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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 OSTI2025

Operando synchrotron X-ray analysis of melt pool dynamics in an Al-Sn immiscible alloy

The melt flow in an Al-50vol.% Sn immiscible alloy, produced by single-track laser melting of Al and Sn elemental powders, was studied in real time. High-speed synchrotron X-ray imaging was used to track Al and Sn liquids’ movements, and also to examine elemental distributions in the laser tracks, complimented by electron microscopy after solidification. Key aspects, including melt pool geometry, keyhole instability, and flow dynamics (flow pattern and velocity), were examined using digital image analysis. Relatively deeper melt pools formed at 400 W and 300 mm/s exhibited greater stability, with smooth surfaces, consistent outward flow, and minor vortices near the keyhole. In contrast, shallower pools produced at higher scanning speeds (>500 mm/s) demonstrated greater instability with increased surface waviness, and stronger velocity fluctuations, leading to numerous micro-vortices and increased Al-Sn heterogeneity. Velocity scale estimations, supported by experimental observations, examined the roles of vapour pressure, Marangoni effect, buoyancy, inertial, and surface tension forces in the flow. The results revealed that vapour pressure and mechanical waves dominated at high scanning speeds (shallow pools), while Marangoni forces were equally significant in deep pools at lower speeds (300 mm/s). Buoyancy was found to have minimal impact in both cases. Furthermore, the interaction between inertial and surface tension forces played a critical role in determining the degree of waviness of the pools’ surfaces. These findings offer valuable insights into melt pool dynamics during laser processing of immiscible alloys and other metallic systems using elemental powders, and provide guidance for developing high-fidelity computational fluid dynamics models.

immiscible alloys↗
DOE OSTI2025

A mechanistic study on environment gas in laser powder bed fusion

A variety of protective or reactive environmental gases have recently gained growing attention in laser-based metal additive manufacturing (AM) technologies due to their unique thermophysical properties and the potential improvements they can bring to the build processes. However, much remains unclear regarding the effects of different gas environments on critical phenomena in laser AM, such as rapid cooling, energy coupling, and defect generation. Through simultaneous high-speed synchrotron x-ray imaging and thermal imaging, we identify distinct effects of two environmental gases in laser AM and gained a deeper understanding of the underlying mechanisms. Compared to the commonly used protective gas, argon, it is found that helium has a negligible effect on cooling the part. However, helium can suppress unstable keyholes by decreasing effective energy absorption, thus mitigating keyhole porosity generation and reducing pore size under certain processing conditions. In conclusion, these observations provide guidelines for the strategic use of environmental gases in laser AM to produce parts with improved quality.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Revealing melt flow instabilities in laser powder bed fusion additive manufacturing of aluminum alloy via in-situ high-speed X-ray imaging

Laser metal additive manufacturing technologies enable the fabrication of geometrically and compositionally complex parts unachievable by conventional manufacturing methods. However, the certification and qualification of additively manufactured parts are greatly hindered by the stochastic melt flow instabilities intrinsic to the process, which has not been explicitly revealed by direct observation. Here, we report the mechanisms of the melt flow instabilities in laser powder bed fusion additive manufacturing process revealed by in-situ high-speed high-resolution synchrotron X-ray imaging. Here we identified powder/droplet impact, significant keyhole oscillation, and melting-mode switching as three major mechanisms for causing melt flow instabilities. We demonstrated the detrimental consequences of these instabilities brought to the process, and presented new understanding on the melt flow evolution and keyhole oscillation. This work provides critical insights into process instabilities during laser metal additive manufacturing, which may guide the development of instability mitigation approaches. The results reported here are also important for the development and validation of high-fidelity computational models.

36 MATERIALS SCIENCE↗
DOE OSTI2022

The interplay between vapour, liquid, and solid phases in laser powder bed fusion

The capability of producing complex, high performance metal parts on demand has established laser powder bed fusion (LPBF) as a promising additive manufacturing technology, yet deeper understanding of the laser-material interaction is crucial to exploit the potential of the process. By simultaneous in-situ synchrotron x-ray and schlieren imaging, we probe directly the interconnected fluid dynamics of the vapour jet formed by the laser and the depression it produces in the melt pool. The combined imaging shows the formation of a stable plume over stable surface depressions, which becomes chaotic following transition to a full keyhole. We quantify process instability across several parameter sets by analysing keyhole and plume morphologies, and identify a previously unreported threshold of the energy input required for stable line scans. The effect of the powder layer and its impact on process stability is explored. These high-speed visualisations of the fluid mechanics governing LPBF enable us to identify unfavourable process dynamics associated with unwanted porosity, aiding the design of process windows at higher power and speed, and providing the potential for in-process monitoring of process stability.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Bubble explosion induced melt pool instabilities in electron beam melting of aluminum alloy

Electron beam melting (EBM) is an additive manufacturing technology that can process materials and manufacture components otherwise impossible or uneconomical. However, defects, including porosity and surface irregularities, are widely reported in EBM-built components, and their formation mechanisms are not fully understood. Here, using in-situ high-speed synchrotron X-ray imaging, we reveal that bubble explosions in Al6061 during EBM induce melt pool instabilities contributing to defect formation. The melt pool and keyhole evolve through three stages: (1) initial formation of a melt pool, (2) subsurface bubble formation and explosion, and (3) periodic keyhole oscillation. During scanning, periodic bubble explosions can eject molten liquid as spatters and disturb the vapor depression and melt pool, contributing to surface humping, that may trigger lack-of-fusion defects in subsequent layers. The physical insights we report could provide guidance for EBM machine development, process innovation, alloy design and model development.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Deep learning with mixup augmentation for improved pore detection during additive manufacturing

In additive manufacturing (AM), process defects such as keyhole pores are difficult to anticipate, affecting the quality and integrity of the AM-produced materials. Hence, considerable efforts have aimed to predict these process defects by training machine learning (ML) models using passive measurements such as acoustic emissions. This work considered a dataset in which keyhole pores of a laser powder bed fusion (LPBF) experiment were identified using X-ray radiography and then registered both in space and time to acoustic measurements recorded during the LPBF experiment. Due to AM’s intrinsic process controls, where a pore-forming event is relatively rare, the acoustic datasets collected during monitoring include more non-pores than pores. In other words, the dataset for ML model development is imbalanced. Moreover, this imbalanced and sparse data phenomenon remains ubiquitous across many AM monitoring schemes since training data is nontrivial to collect. Hence, we propose a machine learning approach to improve this dataset imbalance and enhance the prediction accuracy of pore-labeled data. Specifically, we investigate how data augmentation helps predict pores and non-pores better. This imbalance is improved using recent advances in data augmentation called Mixup, a weak-supervised learning method. Convolutional neural networks (CNNs) are trained on original and augmented datasets, and an appreciable increase in performance is reported when testing on five different experimental trials. When ML models are trained on original and augmented datasets, they achieve an accuracy of 95% and 99% on test datasets, respectively. We also provide information on how dataset size affects model performance. Lastly, we investigate the optimal Mixup parameters for augmentation in the context of CNN performance.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Solidification cracking of laser melted commercial-purity tungsten

The high melting temperature of tungsten (W) makes it an attractive candidate for energy generation applications; however, its use is limited by its poor ductility at low temperatures. This limitation affects even melt-based additive manufacturing (AM) processes such as laser powder bed fusion (PBF-LB), with as-fabricated pure W exhibiting both longitudinal and transverse cracks. Metallurgical and processing factors that affect these cracks are still being explored. This work utilizes powderless single-tracks on pure W plates made over a range of power and velocity combinations in three unique PBF-LB setups: one with flowing argon, one with continuous vacuum, and an in situ high-speed synchrotron X-ray radiography setup. Each had different oxygen activities in their build environments. Longitudinal cracks with oxides appearing to exude from the crack were found for tracks deposited in a build environment with argon shield gas, while no such cracks were observed for builds conducted in vacuum. A calculation showed that direct oxygen ingress into the melt pool from the build environment is negligible when argon shield gas is present. Thus, incorporation of spatter is a likely mechanism for oxygen ingress into the melt pool. Radiography showed extensive keyholing, spatter generation, and crack formation at keyhole porosity. A heat transfer calculation showed the crack formation time was consistent with cooling below its ductile-to-brittle transition (DBT) temperature. This work identifies solidification cracking as a feasible mechanism in pure W beyond the well-known DBT related cracking.

Materials science↗
DOE OSTI2026

Water Observations of Flow/No-Flow for the East-Taylor Watershed, Colorado (June-July 2025 and 2026)

This dataset provides multi-year, ground-truth visual observations of surface water flow/no-flow conditions within the East-Taylor Watershed, Colorado, collected during June and July of 2025 and 2026. In June and July 2025, on-the-ground visual observations of flow/no-flow were collected as part of the Watershed Function Scientific Focus Area (SFA) and Rocky Mountain Biological Laboratory (RMBL) Colorado Headwaters Ecological Spectroscopy Study (CHESS) campaign (further details are provided within the CHESS Project Description). We obtained 377 water observations of flow/no-flow within the East-Taylor Watershed, Colorado. These ground-truth observations were collected to validate classification maps from remote sensing data and model results within the East-Taylor Watershed. In 2025, flow/no-flow measurements were collected using a field-based app for the CHESS Campaign (Zerion iForm). Within the field app, a water observation form was created to collect coordinates and metadata about the observation. Information collected for the water observation points included information about visually-assessed streamflow presence/absence (standard question obtained from Colorado State University’s StreamTracker project), flow estimate, stream or ponded area width, canopy cover, manganese films, iron seeps, and beaver activity. For 2025 water observations, this dataset contains: (1) a data file with the water observations and coordinates (2025_Water_Observations.csv); (2) a Keyhole Markup Language Zipped (KMZ) with the water observation locations and metadata (2025_Water_Observations_Locations.kmz); (3) photos (.jpg and .jpeg) of the water observation points, organized by location, contained within 2025_Water_Observations_FieldPhotographs.zip file; and (4) water observation protocols and figures (2025_Water_Observation_Protocols.pdf). In June and July 2026, on-the-ground visual observations of flow/no-flow were collected as part of the Watershed Function SFA project. We obtained 365 water observations of flow/no-flow within the East-Taylor Watershed, Colorado. The 2026 observations focused on collecting repeat measurements at the 2025 flow/no-flow observation locations conducted as part of the CHESS campaign. These ground-truth observations were collected to understand differences in flow/no-flow in 2026, given the unprecedented 2026 drought in Colorado. In 2026, flow/no-flow measurements were collected using ArcGIS (Geographic Information System) Survey123. Within the field app, a water observation form was created to collect coordinates and metadata about the observation. Information collected for the water observation points included repeat information from the 2025 water observation effort, including visually-assessed streamflow presence/absence (standard question obtained from Colorado State University’s StreamTracker project), flow estimate, stream or ponded area width, canopy cover, manganese films, iron seeps, beaver activity, and a new metadata component of estimated stream depth (for select locations). For 2026 water observations, this dataset contains: (1) a data file with the water observations and coordinates (2026_Water_Observations.csv); (2) a Keyhole Markup Language Zipped (KMZ) with the water observation locations and metadata (2026_Water_Observations_Locations.kmz); (3) photos (.jpg) of the water observation points, organized by location, contained within 2026_Water_Observations_FieldPhotographs.zip file; and (4) water observation protocols and figures (2026_Water_Observation_Protocols.pdf). For 2025 and 2026 water observations, this dataset contains: (1) a location metadata file (locations.csv); (6) a file-level metadata (flmd.csv) file that lists each file contained in the dataset with associated metadata; and (7) a data dictionary (dd.csv) file that contains column/row headers used throughout the files along with a definition, units, and data type. CHESS Project Description: The Colorado Headwaters Ecological Spectroscopy Study (CHESS) comprised a multi-week airborne remote sensing and field observation campaign in the Upper Gunnison Basin, Colorado, conducted in June and July of 2025. Airborne remote sensing was conducted by the National Ecological Observatory Network Airborne Observation Platform (NEON AOP), concurrent with a field campaign run by the Rocky Mountain Biological Laboratory (RMBL), the Lawrence Berkeley National Laboratory (LBNL) and SLAC National Accelerator Laboratory Watershed Function Science Focus Area (SFA), and NASA-JPL (Jet Propulsion Laboratory) Earth Surface Mineral Dust Source Investigation (EMIT) program. Between June 10 and July 18, 2025, the NEON AOP flight team collected high-resolution aerial imaging spectroscopy and Light Detection and Ranging (LiDAR) data over three domains: the Upper East River (CRBU), Almont Triangle (ALMO), and the Upper Taylor Basin (UPTA). In coordination with the flights, a field campaign acquired ground-truth observations, including observations of vegetation composition, foliar traits, forest demography, and subsurface properties in 18 core sampling areas within the domains. Additional surface water observations were taken at over 380 point locations. All CHESS campaign datasets can be found within the CHESS ESS-DIVE data portal: https://data.ess-dive.lbl.gov/portals/chess. This work was supported by the Watershed Function Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-05CH11231. 2026-09-02: This dataset was updated to include 2026 water observation measurements. The 2025 observation files were also updated to ensure a consistent file naming convention across water observation years.

2018 NEON and 2025 CHESS Campaigns↗
DOE OSTI2024

Deep learning with mixup augmentation for improved pore detection during additive manufacturing

In additive manufacturing (AM), process defects such as keyhole pores are difficult to anticipate, affecting the quality and integrity of the AM-produced materials. Hence, considerable efforts have aimed to predict these process defects by training machine learning (ML) models using passive measurements such as acoustic emissions. This work considered a dataset in which keyhole pores of a laser powder bed fusion (LPBF) experiment were identified using X-ray radiography and then registered both in space and time to acoustic measurements recorded during the LPBF experiment. Due to AM’s intrinsic process controls, where a pore-forming event is relatively rare, the acoustic datasets collected during monitoring include more non-pores than pores. In other words, the dataset for ML model development is imbalanced. Moreover, this imbalanced and sparse data phenomenon remains ubiquitous across many AM monitoring schemes since training data is nontrivial to collect. Hence, we propose a machine learning approach to improve this dataset imbalance and enhance the prediction accuracy of pore-labeled data. Specifically, we investigate how data augmentation helps predict pores and non-pores better. This imbalance is improved using recent advances in data augmentation called Mixup, a weak-supervised learning method. Convolutional neural networks (CNNs) are trained on original and augmented datasets, and an appreciable increase in performance is reported when testing on five different experimental trials. When ML models are trained on original and augmented datasets, they achieve an accuracy of 95% and 99% on test datasets, respectively. We also provide information on how dataset size affects model performance. Lastly, we investigate the optimal Mixup parameters for augmentation in the context of CNN performance.

42 ENGINEERING↗
DOE OSTI2021

Modelling the complex evaporated gas flow and its impact on particle spattering during laser powder bed fusion

The additive manufacturing (AM) of metals is becoming an increasingly important production process with the potential to replace traditional techniques such as casting. Laser Powder Bed Fusion (LPBF) is used in many applications to print metal parts from powder. The metal powder is heated locally with sufficient laser radiation that the liquid melt easily reaches its boiling temperature, which leads to a metallic vapour jet that can entrain both powder bed particles and molten droplets. The small size of laser-matter interaction site makes a detailed experimental analysis of the process challenging. Synchrotron X-ray imaging experiments are one of the few methods which can capture the dynamic melting and solidification processes. Comparing such experiments with computer simulations of the process is an important approach in order to better understand the manufacturing process and to analyse the influence of process parameters on the evaporated gas jet and the subsequent impact on particle ejection, leading to potentially reduced AM component quality. The melting and solidification of the metal powder is simulated using an Eulerian multiphase approach based on a control volume discretization of powder bed and substrate and a volume of liquid separation from melt and gas phase. The gas phase modelled as an ideal gas reaches velocities up to 100 m/s. Lagrangian particle tracking in the simulation demonstrates that the velocity fields calculated by the Eulerian multi-phase approach in combination with a standard drag-force model lead to particle accelerations in good agreement with those measured experimentally. In order to avoid numerical laborious Lagrangian calculations, a direct method to compare an Eulerian multiphase simulation with synchrotron X-ray experiments was introduced and validated. This approach is used to analyse the influence of process parameters including laser power and laser speed on the maximal acceleration of particles from the melt pool area. While the particle acceleration increases linearly with line energy in the conduction mode, a linear decrease of the acceleration with increasing line energy can be found in the transition mode before the accel-eration increases again with line energy in the keyhole mode.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Nanoparticle-enabled increase of energy efficiency during laser metal additive manufacturing

The low energy efficiency of the laser metal additive manufacturing (AM) process is a potential sustainability concern for large-scale industrial production. Explicit investigation of the energy efficiency for laser melting requires the direct characterization of melt pool dimension and vapor depression, which is very difficult due to the opaque nature of the molten metal. Here we report the direct observation and quantification of effects of the TiC nanoparticles on the vapor depression and melt pool formation during laser powder bed fusion (LPBF) of Al6061 by in-situ high-speed high-energy x-ray imaging. Based on the quantification results, we calculated the laser melting energy efficiency (defined here as the ratio of the energy needed to melt the material to the energy delivered by the laser beam) with and without TiC nanoparticles during LPBF of Al6061. Here the results show that adding TiC nanoparticles into Al6061 leads to a significant increase of laser melting energy efficiency (114% increase on average, 521% increase under 312 W laser power, 0.4 m/s scan speed). Systematic property measurement, simulation, and x-ray imaging studies enable us, for the first time, to identify that three mechanisms work together to enhance the laser melting energy efficiency: (1) adding TiC nanoparticles increases the absorptivity; (2) adding TiC nanoparticles decreases the thermal conductivity, and (3) adding TiC nanoparticles enables the initiation of vapor depression and multiple reflection at lower laser power (i.e., lowers the laser power threshold for keyholing). The method and mechanisms of using TiC nanoparticles to increase the laser melting energy efficiency during LPBF of Al6061 we reported here may guide the development of feedstock materials for more energy efficient laser metal AM.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Quantifying Equiaxed vs Epitaxial Solidification in Laser Melting of CMSX-4 Single Crystal Superalloy

The competition between epitaxial vs. equiaxed solidification has been investigated in CMSX-4 single crystal superalloy during laser melting as practiced in additive manufacturing. Single-track laser scans were performed on a powder-free surface of directionally solidified CMSX-4 alloy with several combinations of laser power and scanning velocity. Electron backscattered diffraction (EBSD) mapping facilitated identification of new orientations, i.e., “stray grains” that nucleated within the fusion zone along with their area fraction and spatial distribution. Using high-fidelity computational fluid dynamics simulations, both the temperature and fluid velocity fields within the melt pool were estimated. This information was combined with a nucleation model to determine locations where nucleation has the highest probability to occur in melt pools. In conformance with general experience in metals additive manufacturing, the as-solidified microstructure of the laser-melted tracks is dominated by epitaxial grain growth; nevertheless, stray grains were evident in elongated melt pools. It was found that, though a higher laser scanning velocity and lower power are generally helpful in the reduction of stray grains, the combination of a stable keyhole and minimal fluid velocity further mitigates stray grains in laser single tracks.

36 MATERIALS SCIENCE↗
DOE OSTI2021

A Bayesian Approach to the Eagar–Tsai Model for Melt Pool Geometry Prediction with Implications in Additive Manufacturing of Metals

Here, this paper focuses on improving the melt pool geometry predictions and quantifying uncertainties using an adapted version of the Eagar–Tsai (E–T) model that incorporates temperature-dependent properties of the material as well as powder conditions. Additionally, Bayesian inference is employed to predict distributions for the E–T model input parameters of laser absorptivity and powder bed porosity by incorporating experimental results into the analysis. Monte Carlo uncertainty propagation is then used with these parameter distributions to estimate the melt pool depth and associated uncertainty. Our results for the 316L stainless steel suggest that both the absorptivity and powder bed porosity are strongly influenced by the laser power. In contrast, the scanning speed has only a marginal effect on both the absorptivity and powder bed porosity. We constructed a printability map using the Bayesian E–T model based on power-dependent input parameter values to demonstrate the merit of the approach. The Bayesian approach improved the accuracy in predicting the keyhole regions in the laser power-scan speed parameter space for the 316L stainless steel. Although applied to a specific adaptation of the E–T model, the method put forth can be extended to quantify uncertainties in other numerical models as well as in the estimation of unknown parameters.

316L Stainless Steel (SS).↗
DOE OSTI2022

Laser spot welding of additive manufactured 304L stainless steel

Here, the goal of this work is to understand if an additively manufactured 304L stainless steel exhibits similar spot-welding behavior as wrought 304L stainless steel. Due to the many differences between an additively manufactured component and wrought product, it is important to determine how the material interacts with the laser and how it affects the weld bead morphology. In this paper, the laser coupling efficiency, weld size, and solidification of spot welds produced in wrought and additively manufactured 304L stainless steel were investigated. The coupling efficiency of wrought and additively manufactured 304L stainless steel of similar surface condition were approximately the same over a range of applied laser energies. Laser welding of the untreated (rougher) surface of additively manufactured 304L, however, showed improved coupling efficiency ranging between 3.3 and 100%. The rougher surface traps the incoming light and increases the coupling efficiency at lower laser energies, while at higher energy, the absorption efficiency is dominated by intrinsic absorption from the keyhole formation rather than surface roughness. The resulting spot weld microstructures differed from welds made in wrought 304L and additively manufactured 304L. Welds made in wrought 304L were fully austenitic containing what is suspected to be massive austenite, which suggests that these welds solidified as primary ferrite. Welds made in additively manufactured 304L were also fully austenitic and contained both cellular austenite and what is suspected to be massive austenite. These observations mean that welds made in additively manufactured 304L solidified as primary ferrite and primary austenite. The differences in weld microstructures made in wrought and AM 304L can be attributed to differences in the composition and solidification rate.

304L stainless steel↗
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 OSTI2020

Correlation of SWIR imaging with LPBF 304L stainless steel part properties

In the Laser Powder Bed Fusion (LPBF) process, the local thermal history can vary significantly over a part as the heat transfer characteristics and the laser scan path are geometry dependent. The variations introduce the potential for defects that lead to part failure, some of which are difficult to identify non-destructively with common ex-situ evaluation techniques. These defects include significant microstructural and mechanical property differences in the part interiors. In this paper, thermal features are extracted from in-situ Short-Wave Infrared (SWIR) imaging measurements to compile voxel based part representations and understand how the complexities in the thermal history affect part performance. The deviations in thermal features due to different laser processing parameters and complex scan pathing are explored. Empirical correlations are developed to map thermal features with the engineering properties (bulk yield strength, area percentage porosity, and local state) of 304L stainless steel parts manufactured by LPBF. Processing modes (insufficient melting and keyholing) are determined by mapping part property measurements with multiple thermal features. Generating the relationships between thermographic measurements and resulting SLM part properties lays the foundation for in-situ part qualification.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Laser powder bed fusion of Inconel 718 on 316 stainless steel

The joining of Inconel 718 and 316 stainless steel has notable industrial importance. However, traditional arc-welding has many issues. In particular, the formation of the detrimental Nb-rich Laves phase in the heat affected zone can promote crack initiation and propagation, which leads to poor mechanical properties. The small heat input and the rapid solidification of laser powder bed fusion (PBF) has the potential to suppress this problem. Despite the infancy of the multi-material PBF, it is of interest to investigate the relationship among the process parameter, the microstructure at the materials joining point, and the mechanical performance. In this study, we used high-speed x-ray imaging to in-situ monitor the printing of Inconel 718 tracks on a 316H SS substrate via laser powder bed fusion, followed by post-build characterization and testing with SEM, XRD and nanoindentation. We observed that a higher linear energy density resulted in a more diffused interface and a smaller compositional gradient, but as a trade-off, generated more keyhole pores and thermal cracks. Furthermore, we also showed that the built layer exhibited chemical inhomogeneity at two length scales: a tens-of-mu m-scale compositional vortex due to the melt flow, and a sub-mu m-scale Nb-rich dendritic structure due to the non-equilibrium solidification that led to the formation of the Laves phase. It was inferred that the chemical inhomogeneity may benefit the mechanical properties at the interface by providing more interlocking between the two materials and also by limiting the effect of the brittle Laves phase to potentially short range.

36 MATERIALS SCIENCE↗