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DOE OSTI2025

A high-throughput approach for statistical process optimization in Laser Powder Bed Fusion

Process variability is inherent in metal additive manufacturing (AM). However, it is often overlooked in process optimization frameworks, constraining the understanding of process uncertainties and their influence on parameter selection. To address this, we present an integrated framework that combines high-throughput single-track experiments, GAN-based melt pool geometry extraction, robust statistical and machine learning modeling, and uncertainty-quantified process mapping. Process variability is characterized through single-track melt pool behaviors, and its influence on defect formation is systematically quantified to enable statistically guided process parameter optimization. This approach is demonstrated on Laser Powder Bed Fusion (L-PBF) of stainless steel 316L, effectively capturing the interplay between process parameters, melt pool variability, and defect probability. By integrating uncertainty quantification into process optimization, this study provides a structured methodology for addressing variability challenges in AM quality control, ultimately contributing to enhanced manufacturing reliability.

Laser Powder Bed Fusion↗

DOE OSTI2025

Nanoscale interfacial melting enables bonding during high velocity microparticle impacts

Melting during high-velocity particle impact has been understood to be typically detrimental to bonding by lowering the strength at the interface and promoting rebound before solidification can occur. Here we establish a possible remedy to this challenge: by dramatically restricting the volume of molten material, its resolidification is accelerated, effectively forming a nanoscale, braze-type joint during impact. In-situ single particle impact imaging is combined with post-mortem structural and chemical analyses to reveal a regime where adhesion is governed not by extensive plastic deformation, but by the kinetics of melt layer resolidification. Furthermore, these findings redefine the role of melting in impact-based processes, establishing transient melting and rapid solidification as a viable strategy for engineering successful adhesion events.

Additive manufacturing↗

DOE OSTI2020

The origin and formation of oxygen inclusions in austenitic stainless steels manufactured by laser powder bed fusion

The origins of nano-scale oxide inclusions in 316L austenitic stainless steel (SS) manufactured by laser powder bed fusion (L-PBF) was investigated by quantifying the possible intrusion pathways of oxygen contained in the precursor powder, extraneous oxygen from the process environment during laser processing, and moisture contamination during powder handling and storage. When processing the fresh, as-received powder in a wellcontrolled environment, the oxide inclusions contained in the precursor powder were the primary contributors to the formation of nano-scale oxides in the final additive manufactured (AM) product. These oxide inclusions were found to be enriched with oxygen getter elements like Si and Mn. By controlling the extraneous oxygen level in the process environment, the oxygen level in AM produced parts was found to increase with the extraneous oxygen level. The intrusion pathway of this extra oxygen was found to be dominated by the incorporation of spatter particles into the build during processing. Moisture induced oxidation during powder storage was also found to result in a higher oxide density in the AM produced parts. SS 316L powder free of Si and Mn oxygen getters was processed in a well-controlled environment and resulted in a similar level of oxygen intrusion. Finally, microhardness testing indicated that the oxide volume fraction increase from extraneous oxygen did not influence hardness values. However, a marked decrease in hardness was found for the humidified and Si-Mn free AM processed parts.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Ultrafast laser-induced spin–lattice dynamics in the van der Waals antiferromagnet CoPS 3

CoPS 3 stands out in the family of the van der Waals antiferromagnets XPS 3 (X = Mn, Ni, Fe, and Co) due to the unquenched orbital momentum of the magnetic Co 2+ ions, which is known to facilitate the coupling of spins to both electromagnetic waves and lattice vibrations. Here, using a time-resolved magneto-optical pump–probe technique, we experimentally study the ultrafast laser-induced dynamics of mutually correlated spins and lattice. It is shown that a femtosecond laser pulse acts as an ultrafast heater and, thus, results in the melting of the antiferromagnetic order. At the same time, the resonant pumping of the 4 T 1g → 4 T 2g electronic transition in Co 2+ ions effectively changes their orbital momentum, giving rise to a mechanical force that moves the ions in the direction parallel to the orientation of their spins, thus generating a coherent B g phonon mode at the frequency of about 4.7 THz.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2023

Corrosion testing needs and considerations for additively manufactured materials in nuclear reactors

Metal additive manufacturing holds significant promise as an enabling technology for the 21st century nuclear energy industry. Metal additive manufacturing (MAM) can allow the fabrication of novel materials and innovative component designs that are not achievable through conventional manufacturing. Due to its very different fabrication methods, as-fabricated MAM components are characteristically different from conventionally manufactured components. MAM materials exhibit very different microstructures from conventional cast or wrought materials. For example, austenitic stainless steels fabricated by laser powder bed fusion exhibit columnar grain structures, dislocation cell structures, and melt pool fingerprints. In addition, MAM fabrication may result in defects such as porosity, incomplete processing of the feedstock (e.g., lack of fusion in melt-based methods), and oxide inclusions. Heat treatments may further evolve the microstructure, microsegregation, and stresses within the component. Furthermore, MAM components have a rough surface with feature sizes on the order of the feedstock material, as opposed to smooth surfaces resulting from conventional machining and forming operations. As a result, the corrosion behavior of MAM components will likely be significantly different from that of conventionally formed components. Corrosive environments for structural materials within advanced reactor environments include molten fluoride and chloride salts, liquid sodium and lead-bismuth, and high-temperature helium. The Advanced Materials and Manufacturing Technologies program within the Department of Nuclear Energy in the United States Department of Energy is assessing the unique concerns of MAM component corrosion and testing methodologies in advanced nuclear reactor environments. We discuss these concerns and testing strategies in this paper.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2023

Detection of Off-Gassed Products From Molten Salts Using Laser-Induced Breakdown Spectroscopy

The detection of off-gassed sodium from molten sodium nitrate (NaNO 3 ) at temperatures between 330 °C and 505 °C and off-gassed calcium from molten lithium chloride–potassium chloride eutectic (LKE) mixtures at 510 °C with laser-induced breakdown spectroscopy (LIBS) was demonstrated. NaNO 3 and LKE samples were melted in a custom-built crucible that promoted the generation of off-gassed products from the molten sample. The off-gassed products were analyzed with a LIBS system designed to probe the high-temperature environment. Na D emission lines, Na(I)588.99 nm and Na(I) 589.59 nm, were detected from the NaNO 3 samples after reaching a temperature threshold, which indicated the occurrence of phase change. In LKE mixtures, the detection of Ca impurities at a concentration of 78 mg/kg was possible using the emission lines Ca(II) 393.66 nm and Ca(II) 395.85 nm. Furthermore, this work demonstrates the real-time monitoring capabilities of LIBS in high-temperature environments that simulate the conditions of molten salt reactors.

42 ENGINEERING↗

DOE OSTI2020

Accelerating Large-Format Metal Additive Manufacturing: How Controls R&D is Driving Speed, Scale, and Efficiency

This article highlights work at Oak Ridge National Laboratory’s Manufacturing Demonstration Facility to develop closed-loop, feedback control for laser-wire based Directed Energy Deposition, a form of metal Big Area Additive Manufacturing (m-BAAM), a process being developed in partnership with GKN Aerospace specifically for the production of Ti-6Al-4V pre-forms for aerospace components. A large-scale structural demonstrator component is presented as a case-study in which not just control, but the entire 3D printing workflow for m-BAAM is discussed in detail, including design principles for large-format metal AM, toolpath generation, parameter development, process control, and system operation, as well as post-print net-shape geometric analysis and finish machining. In terms of control, a multi-sensor approach has been utilized to measure both layer height and melt pool size, and multiple modes of closed-loop control have been developed to manipulate process parameters (laser power, print speed, deposition rate) to control these variables. Layer height control and melt pool size control have yielded excellent local (intralayer) and global (component-level) geometry control, and the impact of melt pool size control in particular on thermal gradients and material properties is the subject of continuing research. Further, these modes of control have allowed the process to advance to higher deposition rates (exceeding 7.5 lb/hr), larger parts (1-meter scale), shorter build times, and higher overall efficiency. The control modes are examined individually, highlighting their development, demonstration, and lessons learned, and it is shown how they operate concurrently to enable the printing of a large-scale, near net shape Ti-6Al-4V component.

Gibson, Brian↗

DOE OSTI2024

Phase separation during the direct powder bed fusion of SiC

Powder bed fusion (PBF) is an attractive additive manufacturing option for fabrication of SiC object with complex geometries. However, the density and microstructure controls remain a challenge. This study is aimed at understanding laser–SiC interactions, with emphasis on microstructure-processing relationships, to identify potential solutions for the process improvement. SiC tubes were fabricated by PBF of pure SiC powders without sintering additives. Further, comprehensive analysis by X-ray diffraction, Raman spectroscopy, and electron microscopy indicated that binding of SiC particles was achieved by incongruent melting of SiC to a Si/C mixture containing SiC micro- and nanocrystallites. The phase evolution under laser irradiation of SiC was explained by phase diagrams. This study uncovered the PBF SiC microstructure at different length scales and the relationship between the microstructure and the processing parameters.

36 MATERIALS SCIENCE↗

DOE OSTI2020

In Situ Analysis of Laser Powder Bed Fusion Using Simultaneous High-Speed Infrared and X-ray Imaging

Laser powder bed fusion is a metal additive manufacturing technique that has received significant scientific and industrial attention over the past decades. However, the quality and reproducibility of parts manufactured by this technique is still a problem. Overcoming this issue requires an understanding of multiple complex physical phenomena which occur simultaneously during the process. Here, we illustrate a powerful new technique which synchronizes high-speed x-ray imaging with high-speed infrared imaging to study laser powder bed fusion processes in real time. Using this technique, we demonstrate the simultaneous observation of multiple phenomena including three-dimensional melt pool visualization, vapor plume dynamics, spatter formation, thermal history, and point cooling rates. The paired observation of these dynamic phenomena is critical to understanding the fundamentals of laser powder bed fusion, and the overall impact of process parameters on print quality.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Electron Beam Powder Bed Fusion Additive Manufacturing of Refractory Metals

The processing of refractory metals via additive manufacturing (AM) presents an opportunity to fabricatecomponents not possible through traditional powder metallurgy techniques. However, processing via AM isextremely challenging and there exist a host of technological limitations still unaddressed. Molybdenumand tungsten are two materials of particular interest for nuclear energy generation applications. Themajority of existing work on the processing of these materials is focused on laser powder bed fusion whereasa smaller body of work exists for electron beam melting AM. In general, material with acceptable densitycan be obtained via both routes however cracking is much more difficult to mitigate against. Strongcrystallographic fiber textures have been observed across a range of refractory systems. In this work wepresent our recent findings on electron beam melting AM of pure tungsten and molybdenum. Wedemonstrate that defect-free material can be obtained in both systems with appropriate processing. Strongbuild direction crystallographic texture fibers are observed in both systems. In molybdenum the fibertexture has direct impact on the resulting tensile behavior. These results demonstrate that electron beamAM is a suitable technology for processing these difficult to fabricate materials.

Fernandez-Zelaia, Patxi↗

DOE OSTI2024

Deep-learning based artificial intelligence tool for melt pools and defect segmentation

Accelerating fabrication of additively manufactured components with precise microstructures is important for quality and qualification of built parts, as well as for a fundamental understanding of process improvement. Accomplishing this requires fast and robust characterization of melt pool geometries and structural defects in images. This paper proposes a pragmatic approach based on implementation of deep learning models and self-consistent workflow that enable systematic segmentation of defects and melt pools in optical images. Deep learning is based on an image-to-image translation–conditional generative adversarial neural network architecture. An artificial intelligence (AI) tool based on this deep learning model enables fast and incrementally more accurate predictions of the prevalent geometric features, including melt pool boundaries and printing-induced structural defects. We present statistical analysis of geometric features that is enabled by the AI tool, showing strong spatial correlation of defects and the melt pool boundaries. The correlations of widths and heights of melt pools with dataset processing parameters show the highest sensitivity to thermal influences resulting from laser passes in adjacent and subsequent layer passes. The presented models and tools are demonstrated on the aluminum alloy and datasets produced with different sets of processing parameters. However, they have universal quality and could easily be adapted to different material compositions. The method can be easily generalized to microstructural characterizations other than optical microscopy.

additive manufacturing↗

DOE OSTI2024

Direct Visualization of Laser-Induced Coalescence and Alloying of Au into Ag Nanowires

Noble metal nanocomposites with controlled morphology and composition have been largely useful in a wide variety of applications like catalysis, plasmonics, and biological applications. Herein, we show pulsed-laser-assisted fabrication of beaded Au-Ag nanowires with a variety of morphologies and compositions via coalescence and alloying of Au nanoparticles (NP) into a Ag nanowire (NW). Direct visualization of the evolution process with in situ laser heating transmission electron microscopy (ILH-TEM) leads to unambiguous insights into the mechanism of formation for the first time. Ag, being a soft material, melts at the surface to bridge the gap between the NP-NW, forming a cup-like morphology underneath the Au NP via capillary action. Progressive laser irradiation leads to wetting of the Au NP with Ag, forming an envelope around the NP. This is associated with the formation of a valley in Ag NW around the NP, which flattens gradually by partial embedding of the NP. Interdiffusion of Au into Ag and vice versa sets in at this stage, leading to the depletion of Au from the Au-rich NP region. Prolonged irradiation leads to gradual intermixing of Au-Ag forming beaded Au-doped Ag nanowires with homogeneous composition. Such a step-by-step understanding of the coalescence and alloying process has implications in nanowelding, which holds the future in designing efficient transparent conductors and printed electronics.

Isik, Tugba↗

DOE OSTI2026

Melting temperature of bismuth to 55 GPa using synchrotron X-ray phase contrast imaging

The melting temperature of elemental bismuth under high pressure has been measured to 55 GPa using synchrotron X-ray phase-contrast imaging in the laser-heated diamond anvil cell. Imaging of solid-liquid interface formation, combined with radiometric temperature and X-ray diffraction measurements, reveals a pronounced reduction in melting boundary slope in Bi-V with pressure. The unusually steep initial slope is attributed to low configurational entropy of melting, arising from structural ordering and coordination matching in the cool liquid, while slope reduction is driven by entropy increase correlated with significant liquid structure changes with rising pressure and temperature. Finally, the data rule out kinetic effects on melting in shock compression experiments and demonstrate the need for improved theoretical phase diagrams.

Materials science↗

DOE OSTI2025

Controlling mechanical properties of laser powder bed fused AlSi10Mg through manipulation of laser scan rotation

The microstructure and mechanical properties of laser powder bed fused (LPBF) AlSi10Mg alloys can be controlled by many processing parameters. This study focuses on the scan rotation angle, α, between adjacent layers, and establishes the relationship between α and tensile behavior of the as-built LPBF-processed AlSi10Mg alloy. Near-full density cubic coupons were manufactured using the same processing parameters but with systematic variation of α from 0° to 90°. Microscopic observations and X-ray diffraction analysis showed that differences among various coupons mainly include orientations of the melt pools with respect to the build direction and development of the crystallographic texture. The α=0° coupon and α=30° coupon showed the highest and lowest texture index, although the overall crystallographic texture was mild. Tensile specimens were manufactured horizontally and vertically using either α=0° or α=30°, but with various first layer laser direction with respect to the build plate. Notably, the α=0° specimen that was tested along the laser scan direction showed the largest yield strength (283 MPa) and highest tensile ductility (10.1 %). Quantitative image analysis and fractography were performed on all specimens. Results showed that the melt pool orientation with respect to the tensile direction affected the tensile behavior across the different specimens. This was closely related to the localized strain distribution within the melt pool and along the melt pool boundary for the different melt pool orientations observed. Further, these results demonstrate that the laser scan rotation angle between layers can be used to fine tune the mechanical properties of LPBF AlSi10Mg alloy.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Direct Observation of Shock‐Induced Disordering of Enstatite Below the Melting Temperature

Abstract We report in situ structural measurements of shock‐compressed single crystal orthoenstatite up to 337 ± 55 GPa on the Hugoniot, obtained by coupling ultrafast X‐ray diffraction to laser‐driven shock compression. Shock compression induces a disordering of the crystalline structure evidenced by the appearance of a diffuse X‐ray diffraction signal at nanosecond timescales at 80 ± 13 GPa on the Hugoniot, well below the equilibrium melting pressure (>170 GPa). The formation of bridgmanite and post‐perovskite have been indirectly reported in microsecond‐scale plate‐impact experiments. Therefore, we interpret the high‐pressure disordered state we observed at nanosecond scale as an intermediate structure from which bridgmanite and post‐perovskite crystallize at longer timescales. This evidence of a disordered structure of MgSiO 3 on the Hugoniot indicates that the degree of polymerization of silicates is a key parameter to constrain the actual thermodynamics of shocks in natural environments.

Hernandez, J. ‐A.↗

DOE OSTI2022

Analysis of Defects in Metal Additive Manufacturing with Augmented Data Generation

Laser powder bed fusion (LPBF) is a method of additive manufacturing (AM) that selectively melts and fuses together microscopic metallic powder. LPBF offers the benefit of producing custom structures out of high strength metals that can be difficult to fabricate with conventional methods. The challenge of LPBF is that 3D printed structures often have internal pores due to process flaws. Pulsed thermal tomography (PTT) is a method for reconstructing the depth profile of materials, allowing the visualization internal voids in solids. In prior work, we developed a convolutional neural network (CNN) which, having been trained on simulated 2D PTT images of subsurface elliptical defects, was able to classify the semi-major radii, semi-minor radii, and angular orientation of the best-fit ellipses in previously unseen PTT images. The unseen PTT images contained subsurface irregular defect shapes imported from scanning electron microscopy (SEM) images of metallic LPBF-printed specimens. Training the CNN on irregular defect shapes instead of on elliptical shapes would make the resulting classifications more descriptive of actual defect shapes. However, this requires a much higher volume of SEM images of material defects, which are difficult to obtain because of random occurrence of defects in LPBF. To address this challenge, we developed a generative adversarial network (GAN) to augment the existing dataset of SEM defect images. The GAN model is demonstrated to create novel yet realistic defect shapes that can be used as input for simulated PTT images to train CNN.

36 MATERIALS SCIENCE↗

DOE OSTI2024

System and method for high power diode based additive manufacturing

The present disclosure relates to a system for performing an Additive Manufacturing (AM) fabrication process on a powdered material, deposited as a powder bed and forming a substrate. The system makes use of a laser for generating a laser beam, and an optical subsystem. The optical subsystem is configured to receive the laser beam and to generate an optical signal comprised of electromagnetic radiation sufficient to melt or sinter the powdered material. The optical subsystem uses a digitally controlled mask configured to pattern the optical signal as needed to melt select portions of a layer of the powdered material to form a layer of a 3D part. A power supply and at least one processor are also included for generating a plurality of different power density levels selectable based on a specific material composition, absorptivity and diameter of the powder particles, and a known thickness of the powder bed. The powdered material is used to form the 3D part in a sequential layer-by-layer process.

El-Dasher, Bassem S.↗

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).↗