to be replaced with a formal uncertainty quantification (UQ) process if CFD is to play an expanded role in the design research and engineering community, test and evaluation community, and ultimately certification for flight. This is especially true for hypersonic air-breathing propulsion systems due to the environment, scale, and duration limitations of ground test facilities. Accounting for uncertainties in a formal manner is a tedious process. Moreover, the typical CFD practitioner is not likely to be familiar with formal UQ methods. Hence, a major obstacle that has prevented the adoption of UQ methods for engineering design and development work is the lack of a tool set to automate most (if not all) of the UQ workflow. Towards this end, the SANDIA package DAKOTA (which has been developed to drive both UQ and optimization processes) will be tightly wrapped around the VULCAN-CFD code to automate the uncertainty quantification process. The automated process will be applied to an isolator turbulence model validation exercise that has previously been documented using a manual approach to the UQ process. Hence, the focus of this paper will be documenting the level to which automation can hide the UQ process details from the CFD practitioner rather than the UQ method itself.

CFD↗
NASA NTRS2021

Machine Learning to Predict Joint Performance in Epoxy Composites Based on Process Parameters

Polymer matrix composites are gaining popularity in the aerospace industry due to their high specific strength, fatigue properties, and processability. However, based on current FAA certification guidelines, manufacturers utilizing current state-of-the-art composites made with adhesive bonds commonly install redundant fasteners to guarantee the strength of these adhesively bonded composite parts. The number of fasteners in a single-aisle commercial transport aircraft is typically on the order of 105, which reduces manufacturing rate, increases cost tremendously, and reduces the advantage of the specific strength composites provide. Due to this, the Adhesive Free Bonding of Composites (AERoBOND) project at NASA Langley Research Center has developed a novel assembly process to manufacture complex composite parts without the use of adhesives and fasteners. However, optimization of the process is currently challenging due to the complex and interdependent process parameters. To assist with the optimization, four machine learning algorithms utilizing gradient boosting decision trees were created to provide predictions for the mechanical and characterization properties of the composite parts. Approximately 200 random states from each algorithm were tested, and the models from each state were isolated and analyzed based on their accuracy, a validation process, and their feature importance. This analysis concluded that the models created from the machine learning algorithms could accelerate a parametric study for the AERoBOND process by rapidly optimizing process parameters to achieve desired performance characteristics.

Brennen Michael Middleton↗
NASA NTRSDate not supplied

AM Processes Part 2: Principles of Directed Energy Deposition

Directed Energy Deposition (DED) is one of the metal additive manufacturing processes which finds wider industrial applications, particularly for repair and reclamation, and for large structures. This course helps the attendees to understand the fundamental concepts of DED process and covers the topics such as, how the technology works, different DED processes, materials used, and typical applications where this technology is used. The discussion will have a focus on ASTM F3187 – Standard guide for Directed Energy Deposition of Metals. After completing this webinar the attendees will achieve the following: · Identify and understand the different types of DED processes · Understand how to select a DED process and trade criteria · Understand the various materials used for DED processes · Understand the core parameters for the DED processes · Determine potential parts and end application of DED technology

Additive Manufacturing↗
NASA NTRSDate not supplied

Machine Learning to Predict Joint Performance in Epoxy Composites Based on Process Parameters

Polymer matrix composites are gaining popularity in the aerospace industry due to their high specific strength, fatigue properties, and processability. However, based on current FAA certification guidelines, manufacturers utilizing current state-of-the art composites made with adhesive bonds commonly install redundant fasteners to guarantee the strength of these adhesively bonded composite parts.1,2 The number of fasteners in a single-aisle commercial transport aircraft is typically on the order of 105, which reduces manufacturing rate, increases cost tremendously, and reduces the advantage of the specific strength composites provide. Due to this, the Adhesive Free Bonding of Composites (AERoBOND) project at NASA Langley Research Center has developed a novel assembly process to manufacture complex composite parts without the use of adhesives and fasteners.1 However, optimization of the process is currently challenging due to the complex and interdependent process parameters. To assist with the optimization, four machine learning algorithms utilizing gradient boosting decision trees were created to provide predictions for the mechanical and characterization properties of the composite parts. Approximately 200 random states from each algorithm were tested, and the models from each state were isolated and analyzed based on their accuracy, a validation process, and their feature importance. This analysis concluded that the models created from the machine learning algorithms could accelerate a parametric study for the AERoBOND process by rapidly optimizing process parameters to achieve desired performance characteristics.

Brennen M Middleton↗
NASA NTRSDate not supplied

Print-Consistency and Process-Interaction for Inkjet-Printed Copper on Flexible Substrate

Printed electronics is a fastest growing and emerging technology that have shown much potential in several industries including automotive, wearables, healthcare, and aerospace. Its applications can be found not only in flexible but also in large area electronics. The technology provides an effective and convenient method to additively deposit conductive and insulating materials on any type of substrate. Comparing with traditional manufacturing processes, which involves chemical etching, this technology also comes to be relatively environmental friendly. Despite its status, it is not without its challenges. Starting from the material being compatible in the printer equipment to the point of achieving fine resolutions, and with excellent properties are some of the challenges that printed electronics face. Among the myriad of printing technologies such as Aerosol Jet, micro-dispensing, gravure printing, screen printing, Inkjet printing, Inkjet has gained much attention due to its low-cost, low material consumption, and roll-to-roll capability for mass manufacturing. The technology has been widely used in home and office, but recently gained interest in printed electronics in a research and development setting. Conductive materials used in Inkjet printing generally comprises of metal Nanoparticles that need to be thermally sintered for it to be conductive. The preferred metal of choice has been mostly silver due to its excellent electrical properties and ease in sintering. However, silver comes to be expensive than its counterpart copper. Since copper is prone to oxidation, much focus has been given towards photonic sintering that involves sudden burst of pulsed light at certain energy to sinter the copper Nanoparticles. With this technique, only the printed material gets sintered in a matter of seconds without having a great impact on its substrate, due to which it is also preferred in low temperature applications. With all the knowledge, there is still a large gap in the process side with copper where it is important to look how the print process affects the resolution of the print along with the effect of post-print processes on electrical and mechanical properties. In this paper, a copper Inkjet ink is utilized for understanding the effect of Inkjet print parameters on the ejected droplet and its resolution. Post-print process is also quantified using a photonic sintering equipment for excellent electrical and mechanical properties. To demonstrate the complete process, commercial-off-the-shelf components will also be mounted on the additively printed pads via Inkjet. Statistically, control charting technique will be utilized to understand the capability of the Inkjet process.

printed electronics↗
NASA NTRSDate not supplied

Oversimplification of Systems Engineering Goals, Processes, and Criteria in NASA Space Life Support

This paper investigates the oversimplification of the inherently complex systems engineering process in space life support. The standard systems engineering process steps are described. The International Space Station (ISS) life support system is explained with its goals and performance criteria. Although it is not usually emphasized, the essential function of developing a hierarchy of systems and subsystems is to simplify the design process. The System Complexity Metric (SCM) shows how this di-vide-and-conquer approach also reduces the system complexity. The complete systems engineering process has many detailed steps. It is often simplified because of the effort required and the human limitations on working memory and decision span. Systems analysis demands slow, logical, and fo-cused thinking but is often bypassed in favor of quick, intuitive, subconscious “gut feel.” A study of 100 system designs found examples of 12 specific mental mistakes, such as ignoring stakeholder needs, and these mistakes are essentially oversimplifications of the systems engineering process. An analysis of space life support goals, options, criteria, and processes found 11 examples of oversimplifications in systems engineering, such as neglecting safety and cost. All these 11 oversimplifications could be traced to one or more of the 12 previously identified mental mistakes or other well-known ones, such as ig-noring sunk costs. Oversimplification of the systems engineering process is rarely noticed but is a common and harmful problem. A study of failures in 50 different space systems found that problems in systems engineering caused failures and often led to errors in design, development, and test that further contributed to failure. It seems that more diligent systems engineering could prevent many project problems and failures, but projects seem to be more guided by “gut feel” based on tradition, authority, and consensus than on the logical, rational systems engineering approach.

Simplified systems engineering↗
NASA NTRSDate not supplied

Advancement of Metal Additive Manufacturing Processes and Alloys for Rocket Propulsion Applications

NASA has been involved in the development and maturation of metal additive manufacturing (AM) for space applications since the 2000’s. Several efforts have focused on the understanding of AM processes through material characterization and testing, standards development, component fabrication, and infusion into development and flight applications. While many common aerospace alloys have been and continue to be a focus of ongoing development, the need for custom-alloy developments for high performance applications enabled by various AM processes has been realized. The applications being targeted are liquid rocket engines with high heat fluxes, high pressure, and that utilize propellants such as hydrogen, which can degrade the alloy. NASA has recently focused on the development and advancement of novel alloy advancement using AM for use in these harsh environments, such as GRCop-42, GRCop-84, NASA HR-1, and JBK-75. These alloys have been evaluated using powder bed fusion (PBF), directed energy deposition (DED), and solid-state AM processes. The results from these processes have demonstrated that AM can enable rapid development of new alloy systems that can yield higher performances across various metal AM processes. These alloys have undergone the fundamental metallurgical evaluations, heat treatment study, and microstructure characterization and mechanical testing campaign. This, combined with direct application-specific component fabrication and hot-fire testing, enabled the increase of the Technology Readiness Level (TRL). This presentation will provide a background and overview of various AM-enabled novel alloys, a comparison across the AM processes, and development including metallurgical and mechanical property studies. It will also cover the latest advancement in the parallel component development and testing and future developments. The goal of these alloy development and use of various AM processes is to allow for technology infusion into NASA and commercial spaceflight missions as well as to establish and sustain the needed commercial AM supply chain.

Additive Manufacturing↗
NASA NTRSDate not supplied

Game-Theoretic Modeling of Vegetation Composition, Structure, and Dynamics: Physical Constraints, Fundamental Processes, and Emergent Properties

Vegetation structural and compositional dynamics emerge from plant physiological and demographic processes, individual-based competition, vegetation-soil feedbacks, and environmental variations and disturbance events. Predicting long-term changes in vegetation requires scaling plant individual behavior to large scale ecosystem processes. In this presentation, we summarize our studies in the modeling of vegetation demographic processes, competitively dominant plant traits, plant hydraulic processes, and stochastic disturbance effects on ecosystems, and illustrate the roles of the underlying ecological processes and eco-evolutionary optimization in vegetation modeling. With the case studies of evolutionarily stable strategy of allocation, leaf traits, and plant hydraulic processes, we show how the ecosystem processes and vegetation dynamics are determined by the individual-based plant competition and variations of soil and climate conditions. The predictions of ecosystem carbon dynamics can be greatly different with those from the traditional “single-tree” models. We also discuss the tradeoffs of plant traits and evolutionarily optimal strategies in the modeling of terrestrial ecosystem dynamics in an Earth system model.

vegetation models↗
DOE OSTI2025

PROCESS-STRUCTURE-PROPERTY RELATIONSHIPS IN LASER POWDER BED FUSION PRODUCED 17-4 PH STEEL

Laser powder bed fusion (LPBF) is a metal additive manufacturing method that produces non-traditional microstructures as a result of the rapid solidification and thermal cycling inherent to the process. When using LPBF-produced material in application, these unique microstructures challenge the applicability of well developed mechanical property databases achieved by conventional heat treatments. For wider adoption of this technology, a more holistic understanding is necessary on how process attributes develop material structure, which dictate mechanical properties. This dissertation explores the process– structure–property relationships in LPBF 17-4 PH steel through systematic evaluation of atmospheric processing and heat treatment effects on microstructure and mechanical performance. Specimens were fabricated under controlled build environments, subjected to a range of solutionizing, homogenizing, and aging treatments, and characterized using optical microscopy, electron back scatter diffraction (EBSD), and X-ray diffraction (XRD) to quantify phase evolution. Tensile testing was performed to directly link heat treatment pathway and nitrogen absorption to mechanical performance. This work demonstrates where conventional heat treatment standards are applicable to LPBF 17-4 PH steel and where modifications are required. By directly correlating phase stability, nitrogen effects, and tensile response, this work provides practical guidelines for tailoring post-processing strategies. These findings underscore that successful application of LPBF 17-4 PH steel requires explicit consideration of both build environment and post-processing. By linking processing conditions to microstructure and performance, this work advances understanding of critical variables that govern reliability of additively manufactured precipitation-hardened stainless steels in demanding applications.

Brown, Benjamin [Kansas City National Security Cam↗
DOE OSTI2025

Meshfree simulation and prediction of recrystallized grain size in friction stir processed 316L stainless steel

Friction stir processing (FSP) is a promising solid-phase microstructural modification technique that can repair and enhance damaged stainless steel surfaces exposed to harsh environments. The quality of the repaired material is closely correlated to the recrystallized grain size in the stir zone (SZ), which is influenced by the thermomechanical conditions dictated by FSP process parameters. Thus, establishing a reliable relationship between these parameters and recrystallized grain size in the SZ is crucial for optimizing repair quality. However, existing experimental approaches often rely on indirect temperatures measured far from the SZ, along with rough strain rate estimations, which are imprecise and time-consuming. Meanwhile, existing mesh-based modeling methods usually face numerical challenges when dealing with the large material deformations inherent in FSP. Here, to address these issues, this study introduces a meshfree process model for FSP based on the smoothed particle hydrodynamics (SPH) method, aimed at predicting process conditions under different parameters. The model is validated using experimental data from 11 combinations of tool traverse and rotation speeds on 316 L stainless steel. Correlations between process parameters, material flow, temperature, strain, strain rate, and recrystallized grain size are revealed through SPH simulations and electron backscatter diffraction (EBSD) imaging. The results show that in situ SZ temperatures range from 1071 to 1322°C, which exceed the tool temperature by over 300°C. Furthermore, SZ temperature, strain rate, and grain size increase monotonically with higher tool temperature and faster traverse speed. A relationship is then established between the model-predicted Zener-Hollomon parameter and the recrystallized grain size based on EBSD data, expressed as ln(d) = -0.364 ln(Z) + 14.673. Finally, this relationship exhibits satisfactory accuracy with errors of less than 26.9% in predicting grain sizes at various SZ locations, which offers valuable insights for optimizing FSP repair processes for 316 L stainless steel.

316L stainless steel↗
DOE OSTI2025

Mixed-Integer Linear Programming Formulation with Embedded Machine Learning Surrogates for the Design of Chemical Process Families

In previous work, we introduced process family design. The main idea is to design a platform of common elements, and, allowing us to capture additional cost savings, simultaneously design a family of processes, and reducing both engineering and deployment timelines. We formulate this as an optimization problem, specifically a nonlinear generalized disjunctive program (GDP). We have proposed two approaches for reformulating and solving this problem: one based on full-discretization of the design space and one that uses Machine Learning (ML) surrogates to replace the nonlinear process models. Using ML surrogates to predict required system costs and performance indicators allows us to reformulate the nonlinearities in the GDP generate an efficient MILP formulation. In this work, we apply the ML surrogate approach to two case studies. One case study involves designing a family of carbon capture systems to cover a set of different flue gas flow rates and inlet CO 2 concentrations, where we consider the absorber and stripper as common unit module types. The second case study focuses on a water-desalination process, where we design a family of these processes for a variety of salt concentrations and flow rates. In both of these case studies, we demonstrate a scalable optimization approach that enables the design of multiple processes simultaneously, reducing the time-to-market and overall costs by maximizing the cost savings due to both economies of scale and economies of numbers.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1994

Increases in Efficiency and Enhancements to the Mars Observer Non-stored Commanding Process

The Mars Observer team was, until the untimely loss of the spacecraft on August 21, 1993, performing flight operations with greater efficiency and speed than any previous JPL mission of its size. This level of through-put was made possible by a Mission Operations System which was composed of skilled personnel using sophisticated sequencing and commanding tools. During cruise flight operations, however, it was realized by the project that this commanding level was not going to be sufficient to support the activities planned for the mapping operations. The project had committed to providing the science instrument principle investigators with a much higher level of commanding during mapping. Thus, the project began taking steps to enhance the capabilities of the flight team. One mechanism used by project management was a tool available from Total Quality Management (TQM). This tool is known as a Process Action Team (PAT). The Mars Observer PAT was tasked to increase the capacity of the flight team's non-stored commanding process by fifty percent with no increase in staffing and a minimal increase in risk. The outcome of this effort was to, in fact, increase the capacity by a factor of 2.5 rather than the desired fifty percent and actually reduce risk. The majority of these improvements came from the automation of the existing command process. The results required very few changes to the existing mission operations system. Rather, the PAT was able to take advantage of automation capabilities inherent in the existing system and make changes to the existing flight team procedures. This paper will describe in detail the enhancements recommended by the PAT for the non-stored command generation process on Mars Observer. This will be contrasted with the process used by the flight team prior to implementation of these improvements. Finally, there will be a discussion of the applicability of the techniques devised by the PAT for enhancement of the non-stored command process to present and future projects.

Process↗
NASA NTRSDate not supplied

A Novel Additive Manufacturing Process Metric for Predicting Spatter-related Porosity in Laser Powder Bed Fusion

Components fabricated using the powder bed fusion laser beam metallic (PBF) additive manufacturing (AM) process comprise a multitude of sequential weld passes. Porosity defects resulting from weld pass inconsistencies are a concern for PBF materials and have a strong adverse effect on the mechanical properties. In an effort to understand and avoid spatter induced porosity, this work aims to provide a novel computational technique that can be used to quantify and predict the sequence-sensitive impact of spatter upon the PBF process stability and consequential porosity in as-printed material. A novel spatter impact AM model-based process metric (AM-PM) is introduced to model and quantify the impact that spatter has on the PBF process. The occurrence of spatter induced porosity is studied using thermal rise, lack of fusion, and spatter impact AM-PMs synchronized with porosity measured by high-resolution X-ray computed tomography. Six cylindrical specimens were designed to compare the AM-PM correlations with porosity across two different laser powers and three hatch scanning strategies. A point field based computational approach is shown to be effective for testing the influence of the different AM-PMs and interrogating the physical process conditions underlying the porosity formation. Further, characterization using optical metallography and scanning electron microscopy indicated that both keyhole and lack of fusion porosity correlated with the spatter impact AM-PM, which can be exploited to predict and control spatter related porosity by the hatch strategy. Efforts to predict porosity composition within PBF material should incorporate the PBF process disturbances that can result from spatter. The spatter impact AM-PM can be readily incorporated into the development of defect prediction models at the part scale.

Additive Manufacturing↗
DOE OSTI2024

Tetrahydrofuran Processable Organic Solar Cells with 19.45% Efficiency Realized by Introducing High Molecular Dipole Unit Into the Terpolymer

Developing organic solar cells (OSCs) processable with halogen‐free, non‐aromatic solvents is crucial for practical applications, yet challenging due to the limited solubility of most photoactive materials. Here, this study introduces high‐performance terpolymers processable in tetrahydrofuran (THF) by incorporating dithienophthalimide (DPI) into the PM6 backbone. DPI extends the absorption band, lowers HOMO levels, and improves THF solubility and film crystallinity through its large dipole moment effect. Optimal PBD‐10:L8‐BO devices processed with THF achieved a competitive power conversion efficiency (PCE) of 18.79%, approaching chloroform‐processed devices (19.04%). By introducing PBTz‐F as a second donor, ternary OSCs reached an impressive 19.45% PCE when processed with THF. This improvement stems from enhanced photon generation, improved morphology, better charge transport, longer exciton lifetimes, efficient charge dissociation and collection, and suppressed recombination. These PCEs of 18.79% and 19.45% for binary and ternary blend OSCs, respectively, represent the highest reported efficiencies for OSCs processed with halogen‐free, non‐aromatic solvents. This work demonstrates significant progress in eco‐friendly OSC fabrication, paving the way for more sustainable and commercially viable organic photovoltaic technologies.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Solution‐Processed Spin‐Polarized Light‐Emitting Diodes of Colloidal Quantum Wells and Magnetic Nanoparticles

Electrical injection of spin-polarized carriers into semiconductors enables circularly-polarized emission from spin-polarized light-emitting diodes (spin-LEDs). The incredible level of tunability of magnetic and electronic properties in colloidal nanocrystals offers unprecedented opportunities for the modulation of polarization of light in solution-processed spin-LEDs based on magnetic nanoparticles unlike epitaxially grown spin-LEDs restricted by a very limited range of materials for their exploitation, and solution-processed spin-LEDs based on chiral molecules, which do not allow the modulation of polarization in general. Here, it is shown that electrical injection of spin-polarized electrons from magnetic Fe 3 O 4 nanoparticles into CdSe/CdZnS core/shell colloidal quantum wells (CQWs) in solution-processed LEDs that allows for polarization modulation of electroluminescence. In this structure, a monolayer of face-down oriented CQWs is deposited as an active layer to avoid polarization losses due to the hopping of the electrons between the CQWs before the radiative recombination process. In this solution-processed spin-LED, the circular polarization reaches 4.5% at 3 K and survives up to 100 K. A net circular polarization is observed at zero magnetic field up to 100 K because of the remnant magnetization of the Fe 3 O 4 nanoparticles. This new colloidal spin-LED architecture presents significant prospects for future solution-processed advanced opto-spintronic devices.

circularly polarized electroluminescence↗
DOE OSTI2024

Modeling powder spreadability in powder-based processes using the discrete element method

Powder-bed fusion (PBF) processes refer to a subset of Additive Manufacturing (AM) techniques where powder is spread on the build-plate before melting (by a laser or electron beam). While PBF processes are attractive due to their ability for realizing complex structures that are either difficult or impossible to create through conventional means, the parts fabricated with these techniques can exhibit defects such as pores, inclusions, and excessive surface roughness. To minimize these defects, much research has been dedicated towards process maturation by optimizing laser or electron beam parameters. However, these developmental efforts typically do not address the recoating process where achieving dense and uniform layers of powder is a necessity for ensuring process repeatability and part quality. While the recoating process can be studied through experimentation, the dynamics of particle movement are difficult to analyze experimentally. Therefore, here, in this study, powder spreading in PBF was simulated through the Discrete Element Method (DEM) to elucidate the mechanisms that control powder-bed quality. Utilizing the Buckingham Pi theorem, a dimensionless metric referred to as the spreading index is developed that combines powder-bed density, roughness, and particle size to assess the quality of powder layers. The formulated spreading index is then related to several dimensionless quantities that provide insight into the mechanisms dominating powder spreading in PBF. The DEM simulations conducted in this work focused on the scenario where powder is spread onto an existing powder bed and revealed that a reduction in the recoating velocity causes an increase in the spreading index while little to no impact on the spreading index was observed when varying layer thickness from 30 μm to 75 μm.Particle size effects on the powder-bed quality were also investigated.

36 MATERIALS SCIENCE↗