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At least 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Localized keyhole pore prediction during laser powder bed fusion via multimodal process monitoring and X-ray radiography

Systematic fault detection and control during laser powder bed fusion (L-PBF) has been a long-standing objective for system manufacturers and researchers in the additive manufacturing (AM) industry. This manuscript investigates a data fusion approach for detection of keyhole porosity formation during laser irradiation of Ti-6Al-4V substrates by concurrent recording of thermally induced optical emission measured using both off-axis and coaxial photodiode sensors, and acoustic emission. Subsurface defect formation was monitored via high-speed synchrotron X-ray imaging at 20,000 frames per second, enabling temporal registration of keyhole pore formation events to the monitoring signals at a resolution of 50 µs. We developed data fusion machine learning (ML) models for localized prediction of keyhole pore formation at various time scales ranging from 0.5 ms to 2 ms. The signal segments were featurized using two independent approaches: (1) power spectral density (PSD) and (2) highly comparative time series analysis (HCTSA) framework. The extracted features from different sensor modalities were fused together to construct a multimodal feature space and sequential feature selection was used to determine the most informative features for training the ML models. The predictive performance was evaluated for three classifying algorithms: Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Gaussian Naive Bayes (GNB). As a result, pore formation events were predicted with up to 0.95 F1-score, 1.0 recall and 0.94 accuracy. The most heavily weighted features indicate that model performance is chiefly governed by the acoustic monitoring signal, with a secondary contribution from the optical emission sensors.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Prediction of microstructure formation in laser powder bed fusion process.

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

36 MATERIALS SCIENCE↗
DOE OSTI2025

A high strength Al-2Ni-0.5Zr conductor alloy fabricated via laser powder bed fusion

There is a current need for new aluminum alloy design strategies to target applications requiring high strength and conductivity with reductions in mass. A new lightweight Al-2Ni-0.5Zr (wt. %) conductor alloy was fabricated using laser powder bed fusion. A design of experiments probed the alloy's solidification cracking susceptibility. It was observed that solidification cracking was generally reduced with fast scan speeds, above 1500 mm/s, and smaller hatch spacings. The different cooling rates throughout the melt pool produced a heterogeneous distribution of cellular and equiaxed Al 3 Ni precipitates in the as-printed alloy. Additionally, the rapid solidification characteristic of laser powder bed fusion created a super-saturated Zr solid solution. An aging heat treatment at 375 °C for 24 h imparted strengthening through the precipitation of L1 2 -Al 3 Zr nanoprecipitates, which counteracted the softening caused by the fragmentation and coarsening of Al 3 Ni precipitates. The yield strength increased from 138 MPa in the as-printed condition to 168 MPa after aging, while the ductility remained constant at ∼21%. The aging treatment simultaneously increased the electrical conductivity from 40.8% IACS (International Annealed Copper Standard) to 53.5% IACS. Modeling of the strengthening mechanisms and electrical conductivity contributions rationalized the simultaneous increase in strength and conductivity upon aging. Furthermore, the strengthening efficacy of the Al 3 Ni and L1 2 -Al 3 Zr precipitates, combined with the low Ni and Zr solubility in the FCC Al matrix, facilitated both high strength and electrical conductivity. Overall, the combination of strength and electrical conductivity positions this alloy as a suitable choice for additively manufactured lightweight conductors.

Additive manufacturing↗
DOE OSTI2024

Solidification and crystallographic texture modeling of laser powder bed fusion Ti-6Al-4V using finite difference-monte carlo method

Laser powder bed fusion (LPBF) additive manufacturing makes near-net-shaped parts with reduced material cost and time, rising as a promising technology to fabricate Ti-6Al-4V, a widely used titanium alloy in aerospace and medical industries. However, LPBF Ti-6Al-4V parts produced with 67° rotation between layers, a scan strategy commonly used to reduce microstructure and property inhomogeneity, have varying grain morphologies and weak crystallographic textures that change depending on processing parameters. Here, this study predicts LPBF Ti-6Al-4V solidification at three energy levels using a finite difference-Monte Carlo method and validates the simulations with large-area electron backscatter diffraction (EBSD) scans. The developed model accurately shows that a <001> texture forms at low energy and a <111> texture occurs at higher energies parallel to the build direction but with a lower strength than the textures observed from EBSD. A validated and well-established method of combining spatial correlation and general spherical harmonics representation of texture is developed to calculate a difference score between simulations and experiments. The quantitative comparison enables effective fine-tuning of nucleation density (N 0 ) input, which shows a nonlinear relationship with increasing energy level. Future improvements in texture prediction code and a more comprehensive study of N 0 with different energy levels will further advance the optimization of LPBF Ti-6Al-4V components. These developments contribute a novel understanding of crystallographic texture formation in LPBF Ti-6Al-4V, the development of robust model validation and calibration pipeline methodologies, and provide a platform for mechanical property prediction and process parameter optimization.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Laser Powder Bed Fusion of Steels for Nuclear Applications

This report provides an update on laser powder bed fusion of steels for nuclear applications. This report is a Milestone 3 deliverable in FY2024, under work package CT-24AN130401 to support research and qualification activities supported by the Advanced Materials and Manufacturing Technologies (AMMT) program here at Argonne National Laboratory (ANL). Continuing from FY23, the focus of FY24 for ANL includes further optimization of process parameters for three alloys: A709, G91, G92. The work package also includes fabricating test samples to conduct a thorough microstructural analysis and perform preliminary mechanical testing. For the same, we have used CALPHAD-based thermodynamic kinetic modeling to optimize the experimental activities.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Effect of Processing Parameters on Recrystallization During Hot Isostatic Pressing of Stellite-6 Fabricated Using Laser Powder Bed Fusion Technique

Crack-free Stellite-6 alloy was fabricated using the laser powder bed fusion technique equipped with a heating module as the first attempt. Single tracks were printed with a build plate heated to 400 °C to identify the processing window. Based on the melt pool dimensions, two combinations (sample A: 300 W/750 mm/s and sample B: 275 W/1000 mm/s) were identified to print the cubes. The as-printed microstructure comprised FCC-Co dendrites with M7C3 in the interdendritic region. W-rich M6C particles were found in the overlapping regions between the melt pools, matching the Scheil simulations. However, gas pores were observed due to the higher nitrogen and oxygen content of the feedstock requiring hot isostatic pressing (HIP) at 1250 °C and 150 MPa for 2 h. Sample A was partially recrystallized with slightly coarsened M7C3, while sample B underwent complete recrystallization followed by grain growth along with higher coarsening of the M7C3 after HIP. The varying recrystallization behavior can be attributed to the difference in residual stresses and grain aspect ratio in the as-built condition dictated by laser power and scanning speed. The microhardness after HIP was slightly higher than its wrought counterpart, indicating no severe impact of post-processing on the properties of Stellite-6 alloy.

Sridar, Soumya (ORCID:0000000306775408)↗
DOE OSTI2025

Fatigue behavior of low-cost, non-spherical Ti-6Al-4V powder processed via laser powder bed fusion

Hydride-dehydride Ti-6Al-4V powder with particle size distribution of 75–175 μm were used to manufacture fatigue samples. Optimized processing parameters were used to maintain a relative density of >99.5% in the samples. Fatigue tests were carried out in R = σ min /σ max = -1 condition under stresses ranging from 150 to 500 MPa and the results were compared to samples that were additively manufactured using spherical powders. The fatigue lives were similar between the two types of powder; i.e., powder morphology has no effect on fatigue performance. However, the high surface roughness and martensitic microstructure resulting from the laser powder bed fusion process result in poor performance compared to samples machined from conventionally produced materials. In conclusion, crack initiation occurred consistently from surface defects because of high surface roughness of the additively manufactured parts.

36 MATERIALS SCIENCE↗
DOE OSTI2022

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

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

36 MATERIALS SCIENCE↗
DOE OSTI2020

Towards improved speed and accuracy of laser powder bed fusion simulations via representation of multiple time scales

Here, we report the result of investigations into alternative representations of the temporal evolution of the laser powder bed fusion (LPBF) process. In order to provide the reader with sufficient context for the following discussion, the modeling challenge and its underlying cause, as well as current attempts to provide a satisfactory solution, will be discussed. Next, the author will describe two numerical methods (multirate time integration and parallel-in-time) to better represent the temporal scales of the problem. Finally, the results of applying these methods to a two-dimensional version of the heat transfer portion of the LPBF problem will be presented, reporting on both convergence and performance behavior, which indicate the possibility of significant speedup of solution calculations (40–100 times), along with well-characterized effects on solution accuracy.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
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.↗
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↗