Property-map of epoxy-treated and as-printed polymeric additively manufactured materials
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Nha Uyen Huynh↗
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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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Deformation and Failure of Additively Manufactured Materials with Internal Porosity.
Abstract not provided.
Miers, John Carter↗
Process Optimization of Carbon Electrode Materials Manufacturing by Experimental Study and Machine Learning Techniques
Electrospun carbon fibers from coal have been investigated as electrodes for batteries and supercapacitors. Despite the excellent properties of coal-derived carbon fibers (CCNF) for energy storage devices, there still lacks systematic understanding on how various process parameters affect final electrode performances, which poses challenges to scale from pilot to high volume manufacturing. The goals of this project are twofold. First, we focuse on process optimization for converting a new precursor from powder river basin (PRB) coal, referred to as coal-based polyurethane (CPU) to CCNF using electrospinning. Second, different machine learning techniques will be examined using experimental data from this work and open literature. Specifically, for CPU the following process parameters need to be characterized and optimized in order to produce CCNFs with desirable mechanical integrity and physiochemical properties: precursor composition and viscosity, operating voltage and distance, oxidation and carbonization temperature and duration. Consequently, physiochemical properties of the fibers were characterized to correlate these process parameters with desirable electrochemical performance. Given the complex nature of the fiber production process, ML models are assessed for their ability to capture the nonlinear relationship between process parameters and the electrochemical properties in applications including supercapacitors. As such, we applied various machine learning techniques, to determine which technique produces a model that best predicts device function.
Cincotta, Robert E.F.↗
Composite Overwrap of a Lightweight, Additively Manufactured Thrust Chamber: Materials Characterization, Manufacturing, and Hot-fire Testing
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Composite Overwrap↗
Development of Computational Materials Workflows for Additively Manufactured Metallic Materials to Enable Accelerated Prediction of Fatigue Performance
The maturation of computational materials approaches for fatigue performance prediction in a qualification and certification process is stifled by the ability to validate complex, microstructure-based simulations. Such a validation strategy bears immediate challenges including generating accurate virtual microstructures, efficiently solving physics-based mechanical simulations over relevant spatial and temporal scales, and acquiring high-fidelity calibration and validation data at the appropriate length scale. This presentation will overview these common challenges and present a case study to demonstrate a computational materials workflow for additively manufactured metallic materials. In this study, process-specific defects are characterized using segmented X-Ray micro-computed tomography measurements and overlaid on virtual microstructures. Accelerated crystal plasticity-based fatigue simulations are performed to demonstrate cyclic evolution and localization of mechanical fields in the vicinity of defects in response to their precise spatial configuration. An example of how this computational materials workflow may support next-generation qualification is discussed.
computational materials↗
Preparation of Smart Materials by Additive Manufacturing Technologies: A Review
Over the last few decades, advanced manufacturing and additive printing technologies have made incredible inroads into the fields of engineering, transportation, and healthcare. Among additive manufacturing technologies, 3D printing is gradually emerging as a powerful technique owing to a combination of attractive features, such as fast prototyping, fabrication of complex designs/structures, minimization of waste generation, and easy mass customization. Of late, 4D printing has also been initiated, which is the sophisticated version of the 3D printing. It has an extra advantageous feature: retaining shape memory and being able to provide instructions to the printed parts on how to move or adapt under some environmental conditions, such as, water, wind, light, temperature, or other environmental stimuli. This advanced printing utilizes the response of smart manufactured materials, which offer the capability of changing shapes postproduction over application of any forms of energy. The potential application of 4D printing in the biomedical field is huge. Here, the technology could be applied to tissue engineering, medicine, and configuration of smart biomedical devices. Various characteristics of next generation additive printings, namely 3D and 4D printings, and their use in enhancing the manufacturing domain, their development, and some of the applications have been discussed. Special materials with piezoelectric properties and shape-changing characteristics have also been discussed in comparison with conventional material options for additive printing.
36 MATERIALS SCIENCE↗
Invertible neural networks for real-time control of extrusion additive manufacturing
Material extrusion additive manufacturing (AM) has enabled an elegant fabrication pathway for a vast material library. Nonetheless, each material requires optimization of printing parameters generally determined through significant trial-and-error testing. To eliminate arduous, iteration-based optimization approaches, many researchers have used machine learning (ML) algorithms which provide opportunities for automated process optimization. Here, in this work, we demonstrate the use of an ML-driven approach for real-time material extrusion print-parameter optimization through in-situ monitoring of printed line geometry. To do this, we use deep invertible neural networks (INNs) which can solve both forward and inverse, or optimization, problems using a single network. By combining in-situ computer vision and deep INNs, the printing parameters can be autonomously optimized to print a target line width in 1.2 s. Furthermore, defects that occur during printing can be rapidly identified and corrected autonomously. The methods developed and presented in this work eliminate user-intensive, time-consuming, and iterative parameter discovery approaches that currently limit accelerated implementation of extrusion-based AM processes. Furthermore, the presented approach can be generalized to provide real-time monitoring and optimization pathways for increasingly complex AM environments.
36 MATERIALS SCIENCE↗
Insulation Material
Manufactured by Hitco Materials Division of Armco, Inc. a ceramic fiber insulation material known as Refrasil has been used extensively as a heat-absorbing ablative reinforcement for such space systems as rocket motor nozzles, combustion chambers, and re-entry shields. Refrasil fibers are highly porous and do not melt or vaporize until fibers exceed 3,100 degrees Fahrenheit. Due to these and other properties, Refrasil has found utility in a number of industrial high temperature applications where glass, asbestos and other materials fail. Hitco used this insulation to assist Richardson Co., Inc. in the manufacturing of hard rubber and plastic molded battery cases.
Source record↗
Technology transfer and international development: Materials and manufacturing technology
Policy oriented studies on technological development in several relatively advanced developing countries were conducted. Priority sectors defined in terms of technological sophistication, capital intensity, value added, and export potential were studied in Brazil, Venezuela, Israel, and Korea. The development of technological policy alternatives for the sponsoring country is assessed. Much emphasis is placed on understanding the dynamics of the sectors through structured interviews with a large sample of firms in the leading manufacturing and materials processing sectors.
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1st International Conference on Advanced Manufacturing: NASA’s Advanced Composites Materials and Manufacturing Research for the Future
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composites↗
Federal Agencies Perspectives on Composites Materials and Manufacturing
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manufacturing↗
Advanced Materials and Manufacturing Technologies Office
The Advanced Materials and Manufacturing Technologies Office (AMMTO) researches, develops, and demonstrates next-generation materials and manufacturing technologies needed to increase U.S. industrial competitiveness and to advance economy-wide decarbonization. AMMTO supports the national plan to revitalize American manufacturing, secure critical supply chains, and develop diverse innovation ecosystems leading to new manufacturing jobs and increased economic strength and security of the nation. AMMTO provides planning, management, and direction necessary for a balanced program of research, development, demonstration, technical assistance, and workforce development to support domestic manufacturing that is critical to achieving a clean, decarbonized economy.
Next Generation materials and processes, secure an↗
Interpretation of Ion Irradiation and Neutron Irradiation Damage in Additively Manufactured 316 Stainless Steel using Multiscale Modeling
The accelerated adoption of nuclear energy necessitates advanced manufacturing technologies, such as additive manufacturing, to meet heightened supply chain requirements and support innovative reactor technologies. Due to the unique microstructural characteristics of additively manufactured materials under distinct solidification conditions, comprehensive evaluation of their performance in reactor environments is essential. The Advanced Materials and Manufacturing Technologies program under the Department of Energy's Office of Nuclear Energy focuses on understanding the irradiation performance and damage evolution of laser powder bed fusion 316 stainless steel, with an emphasis on integrating ion and neutron irradiation data to accelerate the development and qualification of materials for advanced nuclear reactor applications. While ion irradiation is a cost- and time-effective method, modeling and simulation are required to interpret the data for the broader range of irradiation conditions encountered in advanced reactors. In fiscal year 2025, integrated multiscale modeling and simulations were conducted to assess irradiation damage in additively manufactured 316 stainless steel. Key outcomes include predictions of chromium enrichment at grain boundaries, nickel enrichment at dislocation cell walls and void surfaces, and heterogeneous void evolution under ion and neutron irradiation conditions. Cluster dynamics simulations revealed the coarsening of voids at high irradiation temperatures and the suppression of void growth by high network dislocation density, while also demonstrating significant growth and coarsening of voids and self-interstitial atom loops at low dose rates. Machine learning-accelerated atomistic simulations highlighted the impact of the local environment and chromium concentration on vacancy diffusivity, providing key insights on the influence of composition on void swelling and radiation-induced segregation. Additionally, molecular dynamics simulations demonstrated the presence of defect production bias and a significant effect of carbon content on defect cluster behavior. These combined efforts aim to predict the performance of additively manufactured materials under various reactor conditions, supporting their qualification for nuclear reactor applications by interpreting ion irradiation data. This report underscores the potential of integrated multiscale modeling to analyze ion irradiation data in the effort to accelerate the qualification of additively manufactured materials for nuclear reactor components.
316 stainless steel↗
Fatigue crack growth resistance of a mesoscale composite microstructure Haynes 282 fabricated via electron beam melting additive manufacturing
Materials synthesis via additive manufacturing gives unprecedented control over the solidified microstructure. While a number of studies have demonstrated the ability to produce spatially varying microstructures, little work exists to understand the behavior of such “composite” materials. In this work, we utilized electron beam melting to process Ni-based superalloy Haynes 282 and produce compact tension samples with a spatially varying mesoscale structure. Fatigue crack growth experiments reveal that the crack growth rate is dependent on the degree of microstructural heterogeneity. This work demonstrates that the crack growth resistance can be tailored within a component using electron beam melting additive manufacturing.
36 MATERIALS SCIENCE↗
Materials Scorecards, Phase 1: Advanced Materials and Manufacturing Technology
This report presents preliminary material score cards (Phase 1) based on a survey of industry experts and the limited literature available on the state of advanced manufacturing of materials for nuclear applications. Seven reactor types were considered, and the data is presented in tabular form. The caveat is that the scores are based on limited data and expert judgement. Considerable work remains to be done to prioritize materials for advanced manufacturing to support existing and advanced reactors. A central database of processing conditions, resulting microstructure, post processing, properties, and performance of these materials is needed to support the accelerated adoption of advanced manufacturing by the industry.
36 MATERIALS SCIENCE↗
Systems and methods for additive manufacturing magnetic solenoids
Systems and methods for forming a magnetically-enabled part via additive manufacturing. The method includes depositing a layer of additive manufacturing material on a build plate, melting or sintering the layer of additive manufacturing material, depositing additional layers of additive manufacturing material on previous layers of additive manufacturing material, the additive manufacturing material of at least some of the additional layers being magnetically permeable, and melting or sintering the additional layers of additive manufacturing material such that the magnetically-enabled part has a transition region including at least some of the magnetically permeable additive manufacturing material.
Hatch, Jonathan Douglas↗
Systems and methods for additive manufacturing magnetic solenoids
Systems and methods for forming a magnetically-enabled part via additive manufacturing. The method includes depositing a layer of additive manufacturing material on a build plate, melting or sintering the layer of additive manufacturing material, depositing additional layers of additive manufacturing material on previous layers of additive manufacturing material, the additive manufacturing material of at least some of the additional layers being magnetically permeable, and melting or sintering the additional layers of additive manufacturing material such that the magnetically-enabled part has a transition region including at least some of the magnetically permeable additive manufacturing material.
Hatch, Jonathan Douglas↗