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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 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Leveraging the digital thread for physics-based prediction of microstructure heterogeneity in additively manufactured parts

A major limitation of additive manufacturing (AM) processes is that local conditions of material deposition frequently lead to unintentional heterogeneities in microstructure and properties within a single component, despite nominally uniform process conditions. Up to now, there has been no way to a priori determine the distribution of these heterogeneities, requiring expensive trial-and-error approaches to fabrication, testing, and characterization. Here, a physics-based framework for creating a digital representation of the laser powder bed fusion (PBF) process is proposed to predict the variation in solidification behavior that leads to heterogeneous microstructures in an as-built part. By leveraging in situ process data stored in the part’s digital thread, the scan path and process parameters were input into a heat transfer model which predicted solidification data at the melt pool scale. A two-step unsupervised clustering algorithm was used to first cluster the local solidification conditions (12.5µm 3 voxels) and then to cluster the regional behavior on the scale of multiple scan passes and print layers (250µm 3 super-voxels). This process was used to identify regions with similar solidification characteristics for multiple locations in a Stainless Steel 316-L component. The corresponding as-built part was sectioned and characterized using electron backscatter diffraction (EBSD). Quantitative analysis of the pole figures confirmed that the predicted regions of heterogeneity in the solidification conditions corresponded with differences in the observed microstructure. In conclusion, this work shows a viable path for estimating the microstructural heterogeneity for additively manufactured parts to either limit microstructural variation throughout a part or to enable functionality-based variation of the microstructure.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Uncertainties Induced by Processing Parameter Variation in Selective Laser Melting of Ti6Al4V Revealed by In-Situ X-ray Imaging

Selective laser melting (SLM) additive manufacturing (AM) exhibits uncertainties, where variations in build quality are present despite utilizing the same optimized processing parameters. In this work, we identify the sources of uncertainty in SLM process by in-situ characterization of SLM dynamics induced by small variations in processing parameters. We show that variations in the laser beam size, laser power, laser scan speed, and powder layer thickness result in significant variations in the depression zone, melt pool, and spatter behavior. On average, a small deviation of only ~5% from the optimized/reference laser processing parameter resulted in a ~10% or greater change in the depression zone and melt pool geometries. For spatter dynamics, small variation (10 μm, 11%) of the laser beam size could lead to over 40% change in the overall volume of the spatter generated. The responses of the SLM dynamics to small variations of processing parameters revealed in this work are useful for understanding the process uncertainties in the SLM process.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Time resolved in-situ multi-contrast X-ray imaging of melting in metals

In this work, the application of a time resolved multi-contrast beam tracking technique to the investigation of the melting and solidification process in metals is presented. The use of such a technique allows retrieval of three contrast channels, transmission, refraction and dark-field, with millisecond time resolution. We investigated different melting conditions to characterize, at a proof-of-concept level, the features visible in each of the contrast channels. We found that the phase contrast channel provides a superior visibility of the density variations, allowing the liquid metal pool to be clearly distinguished. Refraction and dark-field were found to highlight surface roughness formed during solidification. This work demonstrates that the availability of the additional contrast channels provided by multi-contrast X-ray imaging delivers additional information, also when imaging high atomic number specimens with a significant absorption.

36 MATERIALS SCIENCE↗
NASA NTRS2017

Relationship of Powder Feedstock Variability to Microstructure and Defects in Selective Laser Melted Alloy 718

Powder-bed additive manufacturing processes use fine powders to build parts layer by layer. For selective laser melted (SLM) Alloy 718, the powders that are available off-the-shelf are in the 10-45 or 15-45 micron size range. A comprehensive investigation of sixteen powders from these typical ranges and two off-nominal-sized powders is underway to gain insight into the impact of feedstock on processing, durability and performance of 718 SLM space-flight hardware. This talk emphasizes an aspect of this work: the impact of powder variability on the microstructure and defects observed in the as-fabricated and full heated material, where lab-scale components were built using vendor recommended parameters. These typical powders exhibit variation in composition, percentage of fines, roughness, morphology and particle size distribution. How these differences relate to the melt-pool size, porosity, grain structure, precipitate distributions, and inclusion content will be presented and discussed in context of build quality and powder acceptance.

Characterization↗
DOE OSTI2020

High-throughput experimentation for microstructural design in additively manufactured 316L stainless steel

In the present study, a combination of high-throughput (HT) and low-throughput (LT) techniques was used to rapidly determine the processing window and generate processing maps for Selective Laser Melting (SLM) of 316L stainless steel. The HT method includes the fabrication of hundreds of hex nut-shaped specimens, each processed with a unique combination of laser power, scanning speed, and hatch spacing. An easily removable scaffolding permitted rapid sample extraction from the base plate, thus saving machining cost and time. Hardness and immersion density measurements were used for HT characterization to identify a processing window for maximum strength and density. Within the defined processing window, a low-throughput (LT) microstructural interrogation of specimens were performed. The microstructural analysis included quantification at various length scales (i.e., grains size and morphology, texture, primary dendrite arm spacing, and melt pool geometry analysis). Microstructure-based processing maps as a function of volumetric energy density were generated. The combination of HT and LT methods produced a predictive relationship between hardness and primary dendrite arm spacing using a Hall-Petch relationship. A model is proposed to explain the dependence of microstructure on the melt pool geometry. Here, the HT method can be applied for the microstructural design of SLM-fabricated components in other alloys.

316L stainless steel↗
DOE OSTI2020

Frequency domain measurements of melt pool recoil force using modal analysis

Recoil pressure is a critical factor affecting the melt pool dynamics during Laser Powder Bed Fusion (LPBF) processes. Recoil pressure depresses the melt pool. When the recoil pressure is low, thermal conduction and capillary forces may be inadequate to provide proper fusion between layers. However, excessive recoil pressure can produce a keyhole inside the melt pool, which is associated with gas porosity. Direct recoil pressure measurements are challenging because it is localized over an area proportionate to the laser spot size producing a force in the mN range. This paper reports a vibrationbased approach to quantify the recoil force exerted on a part in a commercial LPBF machine. The measured recoil force is consistent with estimates from high speed synchrotron imaging of entrained particles, and the results show that the recoil force scales with applied laser power and is inversely related to the laser scan speed. These results facilitate further studies of melt pool dynamics and have the potential to aid process development for new materials.

42 ENGINEERING↗
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↗
NASA NTRSDate not supplied

Zircon (U-Th)/He Impact Crater Thermochronometry and the Effects of Shock Microstructures on Helium Diffusion Kinetics

Accurate and precise age determination of impact cratering events remains challenging and often contentious; less than half of all known craters are regarded as accurately and precisely dated. Zircon (U-Th)/He (ZHe) dating of impactites can be employed to date medium to large impact structures as ZHe ages can be fully reset in minutes at T >1000°C, a plausible scenario in the central melt pool. In contrast, complete resetting of ZHe at 200-300°C, encountered near the crater margins or due to post-impact hydrothermal overprinting, may take >103-6 years. To test the reliability of ZHe impact dating, we have quantified the effects of shock-induced microstructures on helium diffusion kinetics in well-characterized variably shocked zircon. We investigated samples from two impact structures, the Chicxulub multi-ring crater and Ries complex crater, to compare diffusion kinetics from structures with different size, age, and hydrothermal system longevity. Shock microstructures were characterized by backscattered-electron imaging prior to determining the He diffusion kinetics by prograde and retrograde fractional-release experiments via light-bulb furnace with incremental step-heating (10°C) from 300°C to 600°C. Next, we examine the internal interconnectivity and sizes of the diffusion domains by EBSD. While we found that zircon with few shock microstructures exhibited no marked deviation from helium diffusion kinetics of undamaged zircon, zircon grains with planar microstructures and granular textures are characterized by a dramatic decrease in helium retentivity due to the reduction in the effective domain size and the introduction of interconnected fast diffusion pathways. A subset of grains were ZHe dated and showed that less deformed grains yielded a weighted mean age within error of the accepted impact ages, while the grains with planar microstructures or granular textures gave systematically younger ages. These new diffusion data and ZHe ages demonstrate that highly shocked grains are unsuitable for ZHe impact crater dating. Therefore, detailed characterization of impact-induced microstructures is critical for determining accurate ZHe impact ages and offers the possibility of investigating post-impact hydrothermal circulation.

Zircon↗
DOE OSTI2022

Predicting melt pool depth and grain length using multiple signatures from in-situ single camera two-wavelength imaging pyrometry for laser powder bed fusion

In laser powder bed fusion (LPBF), the in-situ process signatures are known to have a direct correlation with the microstructural properties of the solidified melt pool (MP). It is known that the MP cooling and heating rates, and laser processing parameters can critically determine the grain structure and thereby affect the part properties. The objective of this work is to study the feasibility of using in-process, high-speed imaging pyrometry for evaluating the solidified MP properties “below” the surface, such as depth and microstructural properties. To accomplish this, we employ an in-house single camera-based two-wavelength imaging pyrometry (STWIP) system for monitoring the printing of single-scan tracks with Inconel 718 on a commercial LPBF printer (EOS M290). Further, the lab designed STWIP system is a coaxial high-speed (>10,000 fps) imaging system capable of monitoring MP temperature, morphology, and intensity profiles. The temperature measurements from STWIP are emissivity independent. The STWIP measured MP signatures of the printed tracks are correlated with the ex-situ microscopy characterized MP depth and the average grain lengths. From the data analysis, using support vector machine (SVM)-based regression models, we found that the MP temperature signatures are crucial for an accurate prediction of MP depth and the grain length, thus validating the novelty and necessity of the developed in-situ monitoring methods and analysis.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Uncovering grain and subgrain microstructure at the scale of additive manufacturing melt tracks with a scalable cellular automaton solidification model

Metal additive manufacturing, characterized by rapid solidification, yields refined grains with a distinctive cellular subgrain microstructure that plays a pivotal role in determining material properties. Due to the significant computational expense demanded to simulate the required physics with submicron spatial resolution, their numerical simulations have been limited to proof-of-concept studies to either 2D or small subregions of a melt pool. In this study, an open-source, scalable, solidification code, muMatScale, based on the cellular automaton method, has been developed to predict the grain and the underlying subgrain microstructure over an entire melt pool. The model incorporates flexible parallelization schemes, utilizing MPI and OpenMP GPU Offloading, in addition to appropriate multi-physics specific to non-equilibrium rapid solidification in AM. The impact of nucleation parameters on grain microstructures was investigated with a focus on grain size variations and morphology transitions. With selected nucleation parameters, the simulation predicted the grain size, subgrain morphology, crystallographic orientation, and microsegregation aligned with experimental measurements. The model demonstrates that epitaxial grain growth is a dominant factor at the melt pool boundary, influencing grain size variation under different grain sizes in the build plate while maintaining consistent primary dendrite arm spacing under identical thermal conditions. Here, the highly efficient numerical model enables large-scale simulations with a spatial resolution of 100 nm or less, unveiling unprecedented insights into thermal and solutal diffusion driven grain growth, and the subgrains with microsegregation within grains in 3D across scales. muMatScale will enable the linking of submicron length-scale microstructure to part-level material behavior by investigating fundamental solidification problems at the intercellular scale in many-track and many-layer builds.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Multiscale investigation of thermomechanical and compositional developments in Ni alloy 718 under laser processing

Laser processing has been widely employed in various applications due to its exceptional spatial resolution. However, the rapid temperature gradients generated in localized areas present significant challenges for experimental characterization using conventional instruments. To characterize Ni alloy 718 during laser processing, we employed in-situ synchrotron X-ray diffraction with a high-speed detector, a method particularly well-suited for probing processes with high temporal and spatial resolution. Through a series of in-situ experiments, we investigated the local variations in the evolution of microstructures and thermomechanical behaviors within a keyhole mode melt pool. The in situ macroscopic thermomechanical behaviors were quantified using an empirical model derived from diffraction patterns, with experimental results showing reasonable agreement with finite element analysis. Various laser parameters were tested to assess their influences on the residual strains in the melt pools. The results revealed that the residual strain in the keyhole mode melt pool is relatively insensitive to variations in the parameters and is smaller than that in the melt pool created under conduction mode laser scanning. Additionally, we analyzed the shapes of individual diffraction spots, providing insights into the plastic behaviors and compositional developments in the resolidified alloy. The analysis confirmed that compositional variations in a dendritic microstructure manifest as asymmetric broadening of the diffraction spots.

Ni alloy 718↗
DOE OSTI2022

ExaAM: Metal additive manufacturing simulation at the fidelity of the microstructure

Additive manufacturing (AM), or 3D printing, of metals is transforming the fabrication of components, in part by dramatically expanding the design space, allowing optimization of shape and topology. However, although the physical processes involved in AM are similar to those of welding, a field with decades of experimental, modeling, simulation, and characterization experience, qualification of AM parts remains a challenge. The availability of exascale computational systems, particularly when combined with data-driven approaches such as machine learning, enables topology and shape optimization as well as accelerated qualification by providing process-aware, locally accurate microstructure and mechanical property models. We describe the physics components comprising the Exascale Additive Manufacturing simulation environment and report progress using highly resolved melt pool simulations to inform part-scale finite element thermomechanics simulations, drive microstructure evolution, and determine constitutive mechanical property relationships based on those microstructures using polycrystal plasticity. We report on implementation of these components for exascale computing architectures, as well as the multi-stage simulation workflow that provides a unique high-fidelity model of process–structure–property relationships for AM parts. In addition, we discuss verification and validation through collaboration with efforts such as AM-Bench, a set of benchmark test problems under development by a team led by the National Institute of Standards and Technology.

3D printing↗
DOE OSTI2022

High-throughput surface characterization to identify porosity defects in additively manufactured 316L stainless steel

In the present study, a high throughput (HT) methodology is applied to rapidly assess the surface characteristics of 83 additively manufactured 316L stainless steel. The variation in surface roughness (S a ) showed a good correlation with a dimensionless number (π) and provided direct linkages to the internal porosity defects. Lack-of-fusion regime relates to high surface roughness (S a > 5 µm), low dimensionless number (π < 61), and the presence of cavities in between the melt pool tracks. Balling regime correlates to high surface roughness (S a > 5 µm), intermediate dimensionless number (61 < π < 146), and non-uniform melt pool track width. Keyhole regime shows low surface roughness (S a < 5 µm), high dimensionless number (π > 146), and a curved melt pool track. This approach accelerates the process parameter discovery and minimizes the porosity defects for the LPBF process. The impact of the defects on the as-processed tensile mechanical properties demonstrates that samples with porosity exhibit up to 10% less tensile strength and 30% less ductility than optimal samples.

316L stainless steel↗
DOE OSTI2023

An operando synchrotron study on the effect of wire melting state on solidification microstructures of Inconel 718 in wire-laser directed energy deposition

Directed energy deposition (DED) with a coaxial wire-laser configuration has gained significant attention in recent years for the production of large-scale metallic components because of its low directional dependence, fast deposition rate, high feedstock efficiency, and low manufacturing costs. This work studies the coaxial wire-laser DED process of Inconel 718 alloy under a stable deposition condition with a relatively low input volumetric energy density (55.5J/mm 3 ). Post characterization reveals a cluster of refined grains at the center-bottom region of the as-printed track. Operando high-energy synchrotron X-ray experiments and multi-physics modeling are applied innovatively to study the fundamental mechanism responsible for the formation of this microstructure. The X-ray diffraction experiment provides direct evidence, which is supported by the simulation, that the feeding wire can reach the melt pool bottom and release solid particles (primarily carbides) near the mushy zone owing to insufficient melting. Consequently, these sub-micron sized particles suppress the growth of large columnar grains and cause the formation of unique microstructural heterogeneity. In conclusion, this discovery offers new opportunities for tailoring the solidification microstructure by controlling the melting state of the feedstock wire in DED process, in addition to commonly known factors such as the thermal gradient and solidification velocity.

42 ENGINEERING↗
DOE OSTI2025

Real-time tracking and analysis of gas bubble dynamics in laser powder bed fusion using in-situ X-ray characterization and machine learning

Porosity defects remain a significant challenge in the laser powder bed fusion (LPBF) process, adversely affecting the mechanical properties and reliability of additively manufactured components. Here, this study investigates the real-time formation and trajectory of gas bubbles during LPBF of Al6061 alloy using advanced in-situ X-ray characterization and machine learning. The unsupervised Gaussian mixture model and particle tracking algorithm developed are able to precisely track and quantify the properties of gas bubbles and keyhole pores. Our analysis identified five distinct types of gas bubble formation and movement patterns, emphasizing the diverse origins and behaviors of these defects. It enables precise quantification of trajectories, velocities, and morphological changes of gas bubbles, offering a granular view of the subsurface dynamics within the melt pool. Additionally, we explored keyhole-induced pore dynamics, revealing the critical role of keyhole oscillation and collapse for the formation of both large and small gas pores. It defines four different regions of gas bubble movement within the melt pool, providing a clearer understanding of how local fluid dynamics affect pore behavior. The results underscore the importance of integrating in-situ experimental observation and automated machine learning to develop a more robust predictive model for defect formation in LPBF.

In-situ X-ray imaging↗
NASA NTRS2001

Magnetic Damping of g-Jitter Induced Double-Diffusive Convection

This paper describes a numerical study of the g-jitter driven double diffusive convective flows, thermal and concentration distributions in binary alloy melt systems subject to an external magnetic field. The study is based on the finite element solution of transient magnetohydrodynamic equations governing the momentum, thermal and solutal transport in the melt pool. Numerical simulations are conducted using the synthesized single- and multi- frequency g-jitter as well as the real g-jitter data taken during space flights with or without an applied magnetic field. It is found that for the conditions studied, the main melt flow follows approximately a lineal- superposition of velocity components induced by individual g-jitter components, regardless of whether a magnetic field exists or not. The flow field is characterized by a recirculating double diffusive convection loop oscillating in time with a defined frequency equal to that of the driving g-jitter force. An applied magnetic field has little effect on the oscillating recirculating pattern, except around the moment in time when the flow reverses its direction. The field has no effect on the oscillation period, but it changes the phase angle. It is very effective in suppressing the flow intensity and produces a notable reduction of the solutal striation and time fluctuations in the melt. For a given magnetic field strength, the magnetic damping effect is more pronounced on the velocity associated with the largest g-jitter component present and/or the g-jitter spiking peaks. A stronger magnetic field is more effective in suppressing the melt convection and also is more helpful in bringing the convection in phase with the g-jitter driving force. The applied field is particularly useful in suppressing the effect of real g-jitter spikes on both flow and solutal distributions. With appropriately selected magnetic fields, the convective flows caused by g-jitter can be reduced sufficiently and diffusion dominant. solutal transport in the melt is possible.

Shu, Y.↗
DOE OSTI2024

Characterization and Rationalization of Microstructural Evolution in GRCop-84 Processed by Laser-Powder Bed Fusion (L-PBF)

In this study, prismatic geometries of GRCop-84 [Cu-8Cr-4Nb (at. pct)] were built with laser-powder bed fusion (L-PBF) process. The samples were sectioned parallel or perpendicular to the build direction and characterized in the as-built and after post-processing with a hot-isostatically pressing (HIP) treatment. The microstructure and phase evolutions were evaluated with optical microscopy, scanning electron microscopy (SEM), electron backscattered diffraction (EBSD), and high-temperature X-ray diffraction (HTXRD) up to 1223 K. The samples in the as-built conditions exhibited predominantly columnar epitaxial and misoriented Cu-FCC grains. The microstructure evolutions are discussed based on locations within the overall build geometry, the dynamics of small melt pool shape and sectioning effects. The above grain structure did not change significantly during post-process HIP treatment. The stability of this FCC grain structure is attributed to the formation of primary stable Cr 2 Nb (Laves phase) during L-PBF, even before the emergence of FCC grains from liquid. The stability of Cr 2 Nb in both as-built and HIPed samples were evaluated using high-temperature X-ray diffraction measurements and compared with that of gas-atomized powder. The significance of these results is discussed with reference to aerospace applications.

36 MATERIALS SCIENCE↗