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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 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Virtual Volumetric Additive Manufacturing (VirtualVAM)

Abstract Tomographic volumetric additive manufacturing (VAM) produces arbitrary 3D geometries by exposure of a rotating volume of photopolymer resin to tomographically‐patterned illumination. This enables high speed, layer‐less printing of parts from a wide range of photopolymers not amenable to layer‐by‐layer processes. Since the entire geometry is produced at once over the course of a few seconds to minutes, molecular diffusion length scales become significant to the printing process. Understanding these molecular reaction and diffusion processes is imperative for advancing VAM to a usable technology. These processes are experimentally very difficult to monitor and measure. Herein, VirtualVAM ‐ a simulation framework for modeling the tomographic VAM process, is developed and experimentally validated. VirtualVAM simulates reaction, diffusion, and heat generation processes over the course of a print with single‐voxel resolution. From a few experimentally‐determined input parameters and a set of images for projection, VirtualVAM is able to generate a large spatio‐temporal data set for any given tomographic VAM print. Using VirtualVAM, a number of experimentally‐unattainable aspects of the VAM process are investigated such as single‐voxel conversion profiles, effect of molecular oxygen, and stopping time determination. VirtualVAM also enables the optimization of exposure patterns to further improve contrast between in‐part and out‐of‐part delivered dose.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Layer Time Control for Large Scale Additive Manufacturing Using High Performance Computing

This work proposes to optimize an additive manufacturing AM process to reduce energy and printing cost. The polymer AM process is inherently dependent on the time-temperature history of each layer to maintain geometric tolerances and mechanical integrity. Our preliminary study shows that regression-based layer time control model using thermal images could result in up to 30% build time reduction for simple geometries. This proposed work would use high-performance computing (HPC) to couple the data-driven model with thermal simulation for better predicting layer temperature profiles, improving throughput of large-scale additive manufacturing, and reducing its energy cost. We have developed a method to optimize a layer deposition time (a.k.a. layer time) for large-scale AM via physics-based simulations. A long layer time leads to an over-cooled surface on which a new layer is deposited, and therefore, it may result in a weak bonding or debonding between layers, cracking, or warping. A short layer time leads to a high temperature of the structure due to insufficient cooling, and therefore, the structure may not be stiff enough and may collapse during manufacturing. Therefore, it is important to estimate the optimal layer time in additive manufacturing for a high-quality product. The temperature of a top layer right before deposition is recommended to be slightly higher than the glass temperature of the material. A temperature cooling was approximated to an exponential function of time, and the optimized layer time was obtained based on a target temperature while maintaining a minimal printing time. The material used is carbon fiber-reinforced polycarbonate (CF/PC), and the large-scale deposition system used is LSAM TM from Thermwood Corporation. Three different layer time cases were used for experiments, and a series of thermal images were obtained via an infra-red (IR) camera during the entire AM processes. AM process simulations were performed using a finite element method and the temperature profiles from the simulation were in good agreements with those from experiments. The layer time optimization was performed based on the temperature profiles from the simulations. A layer temperature with the optimal layer time was confirmed as the target temperature through simulation. In addition to the development of a layer time optimization method, we have developed a numerical framework for AM simulation with element activations in sync with toolpath, based on an open source finite element framework, DEAL.II. A major portion of this work was presented at SAMPE 2022 Conference and Exhibition on May 2022, and published in Proceedings of SAMPE 2022.

42 ENGINEERING↗
DOE OSTI2020

Post-process annealing of large-scale 3D printed polyphenylene sulfide composites

Large-scale extrusion-based additive manufacturing of high-performance thermoplastic composites like fiber reinforced polyphenylene sulfide (PPS) is well-suited for tooling applications to lower manufacturing costs and lead times. Autoclave tooling requires good mechanical performance at temperatures even above the glass transition temperature (T g ). In this work, the authors have investigated a post-process isothermal annealing technique to improve the mechanical properties of various grades of carbon fiber reinforced PPS components printed on the Big Area Additive Manufacturing (BAAM) system. Since PPS is a semi-crystalline polymer, crystallinity can change during annealing and affect the mechanical properties of the part. In addition, isothermal annealing can also lead to solid-state structural changes in the form of thermal and oxidative branching and/or crosslinking reactions in some grades of PPS which can alter the crystallization process. This work reports the effect of annealing on dynamic mechanical properties of BAAM printed components, along with studies to determine the effect of annealing on crystallinity and the occurrence of oxidative reactions. Results showed that isothermal annealing at 250 °C for 18 h improved the storage modulus of all selected grades (neat and reinforced) of PPS at temperatures above T g . Annealing led to an overall increase in the degree of crystallinity, with secondary crystallization taking place. Although oxidative structural changes were observed to occur more on the surface of the PPS parts, they primarily influenced the size of crystals formed and did not significantly alter the degree of crystallinity at various regions within the sample.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Location-Dependent Mechanical Property Evaluation on Additively Manufacture Materials

This report summarizes research activities conducted at Argonne National Laboratory in support of the development, qualification and certification of additively manufactured (AM) metallic components to allow for innovative reactor design and licensing for the Transformational Challenge Reactor (TCR). Focus is on the evaluation of high temperature mechanical properties including creep and fatigue of 316L stainless steel manufactured by the laser powder bed fusion process. Creep behavior of AM 316L stainless steel was evaluated for rods printed by either or a combination of both lasers of the dual-laser system of a Concept Laser-M2 printer to examine the consistency across the build plate. Creep tests were conducted at temperatures of 550, 575, 600, and 650°C and stresses between 175 and 300 MPa using ASTM standard-sized specimens. The effect of heat treatments at different temperatures on creep properties of AM 316L SS was examined to understand the processing-microstructure-property relationship. Fatigue properties of AM 316L SS were investigated for two print geometries (rod and plate) and in two build orientations of printed plates. Locations of specimens in the build were carefully tracked such that the testing data can be directly related to location-specific in situ data to establish links between printing process, post-printing treatment, microstructure and mechanical properties. The work is to support the development of a digital platform informed approach to AM component qualification and certification for nuclear applications.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

Additive manufacturing of complexly shaped SiC with high density via extrusion-based technique – Effects of slurry thixotropic behavior and 3D printing parameters

Here, additive manufacturing of dense SiC parts was achieved via an extrusion-based process followed by electrical-field assisted pressure-less sintering. The aim of this research was to study the effect of the rheological behavior of SiC slurry on the printing process and quality, as well as the influence of 3D printing parameters on the dimensions of the extruded filament, which are directly related to the printing precision and quality. Different solid contents and dispersant- Darvan 821A concentrations were studied to optimize the viscosity, thixotropy and sedimentation rate of the slurry. The optimal slurry was composed of 77.5 wt% SiC, Y 2 O 3 and Al 2 O 3 powders, 0.25 wt% dispersant and 0.01 wt% defoamer. The printing parameters studied included extrusion pressure, nozzle size, layer height and printing speed; the one that had the most prominent effect on filament width and height was indicated as layer height. The nozzle inner diameter of 1.04 mm, speed of 350 mm/min, layer height of 0.7 mm and extrusion air pressure of 0.31 MPa were the optimal printing parameters. Furthermore, the relationship between the printing parameters and the filament dimensions was successfully predicted by using machine learning and grey system theory. Finally, the relative density of the printed SiC parts sintered at 1900 oC reached 94.7±1.5%.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Additive manufacturing of LiNi 1/3 Mn 1/3 Co 1/3 O 2 battery electrode material via vat photopolymerization precursor approach

Additive manufacturing, also called 3D printing, has the potential to enable the development of flexible, wearable and customizable batteries of any shape, maximizing energy storage while also reducing dead-weight and volume. In this work, for the first time, three-dimensional complex electrode structures of high-energy density LiNi 1/3 Mn 1/3 Co 1/3 O 2 (NMC 111) material are developed by means of a vat photopolymerization (VPP) process combined with an innovative precursor approach. This innovative approach involves the solubilization of metal precursor salts into a UV-photopolymerizable resin, so that detrimental light scattering and increased viscosity are minimized, followed by the in-situ synthesis of NMC 111 during thermal post-processing of the printed item. The absence of solid particles within the initial resin allows the production of smaller printed features that are crucial for 3D battery design. The formulation of the UV-photopolymerizable composite resin and 3D printing of complex structures, followed by an optimization of the thermal post-processing yielding NMC 111 is thoroughly described in this study. Based on these results, this work addresses one of the key aspects for 3D printed batteries via a precursor approach: the need for a compromise between electrochemical and mechanical performance in order to obtain fully functional 3D printed electrodes. In addition, it discusses the gaps that limit the multi-material 3D printing of batteries via the VPP process.

25 ENERGY STORAGE↗
DOE OSTI2026

Establishing an acoustic-property relationship in laser powder bed fusion with machine learning

Quality control of Laser Powder Bed Fusion (PBF-LB) additively manufactured parts is an important hurdle inhibiting the technology’s use structural applications. Acoustic monitoring of the laser powder bed fusion process can detect defects in-situ that are known to degrade mechanical properties. However, processing-structure-property (PSP) relationships are required to extrapolate from detected defects to part performance. Here, this study explores how acoustics may be a suitable signature linking processing conditions to properties, thus effectively substituting for structure in the PSP relationship. Establishing such a relationship would enable a part’s mechanical performance to be directly predicted from its acoustic signature, reducing the need for destructive testing or microstructural analysis to ensure a part will meet performance requirements. One hundred CoCrFeMnNi high entropy alloy tensile bars were printed across 13 process conditions in a series of 6 prints. The acoustic signatures of these tensile bars were used to train machine learning models to predict each part’s mechanical properties. By using both process information and acoustic information to predict mechanical properties, yield strength was predicted 18% more accurately and ductility to failure was predicted 10% more accurately than is achieved when using duplicate parts to predict part performance. Finally, individual acoustic frequencies were investigated to determine why acoustic signatures improve mechanical property predictions and the potential physical origins of these signatures. This work demonstrates how blending acoustics, process information, and machine learning can provide in-situ diagnostics of mechanical properties and improve the reliability of the PBF-LB process.

Acoustic emission↗
DOE OSTI2023

Selective Laser Sintering of Polyolefins

Polyolefins are some of the most produced and used polymers around the globe. They are semi-crystalline thermoplastic polymers known for their low production cost while still exhibiting good mechanical properties and chemical resistance. These polymers are often used in a variety of manufacturing processes, including 3D printing or additive manufacturing (AM). However, due to their semi-crystalline nature, there are often issues with using polyolefins in AM processes (for example, shrinkage leading to dimensional instabilities). One example of an AM process for which polyolefins are of interest is selective laser sintering (SLS), where printability depends on the powdered material’s morphology, flowability, and size. Due to the inherent properties of polyolefins, there are a variety of issues surrounding their use in the SLS process. This paper aims to review the basics of SLS as well the current state of SLS printing of a few polyolefins and what challenges that would need to be overcome to print with these polyolefins.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Vehicle Technology Office (Quarterly Report, FY20 Q2)

This project has three objectives: (1) tuning microstructures of 3D-printed SSE separators, (2) determining material and processing compatibilities with cathode printing, and (3) 3D printing of sintering-free SSE separators.

36 MATERIALS SCIENCE↗
DOE OSTI2021

VTO battery project (Quarterly Report)

This project has three objectives: (1) tuning microstructures of 3D-printed SSE separators, (2) determining material and processing compatibilities with cathode printing, and (3) 3D printing of sintering-free SSE separators.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Vehicle Technologies Office Quarterly Report (FY21Q2)

This project has three objectives: (1) tuning microstructures of 3D-printed SSE separators, (2) determining material and processing compatibilities with cathode printing, and (3) 3D printing of sintering-free SSE separators.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Combining In-situ Diagnostics and Data Analytics for Discovery of Process-Structure-Property Relationships in AM parts – A Step Toward Digital Twins

In-situ additive manufacturing (AM) diagnostic tools (e.g., optical/infrared imaging, acoustic, etc.) already exist to correlate process anomalies to printed part defects. This current work aimed to augment existing capabilities by: 1) Incorporating in-situ imaging w/ machine learning (ML) image processing software (ORNL- developed "Peregrine") for AM process anomaly detection 2) Synchronizing multiple in-situ sensors for simultaneous analysis of AM build events 3) Correlating in-situ AM process data, generated part defects and part mechanical properties The key R&D question investigated was to determine if these new combined hardware/software tools could be used to successfully quantify defect distributions for parts build via SNL laser powder bed fusion (LPBF) machines, aiming to better understand data-driven process-structure-property- performance relationships. High resolution optical cameras and acoustic microphones were successfully integrated in two LPBF machines and linked to the Peregrine ML software. The software was successfully calibrated on both machines and used to image hundreds of layers of multiple builds to train the ML software in identifying printed part vs powder. The software's validation accuracy to identify this aspect increased from 56% to 98.8% over three builds. Lighting conditions inside the chamber were found to significantly impact ML algorithm predictions from in-situ sensors, so these were tailored to each machine's internal framework. Finally, 3D part reconstructions were successfully generated for a build from the compressed stack of layer-wise images. Resolution differences nearest and furthest from the optical camera were discussed. Future work aims to improve optical resolution, increase process anomalies identified, and integrate more sensor modalities.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Multiphysics simulation of microscale copper printing by confined electrodeposition using a nozzle array

3D printing of metals at the microscale and nanoscale is crucial to produce high-density interconnects and intricate structures in electronic devices. Conventional melting and sintering processes are not suitable for these scales due to a reliance on individual metal particles in the size range of tens of micrometers. Confined electrodeposition (CED) is an established alternative to conventional metal 3D printing processes in which an electrolyte is used to selectively induce deposition of the metal on the printing surface. However, commercialization and efficiency of this process have been limited due to a reliance on sub-micrometer nozzles to achieve desirable deposition rates and single nozzle to achieve uniformity of printed structures. Here, we address these challenges by computationally analyzing an array of microscale nozzles. We tailor the convection within the electrolyte to alter both deposition rate and geometric uniformity of the printed structures. The results show that for large nozzles the evaporation alone is not sufficient to obtain high deposition rates, yet an external pressure can be used to increase deposition and alter uniformity (thickness) of printed structures. Our results can be used to design and analyze new experiments toward parallel multi-nozzle deposition using CED toward high-throughput metal printing.

3D printing↗
DOE OSTI2023

High fidelity model of directed energy deposition: Laser-powder-melt pool interaction and effect of laser beam profile on solidification microstructure

Metal additive manufacturing technologies keep receiving a great deal of interest as well as strong requests to develop methods to link the process science to printed parts performance and understand how to overcome inherent limitations. Here, a high-fidelity model based on the multiphysics ALE3D code was developed to reproduce the directed energy deposition process down to the powder scale. This includes resolving the laser-powder-melt pool interactions (powder impingement and incorporation into melt pool, hydrodynamics flow condition and laser absorption inefficiencies) as well as the resulting solidification microstructure. This micrometer scale digital twin captured the effect of powder incorporation process and powder flow rate on porosity. Furthermore, it was used to explore how a ring laser beam profile instead of the standard Gaussian laser profile could decrease the thermal gradient along the solidification front in the melt pool, which in turn can increase propensity for more desirable equiaxed grains.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Inkjet-printed SnO x as an effective electron transport layer for planar perovskite solar cells and the effect of Cu doping

Inkjet printing is a more sustainable and scalable fabrication method than spin coating for producing perovskite solar cells (PSCs). Although spin-coated SnO 2 has been intensively studied as an effective electron transport layer (ETL) for PSCs, inkjet-printed SnO 2 ETLs have not been widely reported. Here, we fabricated inkjet-printed, solution-processed SnO x ETLs for planar PSCs. A champion efficiency of 17.55% was achieved for the cell using a low-temperature processed SnO x ETL. The low-temperature SnO x exhibited an amorphous structure and outperformed high-temperature crystalline SnO 2 . The improved performance was attributed to enhanced charge extraction and transport and suppressed charge recombination at ETL/perovskite interfaces, which originated from enhanced electrical and optical properties of SnO x , improved perovskite film quality, and well-matched energy level alignment between the SnO x ETL and the perovskite layer. Furthermore, SnO x was doped with Cu. Cu doping increased surface oxygen defects and upshifted energy levels of SnO x , leading to reduced device performance. A tunable hysteresis was observed for PSCs with Cu-doped SnO x ETLs, decreasing at first and turning into inverted hysteresis afterwards with increasing Cu doping level. This tunable hysteresis was related to the interplay between charge/ion accumulation and recombination at ETL/perovskite interfaces in the case of electron extraction barriers.

14 SOLAR ENERGY↗