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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 505 records · Page 28
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

A creep-resistant additively manufactured Al-Ce-Ni-Mn alloy

Existing additively manufactured aluminum alloys exhibit poor creep resistance due to coarsening of their strengthening phases and refined grain structures. In this paper, we report on a novel additively manufactured Al-10.5Ce-3.1Ni-1.2Mn wt.% alloy which displays excellent creep resistance relative to cast high-temperature aluminum alloys at 300–400 °C. In this work, the creep resistance of this alloy is attributed to a high volume fraction (~35%) of submicron intermetallic strengthening phases which are coarsening-resistant for hundreds of hours at 350 °C. The results herein demonstrate that additive manufacturing provides opportunities for development of creep-resistant aluminum alloys that may be used in bulk form in the 250–400 °C temperature range. Pathways for further development of such alloys are identified.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Deformation mechanisms in an additively manufactured dual-phase eutectic high-entropy alloy

Nanostructured metals and alloys often exhibit high strengths but at the expense of reduced ductility. Through harnessing the far-from-equilibrium processing conditions of laser powder-bed fusion (L-PBF) additive manufacturing, we develop a dual-phase nanolamellar structure comprised of FCC/L1 2 and BCC/B2 phases in a Ni 40 Co 20 Fe 10 Cr 10 Al 18 W 2 eutectic high-entropy alloy (EHEA), which exhibits a combination of ultrahigh yield strength (>1.4 GPa) and large tensile ductility (~17%). The deformation mechanisms of the additively manufactured EHEA are studied via in-situ synchrotron X-ray diffraction and high-resolution transmission electron microscopy. The high yield strength mainly results from effective blockage of dislocation motion by the high density of lamellar interfaces. The refined nanolamellar structures and low stacking fault energy (SFE) promote stacking fault (SF)-mediated deformation in FCC/L1 2 nanolamellae. The accumulation of abundant dislocations and SFs at lamellar interfaces can effectively elevate local stresses to promote dislocation multiplication and martensitic transformation in BCC/B2 nanolamellae. The cooperative deformation of the dual phases, assisted by the semi-coherent lamellar interfaces, gives rise to the large ductility of the as-printed EHEA. In addition, here we also demonstrate that post-printing heat treatment allows us to tune the non-equilibrium microstructures and deformation mechanisms. After annealing, the significantly reduced SFE and thicknesses of the FCC nanolamellae facilitate the formation of massive SFs. The dissolution of nano-precipitates in the BCC/B2 nanolamellae reduces spatial confinement and further promotes martensitic transformation to enhance work hardening. Our work provides fundamental insights into the rich variety of deformation mechanisms underlying the exceptional mechanical properties of the additively manufactured dual-phase nanolamellar EHEAs.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Process optimization of complex geometries using feed forward control for laser powder bed fusion additive manufacturing

Additive manufacturing (AM) enables the fabrication of complex designs that are difficult to create by other means. Metal parts manufactured by laser powder bed fusion (LPBF) can incorporate intricate design features and demonstrate desirable mechanical properties. However, printing a part that is qualified for its intended application often involves reprinting and discarding many parts to eliminate defects, improve dimensional accuracy, and increase repeatability. The process of iteratively converging on the appropriate build parameters increases the time and cost of creating functional LPBF manufactured parts. This work describes a fast, scalable method for part-scale process optimization of arbitrary geometries. Additionally, the computational approach uses feature extraction to identify scan vectors in need of parameter adaptation and applies results from simulation-based feed forward control models. This method provides a framework to quickly optimize complex parts through the targeted application of models with a range of fidelity and by automating the transfer of optimization strategies to new part designs. The computational approach and algorithmic framework are described, a software package is implemented, the method is applied to parts with complex features, and parts are printed on a customized open architecture LPBF machine.

3D printing↗
DOE OSTI2020

An additive manufacturing design approach to achieving high strength and ductility in traditionally brittle alloys via laser powder bed fusion

Additive Manufacturing (AM) presents unprecedented opportunities to enable design freedom in parts that are unachievable via conventional manufacturing. However, AM-processed components generally lack the necessary performance metrics for widespread commercial adoption. We present a novel AM processing and design approach using removable heat sink artifacts to tailor the mechanical properties of traditionally low strength and low ductility alloys. The design approach is demonstrated with the Fe-50 at.% Co alloy, as a model material of interest for electromagnetic applications. AM-processed components exhibited unprecedented performance, with a 300 % increase in strength and an order-of-magnitude improvement in ductility relative to conventional wrought material. These results are discussed in the context of product performance, production yield, and manufacturing implications toward enabling the design and processing of high-performance, next-generation components, and alloys.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Directed energy deposition additive manufacturing of functionally graded Al-W composites

Directed energy deposition using laser heat sources (DED-L) is a metal additive manufacturing method that can be used to fabricate functionally graded materials by feeding multiple powder streams directly into a laser-generated melt pool. Additionally, the composition can be spatially varied by independent control of the materials feed rates. DED-L additive manufacturing is used in this work to create continuously graded Al/W composites that span 0–55 vol% W in Al over length scales from 0.6 to 1.2 mm with individual layers on the order of 0.1 mm and powder particle sizes on the order of 10 µm. The obtained composition and thickness ranges are comparable to those typically achieved using conventional powder metallurgy approaches and enables fabrication of composites to be used as a high impedance graded density impactor (GDI) in dynamic ramp compression experiments to study matter under extreme conditions. DED-L additive manufacturing of a GDI presents an opportunity to reduce GDI fabrication time to less than a day compared to many weeks by conventional methods. It is also possible to scale the deposition by DED-L to essentially unlimited dimensions in the lateral directions, whereas conventional methods become limited.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Process-Structure-Property Relations for As-Deposited Solid-State Additively Manufactured High-Strength Aluminum Alloy

Solid-state additive manufacturing methods provide innovative solutions to circumvent problems associated with materials susceptible to hot cracking by avoiding liquid solid phase transformations. In this work, the process parameter influence on microstructural evolution and mechanical response of a fully dense aluminum alloy 7050 (AA7050) component manufactured via a rapid, solid-state additive manufacturing process known as Additive Friction Stir Deposition (AFS-D) was quantified for the first time. Three sections (starting dwell, transient, crossover of roads) of the deposition that exhibit differing thermomechanical processing histories were evaluated for the resulting microstructure and mechanical response. The microstructural characterization was performed on the as-deposited AA7050 via Electron Backscatter Diffraction (EBSD), TEM, optical microscopy, and Scanning Electron Microscopy (SEM). The microstructural characterization revealed refined constituent particles and grains throughout the as-deposited AA7050 microstructure. Furthermore, quasi-static tensile experiments were conducted in both the build and transverse directions, in order to quantify the orientation influence on tensile properties of the as-deposited AA7050 build. Spatially dependent tensile properties were observed in the material due to heat input variation coarsening of secondary phases towards the initial layers of the AFS-D build. Post-mortem analysis revealed that voids nucleated and coalesced from the overgrowth of the strengthening precipitates present in the material, resulting in fracture.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Large-scale additive manufacturing of self-heating molds

Large-scale material extrusion additive manufacturing technology is becoming the new mainstream technology for scaled-up composite mold and die applications. This paradigm shift in composite processing technology is primarily driven by out-of-autoclave tooling applications, in which fiber reinforced composite molds with scaled-up sizes and embedded heating elements are attractive. The present research describes the design, manufacturing, and testing of self-heating composite molds fabricated via a large-scale pellet extrusion 3D printing machine with an integrated wire co-extrusion tool. Polycarbonate (PC) composites reinforced with carbon fiber (PC/CF; 20 wt.%) and glass fiber (PC/GF; 20 wt.%) were used to fabricate mold parts. Joule heating thermal test results showed that uniform temperatures (~100 °C) were achieved for both PC/CF and PC/GF mold surfaces, using a custom-made feedback control power supply and infrared thermography. Mechanical characterizations, including tensile and flexural testing were performed on the wire-embedded and un-wired PC/CF and PC/GF base specimens to investigate the impact of the fiber reinforcement as well as the embedded wires. In the direction of extrusion, the ultimate tensile stress of PC/CF was 105 MPa, and that of PC/GF was 73 MPa, while the neat PC value was 64 MPa. Inner-bead voids and interfacial gaps were observed and characterized via optical and scanning electron microscopy. The embedded wires and inner bead impacted the mechanical properties of the composites. Furthermore, the stiffness of the wire-embedded mold was still satisfactory, proving that the technology can be used to fabricate additively manufactured out-of-oven/autoclave molds.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Recent advances in ink-based additive manufacturing for porous structures

The use of porous structures is an ancient wisdom, people found its importance ever since we began to understand nature. The evolution of porous structure gives rise to multi-scale (combined nano-, micro-, and macro-) porous networks. Traditional manufacturing technologies have challenges in creating shape-complex and conformable porous structures for real-world applications. Additive manufacturing (AM) with the capability of assembling various materials with complex and customized architectures is rapidly developing. With the advancement of material development, the integration of AM and porous structure offers unparalleled and emerging opportunities for concept-to-design-to-fabrication of multi-scale porous networks, enabling the multi-functionalities of the 3D printed specimen and broadening its applications. In this work, we provide a comprehensive review of the state-of-the-art ink-based AM techniques for 3D printing porous structures. The ink design principle, ink composition, and printability are thoroughly discussed. The methodologies of various AM technologies for porous structures are analyzed. Readers can find a clear experimental guidance toward 3D printing multi-scale porous structures. The synergistic and collective merits of additive manufacturing and porous structure are highlighted and systematically discussed in this review. The challenges and promises of this field for future research are also outlined.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Effect of N 2 - and CO 2 -containing shielding gases on composition modification and carbonitride precipitation in wire arc additive manufactured grade 91 steel

Additive manufacturing (AM) represents a promising technique to fabricate metallic alloys with greater control of the resulting material features as compared to traditional manufacturing routes. Recently, there is greater interest in AM research on 9 wt% Cr ferritic/martensitic (F/M) steels, which are commonly studied for use in the nuclear energy industry. This work aims to prove that wire arc AM can manufacture F/M steels with adequate mechanical properties in multiple processing atmospheres and aims to study how shielding gas composition can be leveraged during fabrication to induce specific precipitation pathways. The effect of shielding gas composition on MX (M=Nb and/or V, Xdouble bondC and/or N) carbonitride precipitation in a 9 wt% Cr ferritic/martensitic (F/M) steel alloy known as Grade 91 was studied using N 2 and CO 2 gas additions to an inert Ar shielding gas atmosphere during wire arc AM. The N and C atoms present in the processing atmospheres were absorbed into the melt pools during fabrication. Due to their differing affinities for precipitate-forming reactions, the varying levels of C and N between the samples contributed to differences in final carbonitride composition and morphologies. Such precipitate behavior is of interest as carbonitrides have been shown to contribute to increased mechanical performance. This increased performance was studied via electron microscopy and tested for strength, ductility, and fracture properties.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Creep deformation and cavitation in an additively manufactured Al-8.6Cu-0.4Mn-0.9Zr (wt%) alloy

Creep deformation and cavitation were investigated at 300 ºC in both tension and compression for an additively manufactured Al-8.6Cu-0.5Mn-0.9Zr (wt%) alloy in the as-fabricated state and after various aging treatments (aging at 300 °C/200 h or 350 °C/24 h and overaging at 400 °C/200 h). Creep mechanisms at 300 °C were determined by relating the measured creep response to corresponding microstructural and X-ray computed tomography observations. In compression, alloys in the as-fabricated and two aging conditions exhibited similarly high creep resistance. Overaging (400 °C/200 h) led to substantial coarsening of intragranular θ-Al2Cu precipitates and an expected drop in their Orowan strengthening contribution. In tension, minimum strain rates comparable to those in compression were obtained at any given stress; however, upon accumulation of some plastic strain in the matrix, creep cavities started to form, leading to accelerated tertiary stage creep deformation and rupture. Cavitation occurred exclusively along melt pool boundaries due to locally enhanced diffusion enabled by (i) large grain-boundary area in adjacent fine-grained zones and (ii) localization of creep strain in nearby heat-affected zones. Although cavity growth was initially diffusion-controlled, its rate was determined by matrix creep rate, consistent with constrained cavity growth mechanisms. This study reveals how microstructural complexities induced by the additive manufacturing process affect the creep and cavitation behavior of Al-Cu-Mn-Zr alloys. The underlying creep and cavitation mechanisms uncovered in this study point to pathways that improve the high-temperature properties of additively manufactured alloys.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Online thermal profile prediction for large format additive manufacturing: A hybrid CNN-LSTM based approach

Large format additive manufacturing (LFAM) is an advanced 3D printing technique that efficiently fabricates large-scale components through a layer-by-layer extrusion and deposition process. Accurate surface layer temperature monitoring is essential to prevent manufacturing failures and ensure final product quality. Traditional physics-based offline approaches for simulating thermal behavior are often inefficient and complex, posing challenges on real-time, in-situ monitoring. Here, to address this, we propose a data-driven hybrid CNN-LSTM model to predict sequential thermal images of arbitrary length using real-time infrared thermal imaging. In this approach, a Convolutional Neural Networks (CNN) is trained offline to capture spatial features, reduce dimensional complexity, and enhance time efficiency, while a stacked Long Short-Term Memory (LSTM) is applied online to capture temporal information for improved prediction of future thermal behavior in subsequent printing layers. Model performance is evaluated using MSE, SSIM, and PSNR metrics and is benchmarked against stacked LSTM and convolutional LSTM models, demonstrating superior accuracy and applicability. Additionally, to mitigate noise from moving extruders and gantry backgrounds in thermal images, a fine-tuned semantic segmentation model is implemented offline to extract printing geometry, enabling precise temperature tracking along the tool path for further thermal analysis. The frameworks developed in this study significantly advance temperature monitoring, thermal analysis, and in-situ manufacturing control for LFAM, bridging the gap between theoretical modeling and practical application.

Geometry extraction↗
DOE OSTI2026

A meshing framework for digital twins for extrusion based additive manufacturing

Additive manufacturing (AM) allows for manufacturing of complex three-dimensional geometries not typically realizable with standard manufacturing practices. The internal microstructure of AM components has a significant impact on mechanical, vibrational, and shock properties and permits richer design space when this is controllable. Due to complex interactions of internal geometry of an extrusion-based AM component, it is common practice to assume homogeneous behavior or to perform characterization testing on specific toolpath configurations. To avoid testing or material waste, it is necessary to develop a consistently accurate numerical simulation framework with relevant boundary value problems that can handle the complicated geometry of internal material microstructure present in AM components. Herein, a framework is proposed to directly create computational meshes suitable for finite element analysis (FEA) of the fine-scale features generated from extrusion-based AM tool paths to maintain a strong process–structure–property-performance linkage. This mesh can be manually or automatically analyzed using standard FEA simulations such as quasi-static preloading or modal analysis. The framework allows an in-silico assessment of a target AM geometry where fine-scale features greatly impact quantities of design interest such as in soft elastomeric lattices where toolpath infill can greatly influence the self-contact of a structure in compression, which we use as a motivating exemplar. This approach greatly reduces both time and resource waste present in traditional build and test design cycles for non-intuitive design spaces, and acts as a tool for use in the production of a key component of a digital twin, a mesh suitable for finite element analysis. In conclusion, it also further allows for the exploration of toolpath infill to optimize component properties beyond simple linear properties such as density and stiffness.

Additive manufacturing↗
DOE OSTI2020

Grinding of additively manufactured silicon carbide surfaces for optical applications

Many of the properties of Silicon carbide (SiC) are advantageous for optical applications, such as telescope mirrors and industrial laser systems. However, the base shapes of complex components are costly and difficult to manufacture. Leveraging additive manufacturing, near net complex components are readily processed. In this work, we investigate the post processing of additively manufactured SiC (AM SiC) compared to chemical vapor deposited (CVD) SiC. The specific grinding energy for the AM SiC was lower than CVD, however the trends were the same. A specular finish was observed on both materials but the AM SiC finish was limited due to residual porosity.

36 MATERIALS SCIENCE↗
DOE OSTI2023

A framework for hybrid manufacturing cost minimization and preform design

Here, this paper describes preform design optimization in hybrid additive-subtractive manufacturing. In hybrid manufacturing, the question of what form and what geometry the additive preform should take has largely been a matter of intuition and experience, or trial and error. The choice of a more optimal preform depends on the target parameters, such as stiffness, cost, or lead time. We demonstrate a framework for preform optimization using static stiffness, and then the combined cost of additive and subtractive manufacturing, while respecting stable cutting conditions for the tool-part combination. The procedure is illustrated by comparing three preform geometries for a thin wall.

42 ENGINEERING↗
DOE OSTI2020

Kinetic Monte Carlo simulations of structural evolution during anneal of additively manufactured materials

Our experiments indicated that upon a post-processing anneal, an additively manufactured 316L stainless steel exhibits cubic grains rather than the conventional equiaxed grains. In this work, we have used kinetic Monte Carlo simulations to explore the origin of these cubic grains. First, we implemented a new kinetic Monte Carlo model in parallel code SPPARKS to simulate grain growth and recrystallization under a residual energy distribution. Our model incorporates physical properties and real-time, as opposed to generic properties and relative time. We further validated that our SPPARKS simulations reproduced the expected kinetic behavior of single-grain evolution. We then used the validated approach to simulate the anneal of an additively manufactured material under the same conditions used in our experiments. We found that the cubic grains can origin from a periodically varying residual energy that may be present in additively manufactured materials.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Spatiotemporal parallelization of an analytical heat conduction model for additive manufacturing via a hybrid OpenMP + MPI approach

The ability to do thermal simulations for entire additive manufacturing builds is a key computational problem facing the additive manufacturing community; however, complex numerical models considering multiple physical phenomena currently do not have the capacity for simulations at this scale. To this end, conduction only analytic models offer a viable approach due to the massive drop in computational expense. In this work, we extend an existing implementation which uses a governing equation which can be evaluated at any point in space and time. This implementation already utilizes OpenMP with a spatial decompositions scheme stemming from a melt pool tracking algorithm. Furthermore, we then combine this with a parallel in time (PinT) approach to make the problem highly parallelizable. The new scheme, which uses MPI for internode communication and OpenMP for intranode communication, is shown to scale very well across multiple computational nodes. This approach results in the ability to simulate the 3D solidification conditions for entire layers of additively manufactured parts in minutes making part scale thermal simulations more practical.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Understanding structure-processing relationships in metal additive manufacturing via featurization of microstructural images

Understanding and predicting accurate property-structure-processing relationships for additively manufactured components is important for both forward and inverse design of robust, reliable parts and assemblies. While direct mapping of process parameters to properties is sometimes plausible, it is often rendered difficult due to poor microstructural control. Exploring the direct relationship between processing conditions and microstructural features can thus provide significant physical insights and aid the overall design process. Here, in this study, we develop an automated high-throughput framework to simulate an uncertainty-aware additive manufacturing (AM) process, characterize microstructural images, and extract meaningful features/descriptors. A kinetic Monte Carlo (KMC) based model of the AM process is used to simulate microstructural evolution for a diverse set of experimentally relevant processing conditions. We perform a parametric study to explore the relationship between microstructural features and processing conditions. Our results indicate that a many-to-one mapping can exist between processing conditions and typical descriptors; therefore, multiple descriptors are thus necessary to unambiguously represent microstructural images. Our work provides crucial quantitative and qualitative in-formation that would aid in the selection of features for microstructural images. Featurized microstructures could then be utilized to build data-driven models for predictive control of microstructures and thereby properties of additively manufactured components.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Hybrid Modeling in the Era of Smart Manufacturing

Smart manufacturing (SM) is a new paradigm that allows manufacturing to enter its fourth revolution by exploiting state-of-the art sensing, communication and computation as the Industrial Internet of Things (IioT). Via the use of high-performance computing and advanced modeling, SM aims to improve the flexibility and adaptability of manufacturing. This paper addresses this trend by reviewing the combined use of data-driven and knowledge-enabled hybrid models (HM), and discusses how such techniques seamlessly fit in the SM platform. Furthermore, a discussion of the new paradigms of HM enabled by the SM platform is given, highlighting their importance in future large-scale applications of the SM platform.

42 ENGINEERING↗