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At least 487 records · Page 27

Metal Additive Manufacturing Process Selection and Development Lifecycle for Propulsion Components

Metal additive manufacturing (AM) is a generic term that captures a variety of fabrication processes. Each of these manufacturing process has unique advantages and challenges for use in aerospace propulsion applications. The most commonly used AM processes include Powder Bed Fusion (PBF), Directed Energy Deposition (DED), and solid-state processes as in Cold Spray, Ultrasonic Additive Manufacturing, and Additive Friction Stir Deposition. While detailed research has been conducted among many of the AM processes to mature processing parameters and material properties, navigating which processes are best to select is difficult as it is based on specific component requirements. The focus of this presentation is to provide an overview of considerations for each of metal AM process selection for aerospace components based on various key attributes. These key attributes include geometric considerations, metallurgical characteristics, cost basis, post-processing and maturity of the processes. The data for these trade selections are based on studies that NASA as performed internally and with academic and industry partners. These studies include multiple AM build experiments to evaluate (1) geometric variations and constraints within the processes, (2) alloy characterization and mechanical testing, (3) pathfinder component development and hot-fire evaluations, and (4) certification approaches. This presentation summarizes these results and is meant to introduce specific examples which show what to consider when designing a metal AM component for aerospace propulsion applications.

Paul Gradl↗

Making & Breaking the Rules for DED Design

Design for Additive Manufacturing (DfAM) is widely used to describe design activities and guidelines employed in the creation of Additively Manufactured (AM) parts. Basic design rules for widely adopted processes like Powder Bed Fusion (PBF) are relatively understood including material performance, part size constraints, self-supporting angles, overhangs, wall thicknesses, and feature resolution. Complexity is often inherent with PBF using organically inspired solutions through generative design and topology optimization software to create designs that often cannot be realized via traditional manufacturing processes. The design rules for Directed Energy Deposition (DED) are less well known, and, in some cases, the lack of understanding is likely inhibiting broader application of the various DED processes. This presentation covers key design rules for DED fusion processes and elucidates opportunities like multi-material deposition and build approaches ranging from basic features to complex large-scale parts. Complementary to the opportunistic characteristics of DfAM for DED, Manufacture for AM (MfAM) encompasses the restrictive aspects of the manufacturing lifecycle with limitations due to bead width and wall thickness, self-supporting angles, surface texture, microstructural evolution, and residual stresses. AM enabled part design must always consider the entire value chain starting with incoming feedstock (e.g. plate, wire, or powder) and progressing through the printing process, post-processing (e.g. machining, inspection), and final system assembly. Key considerations, like the tradeoffs between feature resolution and build rate, for process and part selection and economic assessments will be discussed as DED is often traded against other AM processes. After highlighting typical DED design rules, we will also show how some of those rules can be broken via use cases and part designs that span the full spectrum of DED processes with examples of various DED part designs.

Additive Manufacturing↗

Effect of Flight on the Noise from Turbulent Jets in the Generalized Acoustic Analogy

This paper presents an extension of a noise prediction method for static jets based on the Generalized Acoustic Analogy to include a non-zero ambient stream to simulate the effects of flight. Extended formulae for the propagator functions, source terms and the far-field acoustic spectrum are derived. The formulations of the equation and the boundary conditions for numerical solution of the scalar adjoint Green’s function in the presence of a non-zero ambient stream are also given. Wherever possible, the corresponding equations in the original papers for the static case and their extensions to include a flight stream are identified. The extended formulation is implemented into an existing code and used to make predictions of the effect of the flight stream on the observed far-field noise for a series of subsonic, unheated, axisymmetric jets over a range of jet-exit and free-stream velocities using Reynolds-averaged Navier-Stokes flow solutions as input. The predictions are compared with data from experiments conducted at NASA Glenn Research Center which extend a fundamental jet noise database to include a flight stream of various speeds. Descriptions of the experimental setup, data acquisition, post-processing and corrections applied to account for the external shear-layer are given. It is shown that the acoustic analogy-based method can provide reasonably good predictions of the impact of an external flight stream on the spectral characteristics of noise from unheated round jets.

Jet noise↗

Introduction to Additive Manufacturing for Propulsion Systems

Additive Manufacturing (AM) has become a prominent technology of interest for manufacturing propulsion components and gaining attention across the aerospace industry. AM is maturing at a rapid pace and providing new design opportunities, novel materials, new industries and supply chains resulting in programmatic and technical performance improvements. The successful use of AM requires a methodical and intentional approach to understanding the concept to utilization lifecycle for AM. This course will provide an overview of the various steps in AM process trades, design, build process, post-processing, certification and infusion of AM. This will provide various lessons learned and experiences that were captured in a recent AIAA textbook titled "Metal Additive Manufactuging for Propulsion Applications" by the session chairs and industry colleagues. This course will include specific liquid rocket engine component examples and that went through the AM lifecycle including hot-fire testing.

Paul R. Gradl↗

ArcjetCV: A New Machine Learning Application for Extracting Time-Resolved Recession Measurements From Arc Jet Test Videos

Arc jet Computer Vision (ArcjetCV) is a software application built to automate analysis of arc jet ground test video footage. This includes tracking material recession, sting arm motion, and the shock-material standoff distance. This provides a new capability to resolve and validate new physics associated with non-linear processes. This is an essential step to reduce testing, modeling, and validation uncertainties for heatshield material performance. ArcjetCV uses several types of machine learning (convolutional neural net: CNN, decision tree: DT, k-means unsupervised clustering: KM) to automate the video processing pipeline. These include inferring the start/stop of time segments of interest (1D CNN), measuring the time-dependent 2D recession of the material samples (2D CNN, DT), measuring the time-dependent shock standoff distance (2D CNN, DT), and post-processing cleaning of the recession data (KM). The software also provides a graphical user interface for ease of use. The results of using this tool on arc jet videos show non-linear time-dependent effects can be important for certain materials.

machine learning↗

Structural Dynamics Observations in Space Launch System Green Run Hot Fire Testing

The Space Launch System (SLS) Core Stage (CS) Thrust Vector Control (TVC) system is comprised of eight mechanical feedback Shuttle heritage Type III TVC actuators and four RS-25 engines, each attached to a Shuttle heritage gimbal block/bearing. Two actuators are used to move each engine in two planes perpendicular to one another (i.e., pitch and yaw). The TVC system design leverages hardware from the Space Shuttle program as well as new hardware designed specifically for the Core Stage. The Green Run Hot Fire (GRHF) of the SLS Core Stage provided a flight-like ground test environment for verification of integrated vehicle TVC performance. A TVC model coupled to a vehicle structural dynamic model has been developed previously and incrementally validated in subsystem tests and simulations. Still, some aspects of TVC performance in GRHF were not anticipated. The ensuing investigation demonstrated the need for well-instrumented test environments, various levels of modeling fidelity, test-representative structural models, and caution in reuse of legacy components. This paper is the sixth installment in a seven-paper series surveying the design, engineering, test validation, and flight performance of the Core Stage Thrust Vector Control system. It introduces the salient structural dynamic phenomena uncovered in ambient and hot fire testing. During the Green Run test campaign, a comparison of ambient and hot fire step responses showed a significant change in apparent damping due to the presence of friction, challenging long standing assumptions that friction could be neglected. Additionally, the characteristic response of the engine and thrust structure during GRHF proved to be more complex than anticipated, as evidenced by the available actuator, thrust structure, and engine measurements. While the string-potentiometer based test instrumentation was intended to allow for reconstruction of the engine angles along the two control axes, the geometric placement, location uncertainty, and responses in overlapping frequency spectra revealed additional phenomena requiring further analysis and post-processing. The observations from both modal and frequency response testing during the Green Run ambient and hot fire configurations led to Engine and Core Stage FEM (finite element model) updates. When evidence of unexpected engine motion was found in engine section accelerometer data, the authors pursued additional structural analysis leading to FEM updates associated with the TVC gimbal and thrust structure. Through collaboration between structures, TVC, and flight control disciplines, the test-informed models and root-cause analysis led to confident flight rationale for the first flight of the SLS launch vehicle.

Richard Moore↗

LAADS DAAC Migrates to the Cloud: Lessons Learned from Communicating About Earth Science Data on the Cloud

The Level-1 and Atmosphere Archive Distribution System (LAADS) Distributed Active Archive Center (DAAC) is migrating data to the cloud. As one of twelve DAACS supported by NASA’s Earth Science Data and Information System (ESDIS), LAADS is using moving away from on premise data storage facilities to migrating to Amazon Web Services, where the massive archive of data from the Moderate Imaging Spectroradiometer (MODIS) and the Visible Infrared Imaging Radiometer Suite (VIIRS) will be available for download and post-processing transformations online. The migration is happening in three phases and LAADS is concluding its beta testing period. This poster shows the lessons learned from communicating with a select group of users about how to effectively educate data users on using data in the cloud.

Tassia Owen↗

Flight Software for the LOFTID Re-Entry Vehicle

NASA’s Low-Earth Orbit Flight Test of an Inflatable Decelerator, or LOFTID, demonstrated re-entry from low-Earth orbit of a 6-meter aeroshell designed to be used as a heat shield that is larger than the rocket shroud, enabling the return of much greater mass. The LOFTID Flight Software was developed in-house and consists of three components: the Power Distribution software, the Data Recorder software, and the Camera Controller software, along with the Ground Software used for testing, operations, data integrity, and data processing. This presentation will discuss issues found in developing the Power Distribution software in cFS on a non-Linux POSIX platform, designing an efficient architecture for recording of high bandwidth data, controlling over a dozen cameras in-flight, and creating the ground support tools to verify data integrity and support the post-processing of flight data.

Paul Brewster↗

Analysis of Eight Magnetic Chemically Peculiar Stars With Rotational Modulation

Since the end of 2018, the Transiting Exoplanet Survey Satellite (TESS) has provided stellar photometry to the astronomical community. We have used TESS data to study rotational modulation in the light curves of a sample of chemically peculiar stars with measured large-scale magnetic fields (mCP stars). In general, mCP stars show inhomogeneous distributions of elements in their atmospheres that lead to spectroscopic (line profile) and photometric (light curve) variations commensurate with the rotational period. We analyzed the available TESS data from 50 sectors for eight targets after post-processing them in order to minimize systematic instrumental trends. Analysis of the light curves allowed us to determine rotational periods for all eight of our targets. For each star, we provide a phase diagram calculated using the derived period from the light curves and from the available measurements of the disc-averaged longitudinal magnetic field B(sub z). In most cases, the phased light curve and B(sub z) measurements show consistent variability. Using our rotation periods, and global stellar parameters derived from fitting Balmer line profiles, and from Geneva and Strömgren–Crawford photometry, we determined the equatorial rotational velocities and calculated the respective critical rotational fractions v(sub eq)/v(crit). We have shown from our sample that the critical rotational fraction decreases with stellar age, at a rate consistent with the magnetic braking observed in the larger population of mCP stars.

Chemically peculiar stars↗

Impact Ice Microstructure Segmentation Using Transfer Learned Model

A process of using machine learning to segment impact ice microstructure is presented and analyzed. The segmentation was conducted with the goal of obtaining average grain size estimations. The model was trained on a set of micrographs of impact ice grown at NASA Glenn’s Icing Research Tunnel. The model leveraged a model pre-trained on a large set of micrographs of various materials as a starting point. Post-processing of the segmented images was done to connect broken boundaries. An automatic method of determining grain size following an ASTM standard was implemented. Segmentation results using different training sets as well as different encoder and decoder pairs are presented. Calculated sizes are compared to manual grain size measurement methods. Results show promise in accuracy as well as a possible improvement in repeatability and consistency. Next steps for improving the model are suggested.

Machine learning↗

MLtool Python Code

Machine Learning (ML) is a subfield of Artificial Intelligence that gives computers the ability to learn from past data without being explicitly programmed. The predictive capabilities of ML models have already been used to facilitate several scientific breakthroughs. However, the practical application of ML is often limited due to the gaps in technical knowledge of its users. The common issue faced by many scientific researchers is the inability to choose the appropriate ML pipelines that are needed to treat real-world data, which is often sparse and noisy. To solve this problem, we have developed an automated Machine Learning tool (MLtool) that includes a set of ML algorithms and approaches to aid scientific researchers. The current version of MLtool is implemented as an object-oriented Python code that is easily extensible. It includes 44 different regression algorithms used to model data. MLtool helps users select the best model for their data, based on the scoring metrics used. Besides regression algorithms, MLtool also includes a suite of pre- and post-processing techniques such as missing value imputation, categorical variable encoding, input feature normalization, uncertainty quantification, exploratory data analysis (EDA), etc. MLtool was tested on several publicly available multi-dimensional data sets and was found capable of making accurate predictions.

Machine Learning↗

Differential Ranging Experiments with NASA’s High-Gain Deep Space Network Antennas

Radar experiments are currently under way at the Goldstone Deep Space Communications Complex (GDSCC) to determine the range of cis-lunar objects using a single high-gain 34m antenna transmitting 20 kW at X-band wavelengths, and two 34m receivers located at the GDSCC. The 34m antennas of the Deep Space Network (DSN) have effective fields-of-view of approximately 1 milli-radians, hence predetermined pointing predicts with accuracies on the order of 1 milli-degree are required. The extremely accurate 1 nano-second timing distribution of the DSN network enables sub-meter level ranging in the radial direction, by measuring delays in phase-modulated pseudonoise (PN) echoes reflected from the target. In this paper we demonstrate experimental verification of the proposed techniques by post-processing radar reflections obtained from a decommissioned geo-synchronous satellite. The characteristics of correlation-based delay measurements obtained simultaneously with two different receiving antennas are described. These are the fundamental quantities used to form the “time difference of arrival” (TDOA) observables commonly used in GPS positioning algorithms, and the results interpreted in the context of hyperbolic position location that could be used in the future to monitor spacecraft in cis-lunar space.

Cheung, Kar-Ming↗

Mars 2020 Entry, Descent, and Landing as Observed by Radio Science Techniques at UHF and X-band Frequencies

On February 18, 2021, NASA’s Mars 2020 Rover “Perseverance” conducted the Entry, Descent, and Landing (EDL) sequence to land safely on the surface of Mars at Jezero Crater. Due to the criticality of the EDL sequence, redundant telecommunications strategies were utilized to observe the event. The rover transmitted an X-band (8.4 GHz) signal encoded with Multiple Frequency Shift Keying tones direct-to-Earth and a UHF (401.5 MHz) signal with 8 kbps encoded telemetry to orbiters, which relayed the signal back to Earth at X-band. NASA’s Deep Space Network complexes at Goldstone, California and Madrid, Spain received the direct-to-Earth X-band signal. Two of the largest steerable radio telescopes in the world, the 100-meter Green Bank Telescope in West Virginia and the Effelsberg 100-meter Radio Telescope in Germany also received the UHF carrier signal on Earth. During the EDL event, full spectrum recordings of the UHF and X-band signals were captured at these observatories using open-loop receivers. Real-time and post-processing signal analysis to determine power level, received frequency, and Doppler shift allowed determination of the spacecraft’s health and safety state during critical sequences, including event detection such as atmospheric entry, plasma blackouts, and parachute deployment. Simultaneous observations of the UHF and X-band radio links allow for redundancy in the event of a fault and greatly improve confidence in Doppler event detection.

Gladden, Roy↗

Metrics for Flight Operations: Application to Europa Clipper Tour Selection

Objective measures are ubiquitous in the formulation, design and implementation of deep space missions. Tour durations, flyby altitudes, propellant budgets, power consumption, and other metrics are essential to developing and managing NASA missions. But beyond the simple metrics of cost and workforce, it has been difficult to identify objective, quantitative measures that assist in evaluating choices made during formulation or implementation phases in terms of their impact on flight operations. As part of the development of the Europa Clipper Mission system, a set of operations metrics have been defined along with the necessary design information and software tooling to calculate them. We have applied these methods and metrics to help assess the impact to the flight team on the six options for the Clipper Tour that are currently being vetted for selection in the fall of 2021. To generate these metrics, the Clipper MOS team first designed the set of essential processes by which flight operations will be conducted, using a standard approach and template to identify (among other aspects) timelines for each process, along with their time constraints (e.g., uplinks for sequence execution). Each of the resulting 50 processes is documented in a common format and concurred by stakeholders. Process timelines were converted into generic schedules and workforce-loaded using COTS scheduling software, based on the inputs of the process authors and domain experts. Custom code was generated to create an operations schedule for a specific portion of Clipper's prime mission, with instances of a given process scheduled based on specific timing rules (e.g., process X starts once per week on Thursdays) or relative to mission events (e.g., sequence generation process begins on a Monday, at least three weeks before each Europa closest approach). Over a 5-month period, and for each of six Clipper candidate tours, the result was a 20,000+ line, workforce-loaded schedule that documents all of the process-driven work effort at the level of individual roles, along with a significant portion of the level-of-effort work. Post-processing code calculated the absolute and relative number of work hours during a nominal 5 day / 40 hour work week, the work effort during 2nd and 3rd shift, as well as 1st shift on weekends. The resultant schedules and shift tables were used to generate objective measures that can be related to both human factors and to operational risk and showed that Clipper tours which utilize 6:1 resonant (21.25 day) orbits instead of 4:1 resonant (14.17 day) orbits during the first dozen or so Europa flybys are advantageous to flight operations. A similar approach can be extended to assist missions in more objective assessments of a number of mission issues and trades, including tour selection and spacecraft design for operability.

Sarrel, Marc↗

Precision of Spacecraft Doppler Tracking at Low Signal-to-Noise Ratios

The signal-to-noise ratio received at Earth is typically larger than 10 dB-Hz for radio tracking of a spacecraft in deep space, allowing a phase-locked loop to execute reliable carrier tracking. Recently, missions have been proposed to utilize low-gain antennas for Doppler tracking where the signal-to-noise ratio may be at the single-digit (dB-Hz) level. In this work, we discuss spacecraft Doppler tracking at these low signal-to-noise ratios through an analysis of thermal noise on the radio link, results from ground testing with the Deep Space Network, and demonstrations with an active spacecraft. We show that by utilizing an open-loop receiver to capture the carrier signal and by applying post-processing techniques, radio data with the signal-to-noise ratio as low as 4 dB-Hz can be used to derive Doppler data with precision sufficient for tracking a spacecraft in deep space.

D. R. Buccino↗

Initial Development of A Digital Twin Model for an Electrified Aircraft Propulsion Emulation Rig

In support of aviation fuel burn and emission reduction goals, NASA is pursing high-payoff research investments that promise to transform aviation. This includes investments in Electrified Aircraft Propulsion (EAP), which relies on the generation, storage, transmission, and use of electrical power for producing thrust and optimizing propulsion system efficiency. Multiple technology challenges must be addressed to unlock the full potential of EAP. This includes advances in propulsion controls, which will be vital for ensuring coordinated efficient operation of the complex integrated subsystems that comprise EAP architectures. To support EAP controls research, the NASA Glenn Research Center has developed the Hybrid Propulsion Emulation Rig (HyPER). The HyPER laboratory hardware includes shaft-mounted electric machines, power converters, power supplies, power distribution cables, and an energy storage device that can be reconfigured to represent a variety of EAP architectures. It also includes an integrated real-time computer system that hosts developed EAP control software and turbomachinery simulations. This enables the electrical system and rotating shafts of EAP designs to be implemented in actual hardware and integrated with turbomachinery simulations and system-level EAP control logic implemented in software. In this form, the HyPER laboratory provides a partially simulated, partially hardware-in-the-loop test environment enabling the initial development and evaluation of EAP control technology. A prerequisite for the development of EAP control designs is the availability of a system model that accurately reflects the operation of the electrical system hardware. To support this need, a digital twin model of the HyPER electrical system hardware is under development. This model is being coded in the MATLAB Simulink environment and uses the NASA-developed Electrical Modeling and Thermal Analysis Toolbox (EMTAT) to construct a digital twin framework. EMTAT contains generic electrical component building blocks that are simulated at turbomachinery timescales. Associated inputs and outputs allow the blocks to be combined to model complete electrical systems. The EMTAT blocks also contain adjustable internal maps and parameters that can be set to reflect the operation of a specific electrical component. For the HyPER digital twin, the settings of these EMTAT block internal maps and parameters is determined through machine learning approaches applied to characterization run data collected from the laboratory. During characterization runs the laboratory electrical system hardware is subjected to a full range of torque, speed, and power settings. Acquired data is then used to estimate EMTAT block parameters using a variety of machine learning techniques. The resulting digital twin model is found to match the operation of actual HyPER hardware with an accuracy suitable for control development purposes. It also holds promise for other applications including modeling the performance of HyPER laboratory reconfigurations and model-based anomaly detection. Planned follow-on work to automate post-processing of acquired laboratory data to update the HyPER digital twin model will also be presented and discussed.

Electrified Aircraft Propulsion↗

Using Thermal Desktop to Determine Equivalent Solar Hours on Spacecraft Surfaces

Equivalent solar hours (ESH) are the duration of exposure of a surface to solar radiation, particularly ultraviolet (UV) radiation. Since UV light causes degradation to optical properties of thermal control surfaces, the determination of ESH is important to calculate end of life (EOL) optical properties for spacecraft external surfaces. A higher ESH value generally correlates to greater UV degradation experienced by the surface, resulting in an increase in solar absorptivity. This paper details a novel approach for calculating ESH using Thermal Desktop. Since the ESH value is useful for thermal analysts, this technique is beneficial to quickly perform the analysis internally with tools readily available without relying on external support and funding. The example presented in this paper shows how this method was developed and used for NASA's lunar Gateway spacecraft. This method of analysis is used to calculate ESH for the transit, assembly in orbit, and operational phases for Gateway, utilizing each of the program’s planned configurations throughout its 15-year lifetime. ESH values were calculated using the Monte-Carlo ray tracing capability of Thermal Desktop using both direct solar heating and indirect solar heating due to reflections from other surfaces. The output of this analysis includes an ESH gradient map across the spacecraft. Post-processing of these results enabled identification of the average and maximum ESH on critical surfaces such as radiators. The final ESH values for each surface are then used for determination of EOL optical properties.

Thermal Control↗

Microstructural Evolution and Mechanical Properties of LP-DED NASA HR-1 – A Hydrogen Resistant AM Superalloy for Space Propulsion Applications

The National Aeronautics and Space Administration (NASA) has actively pursued metal additive manufacturing (AM) technologies for spaceflight applications since the late 2000s. AM offers transformative advantages in cost, schedule, part consolidation, and design flexibility. Among the various AM techniques, laser powder directed energy deposition (LP-DED) is particularly well suited for fabricating complex geometries with fine feature resolution. In propulsion systems that utilize high-pressure gaseous hydrogen—such as liquid hydrogen rocket engines—hydrogen environment embrittlement (HEE) presents a serious threat to material performance 1,2 . Mechanical property degradation under these conditions can compromise component reliability, especially under cyclic loading. To address this challenge, NASA developed NASA HR-1 (Hydrogen Resistant-1) as a solution for liquid rocket engine components operating in hydrogen-rich environments, using the LP-DED technique 3-9 . A key component in a liquid rocket engine is the exhaust nozzle, which is typically regeneratively cooled (regen) due to the high heat flux. NASA HR-1 was specifically developed for regen nozzle applications using hydrogen as a propellant, providing resistance to HEE, a critical issue for many materials. The AM version of NASA HR-1 was also formulated to achieve high ultimate tensile strength, along with high yield strength and ductility in this environment 5,6 . Low-cycle fatigue (LCF) is another important consideration in nozzle design, as components are expected to endure multiple starts and missions. Additionally, the LP-DED version of the alloy exhibits improved thermal conductivity compared to its wrought counterpart, which benefits nozzle cooling. Overall, NASA HR-1 offers an excellent balance of high strength, HEE resistance, LCF performance, thermal conductivity, and ductility to meet the demanding requirements of channel-cooled nozzles and other components used with hydrogen and other propellants. The LP-DED–processed NASA HR-1 requires several post-processing heat treatment steps to achieve the material properties desirable for its intended application 6 . These steps include stress relief, homogenization, solution annealing, and aging for precipitation hardening. The stress relief treatment mitigates residual stresses accumulated during the LP-DED process and minimizes the potential for distortion. Homogenization, a common step for AM materials, reduces elemental segregation and promotes recrystallization to develop a more equiaxed grain structure. The subsequent solution annealing treatment heats the part to a solid solution temperature to dissolve the undesirable η-phase that forms during cooling from homogenization, followed by rapid cooling to retain an η-phase–free microstructure. Finally, aging promotes precipitation of the strengthening γ′ phase in the alloy. The integration of compositional design and optimized thermal processing enables high-quality LP-DED NASA HR-1 components with excellent microstructural and mechanical stability. Improved chemical and microstructure homogeneity enhances ductility and fatigue resistance—both critical for safe and reliable operation in high-pressure hydrogen environments. NASA has successfully fabricated and hot-fire tested multiple subscale and full-scale channel wall nozzles using LP-DED NASA HR-1 5,6, 9-14 . These efforts included process refinements to support thin-wall construction and various channel geometries. Throughout development, several key observations emerged. After homogenization, the as-built columnar grain structure transforms into a fully equiaxed microstructure. However, subsequent treatments—such as solution annealing and aging—result in changes that are more difficult to track. The grain structure remains largely unchanged, and the γ′ precipitates, typically 5–10 nm in diameter, are beyond the resolution of scanning electron microscopy (SEM). While transmission electron microscopy (TEM) can resolve these fine precipitates, TEM sample preparation is time-consuming and difficult for LP-DED material. As an alternative, differential scanning calorimetry (DSC) offers a useful, qualitative approach to monitor precipitate evolution throughout different stages of heat treatment. The overall goal is to improve the understanding of how heat treatment affects the microstructure and mechanical performance of LP-DED NASA HR-1. This paper presents heat treatment design considerations, microstructural characterization, mechanical testing – including tensile and LCF testing in both air and hydrogen environments.

Superalloy↗