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

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At least 1,531 records · Page 85
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Manufacturing and Metal Extraction Lunar Technology (MMELT)

Extraction processes of oxygen from regolith under development include carbothermal, molten regolith electrolysis (MRE), ionic liquids, and others, all of which result in a metal slag byproduct. The separation of some of these metals from the slag byproduct have been accomplished on small scales. However, no system currently exists that can extract aluminum, from these processes at scale, to create suitable feedstocks for manufacturing. Funding for MMELT will be used to develop a demonstration of the first end-to-end process to additively manufacture aluminum wire from lunar regolith in a simulated environment.

Manufacturing↗
NASA NTRSDate not supplied

Outfitting the In-Space Manufacturing Advancement Center (ISMAC)

Marshall Space Flight Center has begun investment into a new collaborative workspace where ISM technologies can be advanced utilizing multidisciplinary teams. Equipment has been commissioned to explore ISM technologies related to electronics, welding, additive manufacturing, recycling, and metal extraction from Lunar or Martian regolith. This is an overview of the latest capabilities outfit to the new In-Space Manufacturing Advancement Center.

In-Space Manufacturing↗
NASA NTRSDate not supplied

Toward a Fully Capable In-Space Manufacturing Ecosystem

As technology for in-space refueling and servicing continues to mature, in-space assembly and manufacturing efforts can expand to leverage these new capabilities. This paper expands upon previous work and describes four use cases for a fully capable in-space manufacturing ecosystem. Based on those use cases, enabling technology areas are identified and shortfalls in those technologies are noted for seven key categories. Finally, a survey of options for in-space experiments and demonstrations are presented, showing pathways for technology development in relevant environments.

in-space manufacturing ecosystem↗
NASA NTRSDate not supplied

Off‑World Additive Manufacturing - Augmenting NASA‑STD‑6030

This slide presents a summary for a soon to be released white paper on augmenting NASA-STD-6030 for off-world manufacturing. It examines the driving need for in-space manufacturing and its corresponding risks, challenges, potential solutions, and technology development needed to qualify a process.

NASA-STD-6030↗
DOE OSTI2016

Manufacturing in the Clean Energy Race

There is an energy and manufacturing revolution in the world today. Here is what the United States Department of Energy has done through collaborations in pursuit of American prosperity in the energy and manufacturing industry of tomorrow.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2016

Boosting Manufacturing through Modular Chemical Process Intensification

Manufacturing USA's Rapid Advancement in Process Intensification Deployment Institute will focus on developing breakthrough technologies to boost domestic energy productivity and energy efficiency by 20 percent in five years through manufacturing processes.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2019

Manufacturing Competitiveness Analysis for PEM and Alkaline Water Electrolysis Systems

To examine the factors that drive regional competitiveness in the manufacturing of water electrolysis systems, bottom-up manufacturing cost analysis was developed for PEM and alkaline stacks and systems. Results show that alkaline electrolyzer stack tends to have a lower cost in ($/kW) basis, but this comes with lower power density and lower hydrogen production capacities for alkaline electrolyzers making PEM more economic at the long run.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2024

Additive Manufacturing with Cellulose-Based Composites: Materials, Modeling, and Applications

Recent advances in large-scale additive manufacturing (AM) with polymer-based composites have enabled efficient production of high-performance materials. Cellulose nanomaterials (CNMs) have emerged as bio-based feedstocks due to their exceptional strength and sustainability. However, challenges such as hornification and poor dispersion in polymer matrices still limit large-scale CNM–polymer composite manufacturing, requiring novel strategies. Here, this review outlines an approach starting with atomic-level simulations to link molecular composition to key parameters like bulk density, viscosity, and modulus. These simulations provide data for finite element analysis (FEA), which informs large-scale experiments and reduces the need for extensive trials. The strategy explores how atomic interactions impact the morphology, adhesion, and mechanical properties of CNM-based composites in AM processes. The review also discusses current developments in AM, along with predictions of mechanical and thermal properties for structural applications, packaging, flexible electronics, and hydrogel scaffolds. By integrating experimental findings with molecular dynamics (MD) simulations and finite element modeling (FEM), valuable insights for material design, process optimization, and performance enhancement in CNM-based AM are provided to address ongoing challenges.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Unravelling Microstructure Selection in an Additively Manufactured Eutectic High‐Entropy Alloy

High-entropy alloys (HEAs) are promising candidates for advanced structural applications due to their excellent mechanical properties. Additive manufacturing (AM), with its rapid solidification conditions, enables the creation of unique nonequilibrium microstructures. To fully leverage the synergy between AM and HEAs, understanding how processing affects structure and properties is essential. Here, how solidification rate influences microstructure evolution and phase transformation pathway in laser additively manufactured AlCrFe2Ni2 eutectic HEAs is investigated. By increasing the laser scan speed and hence the solidification rate, distinct solidification modes evolving from coupled eutectic to anomalous eutectic and eventually to single-phase solidification are revealed. These transitions result in distinct microstructures and a wide range of mechanical properties. Thermodynamic modeling and molecular dynamics simulations reveal that low cooling rates allow for sufficient atomic diffusion and phase separation, facilitating coupled eutectic growth. In contrast, rapid cooling suppresses diffusion and destabilizes the solid–liquid interface, promoting anomalous or single-phase solidification. This integrated experimental and computational approach provides a multiscale understanding of solidification mechanisms in HEAs and underscores how kinetic effects can over-ride thermodynamic predictions under nonequilibrium conditions. Furthermore, these results demonstrate that AM can serve as a powerful tool to design HEAs with tailored microstructures and properties.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Acoustic‐Modulated, Selective Particle Deposition for Muti‐Material Additive Manufacturing

Powder‐based additive manufacturing (AM) is an important technology in fabricating complex 3D products. In powder‐based AM, a powder deposition system plays a key role in determining the resolution, density, and properties of printed parts. An acoustic‐actuated particle deposition approach, which utilizes acoustic waves to actuate a glass nozzle, enables the deposition of particles in a cost‐effective way. However, the capability to programmably deposit different particles using acoustic waves has yet to be fully demonstrated. Herein, a selective particle deposition approach via acoustic modulation for muti‐material AM is demonstrated. Sequential and parallel depositions of two types of particles by modulating the signal inputs and resonant frequencies of piezo transducers are demonstrated. An AM process is demonstrated by integrating this acoustic‐actuated particle deposition approach with a heat‐curing mechanism. Several products from this system are produced as well. It is expected that this particle deposition approach can be used in a wide spectrum of applications, such as direct manufacturing of multi‐functional materials/devices.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Enhancing Operational Performance and Productivity Benefits in Breweries through Smart Manufacturing Technologies

This article uses case studies to demonstrate the potential of smart manufacturing (SM) and Internet of Things (IoT) technologies to enhance operational performance and productivity in industry. The analysis highlights benefits such as cost reduction, production flexibility, shorter product time-to-market, energy/water efficiency and environmental impact reduction, and increased productivity. To illustrate the effectiveness of SM and IoT approaches, the authors sought out manufacturers currently implementing or seeking to implement SM and IoT technologies. The authors identified beer brewing (NAICS Code 312120) as a rapidly expanding industry whose members appear eager to implement SM technologies to optimize production lines by revealing bottlenecks and identifying performance-reducing nodes. This article presents two case studies based on SM and IoT technologies in breweries. It briefly describes a systematic framework introduced elsewhere by the authors and uses it to assess the energy productivity and competitiveness of SM applications in breweries. This article addresses questions concerning the information and communications technology infrastructure needed to build smart breweries and how corporations simplify the installation and deployment of SM and IoT components. This article was originally published in the Proceedings of the 2019 ACEEE Summer Study on Energy Efficiency in Industry.

42 ENGINEERING↗
DOE OSTI2024

Boron‐polymer composites engineered for compression molding, foaming, and additive manufacturing

Abstract Boron (specifically 10 B) is the element of choice to shield thermal neutrons due to its large (n, α) cross‐section; however, very few polymer composites containing high boron concentrations are available. This study aimed to determine the maximum possible amount of boron that could be introduced into a polymer matrix. Diverse manufacturing techniques, ranging from additive manufacturing to compression molding, were employed to fabricate inks and filaments for 3D printing, foams, and flexible pads. Composites using siloxanes, poly(lactic acid), and acrylonitrile butadiene styrene containing up to 80 wt% boron were sucessufully fabricated. The addition of known plasticizers (polyethylene glycol) and reinforcing agents (carbon nanofibers and fumed silica) helped to overcome fabrication problems such as clogging of the printing nozzle or crumbling of compression molded parts. In addition, the thermal‐mechanical properties of these novel boron composites were determined and shown to vary according to boron concentration, presence of additives, and fabrication techniques utilized.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Anisotropic thermal behavior of extrusion-based large scale additively manufactured carbon-fiber reinforced thermoplastic structures

Large format additive manufacturing (AM) enables rapid manufacturing of large parts and structures with minimum waste in material and energy. Extrusion-based AM deposition processes provide parts with highly anisotropic thermal properties, which are not typically reflected in textbook values for these materials. In order to develop accurate models that describe the directionally dependent thermal behavior of these materials in processing and service, accurate measurements of specific heat capacity and thermal conductivity are required. Here, this work characterizes, documents, and analyzes the effect of the anisotropic nature of the extrusion-based deposition process on the specific heat capacity and thermal conductivity of the resulting AM products. All measurements were made over a temperature range of 20–180°C using the transient plane source technique, also referred to as the hot disk technique. Three of the most commonly used large format AM feedstock materials that utilize carbon fiber reinforcement were examined in this work: acrylonitrile butadiene styrene, polyphenylene sulfide and polyphenylsulfone. Finally, these findings can serve as a thermal design/process guideline for future large format AM applications.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Predicting Low Toxicity and Scalable Solvent Systems for High–Speed Roll–to–Roll Perovskite Manufacturing

Printed lead-based perovskite photovoltaics (PV) have gained interest due to their potential to be manufactured with scalable roll-to-roll techniques. In industrial scale-up, toxicity of inks can constrain roll-to-roll manufacturing due to the added cost of managing toxic effluents. Due to solvent toxicity, few perovskite solution chemistries in published works are scalable to gigawatt production capacity at low cost. Herein, it is shown that for scalable PV production, the use of aprotic polar solvents should be avoided due to their overall toxicity. Compliance with worldwide worker safety regulations for solvent exposure limits could require additional air handling requirements for some solvents, which in turn would affect cost-effectiveness. It is shown that costs associated with handling of hazardous substances can be significant and estimate an added cost of ¢3.7/W for dimethylformamide (DMF)-based inks. To solve this problem, a new perovskite ink solvent system is developed that is composed entirely of ether and alcohol, which has an effective exposure limit 14× higher than DMF, making it suitable for industrial coating processes. Here, it is shown that the new ink solvent system is capable of fabricating high-efficiency perovskite solar cells processed in 1 min on a standard roll-to-roll system.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Techno‐Economic Analysis of Additively‐Manufactured Wind Turbine Blade Tips That Enable Technology Integration

The Additively‐Manufactured, System‐Integrated Tip (AMSIT) project is leveraging the flexibility of 3D printing to integrate several technologies in a wind turbine blade tip, while reducing the levelized cost of electricity (LCOE) produced. The design integration is demonstrated for a 200‐kW–scale turbine with 13‐m blades, with the outer 15% of the blade replaced with a 3D‐printed design. Aerodynamic performance is enhanced without increasing swept area through inclusion of a winglet and surface texturing, both challenging for traditional manufacturing. Longevity and durability are improved through integrated lightning and leading edge erosion protection. Increased power, reduced repair frequency, and ease of repair through blade modularity all contribute to reduced LCOE. The analysis is also extended to modern MW‐scale designs to estimate the impact of the technology at scale, demonstrating the potential to reduce LCOE significantly for modern onshore turbines, with even higher potential savings offshore.

3D printing↗
DOE OSTI2023

Differential Property Prediction: A Machine Learning Approach to Experimental Design in Advanced Manufacturing

Advanced manufacturing techniques have enabled the production of materials with state-of-the-art properties. In many cases however, the development of physics-based models of these techniques lags behind their development in the lab. This means that material and process development proceeds largely via trial and error. This is sub-optimal since experiments are cost-, time-, and labor-intensive. In this work we propose a machine learning framework, differential property classification (DPC), which enables an experimenter to leverage machine learning's unparalleled pattern matching capability to pursue data-driven experimental design. DPC takes two possible experiment parameter sets and outputs a prediction of which will produce a material with a more desirable property specified by the operator. We demonstrate the success of DPC on AA7075 tube manufacturing process and mechanical property data using shear assisted processing and extrusion (ShAPE), an emerging solid phase processing technology. We show that by focusing on the experimenter's need to choose between multiple candidate experimental parameters, we can reframe the challenging regression task of predicting material properties from processing parameters, into a classification task on which machine learning models can achieve good performance.

advanced manufacturing, machine learning, ShAPE↗
DOE OSTI2022

Alternating chempolishing and electropolishing for interior and exterior surface finishing of additively manufactured (AM) metal components

Additively manufactured (AM) components’ surface finishing is crucial in adopting them for intended applications in challenging environments involving fatigue, corrosion, high temperature, and nuclear radiation. In our prior research, chempolishing (C) was utilized as an electroless etching process that uniformly smoothens complex AM components’ accessible interior and exterior surfaces (Tyagi et al. Additive Manufacturing, 25:32–38, 22). A wide range of electropolishing (E) has been demonstrated for AM surface finishing. However, electropolishing can impact a surface that can be juxtaposed to counter electrode and can yield a very smooth surface to sub-micrometer level roughness. However, a knowledge gap exists about the impact of applying both approaches on the same surface one after another and what new advantages may arise because of combining two methods. This paper uses dual-stage liquid-based surface finishing strategies produced by alternating the chempolishing (C) and electropolishing (E) steps. Two dual-stage surface finishing approaches, i.e., chempolishing followed by electropolishing (CE) and electropolishing followed by chempolishing (EC), were performed on the 316 stainless AM steel component. Impacts of EC and CE approaches were compared with single-stage C and E surface finishing approaches. An optical microscope and mechanical profilometer were utilized to investigate the wide range of surface roughness parameters. CE and EC produced Ra ~ 1.4 µm and ~ 1.6 µm, respectively. Surface roughness on CE- and EC-treated AM samples was lower than those individually treated by C and E approaches. Scanning electron microscopy provided further insights into the microstructural difference between CE- and EC-treated AM samples. This paper reports a liquid contact angle study on CE- and EC-treated AM samples to provide insights into the relative difference in surface energy that is crucial for making coatings on AM parts. A spectroscopic reflectance study was also employed to register the difference in physical properties of AM components treated with CE and EC approaches. Further, this study reveals industrially practicable interior and exterior surface finishing approaches for complex AM metal components that require minimum tooling and real-time process monitoring.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Vision-based localization for cooperative robot-CNC hybrid manufacturing

Wire and arc additive manufacturing (WAAM) has shown promise in recent years for producing large-scale parts with higher deposition rates than other additive processes. WAAM is often combined with subtractive machining to form a hybrid manufacturing process. This hybrid process can be realized by retrofitting computer numerical control (CNC) machines with deposition heads, adding spindles and deposition heads to robots, or developing part localization methods to transfer parts from an additive cell to a CNC machine. Here, a novel, robot-CNC hybrid configuration is introduced where a maneuverable robot is placed in front of a CNC machine to deposit material within the machine envelop. Furthermore, this method removes the need for part localization and the extensive machine modifications required for retrofitting; however, the problem of robot localization is also added. In this work, the effects of error in vision-based, contactless robot localization on machining parameters in a robot-machine hybrid process were studied. Performance was characterized on an implementation of this system using classical computer vision techniques. In addition, machining simulations were conducted to evaluate the effects of image-induced error on chip thickness, material removal rate, and machining allowance. Initial tests show that computer vision could adequately locate a robot for the hybrid WAAM process within .5 mm.

Hybrid manufacturing↗