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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 631 records · Page 35
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Dynamics of pore formation during laser powder bed fusion additive manufacturing

Abstract Laser powder bed fusion additive manufacturing is an emerging 3D printing technique for the fabrication of advanced metal components. Widespread adoption of it and similar additive technologies is hampered by poor understanding of laser-metal interactions under such extreme thermal regimes. Here, we elucidate the mechanism of pore formation and liquid-solid interface dynamics during typical laser powder bed fusion conditions using in situ X-ray imaging and multi-physics simulations. Pores are revealed to form during changes in laser scan velocity due to the rapid formation then collapse of deep keyhole depressions in the surface which traps inert shielding gas in the solidifying metal. We develop a universal mitigation strategy which eliminates this pore formation process and improves the geometric quality of melt tracks. Our results provide insight into the physics of laser-metal interaction and demonstrate the potential for science-based approaches to improve confidence in components produced by laser powder bed fusion.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Development of a Multi-physics Model to Optimize Continuous Liquid Interface Production (CLIP) for Additive Manufacturing (Carbon3D HPC4Mfg Final Report)

Continuous Liquid Interface Production (CLIP) is poised to bring additive manufacturing to multiple American manufacturing sectors owing to its unique combination of rapid print speeds and material options that resemble injection molding thermoplastics. In spite of these benefits, CLIP is still a maturing process. To improve our understanding and control of the process, this project developed a multi-physics computational model that encompasses the coupled chemical-physical processes of photopolymerization and fluid flow to predict part outcomes. A physically predictive model can enable rapid optimization of CLIP and reduce the current cycle time and waste associated with optimization by four-fold. Ultimately, this effort will help open the U.S. $400B plastic manufacturing industry to the key benefits of additive manufacturing, namely mass customization, unlimited design space, and a cost- and energy-effective path to mainstream manufacturing. In the course of the project we produced a model which coupled fluid-flow, species transport, and photochemistry to model the Carbon CLIP process. As the development proceeded, we validated the code against theory and experiments. The resulting code was transferred to Carbon, and we expect it to be a useful part of their modeling capability. Future work includes improvements to the fluid solver’s robustness and performance, along with the addition of additional physics models.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Multi-physics modeling of grain growth during solidification in electron beam additive manufacturing of Inconel 718

While experimental work has shown promising results regarding control of additive manufacturing metal grain structure, the effects of processing parameters on the grain structure is difficult to understand and predict from experiment alone. To this end, a modeling framework is developed which sequentially couples a macro-scale, semi-analytic thermal model, and a meso-scale, cellular automata-based microstructure model. This framework is applied to electron beam additive manufacturing of Inconel 718 using a complex spot scan pattern. The model shows that, with the same scan pattern, variations in the spot time and electron-beam current produce thermal histories with significant spatial and temporal differences, which then produce complex solidification conditions from the interplay between molten pools in the same layer and subsequent layers, resulting in vastly different grain structures. Additionally, it is noted that the framework can significantly reduce the computational expenses for coupled thermal-metallurgical problems, and has the potential to be used for component level problems.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Additively manufactured polymer composites for heat exchanger applications: evaluation of critical thermophysical properties

Polymeric heat exchangers (PHXs) have been used in applications involving weight restrictions, chemical compatibility, and fouling issues. Additive manufacturing (AM) or 3D printing provide new solutions to previously inaccessible combinations of properties and geometries. There are some advancements in the PHXs by AM; however, the process and the properties of materials still need further investigation to improve the overall performance. In this study, additively manufactured polyethylene terephthalate glycol (PETG) composites reinforced with graphite and pitch-based carbon fibers were evaluated for their potential application as PHXs. The thermal conductivity, volumetric heat capacity, coefficient of thermal expansion, creep behavior, and long-term performance were studied in detail. Our results reveal that the composites have an anisotropic thermal conductivity. The thermal conductivity along the printing direction is higher than the layer building direction due to the shear-induced alignment of the fillers. The printed composites achieve good thermal stability with 80% lower CTE at room temperature than neat PETG. Creep tests suggest the creep and creep recovery were highly temperature-dependent, and the deformation can be recovered when the temperature is below glass transition. These results suggest additive manufactured composites be potentially used for heat exchanger applications at low temperatures.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Combined DSC/TGA Analysis to Rationalize the As-Sintered Microstructure in Binder Jet Additively Manufactured H13 Tool Steel

In this study, a combined DSC/TGA analysis has been used to rationalize the constant liquid channel width at higher sintering temperatures in H13 tool steel manufactured by binder jet additive manufacturing. Using enthalpy conservation along with coupled DSC + TGA analysis, the phase fractions of liquid and austenite phases during heating and cooling respectively were estimated. The estimates from the DSC/TGA analysis were compared with equilibrium phase fraction predictions from ThermoCalc. The resulting phase evolution was correlated to the as-sintered microstructure to rationalize the unexpected lack of change in liquid channel width at higher sintering temperatures.

Kannan, Rangasayee↗
DOE OSTI2017

Determination of Process Parameters for High-Density, Ti-6Al-4V Parts Using Additive Manufacturing

In our earlier work, we described an approach for determining the process parameters that re- sult in high-density parts manufactured using the additive-manufacturing process of selective laser melting (SLM). Our approach, which combines simple simulations and experiments, was demon- strated using 316L stainless steel. We have also used the approach successfully for several other materials. This short note summarizes the results of our work in determining process parameters for Ti-6Al-4V using a Concept Laser M2 system.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Advanced Materials & Manufacturing Technology (AMMT): Development of Additive Manufacturing Agnostic Process Parameter Procedure, 316H Stainless Steel Readiness Level Data Sets, and Machine Maintenance Plan

The University of California, Davis is involved in a project to deploy and enhance an artificial intelligence (AI) system for predicting and preventing plasma disruptions on the DIII D tokamak, under the funding from Department of Energy DE-SC0023500 (title: AI/Deep Learning FRNN Software for Prediction & Real-Time Control of DIII-D Plasma Control System (PCS)). The overarching goal is to demonstrate that real-time, AI-guided intervention can proactively modify the plasma state to avoid or mitigate disruptions—a critical challenge for the future of fusion energy.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Reclaimed Carbon Fiber Reinforced Automotive Part Using 3-DEP® Preforming Technology on Additive Manufacturing Tool Made with Reclaimed Carbon Fibers

The project focused on how well tools designed and built using reclaimed carbon fiber on the Big Area Additive Manufacturing (BAAM) can be used to manufacture preforms made with reclaimed carbon fibers. The project supported multiple Institute for Advanced Composite Manufacturing Innovation (IACMI) goals: • Enables the use of recycled carbon fibers in two areas: o Tooling material compounded with reclaimed carbon fiber. o Preforms made with recycled carbon fiber (rCF). • Lowers manufacturing costs by producing low cost 3-DEP® preform tooling. • Reduces manufacturing cycle time with quick additive manufacturing techniques. • Produces lightweight automotive components that will increase fuel economy which will in turn reduce greenhouse gas emissions. The project demonstrated a tool made from reclaimed carbon fibers, gathered technical data that will guide optimization of tooling materials, evaluated preforms made with the BAAM printed tool, and developed cost evaluations. This project has been a total success. First the project demonstrated that reclaimed carbon fiber can be compounded and successfully printed in the BAAM equipment. Second preforms were successfully made from reclaimed carbon fiber using a reclaimed fiber printed tool. Third the team successfully printed a molding tool out of reclaimed carbon fiber. Fourth the project went beyond the scope of this phase I project in 3 areas: (1)- Techmer PM compounded rCF in 2 resin systems instead of just one system (2)- Local Motors evaluated and collected data that will support their IACMI 3.6 project (Robert Bedsole, 2017) . (3)- University of Tennessee Knoxville (UTK) molded a part using the reclaimed preform on a molding tool made from printed reclaim fiber.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Wire-arc Additive Manufacturing Benchmark

This is the dataset associated with the 2022 SRP Additive Manufacturing Prediction Challenge, originally hosted on Github at https://github.com/SRP-AM/SRP_AM_Prediction_Challenge. The benchmark was designed for validating prediction for the temperature history, residual stress, and distortion of an additively manufactured metal part with relatively simple geometry. A calibration problem with the same as-built geometry is provided with measured quantities of interest; including temperature histories at selective locations, post-build residual stress at selective locations, and overall distortion measurements. The challenge problem is presented with a different build sequence (i.e. thermal history). In this dataset, we include the actual recorded calibration and challenge measurements, as well as benchmark template files for testing predictions without incorporating the challenge data. Supplementary files around the materials and setup are available for transparency and reproducibility.

Bachus, Nicholas [UC Davis, Davis, CA]↗
DOE OSTI2022

X-ray Computed Tomography as a Metrology Technique for the Analysis of Additively Manufactured Material

X-ray computed tomography (X-ray CT) is an analytical technique used in materials science to non-destructively characterize features in a variety materials like polymer, metals, composites, and explosives. It also has the capability of imaging additively manufacture, machine and assembled parts. The non-destructive imaging allows for the analysis of features (voids and cracks), which give a fundamental understanding of the material characteristics. Additionally, X-ray CT can obtain accurate measurements of dimensional and topographic variations due to different stimuli and assess the accuracy of material production. This study focuses on parts manufactured via metal additive manufacturing (AM). Although AM produces parts faster and easier, the printing process can produce defects (pores and surface roughness) that undermine the part’s mechanical properties and performance. The analysis of 3D printed objects has an asset in that the material has an STL file from which the item was printed, which is not available in many manufactured materials (i.e., foams) due to stochastic structures. For this study, the print accuracy of four additively manufactured cylinders will be assessed via X-ray CTto approximate the surface roughness and visualize any major morphological changes to assess the dimensional accuracy of complex additively manufactured parts. It was concluded that using X-ray CT to measure surface roughness was affective because reasonable surface roughness values were measured. Additionally, itwas determined that small-scale features can be produced via additive manufacturing with strong dimensional accuracy so long as the features are highly complex with sharp grooves.

36 MATERIALS SCIENCE↗
DOE OSTI2022

When physics-informed data analytics outperforms black-box machine learning: A case study in thickness control for additive manufacturing

Aerosol jet printing (AJP) has emerged as a promising noncontact additive manufacturing method for high-resolution printing for a wide range of material systems. A key challenge limiting the broader adoption of AJP in the material science community is the lack of methods to precisely control thickness. Herein, we develop a model-based design of experiment (MBDoE) framework that integrates physics-informed models, nonlinear regression, and information criteria to postulate, select and calibrate the best model to describe and optimize the AJP manufacturing process. Starting with already available data from system commissioning (e.g., prior single variable sensitivity analysis), four candidate physics-informed models are postulated and trained. MBDoE identifies a single additional optimal experiment to validate these predictive models with quantified uncertainties, which are then used to determine the best experimental conditions to control printed film thickness. As a comparative benchmark, the analysis is repeated using the same dataset with nonparametric Gaussian process regression (GPR) model that does not incorporate physical information. Using MBDoE principles, we find that only five experiments are necessary to calibrate the nonlinear physics-informed parametric model, and with said limited data, this model outperforms the black-box machine learning GPR model. This key result underscores an emerging trend in the data science community: incorporating physical information into predictive models often drastically reduces the data requirements. Leveraging MBDoE further increased the data efficiency. By design, the proposed data science framework is general in nature and can be easily extended to other experimental and additive manufacturing systems beyond AJP.

Aerosol jet printing↗
DOE OSTI2016

CRADA/NFE-15-05761 Report: Additive Manufacturing of Isotropic NdFeB Bonded Permanent Magnets

The technical objective of this technical collaboration phase I proposal is to fabricate net shape isotropic NdFeB bonded magnets utilizing additive manufacturing technologies at the ORNL MDF. The goal is to form complex shapes of thermoplastic and/or thermoset bonded magnets without expensive tooling and with minimal wasted material. Two additive manufacturing methods; the binder jet process; and big area additive manufacturing (BAAM) were used. Binder jetting produced magnets with the measured density of the magnet of 3.47 g/cm 3 , close to 46% relative to the NdFeB single crystal density of 7.6 g/cm 3 were demonstrated. Magnetic measurements indicate that there is no degradation in the magnetic properties. In addition, BAAM was used to fabricate isotropic near-net-shape NdFeB bonded magnets with magnetic and mechanical properties comparable or better than those of traditional injection molded magnets. The starting polymer magnet composite pellets consist of 65 vol% isotropic NdFeB powder and 35 vol% polyamide (Nylon-12). The density of the final BAAM magnet product reached 4.8 g/cm 3 , and the room temperature magnetic properties are: Intrinsic coercivity Hci = 8.65 kOe, Remanence Br = 5.07 kG, and energy product (BH) max = 5.47 MGOe (43.50 kJ/m 3 ). This study provides a new pathway for preparing near-net shape bonded magnets for various magnetic applications.

36 MATERIALS SCIENCE↗
DOE OSTI2024

New insights into the mechanism of ultrasonic atomization for the production of metal powders in additive manufacturing

Ultrasonic atomization is one of the promising technologies for producing metal powders for additive manufacturing, where precise control of particle size and morphology is essential. In this study, we coupled an ultrasonic transducer with a carbon fiber plate and atomized liquid droplets and films under different vibration amplitudes. Water, glycerol, and pure aluminum melt were used to study the atomization mechanism and the resulting droplet/powder characteristics, respectively. High-speed optical and ultrafast synchrotron X-ray imaging were used to study in situ the ultrasonic atomization dynamics, including pulsation and clustering of cavities inside the liquid layer/films, development of capillary waves, and formation of liquid droplets. For the first time, we observed and captured the occurrence of cavitation during the atomization of resting drops, films and impact droplets. The inertial cavitation events interfered with the capillary waves across the interphase boundary, puncturing and breaking the boundary to produce atomized mist. The in situ observation revealed the intricate dynamics of ultrasonic atomization and underscored the pivotal role of cavitation events throughout the entire atomization process. We also conducted experiments on ultrasonic atomization of liquid aluminum, producing particles of perfectly spherical shape. The particle size tended to decrease with reduced vibration amplitude Our work has demonstrated the important processing strategies on how to tailor the particle size while ensuring consistent particle shape and morphology, which is the key processing capability for producing high quality powders for additive manufacturing applications.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Computational Tools for Additive Manufacture of Tailored Microstructure and Properties

Additive manufacturing has the potential to revolutionize industrial hardware and unlock efficiency gains through the fabrication of geometries and architectures not possible by conventional processing. Currently most additive builds use a single set of process parameters (e.g. laser power and scan speed) which results in a part with a homogenous microstructure that provides a singular performance level. To move beyond this state, Raytheon Technologies Research Center worked to create a set of computational tools to track material evolution through each step of the additive process. Computational fluid dynamics and phase field models for microstructure evolution as a function of processing parameters, and crystal plasticity models fully coupling microstructure and mechanical properties for performance predictions were leveraged to establish a connection between additive parameters and the final microstructure. This framework was utilized to tailor spatially-varying mechanical properties in a part by appropriately controlling the microstructure evolution during the additive process. Specifically, a turbine blade was 3D printed from nickel superalloy IN718 using laser powder bed fusion with coarse grains in the airfoil section which experiences the highest temperatures and is creep limited while finer grains were printed in the root of the blade which experience higher stresses but at lower temperatures and is therefore fatigue limited. The benefit of being able to intentionally insert coarse grains in the high temperature region of the blade was showcased with a microstructure sensitive creep model that indicates longer creep life for coarser grains.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2020

Utility of Big Area Additive Manufacturing for Part Production for Low-Head Hydropower (CRADA NFE-18-07280 Final Report)

ORNL worked with Cadens, LLC, to explore the use of additive manufacturing (AM) to produce low-cost parts for low-head hydropower systems. Cadens develops design optimization software that leverages the flexible, low-cost, high-strength benefits of AM and composite materials. Cadens also operates a micro-hydro lab to test AM components in controlled environments. Until now, Cadens’ tests have been limited to components of a size that they can cost-effectively manufacture using local commercial systems. This project provided an opportunity for Cadens to scale up their modular AM hydropower parts using the capabilities of Big Area Additive Manufacturing (BAAM). This project was a success and resulted in the design and fabrication of several end-use parts of a hydropower system, using a Big Area Additive Manufacturing (BAAM) system. The fabricated parts include a draft tube, thimble, runner housing mold, PVC end fitting and two PVC pipe supports. In addition, a runner system was fabricated using fused deposition modeling on a 3D Platform Workbench 400 Series System.

13 HYDRO ENERGY↗
DOE OSTI2020

Toward large-scale simulation of residual stress and distortion in wire and arc additive manufacturing

In this study, we aim to advance the structural analysis of wire and arc additive manufacturing (WAAM) by considering the thermomechanical features inherent in direct energy deposition. Simulation approaches including the iterative substructure method (ISM), dynamic mesh refining method (DMRM), and graphics processing unit (GPU) based explicit finite element method (FEM) were developed for accelerating additive manufacturing stress analysis that is very time consuming by conventional numerical methods. ISM and DMRM take advantage of the strong nonlinearity phenomenon near the moving heat source by reducing the global iterations and refining the local mesh, respectively. In addition to the use of GPUs, the explicit FEM is accelerated by a time scaling technique based on the inherent strain concept. The residual stress and distortion of two large builds were analyzed, showing very consistent numerical results and good agreement with experiments. Compared with the commercial software Abaqus, the novel approaches reduced the computational cost substantially without compromising accuracy. The GPU code showed the highest computational efficiency (30~70×), while DMRM and ISM had acceleration factors of 9× and 3×, respectively. Finally, such high-fidelity modeling approaches will be very useful for building up a digital twin of WAAM to reduce development time and cost.

36 MATERIALS SCIENCE↗