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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 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Characterization of ablator dynamics initiated by picket-pulse conditions

Direct-drive laser-fusion targets may include low-cost plastic ablator material. Here, this work describes a tabletop experimental system designed to enable the investigation of the interaction of such ablator material as a function of the laser wavelength and the associated excitation mechanism. This experimental system offers time-resolved imaging of the material response with subpicosecond temporal resolution and ≈1-μm spatial resolution. The system involves synchronized femtosecond and picosecond lasers to initiate and characterize the dynamics of polystyrene planar targets irradiated by conditions similar to a “picket” prepulse from a direct-drive pulse shape. Ultrafast imaging of the initial excitation process followed by the resulting critical plasma formation, shock-wave propagation, and blowoff plasma expansion is used to compare the impact of excitation wavelength, 355 nm or 266 nm, on the ensuing dynamics. This work aims to investigate fundamental laser material interactions to help advance physics models that can inform the development of laser driver and target designs.

42 ENGINEERING↗
DOE OSTI2022

Investigating radiatively driven, magnetized plasmas with a university scale pulsed-power generator

We present first results from a novel experimental platform that is able to access physics relevant to topics including indirect-drive magnetized inertial confinement fusion, laser energy deposition, various topics in atomic physics, and laboratory astrophysics (for example, the penetration of B-fields into high energy density plasmas). This platform uses the x rays from a wire array Z-pinch to irradiate a silicon target, producing an outflow of ablated plasma. The ablated plasma expands into ambient, dynamically significant B-fields (∼5 T), which are supported by the current flowing through the Z-pinch. The outflows have a well-defined (quasi-1D) morphology, enabling the study of fundamental processes typically only available in more complex, integrated schemes. Experiments were fielded on the MAGPIE pulsed-power generator (1.4 MA, 240 ns rise time). On this machine, a wire array Z-pinch produces an x-ray pulse carrying a total energy of ∼15 kJ over ∼30 ns. This equates to an average brightness temperature of around 10 eV on-target.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Part-scale microstructure prediction for laser powder bed fusion Ti-6Al-4V using a hybrid mechanistic and machine learning model

Laser powder bed fusion (LPBF) Ti-6Al-4V is widely studied for use in structural applications in aerospace and medical industries, but mechanical anisotropy and microstructural inhomogeneity prohibits its wider adoption. Although successful microstructure prediction models have been developed, a remaining challenge is their limited integration across length/time scales and validation by experimental studies. Here, this work proposes a physics-augmented machine learning surrogate model to unite predictions of LPBF temperature, β phase morphology and texture, and α/α’ formation into a single framework that is calibrated and validated with experiments. First, a phase field (PF) model of the martensitic β→α’ transformation is developed and calibrated using data from in-situ synchrotron cyclic heating/cooling studies quantifying the variation of α phase fraction with time. In parallel, an established finite difference-Monte Carlo (FDMC) model predicts the part-scale temperature profile and β grain formation during solidification. A dataset is developed using LPBF cyclic temperature descriptors from the FDMC model as inputs and corresponding α/α’ phase fraction and width from the PF model as outputs. Five machine learning (ML) regression models are tested and optimized, having mean absolute error in testing ≤ 4 %, and the k-nearest neighbors (KNN) model is selected as the best performing. The KNN model is called at the nodal level during post-processing of the FDMC model to replace and downscale the response of the PF model. The combined agility and accuracy of the hybrid FDMC-ML model enables part-scale microstructure predictions that can be further used for property predictions to accelerate AM process optimization.

36 MATERIALS SCIENCE↗
DOE OSTI2023

A framework for forming thermoset polymer networks during laser powder bed fusion additive manufacturing

It is commonly reported that the limited availability of material types is a major barrier to widespread adoption for polymer laser powder bed fusion (PBF/L) additive manufacturing (AM). A deeper understanding of the physico-chemical relationships between AM process stimuli and polymer responsive behavior is needed to make data-driven decisions for PBF/L specific material development and formulation. Many researchers have cumulatively built the current state of PBF/L “science-based manufacturing” framework where an initial guess for process parameter values can be determined from measured polymer properties and the first-principles of physics. However, published literature has focused on developing this framework for thermoplastic processing of polymers omitting many specific concerns unique to processing thermosetting materials. The authors consider in the present work the implications of many specific aspects unique to thermosetting polymers during PBF/L manufacturing. These aspects are contextually discussed according to the five sub-functions of PBF/L and are described mathematically where appropriate alongside example data from thermosetting materials commercially available in powder form. Further, this expansion of the prevailing material development framework to include thermosetting polymers paves the way for the unique performance properties offered by that class to be manifest through AM’s geometric freedom.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Mitigating keyhole pore formation by nanoparticles during laser powder bed fusion additive manufacturing

Keyhole pore formation is one of the most detrimental subsurface defects in the laser metal additive manufacturing process. However, effective ways to mitigate keyhole pore formation beyond tuning laser processing conditions during keyhole mode laser melting are still lacking. Here we report a novel approach to mitigate keyhole pore formation during laser powder bed fusion (LPBF) process by using stable nanoparticles. The critical keyhole depth for keyhole pore generation (i.e., the largest keyhole depth without keyhole pore formation) during LPBF of Al6061 increases from 246 µm to 454 µm (85% increase) after adding TiC nanoparticles. In-depth x-ray imaging studies and thermo-fluid dynamics simulation enable us to identify that two mechanisms work together to mitigate keyhole pore generation: (1) adding nanoparticles prevents the keyhole from collapsing by increasing the liquid viscosity to impede the protrusion development; (2) adding nanoparticles slows down the keyhole pore movement by increasing the liquid viscosity, resulting in the recapturing of the pore by the keyhole. We further demonstrate that adding TiC nanoparticles can also eliminate the keyhole fluctuation induced keyhole pore during LPBF of Al6061. Our research provides a potential way to mitigate keyhole pore formation for defect lean metal additive manufacturing.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Prediction of microstructure formation in laser powder bed fusion process.

The datasets are results analyzing the predicted microstructures in a single track during the laser powder bed fusion additive manufacturing process. They are the outputs by running the opensource code, muMatScale (The code can be cited at: Yuan, Lang, Fattebert, Jean-Luc, and Sabau, Adrian. (2023, August 03). muMatScale. [Computer software]. https://github.com/lang-yuan/muMatScale. https://doi.org/10.11578/dc.20240112.2.) For each set of data, it contains the time-dependent information of Temperature, Fraction Solid, Grain ID, Grain Angle ( crystallographic orientations by Euler angles), and solute Composition. The dataset can be visualized by Paraview (https://www.paraview.org/ ). The 6 datasets are: 1. Baseline_base1_n4e14_dt20: baseline case with bulk nucleation density of 4E+14/m^3, undercooling of 20K, substrate nucleation density of 1.5E+15/m^3 2. Nuc_n4e15_dt20: case with bulk nucleation density of 4E+15/m^3, undercooling of 20K, substrate nucleation density of 1.5E+15/m^3 3. Nuc_n4e15_dt50: case with bulk nucleation density of 4E+15/m^3, undercooling of 50K, substrate nucleation density of 1.5E+15/m^3 4. Nuc_n6e15_dt20: case with bulk nucleation density of 4E+16/m^3, undercooling of 20K, substrate nucleation density of 1.5E+15/m^3 5. Base4_n4e14_dt20: case with bulk nucleation density of 4E+14/m^3, undercooling of 20K, substrate nucleation density of 6.0E+15/m^3 6. Base16_n4e14_dt20: case with bulk nucleation density of 4E+14/m^3, undercooling of 20K, substrate nucleation density of 2.4E+16/m^3

36 MATERIALS SCIENCE↗
DOE OSTI2024

System and method for large-area pulsed laser melting of metallic powder in a laser powder bed fusion application

The present disclosure relates to systems and methods for performing large area laser powder bed fusion (LBPF) to form a plurality of layers of a 3D part in a layer-by-layer fashion using meltable powder particles. In one implementation the system makes use of a first light source, which may be a diode laser subsystem, for generating a first light pulse of a first duration. The first light is used to preheat a substrate underneath a new layer of powder particles, wherein the substrate is formed from a previously fused quantity of the powder particles. A second light source, which may be a pulse laser, generates a second light pulse subsequent to the first light pulse. The second light pulse has a second duration shorter than the first duration by a factor of at least about 10, and fully melts the new layer of powder particles in addition to the substrate, to achieve a smooth printed layer. The wavelength of the first light pulse also differs from a wavelength of the second light pulse.

Khairallah, Saad A.↗
DOE OSTI2026

Microstructural evolution, defect mitigation, and precipitation behavior in AA6061 via laser powder bed fusion with high-temperature substrate heating

Defects, particularly solidification cracking, remain persistent challenges in the laser powder bed fusion (LPBF) processing of AA6061 aluminum alloy. This study systematically investigates defect mitigation, microstructural evolution, and mechanical properties associated with high-temperature substrate preheating at 500 °C. Comprehensive microstructural analyses, including characterization of defects, grain structures, and precipitation behavior, were performed on samples in both as-built and T6 heat-treated states. Elevated preheating substantially reduced solidification cracking across a wide processing window, while demonstrating decreased crack sensitivity to laser parameters. Columnar cracks along the build direction were observed despite substrate preheating. Lack-of-fusion and keyhole porosity were effectively eliminated, though gas-induced microporosity persisted at higher powers. In-depth characterization of two distinct laser power and speed conditions confirmed the formation of micron-sized, non-coherent Mg 2 Si precipitates under heated substrate conditions, alongside α-AlFeCrMnSi intermetallic phases indicative of in-situ thermal effects during fabrication. Subsequent T6 heat treatment revealed the formation of fine, coherent needle-shaped β″ strengthening precipitates. Despite substantial differences in processing parameters, comparable mechanical properties were measured in the as-built samples (∼52 MPa yield strength, ∼130 MPa tensile strength), primarily due to reduced strain hardening effects and consistent precipitation characteristics. Meanwhile, the T6 heat treatment led to significant improvement in properties, enhancing yield strength by over 400%, aligning closely with the performance of conventional wrought AA6061-T6. These findings underscore that high-temperature substrate preheating offers an effective means to suppress cracks, control precipitation, and enhance mechanical performance in LPBF-processed AA6061.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Carbon nanotube (CNT) reinforced 316L stainless steel composites made by laser powder bed fusion: Microstructure and wear response

Carbon nanotube (CNT) reinforced 316L stainless steel (SS) composites were fabricated by laser powder bed fusion (LPBF) additive manufacturing (AM). This study focuses on microstructure evolution, hardness, and wear behavior. The wear behavior for AM 316L SS and 1 wt% CNT/316L SS was studied by a dry sliding test under normal loads of 10, 15, and 25 N. The addition of CNT to 316L SS led to the change of solidification mode from cellular/columnar to dendritic growth, producing grain refinement. Nanoscale dendrites were observed. Although TEM characterization confirmed the existence of CNT in cell interior, most CNT were damaged, resulting in carbon material segregation along cellular/dendritic boundaries. The wear rate was found dramatically decreased due to the improved hardness. Compared to the adhesive and abrasive wear in AM 316L SS, the wear mechanism of CNT/316L composite was adhesion and oxidative wear. Furthermore, enhanced cellular/dendritic boundary strengthening by carbon segregation was the main strengthening mechanism of CNT/316L composite.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Height-resolved emission spectroscopy and high-speed imaging of the TiAl6V4 vapor plume under laser powder bed fusion conditions

Optical emission spectroscopy is increasingly used as an in situ monitoring technique during laser powder bed fusion (LPBF) because plume emission holds elemental information not found in other in situ sensing techniques. This work explores the shape, stability, and temperature of the emission plume above the melt pool of Ti6Al4 V undergoing laser scans under LPBF-like processing conditions, using high-speed video and height-resolved spectroscopy to study the details of plume dynamics. Optical emission spectroscopy is conducted in the 480 nm to 525 nm region, where Ti emission is strong, with 0.3 mm vertical resolution above the baseplate. The Boltzmann plot method is used to determine temperature at each probed elevation, which indicates that the hottest location of the plume is occasionally elevated 0.3 mm to 0.6 mm above the scanning surface. The results show that the plume shape, stability, temperature, and spectra are highly dependent on the processing conditions. We highlight some of the complexities of optical emission spectroscopy and discuss potential challenges for implementing optical emission spectroscopy across an entire build.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Direct mechanistic connection between acoustic signals and melt pool morphology during laser powder bed fusion

Various nondestructive diagnostic techniques have been proposed for in situ process monitoring of laser powder bed fusion (LPBF), including melt pool pyrometry, whole-layer optical imaging, acoustic emission, atomic emission spectroscopy, high speed melt pool imaging, and thermionic emission. Correlations between these in situ monitoring signals and defect formation have been demonstrated with acoustic signals having been shown to predict pore formation with especially high confidence in recent machine learning studies. Here, in this work, time-resolved acoustic data are collected in both the conduction and keyhole welding regimes of LPBF-processed Ti-6Al-4V alloy. A non-dimensionalized Strouhal number analysis, used in whistle aeroacoustics, is applied to demonstrate that the acoustic signals recorded in the keyhole regimes can be directly associated with the vapor depression morphology. This mechanistic understanding developed from whistle aeroacoustics shows that acoustic monitoring during the LPBF process can provide a direct probe into the vapor depression dynamics and defect occurrence, especially in the keyhole regimes relevant to printing and defect formation.

36 MATERIALS SCIENCE↗
DOE OSTI2024

A Microchannel Heat Exchanger Produced From a Metal Matrix Composite by Hybrid Laser Powder Bed Fusion and Inkjet Printing

Abstract This paper explores the production of an oxide dispersion strengthened (ODS) 304L stainless steel microchannel heat exchanger (HX) using a hybrid additive manufacturing process of laser powder bed fusion and inkjet printing. The study investigates the capabilities and economics of the hybrid inkjet-laser powder bed fusion (LPBF) process and evaluates the dimensional accuracy, functionality, and mechanical properties of the resulting ODS alloy. The effectiveness and pressure drop of the ODS heat exchangers produced by the hybrid LPBF tool are also determined. Results show that the inkjet-doped samples have a lower mean channel height with higher standard deviation than samples produced by LPBF alone. This is attributed to greater absorption of laser energy for the powder coated with the oxide precursor. The economic analysis shows that the hybrid process has a potential for reducing the unit cost of the heat exchanger based on cost modeling assumptions.

Engineering↗
DOE OSTI2023

Remarkable enhancement in thermal conductivity of stainless-steel leveraging metal composite via laser powder bed fusion: 316L-Cu composite

This work aimed to significantly enhance the thermal conductivity of the SS matrix by fabricating a metal composite SS-Cu system utilizing a laser powder bed fusion process. The impact of process parameters on the microstructure of SS-Cu samples is investigated. Here, the processing and post-processing factors that influence the effective thermal conductivity of the metal composite SS-Cu system are discussed. Then, the effective thermal conductivity and mechanical properties of the SS-Cu composite are measured at room temperature and operating temperature of 300 °C. The optimized laser powder bed fusion parameters led to low Marangoni convection, high cooling rate, greater macro-segregation, and higher thermal conductivity values albeit at the cost of ductility in SS-Cu metal composite likely due to the presence of the unmelted powder particles. As-printed SS-60 vol % Cu and annealed SS-60 vol % Cu had 2.5X and 6.6X thermal conductivity enhancement, respectively. During annealing, phase separation, partial melting of Cu, and formation of a continuous network of Cu surrounding SS play a key role in enhancing the thermal conductivity of the SS-Cu metal composite structure.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Effect of laser power on roughness and porosity in laser powder bed fusion of stainless steel 316L alloys measured by X-ray tomography

The quality of metal objects fabricated via laser powder bed fusion are highly affected by process parameters, and their influence on final products is yet to be fully explored. In this work, pyrometry signals of the melt pool were collected from a set of stainless-steel samples during manufacturing and the effect of laser power on porosity and roughness of final printed parts was analyzed. Results show that the melt pool pyrometry signal of contours increases with higher laser power, whereas it is lower and decreases for the infilled part. Post-built X-ray computed tomography imaging reveals that porosity decreases while sample roughness increases upon increasing laser power. The decrease in porosity with increasing laser power is attributed to the larger size of the contour welds that were printed first, leading to an increase in dimension of the final products.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Topology-driven compressive behavior of Inconel 718 lattice structures with Z-strut reinforcement fabricated by laser powder bed fusion

This study investigates the compressive deformation behavior and mechanical performance of Inconel 718 lattice structures fabricated by laser powder bed fusion (LPBF). Four unit-cell topologies—BCC, FCC, BCCZ, and FCCZ—were designed with a unit-cell size of 3 mm and fabricated under identical process conditions to isolate the effect of topology. Measured relative densities ranged from 14.65% to 17.72%. Compressive testing showed that Z-strut-reinforced topologies (BCCZ: 54.6 MPa; FCCZ: 80.2 MPa) exhibited higher strength than their unreinforced counterparts, which may be associated with mixed-mode deformation behavior enabled by the vertically aligned Z-struts. Finite element simulations and Digital Image Correlation (DIC) analysis support the observation of a transition from node-dominated deformation in BCC/FCC to mixed-mode deformation in BCCZ/FCCZ. These findings suggest that unit-cell topology is a key design variable for tailoring deformation mechanisms in LPBF lattice structures.

36 MATERIALS SCIENCE↗
DOE OSTI2026

In-situ sensor monitoring of multi-class gas porosity formation in laser powder bed fusion using convolutional neural network

In-situ monitoring of defect formation remains a significant challenge in the laser powder bed fusion (LPBF) process. Recent advances have enabled real-time defect detection with machine learning and in-situ sensing technologies; however, most studies focus on binary classification of keyhole pores, limiting nuanced multi-class pore differentiation and formation mechanisms. This work introduces a multi-class pore detection framework (no pore, small pores < 15 µm, and large pores > 15 µm) by leveraging photodiode sensor data alongside high-fidelity synchrotron X-ray imaging. The 15 µm threshold is selected to distinguish between two fundamentally different defect mechanisms, following the physical size-mechanism boundary established by prior high-resolution synchrotron X-ray characterization of Al6061 LPBF. Distinguishing these classes is critical because large keyhole pores are structurally detrimental, whereas small gas pores are often benign, requiring different process control strategies. Thermal emission monitoring data collected simultaneously with high-speed X-ray imaging at the Stanford Synchrotron Radiation Lightsource (SSRL), are correlated with subsurface melt pool dynamics to establish ground truth. Continuous Wavelet Transform (CWT) with optimized parameters converts the photodiode time-series signals into time–frequency images, facilitating feature extraction. Convolutional Neural Networks (CNN) are then applied for real-time multi-class pore classification in an average inference time of 1 ms per signal window. It achieves 79% accuracy and an Area Under the Receiver Operating Characteristic curve (AUC ROC) score of 0.89 with five-fold cross-validation. The results demonstrate that coupling CWT-based feature engineering with CNN architecture enables reliable multi-class pore detection in Al6061 builds using affordable in-situ sensors. This approach advances scalable and affordable quality assurance in additive manufacturing by moving beyond binary defect detection toward more nuanced classification of porosity mechanisms with in-situ sensors and machine learning.

Laser powder bed fusion, Multi-class pores, In-sit↗
DOE OSTI2023

Machine learning–aided real-time detection of keyhole pore generation in laser powder bed fusion

Porosity defects are currently a major factor that hinders the widespread adoption of laser-based metal additive manufacturing technologies. One common porosity occurs when an unstable vapor depression zone (keyhole) forms because of excess laser energy input. With simultaneous high-speed synchrotron x-ray imaging and thermal imaging, coupled with multiphysics simulations, we discovered two types of keyhole oscillation in laser powder bed fusion of Ti-6Al-4V. Amplifying this understanding with machine learning, we developed an approach for detecting the stochastic keyhole porosity generation events with submillisecond temporal resolution and near-perfect prediction rate. Finally, the highly accurate data labeling enabled by operando x-ray imaging allowed us to demonstrate a facile and practical way to adopt our approach in commercial systems.

42 ENGINEERING↗
NASA NTRSDate not supplied

Towards Qualification and Certification of Laser Powder Bed Fusion Ti-6Al-4V with In-Situ Process Monitoring and Automated Defect Detection

Qualification and certification of laser powder bed fusion (LPBF) parts are two challenges that must be answered to ensure suitability for critical applications. In-situ monitoring using high frame rate thermal and conventional optical imaging sensors is applied to the LPBF build process. Currently, the large volume of data from such sensors becomes untenable for manual inspection in production environments. This presentation serves to address this in-situ monitoring deficiency in two ways. First, a framework for managing data streams from LPBF process monitoring sensors is described. Second, two candidate image analysis techniques are presented: one is a set of heuristics that are easily interpretable, and the other is an uninterpretable convolutional neural network. These strategies are compared in terms of performance, computational expense, and speed. These methodologies represent platforms for connecting processing conditions to process modeling efforts aligned with the qualification and certification mission for LPBF Ti-6Al-4V components.

Qualification↗