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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 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Experiment Based Superposition Thermal Modeling of Laser Powder Bed Fusion

This paper evaluates experiment-based superposition thermal modeling for Laser Powder Bed Fusion (LPBF) with a pulsed laser. An analytical pulsed laser thermal model establishes the modeling procedure. The framework inverts a powder bed’s single pulse temperature response from experimental spatiotemporal Short-Wave Infrared (SWIR) camera data. Superimposing this response along a scan path simulates multi-pulse LPBF. Results show the experimentally informed superposition model rapidly and accurately predicts a layer’s temperature history. The model has applications in correction of thermally driven LPBF errors and in-situ part qualification.

Lough, Cody↗
DOE OSTI2020

Microstructure–Property Correlation in a Laser Powder Bed Fusion Processed High-Strength AF-9628 Steel

Laser powder bed fusion additive manufacturing (LPBF-AM) of a low-alloy, high-performance AF-9628 steel results in exceptionally high strength and good ductility. The reasons for such mechanical properties are investigated through detailed microscopy performed at several length scales. Thus, the characterization of melt pool, porosity, grain morphology, phases, and dislocations is performed in the as-printed material. The as-printed material consists of only 0.004 vol% of uniformly distributed porosity, single-phase martensitic laths with an average lath size of ≈2.5 μm, the absence of carbides indicating interstitial trapping of C atom, and high dislocation density in the martensitic laths. Experimental data through microscopy are then fed in analytical models for calculating strengthening contributions from various strengthening mechanisms. Calculated yield strength agrees well with experimentally determined value, and therefore, activation of various strengthening mechanisms is established in as-printed AF-9628.

36 MATERIALS SCIENCE↗
DOE OSTI2026

A transport-based framework for solidification cracking in Ni-based superalloys processed by laser powder bed fusion

Solidification cracking remains a persistent barrier to laser powder bed fusion (LPBF) processing of solid-solution-strengthened (SSS) Ni-based superalloys and is commonly attributed to carbide formation, liquation-type failure, or elemental segregation. In this work, the cracking behavior of three SSS Ni-based superalloys—Inconel 625 (625), Inconel 617 (617), and Haynes 230 (230)—is examined under comparable LPBF conditions to evaluate the origins of their cracking susceptibility. Carbides were present in both 625 and 230, yet cracking behavior differed significantly. MC-type carbides in 625 remain discrete and preserve liquid connectivity, whereas Cr-rich M23C6 carbides in 230 form at cellular triple junctions that bottleneck the interdendritic liquid network, influencing cracking through liquid connectivity rather than as an independent cause. Alloy 617 exhibited anomalous segregation without resolvable secondary phases yet cracked mildly, indicating that extensive carbide formation is not a prerequisite for cracking. To rationalize these observations, a transport-limited framework is proposed in which cracking occurs when terminal-liquid feeding cannot accommodate solidification-induced strain; among the mechanisms evaluated, this framework is most consistent with the physical constraints imposed by LPBF solidification. Transport-based crack-susceptibility indexes incorporating permeability, viscosity, and solidification kinetics distinguish the alloy hierarchy; permeability ratios Krat increased from approximately 7 (625) to 11.5 (617) and 14.5 (230), with corresponding increases in the μ-weighted mushy zone risk index (CSIMZRM). These results are consistent with transport-limited liquid feeding as a rate-limiting contributor to solidification cracking in LPBF-processed SSS Ni-based superalloys, providing a physically grounded basis for alloy and process design.

Hyer, Holden [ORNL] (ORCID:0000000343915561)↗
DOE OSTI2025

Laser Powder Bed Fusion Microstructure Surrogate Model

SAND2025-11467O The Laser Powder Bed Fusion (LPBF) Microstructure Surrogate Model is a machine-learning-based tool. It predicts statistics of microstructures that are produced by the LPBF additive manufacturing process. It includes a series of codes for training, testing, and analyzing the model as well as utility scripts for handling data. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Moser, Daniel [Sandia National Lab. (SNL-CA), Live↗
DOE OSTI2022

Photodiode-based machine learning for optimization of laser powder bed fusion parameters in complex geometries

We report the quality of parts produced through laser powder bed fusion additive manufacturing can be irregular, with complex geometries sometimes exhibiting dimensional inaccuracies and defects. For optimal part quality, laser process parameters should be selected carefully prior to printing and adjusted during the print if necessary. This is challenging since approaches to control and optimize the build parameters need to take into account the part geometry, the material, and the complex physics of laser powder bed fusion. This work describes a data-driven approach using experimental diagnostics for the optimization of laser process parameters prior to printing. A training dataset is generated by collecting high speed photodiode signal data while printing simple parts containing key geometry features with various process parameter strategies. Supervised learning approaches are employed to train both a forward model and an inverse model. The forward model takes as inputs track-wise geometry features and laser parameters and outputs the photodiode signal along the scan path. The inverse model takes as inputs the geometry features and photodiode signal and predicts the laser parameters. Given the part geometry and a desired photodiode signal, the inverse model can thus determine the required laser parameters. Two test parts which contain defect-prone features are used to assess the validity of the inverse model. The use of the model leads to improved part quality (higher dimensional accuracy, reduced dross, reduced distortion) for both test geometries.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Sub-millisecond keyhole pore detection in laser powder bed fusion using sound and light sensors and machine learning

Laser powder bed fusion is a mainstream additive manufacturing technology widely used to manufacture complex parts in prominent sectors, including aerospace, biomedical, and automotive industries. However, during the printing process, the presence of an unstable vapor depression can lead to a type of defect called keyhole porosity, which is detrimental to the part quality. In this study, we developed an effective approach to locally detect the generation of keyhole pores during the printing process by leveraging machine learning and a suite of optical and acoustic sensors. Simultaneous synchrotron x-ray imaging allows the direct visualization of pore generation events inside the sample, offering high-fidelity ground truth. A neural network model adopting SqueezeNet architecture using single-sensor data was developed to evaluate the fidelity of each sensor for capturing keyhole pore generation events. Our comparative study shows that the near infrared images gave the highest prediction accuracy, followed by 100 kHz and 20 kHz microphones, and the photodiode sensitive to processing laser wavelength had the lowest accuracy. Using a single sensor, over 90% prediction accuracy can be achieved with a temporal resolution as short as 0.1 ms. A data fusion scheme was also developed with features extracted using SqueezeNet neural network architecture and classification using different machine learning algorithms. Our work demonstrates the correlation between the characteristic optical and acoustic emissions and the keyhole oscillation behavior, and thereby provides strong physics support for the machine learning approach.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Formation of functionally graded steel by laser powder bed fusion via in-situ carbon doping

Additive Manufacturing (AM) enables functional integration by combining multiple components into a single part to shorten assembly time, reduce weight, and improve performance. Laser Powder Bed Fusion (LPBF) is an important AM method due to excellent spatial resolution, surface finish, and material properties without the need for extensive post-processing. Functional integration could be enhanced by spatial tuning of properties, but LPBF cannot readily vary material composition. Here, this paper addresses a method to add spatial composition control by printing small quantities of dopants via liquid carrier prior to laser fusion. The impact of carbon black suspension added to select regions of a Stainless Steel 316 L powder bed on melt pool dimension, hardness, and porosity is reported. The distribution of the carbon between the doped and plain layers and the resulting spatial variation in hardness is measured. Optical microscopy and composition analysis show that the carbon dispersed uniformly within the layer of deposition and diffused as little as 50 μm in the build direction. Keyhole conditions dramatically increase the inter-layer transport of the dopant. The added carbon increased hardness by >50 %. Porosity increased in doped regions but remained below 1.5 % for the best processing parameters. These results demonstrate that the composition of LPBF parts could be controlled in 3-dimensions using a dopant that is soluble in the melt pool. Additional work will be required to evaluate different dopant materials and optimize processing conditions for full density, but microalloying with soluble dopants appears to be a plausible solution to enhance functional integration with LPBF.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Effect of heat treatment on microstructural evolution and hardness homogeneity in laser powder bed fusion of alloy 718

The thermal history developed in laser powder bed fusion (LPBF) processes has been shown to be complex resulting in equally complex microstructures and mechanical properties. Microscopic observations and Vickers micro-hardness mapping measurements were carried out on diff ;erent section planes of LPBF alloy 718 cuboids. Three-dimensional finite element analysis was used to simulate thermal history and to predict the residual stress distribution in the as-built material. Computational thermodynamics was used to predict the micro-segregation and nucleation driving force of various phases in the bulk and in segregated regions. Varied heat-treatments such as simulated hot isostatic pressing, and double aging were applied. Their influence on the microstructure, microsegregation, precipitate formation, and micro-hardness variations of LPBF alloy 718 were investigated. Hardness map results showed heterogeneous micro-hardness on the xy- and xz-planes of the as-built parts where the bottom plane and center regions had larger hardness of ~315 HV 0.5 while the top plane and contours showed hardness of ~300 HV 0.5 . It was found that the aging treatment increased the overall hardness of the as-built condition from ~310 HV 0.5 to 470 HV 0.5 but also increased the hardness gradient throughout the coupon. After simulated hot isostatic pressing process (i.e., without applied pressure) at 1020 °C for 4 h followed by water quench (HIPWQ), the hardness gradient and hardness was minimized (~210 HV 0.5 ) as the microstructure transitioned from heterogeneous columnar grains in the as-built condition to more uniform recrystallized grains. A double aging treatment was applied to enhance hardness from ~210 HV 0.5 to ~440 HV 0.5 . Finally, HIPWQ followed by double aging produced a homogeneous microstructure and more uniform hardness map with enhanced mechanical properties in LPBF alloy 718 coupons.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Island scanning pattern optimization for residual deformation mitigation in laser powder bed fusion via sequential inherent strain method and sensitivity analysis

Laser powder bed fusion (L-PBF) has emerged as one of the mainstream additive manufacturing approaches for fabricating metal parts with complex geometries and intricate internal structures. However, large deformation associated with rapid heating and cooling can lead to build failure and requires post-processing which may increase manufacturing cost and prolong the production period. Here in this work, an island scanning pattern design method is proposed to optimize the scanning direction of each island in order to reduce part deformation after cutting off the build platform. The objective of this optimization is to minimize the upward bending of the part after sectioning, which allows the part deformation to satisfy the tolerance requirement or reduce the post heat treatment time. Inherent strain method is employed in the sequential finite element analysis consisting of layer-by-layer activations and sectioning for fast residual distortion prediction. Full sequential sensitivity analysis for the formulated optimization is provided to update the island scanning directions. To show the feasibility and effectiveness of the proposed method, the scanning patterns of a block structure and a connecting rod were designed and parts were fabricated using an open architecture L-PBF machine. The fabrication experiments demonstrated that the residual deformation of both parts fabricated by optimized scanning pattern can be reduced by over 50% compared to the initial scanning patterns, which demonstrate the effectiveness of the proposed method.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Revealing transient powder-gas interaction in laser powder bed fusion process through multi-physics modeling and high-speed synchrotron x-ray imaging

Laser powder bed fusion (LPBF) is an emerging metal additive manufacturing process. The gas-driven powder motions in laser powder bed fusion have significant influence on the build quality. However, the transient powder-gas interaction has not been well understood due to the challenges in quantitative experiment measurements. In this work, the powder-gas interaction for a single pulse laser illuminating on the powder bed is studied. We establish a multi-physics model to simulate the complex liquid/gas flow as well as the gas-driven powder motions, which is substantiated by high-speed synchrotron x-ray imaging. We identify and quantify four characteristic modes of powder-gas interaction in LPBF. The motion of a powder is controlled by one or multiple interaction modes collectively. As revealed by simulations and confirmed by experiments, powders can merge into the molten pool from its rim, be ejected at different divergence angles (powder spattering), or dive into the molten pool to cause significant molten pool fluctuation. Overall, our results provide insights toward the driving forces controlling the dynamic powder behavior, which pave the way for reducing structure defects during the build process.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Predictive process mapping for laser powder bed fusion: A review of existing analytical solutions

One of the main challenges in the laser powder bed fusion (LPBF) process is making dense and defect-free components. These porosity defects are dependent upon the melt pool geometry and the processing conditions. Power-velocity (PV) processing maps can aid in visualizing the effects of LPBF processing variables and mapping different defect regimes such as lack-of-fusion, under-melting, balling, and keyholing. This work presents an assessment of existing analytical equations and models that provide an estimate of the melt pool geometry as a function of material properties. The melt pool equations are then combined with defect criteria to provide a quick approximation of the PV processing maps for a variety of materials. Finally, the predictions of these processing maps are compared with experimental data from the literature. Here, the predictive processing maps can be computed quickly and can be coupled with dimensionless numbers and high-throughput (HT) experiments for validation. The present work provides a boundary framework for designing the optimal processing parameters for new metals and alloys based on existing analytical solutions.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Determining processing behaviour of pure Cu in laser powder bed fusion using direct micro-calorimetry

We report copper is challenging to process by laser powder bed fusion (LPBF) given its high reflectivity at common infrared laser diode wavelengths and high thermal conductivity. Successful deposition of copper in a predictable and repeatable fashion relies on understanding the development of the keyhole melting regime, as well as heating, melting, boiling and vapour formation behaviour when interacting with a laser beam within an LPBF environment. In this study, in situ optical absorptivity measurements are used to clarify the complex physics of the laser material interaction. Absorptivity of laser energy is measured using direct micro-calorimetry and compared to melt pool depth in correlation to processing parameters. The measured absorptivity for a 100 μm layer thickness of powder was found to be approximately four times higher than that of the bare polished discs. It was also shown that high laser power above 500 W and scan speed up to 150 mm/s are appropriate for effective melting of the powder layer, with these parameters overcoming the threshold required to achieve keyhole melting. This is explained by multiple reflections withing the powder particles and the lower thermal conductivity of packed powder in comparison to bare discs. Melt pool formation was found to be highly unstable and an explosive behavior was observed when in the keyhole regime, caused by high fluctuations in absorptivity values. This work demonstrates calorimetry can be used to monitor melting behaviour in a real-time fashion during processing for this challenging to process material, thereby avoiding unnecessary parametric optimisation. In addition, the parametric window for optimum processing revealed here can inform future work.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Effect of hatch spacing and laser power on microstructure, texture, and thermomechanical properties of laser powder bed fusion (L-PBF) additively manufactured NiTi

This study systematically evaluates the effects of laser powder bed fusion additive manufacturing (L-PBF-AM) parameters (hatch spacing and laser power) on the thermomechanical behavior and microstructure of Ni 50.8 Ti 49.2 shape memory alloy. The samples were fabricated with hatch spacings from 40 to 240 µm and laser powers of 50 and 100 W at a constant scanning speed of 125 mm/s, resulting in parts with volumetric energy density levels from 55 to 666 J/mm 3 and two sets of linear energy densities of 0.4 and 0.8 J/mm. The results showed a reduced melt pool size and discontinuity of scan tracks with decreased laser power. Additionally, the porosity level was increased with larger hatch spacing and lower laser power. More notably, the transformation temperatures increased, and the critical stress, recoverable strain, and functional stability of samples improved with lower hatch spacing, where the recovery ratio of up to 90% was observed, regardless of the employed laser power. This study also discussed the relationship between the fabrication process and texture formation in the L-PBF-AM process. In conclusion, the advantage of L-PBF-AM was revealed in tailoring the microstructure from highly textured samples in [1 1 1] or [0 0 1] direction when hatch spacing lower than laser beam focused was employed, to the appearance of equiaxed solidification front with island grains and random orientations.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Clarifying the formation of equiaxed grains and microstructural refinement in the additive manufacturing of Ti-Cu

Controlling microstructural evolution in metallic additive manufacturing (AM) is difficult, especially in producing refined as-built grains instead of coarse, directional grains. Traditional solutions involve adding inoculants to AM feedstocks, but titanium (Ti) alloys cannot employ this approach without producing detrimental secondary phases. Ti-Cu (Ti-copper) alloys offer a solution through constitutional supercooling and/or solid state thermal cycling under AM conditions. This work analyzes a compositionally graded directed energy deposition (DED) Ti-Cu build, single-melt laser tracks, and dilatometric heat treatments to evaluate if, when, and by what mechanism(s) microstructural refinement occurs. Refinement by inoculation of unmelted powder particles was also considered. Constitutional supercooling produced no net microstructural refinement as any equiaxed dendrites which form are remelted with new deposition. This finding agreed with solidification modeling of powder bed fusion-laser beam (PBF-LB) and DED builds. Solid state thermal cycling refined microstructures only during ex-situ dilatometric heat treatments, suggesting build parameter optimization is needed to achieve refinement in-situ. Accidental heterogeneous nucleation on unmelted Ti powder, originating from the different thermophysical properties of Ti and Cu, provided the most significant microstructural refinement. This work systematically assesses the microstructural refinement mechanisms of Ti-Cu in AM builds and offers insights into microstructural control in eutectoid alloys.

36 MATERIALS SCIENCE↗
DOE OSTI2024

ExaCA grain structure predictions for laser powder bed fusion processing

This dataset contains simulated cross-sections of laser powder bed fusion grain structures produced using the microstructure model ExaCA, in turn using time-temperature history data produced using the heat transport model AdditiveFOAM. These predictions show the grain structure using various permutations of hatch spacing and nucleation density. Also contained in the dataset are the input files necessary to reproduce the results. AdditiveFOAM (https://github.com/ORNL/AdditiveFOAM) and ExaCA (https://github.com/LLNL/ExaCA), both open source software, are required to reproduce the results in this dataset using the given input files. This DOI was updated 12/09/2025.

36 MATERIALS SCIENCE↗
DOE OSTI2023

A Data-Driven Framework for Direct Local Tensile Property Prediction of Laser Powder Bed Fusion Parts

This article proposes a generalizable, data-driven framework for qualifying laser powder bed fusion additively manufactured parts using part-specific in situ data, including powder bed imaging, machine health sensors, and laser scan paths. To achieve part qualification without relying solely on statistical processes or feedstock control, a sequence of machine learning models was trained on 6299 tensile specimens to locally predict the tensile properties of stainless-steel parts based on fused multi-modal in situ sensor data and a priori information. A cyberphysical infrastructure enabled the robust spatial tracking of individual specimens, and computer vision techniques registered the ground truth tensile measurements to the in situ data. The co-registered 230 GB dataset used in this work has been publicly released and is available as a set of HDF5 files. The extensive training data requirements and wide range of size scales were addressed by combining deep learning, machine learning, and feature engineering algorithms in a relay. The trained models demonstrated a 61% error reduction in ultimate tensile strength predictions relative to estimates made without any in situ information. Lessons learned and potential improvements to the sensors and mechanical testing procedure are discussed.

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↗