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At least 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
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 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 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 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.↗
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↗
DOE OSTI2024

Investigation of Microstructures and Tensile Properties of 316L Stainless Steel Fabricated via Laser Powder Bed Fusion

In this study, a thorough investigation of the microstructures and tensile properties of 316L stainless steel fabricated via laser powder bed fusion (L-PBF) was done. 316L stainless steel specimens with two different thicknesses of 1.5 mm and 4.0 mm fabricated under similar conditions were utilized. Microstructural characterization was performed using optical microscopy (OM) and scanning electron microscopy (SEM) equipped with electron backscatter diffraction (EBSD). Melt pools and cellular structures were observed using OM, whereas EBSD was utilized to obtain the grain size, grain boundary characteristics, and crystallographic texture. The 1.5 mm thick sample demonstrated a yield strength (YS) of 538.42 MPa, ultimate tensile strength (UTS) of 606.47 MPa, and elongation to failure of 69.88%, whereas the 4.0 mm thick sample had a YS of 551.21 MPa, UTS of 619.58 MPa, and elongation to failure of 73.66%. These results demonstrated a slight decrease in mechanical properties with decreasing thickness, with a 2.4% reduction in YS, 2.1% reduction in UTS, and 5.8% reduction in elongation to failure. In addition to other microstructural features, the cellular structures were observed to be the major contributors to the high mechanical properties. Using the inverse pole figure (IPF) maps, both thicknesses depicted a crystallographic texture of {001} <101> in their as-built state. However, when subjected to tensile loads, texture transitions to {111} <001> and {111} <011> were observed for the 1.5 mm and 4.0 mm samples, respectively. Additionally, EBSD analysis revealed the pre-existence of high-density dislocation networks and a high fraction of low-angle grain boundaries. Interestingly, twinning was observed, suggesting that the plastic deformation occurred through dislocation gliding and deformation twinning.

316L stainless steel↗
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 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↗
DOE OSTI2026

Which way does the dendrite grow? Competition among epitaxy, preferred growth direction, and thermal gradients in powder bed fusion additive manufacturing

The as-processed microstructure of metal alloy parts manufactured through laser powder bed fusion (LPBF) is heavily derived from the cellular dendritic solidification. The growth direction of dendrites within the melt pool is determined through competition among epitaxial growth, preferred growth directions, and maximum thermal gradients. However, the dominant factor and the specific role of each in developing melt pool microstructures remain unknown. Here, in this study, we performed single laser track scans on an SS316L single crystal substrate and combined experimental characterization of microstructure and crystal orientations with Computational Fluid Dynamics simulations of thermal gradients to evaluate the role of each factor in determining dendritic growth direction and evolution. Our results reveal that epitaxial growth dominates microstructure development by preferentially growing along a single 〈100〉 variant of the single crystal substrate adjacent to the melt pool boundary. Under LPBF’s highly curved and rapidly evolving thermal field, this preferential dendrite variant selection and its continued growth from the melt pool boundary to the centerline are governed by the local temperature gradient magnitude at the solid-liquid interface, rather than by the instantaneous maximum temperature gradient direction alone. Using these findings, we successfully predict changes in the dendrite growth direction with changing laser scan direction on a single crystal substrate, and show that the geometric melt pool centerline can deviate from the microstructural centerline because asymmetric local temperature gradient magnitudes transiently limit growth, resulting in different dendrite travel distances on each side of the melt pool.

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 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↗
DOE OSTI2022

Study of the Printability, Microstructures, and Mechanical Performances of Laser Powder Bed Fusion Built Haynes 230

The nickel-based superalloy, Haynes 230 (H230), is widely used in high-temperature applications, e.g., heat exchangers, because of its excellent high-temperature mechanical properties and corrosion resistance. As of today, H230 is not yet in common use for 3D printing, i.e., metal additive manufacturing (AM), primarily because of its hot cracking tendency under fast solidification. The ability to additively fabricate components in H230 attracts many applications that require the additional advantages leveraged by adopting AM, e.g., higher design complexity and faster prototyping. In this study, we fabricated nearly fully dense H230 in a laser powder bed fusion (L-PBF) process through parameter optimization. The efforts revealed the optimal process space which could guide future fabrication of H230 in various metal powder bed fusion processes. The metallurgical analysis identified the cracking problem, which was resolved by increasing the pre-heat temperature from 80 °C to 200 °C. A finite element simulation suggested that the pre-heat temperature has limited impacts on the maximum stress experienced by each location during solidification. Additionally, the crack morphology and the microstructural features imply that solidification and liquation cracking are the more probable mechanisms. Both the room temperature tensile test and the creep tests under two conditions, (a) 760 °C and 100 MPa and (b) 816 °C and 121 MPa, confirmed that the AM H230 has properties comparable to its wrought counterpart. The fractography showed that the heat treatment (anneal at 1200 °C for 2 h, followed by water quench) balances the strength and the ductility, while the printing defects did not appreciably accelerate part failure.

14 SOLAR ENERGY↗
DOE OSTI2021

Deformation and fracture behavior of a laser powder bed fusion processed stainless steel: In situ synchrotron x-ray computed microtomography study

Tensile plastic deformation and fracture behavior of a laser powder bed fusion processed 316L stainless steel alloy was investigated in situ using high-resolution synchrotron x-ray computed microtomography (sXCT). Three different cases were studied: (1) specimen A1, near full-density with initial porosity of 0.04%, (2) specimen B1, initial lack-of-fusion (LOF) porosity of about 3% with the major axis of defects aligned nearly perpendicular to the tensile loading direction (LD), and (3) specimen B, similar to B1 but with the major axis of LOF defects aligned nearly parallel to LD. The effects of type, density, and orientation of defects in as-printed samples on plastic deformation and failure modes were studied by analyzing sXCT image sequences and quantitative defect properties. Specifically, the evolutions of porosity, size, aspect ratio, and angle of gas pores and LOF defects were characterized as a function of applied strain. Moreover, interactions between as-printed defects and deformation-induced crack propagation were investigated. The tensile behavior of specimen A1 was comparable to that of a commercial wrought plate. Furthermore, the quantitative in-situ sXCT results showed a void-growth phenomenon in A1 but without noticeable coalescence, resulting in a ductile cup-cone failure. Specimen B1 showed damage-driven brittle failure with limited plasticity due to early and rapid propagation of open voids. On the other hand, specimen B, with a similar amount of LOF defects as B1, showed deceivingly strong and ductile tensile behavior similar to A1 without any major crack propagations due to the favorable orientation relationship between LOF defects and the LD.

36 MATERIALS SCIENCE↗
DOE OSTI2022

On the thermal coarsening and transformation of nanoscale oxide inclusions in 316L stainless steel manufactured by laser powder bed fusion and its influence on impact toughness

The thermal evolution of nanoscale oxide inclusions in 316L stainless steel (SS) manufactured by laser powder bed fusion additive manufacturing (AM) was explored. The size, chemical composition, morphology, and distribution of the oxides were characterized as the function of heat treatment conditions. The study revealed the mechanistic driving force of the rapid oxide coarsening during recrystallization. Ostwald ripening governs oxide coarsening. The active grain boundary-oxide interaction at the early stage of recrystallization accelerated oxide coarsening via enhanced solute diffusion along grain boundaries. Pipe diffusion along dislocation cellular boundaries has a negligible contribution to oxide coarsening. At high temperatures (T> 1065 °C), although lattice diffusion primarily controlled the oxide growth, the contribution from the grain-boundary diffusion was necessary. The transformation from MnSiO 3 to CrMn 2 O 4 took place in the un-recrystallized grains but was not observed when recrystallization started. The interaction of grain boundary and oxides during recrystallization resulted in a high fraction of oxides accumulated at grain boundaries. While oxide coarsening does not significantly alter the toughness value, grain-boundary oxides promote microvoid formation and intergranular fracture under Charpy impact in the recrystallized AM 316L SS.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Design of heterogeneous structured Al alloys with wide processing window for laser-powder bed fusion additive manufacturing

Required microstructural attributes of an alloy vary with structural applications. The microstructural fine-tuning capability of laser-powder bed fusion (L-PBF) additive manufacturing (AM) enables application specific manufacture of the components. Such manufacture with L-PBF AM requires alloys that exhibit wide processing window and are amenable to multiple deformation mechanisms. However, high hot cracking susceptibility of Al alloys poses a barrier to such printability-performance synergy. In this work we show that an integration of, a) grain refinement through heterogeneous nucleation, and b) eutectic solidification, leads to crack-free parts at wide range of process parameters, microstructural heterogeneity, and hierarchy in the Al-Ni-Ti-Zr alloy. Such an integration targets hot cracking at multiple stages of solidification in L-PBF as opposed to the contemporary alloy design strategies that target hot-cracking at only specific stages of solidification. The Al-Ni-Ti-Zr alloy exhibits excellent printability and a high as-built tensile performance. Due to the wide processing window and amenability to multiple deformation mechanisms, the alloy microstructure and subsequently the performance, can be fine-tuned. Here, such strategy opens the gateway for application-specific manufacture of Al alloys with L-PBF AM and establishes a fundamental shift in current methodologies for design of these alloys for L-PBF AM.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Defects and anomalies in powder bed fusion metal additive manufacturing

Metal additive manufacturing is a disruptive technology that is revolutionizing the manufacturing industry. Despite its unrivaled capability for directly fabricating metal parts with complex geometries, the wide realization of the technology is currently limited by microstructural defects and anomalies, which could significantly degrade the structural integrity and service performance of the product. Accurate detection, characterization, and prediction of these defects and anomalies have an important and immediate impact in manufacturing fully-dense and defect-free builds. As such, this review seeks to elucidate common defects/anomalies and their formation mechanisms in powder bed fusion additive manufacturing processes. They could arise from raw materials, processing conditions, and post-processing. While defects/anomalies in laser welding have been studied extensively, their formation and evolution remain unclear. Additionally, the existence of powder in powder bed fusion techniques may generate new types of defects, e.g., porosity transferring from powder to builds. Practical strategies to mitigate defects are also addressed through fundamental understanding of their formation. Such explorations enable the validation and calibration of models and ease the process qualification without costly trial-and-error experimentation.

42 ENGINEERING↗
DOE OSTI2022

Monitoring and prediction of porosity in laser powder bed fusion using physics-informed meltpool signatures and machine learning

In this work we accomplished the monitoring and prediction of porosity in laser powder bed fusion (LPBF) additive manufacturing process. This objective was realized by extracting physics-informed meltpool signatures from an in-situ dual-wavelength imaging pyrometer, and subsequently, analyzing these signatures via computationally tractable machine learning approaches. Porosity in LPBF occurs despite extensive optimization of processing conditions due to stochastic causes. Hence, it is essential to continually monitor the process with in-situ sensors for detecting and mitigating incipient pore formation. In this work a tall cuboid-shaped part (10 mm × 10 mm × 137 mm, material ATI 718Plus) was built with controlled porosity by varying laser power and scanning speed. This test caused various types of porosity, such as lack-of-fusion and keyhole formation, with varying degrees of severity in the part. The meltpool was continuously monitored using a dual-wavelength imaging pyrometer installed in the machine. Physically intuitive process signatures, such as meltpool length, temperature distribution, and ejecta (spatter) characteristics, were extracted from the meltpool images. Subsequently, relatively simple machine learning models, e.g., K-Nearest Neighbors, were trained to predict both the severity and type of porosity as a function of these physics-informed meltpool signatures. These models resulted in a prediction accuracy exceeding 95% (statistical F1-score). The same analysis was carried out with a complex, black-box deep learning convolutional neural network which directly used the meltpool images instead of physics-informed features. The convolutional neural network produced a comparable F1-score in the range of 89–97%. Finally, these results demonstrate that using pragmatic, physics-informed meltpool signatures within a simple machine learning model is as effective for flaw prediction in LPBF as using a complex and computationally demanding black-box deep learning model.

36 MATERIALS SCIENCE↗