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At least 361 records · Page 20

Investigation of Additive Manufactured GRCop-42 Alloy Developed by Directed Energy Deposition Methods

GRCop is an alloy family constructed of copper, chromium, and niobium and was developed by NASA for high heat flux applications. GRCop-alloys were specifically formulated for the requirements in channel-cooled main combustion chambers allowing for repeat use in high heat flux environments[1]. GRCop-84 was evolved using additive manufacturing techniques under a NASA development program. To further increase thermal conductivity while maintaining material strength characteristics, the percentage of alloying elements were cut in half and GRCop-42 was developed. In recent years, NASA has successfully additively manufactured GRCop-42 with comparable material characteristics to extrudedGRCop-42 using a Laser Powder Bed Fusion (L-PBF) process. Benefits of this process include fabrication of intricate internal cooling channels as well as a decrease in manufacturing time. However, there are some large disadvantages in using this process. The nature of the powder bed process imposes a strict volume constraint as well as an excessive amount of material inventory required. A Directed Energy Deposition (DED) process addresses these limitations while also speeding up the manufacturing process. With little data on how DED performs with GRCop-42, an investigation into the mechanical properties was conducted. More specifically, Blown Powder Directed Energy Deposition (BPD), was used to compare material properties to that of the L-PBF manufactured GRCop-42. The DED manufactured material was found to have less than 0.1% porosity. Tensile tests concluded that the DED manufactured GRCop-42 had lower tensile strengths at room temperature. The results point towards a process capable of producing fully dense parts capable of meeting mechanical strength requirements with some possible refinement of printing parameters.

GRCop-42↗

Fused Deposition Modeling Additive Manufacturing of Carbonized Structures via Waste-Enhanced Filaments

This report details the development, characterization, and use of coal-enhanced composite materials in additive manufacturing applications. High coal loading formulations—containing up to 70 wt.% coal—were successfully extruded and processed using commercially available 3D printers. Extensive experimental testing was conducted to assess the mechanical, thermal, and microstructural properties of the composites. In parallel, multi-scale computational modeling was employed to elucidate atomistic interactions and evaluate the effects of printing-induced defects on structural performance. Large-scale printability trials demonstrated the feasibility of fabricating complex components for tooling and construction applications, including wind turbine blade molds and modular wall sections. Techno-economic analyses demonstrated the cost-effectiveness and scalability of coal-enhanced composites for large-scale additive manufacturing applications such as wind turbine blade tooling.

01 COAL, LIGNITE, AND PEAT↗

Application of Additively Manufactured Components in Rocket Engine Turbopumps

The use of additive manufacturing technology has the potential to revolutionize the development of turbopump components in liquid rocket engines. When designing turbomachinery with the additive process there are several benefits and risks that are leveraged relative to a traditional development cycle. This topic explores the details and development of a 90,000 RPM Liquid Hydrogen Turbopump from which 90% of the parts were derived from the additive process. This turbopump was designed, developed and will be tested later this year at Marshall Space Flight Center.

Calvert, Marty, Jr.↗

Rapid data acquisition and machine learning-assisted composition design of functionally graded alloys via wire arc additive manufacturing

Abstract The lack of high-quality datasets in materials science hinders artificial intelligence (AI)-driven alloy design. To address this challenge, wire arc additive manufacturing (WAAM) was employed to fabricate graded alloys, generating extensive data for machine learning (ML)-assisted property prediction. ML models were developed using high-throughput experiments, computational models, and genetic algorithm to optimize feature selection, successfully predicting hardness and porosity. The ML model demonstrated its efficacy by designing a gradient alloy with enhanced properties. However, scaling up revealed uncertainties in tensile property and porosity due to differences in size and thermal conditions between the designed alloy build and the gradient print used to construct the ML model. This underscores the need for uncertainty quantification and process optimization in WAAM-driven alloy design. Our work advances AI-integrated additive manufacturing, offering a rapid approach to exploring process–structure–property relationships and accelerating materials development.

Wang, Xin↗

Nondestructive Evaluation of Additive Manufacturing State-of-the-Discipline Report

This report summarizes the National Aeronautics and Space Administrations (NASA) state of the art of nondestructive evaluation (NDE) for additive manufacturing (AM), or "3-D printed", hardware. NASA's unique need for highly customized spacecraft and instrumentation is suited for AM, which offers a compelling alternative to traditional subtractive manufacturing approaches. The Agency has an opportunity to push the envelope on how this technology is used in zero gravity, an enable in-space manufacturing of flight spares and replacement hardware crucial for long-duration, manned missions to Mars. The Agency is leveraging AM technology developed internally and by industry, academia, and other government agencies for its unique needs. Recent technical interchange meetings and workshops attended by NASA have identified NDE as a universal need for all aspects of additive manufacturing. The impact of NDE on AM is cross cutting and spans materials, processing quality assurance, testing and modeling disciplines. Appropriate NDE methods are needed before, during, and after the AM production process.

Waller, Jess M.↗

Evaluation of additive manufacturing side channels for nuclear nonproliferation applications

The nuclear industry is continuing to grow and adopt new technologies to manufacture components. One of the technologies under consideration is additive manufacturing (AM). AM could make production of components at lower cost and could lessen lead times significantly. However, AM could be prone to proliferation risks. AM machines give off signatures during the manufacturing process, and these signatures offer an alternative means of observing the component. Correlations between these signatures and the geometry being manufactured could be developed; enabling proliferators as they could take advantage of these correlations to steal AM manufacturing instructions relating to nuclear technology. This work pertains to preliminary work exploring the feasibility of tapping into AM side channels to predict geometries being manufactured on AM machines. The feasibility of utilizing vibration to draw geometric correlations will be assessed on an Ultimaker 2 + thermoplastic 3D printer.

Additive manufacturing↗

Reactive Additive Manufacturing for Fourth Industrial Revolution Exploration Systems (Ramfire) Aluminum 6061-Ram2 Nozzle Testing

The NASA Marshall Space Flight Center (MSFC) has applied various forms of metallic additive manufacturing (AM) in liquid rocket engine component design, development, and testing since 2010. These AM techniques reduce hardware cost, shorten fabrication schedules, increase reliability by reducing the number of joints, and improve hardware performance by allowing unconventional design. The RAMFIRE project, funded under Space Technology Mission Directorate (STMD) Game Changing Development (GCD) Program, has furthered the use of novel AM liquid rocket nozzles in collaboration with Elementum 3D. The project advanced novel large-scale AM aluminum material technology for significant weight savings in rocket engines and launch vehicles. Previously, aluminum alloys were difficult to weld and print using additive manufacturing. Elementum 3D’s patented Aluminum 6061-RAM2 alloy allows for aluminum alloys to be printed using various AM techniques and at various scales. The alloy can be leveraged for welding wire, showing drastic improvements in aluminum weldability. The RAMFIRE project focuses on five key areas: 1) Laser Powder Directed Energy Deposition (LP-DED) AL6061-RAM2 feedstock specification and verification, 2) LP-DED process development and validation, 3) LP-DED printed AL6061-RAM2 microstructural and mechanical property characterization, 4) Hot-fire test a 7k-lbf thrust class regeneratively cooled nozzle, 5) Print large scale regeneratively cooled nozzle. Hot-fire testing demonstrates potential of advanced space technologies to NASA and potential users by providing the relevant environments to advance TRL levels to the 5/6 range.

Al6061-RAM2↗

In-Process Monitoring and Structural Health Monitoring of Large-Scale Additive Manufacturing Using Acoustic Emission Technique

ORNL collaborated with MISTRAS Group, Inc. to investigate acoustic emission (AE) as a structural health monitoring (SHM) method for large-scale additive manufacturing (AM). Large-scale AM is being adapted as method of producing large structures in a short lead time and cost-effective way. With the growing advancement in AM techniques and application, machine monitoring and part qualification is highly needed. There has been leading research focused on the manufacturing, feedstock material but minimum research on the SHM, defect detection, and nondestructive evaluation (NDE) for AM. Scanning large structure using conventional nondestructive testing (NDT) techniques, such as ultrasound or X-ray, and searching for potential defects can be very time consuming, challenging and cost prohibitive. AE is a passive technique that can be used to monitor and locate defect progression in large structure by distributing group of sensors around the part. This project utilized AE technique and system manufactured/designed by MISTRAS Group to monitor large-scale AM equipment (i.e. Big Area Additive Manufacturing (BAAM) system located at the Oak Ridge National Laboratory – Manufacturing Demonstration Facility (ORNL-MDF) and the printed parts it produces. The AE system provided valuable insight on defect development/progression during and post-printing process.

36 MATERIALS SCIENCE↗

Developing tools and process controls to manufacture energy-efficient powders for additive manufacturing feedstocks: Computational analysis of metal powder manufacturing via machining

Traditionally, metal powders have been produced through methods such as grinding, atomization, and electrolysis. In contrast to these techniques, Metal Powder Works, Inc. has pioneered a methodology based on metal cutting. This innovative approach utilizes a vibrating cutting tool to machine metal particles, in the form of chips, from a workpiece. This technique allows for control of powder particle size, morphology, and avoids any thermally induced material changes. This collaboration aims to elucidate metal cutting characteristics and assess performance on tough materials like Inconel alloys. Computational models, using FEA and SPH techniques, will be developed initially, focusing on aluminum alloy (Al 7075-T6) for studying mesh sensitivity, cutting forces, and chip morphology.

99 GENERAL AND MISCELLANEOUS↗

Developing tools and process controls to manufacture energy-efficient powders for additive manufacturing feedstocks

Traditionally, metal powders have been produced through methods such as grinding, atomization, and electrolysis. In contrast to these techniques, Metal Powder Works, Inc. has pioneered a methodology based on metal cutting. This innovative approach utilizes a vibrating cutting tool to machine metal particles, in the form of chips, from a workpiece. This technique allows for control of powder particle size, morphology, and avoids any thermally induced material changes. This collaboration aims to elucidate metal cutting characteristics and assess performance on tough materials like Inconel alloys. Computational models, using FEA and SPH techniques, will be developed initially, focusing on aluminum alloy (Al 7075- T6) for studying mesh sensitivity, cutting forces, and chip morphology.

36 MATERIALS SCIENCE↗

Assessment of Segmentation-Induced Deviations of Porosity Metrics in Powder Bed Fusion Additively Manufactured Components

Post processing X-ray computational tomography (CT) inspection data for additively manufactured (AM) components can induce deviations in defect quantification, affecting subsequent fatigue and failure predictions. To assess the influence and potential impact of segmentation-induced measurement deviations, this paper applies several segmentation techniques to X-ray CT data for powder bed fusion Ti-6Al-4V specimens exhibiting porosity conditions. X-ray CT reconstructions were segmented with varying techniques including Otsu’s thresholding, random forest, k-nearest neighbors, and the multilayer perceptron. Metrics such as pore size and global porosity were compared for internal validity. Then, top-down X-ray CT measurements of surface-breaking porosity were compared to optical profilometry for cross-validation.

Data fusion↗

Aging heat treatment design for Haynes 282 made by wire-feed additive manufacturing using high-throughput experiments and interpretable machine learning

Wire-feed additive manufacturing (WFAM) produces superalloys with complex thermal cycles and unique microstructures, often requiring optimized heat treatments. To address this challenge, we present a hybrid approach that combines high-throughput experiments, precipitation simulation, and machine learning to design effective aging conditions for the WFAM Haynes 282 superalloy. Our results demonstrate that the γ’ radius is the critical microstructural feature for strengthening Haynes 282 during post-heat treatment compared with the matrix composition and γ’ volume fraction. New aging conditions at 770°C for 50 hours and 730°C for 200 hours were discovered based on the machine learning model and were applied to enhance yield strength, bringing it on par with the wrought counterpart. This approach has significant implications for future AM alloy production, enabling more efficient and effective heat treatment design to achieve desired properties.

CALPHAD↗

Integrated Process-Structure-Property Simulations for Additive Manufacturing Using the Open-Source Materialite Package

The microstructure and properties of additively manufactured (AM) metals are strongly dependent on process conditions. Therefore, process-structure-property (PSP) simulations are a useful tool for exploring process parameter space, studying process variations, and quantifying uncertainty in material properties. However, integrating process-structure and structure-property simulations often involves connecting multiple software packages. Each package may use unique data structures and require substantial domain knowledge. This presentation demonstrates PSP simulation capabilities of Materialite, an open-source package developed at NASA Langley Research Center. Materialite simplifies model linkages by using a common data structure and model interface, enabling straightforward simulation across a PSP model chain. Physics-based models, including kinetic Monte Carlo and crystal plasticity, are implemented within the package. The model interface is also intended to simplify implementation of new models and enable integration with external simulation tools. Example use cases include uncertainty quantification with PSP models and GPU-accelerated powder bed fusion AM process models.

additive manufacturing↗

Polymer Additive Manufacturing for Marine Renewable Energy Applications: Best Practices, Research Trends, and Current Challenges

Additive manufacturing (AM) is a rapidly growing technology space, not only for prototyping, but is also becoming more feasible at larger scales and increasing component quantities. There are a large variety of AM processes and materials available to users and effectively applying those processes and materials to a specific use case can be challenging. One specific area where AM could be particularly beneficial is marine renewable energy (MRE). Not only is MRE a relatively nascent industry with a near-term need for rapid deployments and prototype testing, but developers could also see long-term benefits from the broad variety of environmentally resistant materials available and the ability to manufacture complex geometries that AM technologies offer. Over the past 4 years, AM materials have played an increasing role in the Advanced Materials project; a multi-year, multi-laboratory research project funded by the U.S. Department of Energy's Water Power Technologies Office, with the main goal of reducing barriers to the adoption of complex materials in the MRE industry. The primary focus of this project is to develop test methods and generate datasets to understand the long-term performance of advanced materials in marine environmental and address specific material challenges as they arise. This report provides an extensive overview of the research that has been performed specific to AM polymers as part of the Advanced Materials project. The intention of this document is to provide recommendations of best practices with regards to material selection, mechanical test method development, and design practices, lessons learned along the way, current research trends, and ongoing challenges with regards to AM polymers in marine environments. In particular, this report focuses on several key aspects: Material and process selection, Environmental conditioning and subsequent degradation quantification through mechanical characterization, Composite reinforcements on AM polymer substrates, Adhesion of instrumentation for mechanical characterization and loads measurements, Protective coatings for preventing biofouling and water ingress, Other MRE case studies where AM has proved particularly useful. Ultimately, we hope that the test methods that have been developed, data generated, and lessons learned from this research will be valuable to the MRE community (researchers and developers alike), as well as other industries, and can be used as a reference point as the respective MRE and AM industries continue to grow and mature.

16 TIDAL AND WAVE POWER↗

Multiscale Characterization of Additive Manufacturing Components with Computed Tomography, 3D X-ray Microscopy, and Deep Learning

Additive manufacturing (AM) facilitates the creation of complex-geometry parts, driving advancements in lightweight aerospace components, high-efficiency engine cooling channels, and customized medical implants. However, ensuring the quality and reliability of AM parts remains challenging due to internal defects, surface irregularities, porosity, and residual trapped powder, which are often inaccessible to traditional inspection methods. Recent developments in X-ray computed tomography (XCT) and 3D X-ray microscopy (XRM), particularly systems equipped with resolution-at-a-distance (RaaD™) capabilities, enable high-resolution, non-destructive evaluation of AM components across multiple scales, from sub-micrometer to macroscopic levels. This paper explores modern XCT and XRM techniques for multiscale characterization of AM parts, focusing on their ability to detect and analyze defects such as porosity, cracks, inclusions, and surface roughness, while offering insights into defect formation mechanisms, material properties, and process-induced variations. The integration of deep learning (DL) frameworks, including Simurgh, DeepRecon, and DeepScout, enhances XCT/XRM workflows by reducing scan times, improving resolution recovery, and enabling accurate defect detection even with limited projection data. These DL-based methods overcome limitations of traditional reconstruction techniques, enabling faster, more reliable characterization of dense materials like Inconel 718 and novel alloys such as AlCe. Applications include process parameter optimization, high-throughput quality control, and multistage AM process evaluation, with DL-enhanced workflows accelerating analysis times from weeks to days. Correlative imaging approaches further validate XCT and XRM data against scanning electron microscopy (SEM) images of physically sectioned samples, confirming the accuracy of DL-based reconstructions and enabling comprehensive defect analysis. While challenges remain in generalizing DL models to diverse materials and imaging conditions, improvements in resolution, noise reduction, and defect detection highlight the transformative potential of these methods. This multiscale and correlative approach enables precise identification and correlation of microstructural features with the overall performance of AM components. By integrating advanced XCT, XRM, and DL techniques, this paper demonstrates a significant leap forward in AM characterization, offering valuable insights into the relationships between processing parameters, microstructure, and part performance, and driving innovations that enhance the quality and reliability of AM products for demanding industrial applications.

Additive manufacturing↗

Additive Manufacturing Evaporative Casting

Traditional lost foam casting has been around for decades. The process uses foam forms blown by an injection molding like process or CNC milled into the desired shape, then pouring molten metal over the foam pattern to create a metallic object. Additive Manufacturing Evaporative Casting (AMEC) is a new process that eliminates foam forms by using additive manufacturing to 3D print the desired cast geometry. This not only saves time and money but allows for more advanced and complex designs that can’t be achieved by carving foam. Additionally, AMEC doesn’t require molds and tooling like other casting and foundry options. Because the AMEC process is new, extensive testing is needed to develop a better understanding of the process to minimize defects, quantify material properties, and start computer modeling for the process. This CRADA (collaborative research and development agreement) between ORNL and Skuld seeks to improve the process, develop a computational model, characterize material properties, and explore new applications.

36 MATERIALS SCIENCE↗

A Fully Non-Metallic Gas Turbine Engine Enabled by Additive Manufacturing

The Non-Metallic Gas Turbine Engine project, funded by NASA Aeronautics Research Institute, represents the first comprehensive evaluation of emerging materials and manufacturing technologies that will enable fully nonmetallic gas turbine engines. This will be achieved by assessing the feasibility of using additive manufacturing technologies to fabricate polymer matrix composite and ceramic matrix composite turbine engine components. The benefits include: 50 weight reduction compared to metallic parts, reduced manufacturing costs, reduced part count and rapid design iterations. Two high payoff metallic components have been identified for replacement with PMCs and will be fabricated using fused deposition modeling (FDM) with high temperature polymer filaments. The CMC effort uses a binder jet process to fabricate silicon carbide test coupons and demonstration articles. Microstructural analysis and mechanical testing will be conducted on the PMC and CMC materials. System studies will assess the benefits of fully nonmetallic gas turbine engine in terms of fuel burn, emissions, reduction of part count, and cost. The research project includes a multidisciplinary, multiorganization NASA - industry team that includes experts in ceramic materials and CMCs, polymers and PMCs, structural engineering, additive manufacturing, engine design and analysis, and system analysis.

Ceramics↗

The Crucial Role of Additive Manufacturing at NASA

At NASA, the first steps of the Journey to Mars are well underway with the development of NASA's next generation launch system and investments in research and technologies that should increase the affordability, capability, and safety of exploration activities. Additive Manufacturing presents a disruptive opportunity for NASA to design and manufacture hardware with new materials at dramatically reduced cost and schedule. Opportunities to incorporate additive manufacturing align very well with NASA missions and with most NASA programs related to space, science, and aeronautics. The Agency also relies on many partnerships with other government agencies, industry and academia.

Vickers, John↗