Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Power bed fusion”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Investigating the Potential and Limitations of Fast Scanning Calorimetry to Simulate the Laser Power Bed Fusion Coalescence Process

Laser-Based Powder Bed Fusion (PBF-LB) • PBF-LB is part of one of seven additive manufacturing techniques • Layer wise printing that selectively coalesces discrete powder particles into a solid object o Advantages: Low anisotropy, use of engineering grade materials o Weaknesses: lack of method standardization, challenges in controlled post print quality

Blackman, Malik A. [Georgia Inst. of Technology, A↗
DOE OSTI2023

Temporally continuous thermofluidic–thermomechanical modeling framework for metal additive manufacturing

Additive manufacturing (AM) is known to generate large magnitudes of residual stresses (RS) within builds due to steep and localized thermal gradients. In the current state of commercial AM technology, manufacturers generally perform heat treatments in effort to reduce the generated RS and its detrimental effects on part distortion and in-service failure. Computational models that effectively simulate the deposition process can provide valuable insights to improve RS distributions. Accordingly, it is common to employ Computational fluid dynamics (CFD) models or finite element (FE) models. While CFD can predict geometric and thermal-fluid behavior, it cannot predict the structural response (e.g., stress–strain) behavior. On the other hand, an FE model can predict mechanical behavior, but it lacks the ability to predict geometric and fluid behavior. Thus, an effectively integrated thermofluidic–thermomechanical modeling framework that exploits the benefits of both techniques while avoiding their respective limitations can offer valuable predictive capability for AM processes. In contrast to previously published efforts, the work herein describes a one-way coupled CFD-FEA framework that abandons major simplifying assumptions, such as geometric steady-state conditions, the absence of material plasticity, and the lack of detailed RS evolution/accumulation during deposition, as well as insufficient validation of results. Here, the presented framework is demonstrated for a directed energy deposition (DED) process, and experiments are performed to validate the predicted geometry and RS profile. Both single- and double-layer stainless steel 316L builds are considered. Geometric data is acquired via 3D optical surface scans and X-ray micro-computed tomography, and residual stress is measured using neutron diffraction (ND). Comparisons between the simulations and measurements reveal that the described CFD-FEA framework is effective in capturing the coupled thermomechanical and thermofluidic behaviors of the DED process. The methodology presented is extensible to other metal AM processes, including power bed fusion and wire-feed-based AM.

42 ENGINEERING↗
DOE OSTI2024

GrainGNN: A dynamic graph neural network for predicting 3D grain microstructure

We propose GrainGNN, a surrogate model for the evolution of polycrystalline grain structure under rapid solidification conditions in metal additive manufacturing. High fidelity simulations of solidification microstructures are typically performed using multicomponent partial differential equations (PDEs) with moving interfaces. The inherent randomness of the PDE initial conditions (grain seeds) necessitates ensemble simulations to predict microstructure statistics, e.g., grain size, aspect ratio, and crystallographic orientation. Here, currently such ensemble simulations are prohibitively expensive and surrogates are necessary.In GrainGNN, we use a dynamic graph to represent interface motion and topological changes due to grain coarsening. We use a reduced representation of the microstructure using hand-crafted features; we combine pattern finding and altering graph algorithms with two neural networks, a classifier (for topological changes) and a regressor (for interface motion). Both networks have an encoder-decoder architecture; the encoder has a multi-layer transformer long-short-term-memory architecture; the decoder is a single layer perceptron.We evaluate GrainGNN by comparing it to high-fidelity phase field simulations for in-distribution and out-of-distribution grain configurations for solidification under laser power bed fusion conditions. GrainGNN results in 80%–90% pointwise accuracy; and nearly identical distributions of scalar quantities of interest (QoI) between phase field and GrainGNN simulations compared using Kolmogorov-Smirnov test. GrainGNN's inference speedup (PyTorch on single x86 CPU) over a high-fidelity phase field simulation (CUDA on a single NVIDIA A100 GPU) is 150×–2000× for 100-initial grain problem. Further, using GrainGNN, we model the formation of 11,600 grains in 220 seconds on a single CPU core.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Extend an innovative HPC-Compatible Multiple Temporal-spatial Resolution Concurrent Finite Element Modeling Approach to Guide Laser Powder Bed Fusion Additive

Laser power bed fusing (PBF) additive manufacturing is a key enabling technology to manufacture highly complex and integrated automotive structures. However, the geometric complexity of PBF-AM technique also leads to highly non-uniform heating and cooling rate in the manufactured part, which may cause flaw formation and produce excessive and nonuniform residual stresses, which increase quality uncertainties and manufacture issues, leading to increases in cost and energy consumption in the form of rejected parts. In this research project, we developed an innovative Multi-Spatial-Temporal-Resolution Finite Element (MUST-FE) method and completed the corresponding high performance computation (HPC) platform-based in-house code, which enables high accuracy prediction of temperature and residual stress fields for component-scale PBF-AM manufacture in efficient computation time. The MUST-FE model is calibrated and validated with a “2D pad” AlSi10Mg experiments by matching the melt pool shape and dimension, and with a “XY-cross” AlSi10Mg experiment by matching the thermal distortion and residual stress. The innovative multi-resolution and concurrent modeling approach adopted in this code ensures accuracy and computational efficiency, which will enable energy-efficient and high-yield, low-cost manufacturing of optimized, qualifiable automotive structures and contribute towards reaching technical targets outlined in AMO’s Program Plan to develop additive manufacturing systems that deliver consistently reliable parts with predictable properties.

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 OSTI2020

Process optimization of complex geometries using feed forward control for laser powder bed fusion additive manufacturing

Additive manufacturing (AM) enables the fabrication of complex designs that are difficult to create by other means. Metal parts manufactured by laser powder bed fusion (LPBF) can incorporate intricate design features and demonstrate desirable mechanical properties. However, printing a part that is qualified for its intended application often involves reprinting and discarding many parts to eliminate defects, improve dimensional accuracy, and increase repeatability. The process of iteratively converging on the appropriate build parameters increases the time and cost of creating functional LPBF manufactured parts. This work describes a fast, scalable method for part-scale process optimization of arbitrary geometries. Additionally, the computational approach uses feature extraction to identify scan vectors in need of parameter adaptation and applies results from simulation-based feed forward control models. This method provides a framework to quickly optimize complex parts through the targeted application of models with a range of fidelity and by automating the transfer of optimization strategies to new part designs. The computational approach and algorithmic framework are described, a software package is implemented, the method is applied to parts with complex features, and parts are printed on a customized open architecture LPBF machine.

3D printing↗
DOE OSTI2025

Influence of Process Parameter and Build Rate Variations on Defect Formation in Laser Powder Bed Fusion SS316L

Laser powder bed fusion (LPBF) is an additive manufacturing process that has gained interest for its material fabrication due to multiple advantages, such as the ability to print parts with small feature sizes, good mechanical properties, reduced material waste, etc. However, variations in the key process parameters in LPBF may result in the instantiation of porosity defects and variation in build rate. Particularly, volumetric energy density (VED) is a variable that encapsulates a number of those parameters and represents the amount of energy input from the laser source to the feedstock. VED has been traditionally used to inform the quality of the printed part but different values of VED are presented as optimal values for certain material systems. An optimal VED value can be maintained by changing the key process parameters so that various combinations yield a constant value. In this study, an optimal constant VED value is maintained while printing SS316L with variable key processing parameters. Porosity analysis is performed using optical microscopy, as well as X-ray computed tomography, to reveal the volume density and distribution of those pores. Two primary defect categories are identified, namely lack of fusion and porosity induced by balling defects. The findings indicate that, even at optimal VED, variations in process parameters can significantly influence defect type, underscoring the sensitivity of defect formation to the variation of these parameters. Furthermore, a minor change in the build rate, driven by adjustments in process parameters, was found to influence defect categories. These findings emphasize that fine tuning the process parameters and build rate is essential to minimize defects. Finally, fiducial marks have been identified as a source of unintentional porosity defects. These results enable the refinement of process parameters, ultimately optimizing LPBF to achieve enhanced material density and expedite the printing.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Additive manufacturing power map to mitigate defects

A laser powder bed fusion additive manufacturing system for producing a part by creating a power map that is an intelligent feed forward model to control the laser powder bed fusion additive manufacturing for producing the part and using the power map to control the laser powder bed fusion additive manufacturing for producing the part. This includes an apparatus for producing a part including a powder bed, a laser that produces a laser beam, a proportional integral derivative controller that creates a power map that describes laser power requirements as the laser moves along a path, wherein the laser power requirements prevent defects in the part.

Khairallah, Saad A.↗
DOE OSTI2022

Additive manufacturing power map to mitigate overhang structure

A laser powder bed fusion additive manufacturing system for producing a part by creating a power map that is an intelligent feed forward model to control the laser powder bed fusion additive manufacturing for producing the part and using the power map to control the laser powder bed fusion additive manufacturing for producing the part. This includes an apparatus for producing a part including a powder bed, a laser that produces a laser beam, a proportional integral derivative controller that creates a power map that describes laser power requirements as the laser moves along a path, wherein the laser power requirements prevent defects in the part.

Khairallah, Saad A.↗
DOE OSTI2020

Effect of laser power on roughness and porosity in laser powder bed fusion of stainless steel 316L alloys measured by X-ray tomography

The quality of metal objects fabricated via laser powder bed fusion are highly affected by process parameters, and their influence on final products is yet to be fully explored. In this work, pyrometry signals of the melt pool were collected from a set of stainless-steel samples during manufacturing and the effect of laser power on porosity and roughness of final printed parts was analyzed. Results show that the melt pool pyrometry signal of contours increases with higher laser power, whereas it is lower and decreases for the infilled part. Post-built X-ray computed tomography imaging reveals that porosity decreases while sample roughness increases upon increasing laser power. The decrease in porosity with increasing laser power is attributed to the larger size of the contour welds that were printed first, leading to an increase in dimension of the final products.

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 OSTI2025

The influence of laser power modulation on melt pool dynamics in laser powder bed fusion

While the majority of laser powder bed fusion (LPBF) metal additive manufacturing uses a continuous wave (CW) laser heat source, some commercial applications of LPBF additive manufacturing instead involve the modulation of the laser power on tens-of-microsecond timescales as an adjustable process variable. This article reports the use of in situ, high speed x-ray and optical imaging to probe melt pool fluid flow, defect formation, and nearby powder motion during LPBF with both modulated and CW laser heat sources. We observe melt pool dynamics unique to modulated laser melting even at very high duty cycles that are related to fluctuations in vapor depression depth, complex pore formation mechanisms, and changes to denudation physics when compared to CW melting. These behaviors are present in Ti–6Al–4V, 316L stainless steel, and AL1100 alloys but vary slightly as a function of material, indicating a substantial dependence on the viscosity and surface tension of the liquid metal. While high duty cycles produce weld tracks of comparable quality to CW melting, lower duty cycles introduce substantial defect concentrations. At intermediate duty cycles, careful control of modulation parameters can repeatably and precisely yield one pore per laser pulse, suggesting a method for intentionally inserting engineered porosity at specific sites during an LPBF build.

3D printing↗
DOE OSTI2021

The causal relationship between melt pool geometry and energy absorption measured in real time during laser-based manufacturing

During laser powder bed fusion additive manufacturing, laser power absorption is governed by a pro -tean pool of molten metal that can present as a highly reflective surface, a deeply absorbing cavity, or some amalgamation thereof. These melt pool dynamics have been linked to defect creation, porosity, and surface finish quality. Although these are therefore critical for determining final part quality, their in-stantaneous influence on laser absorption have only been explored through simulation. To date, direct real-time observations have been elusive due to the locally extreme environment. In this work, we fo-cus a laser on Ti-6Al-4V powder and bare plate while quantifying the time-dependent, absolute energy absorption by monitoring omnidirectional backscattered laser intensity. We also simultaneously record the projective melt pool geometries with high-speed synchrotron x-ray imaging. We find that laser ab-sorption strongly reflects the stability of the vapor depression over a wide range of applied laser powers, oxygen content in the processing atmosphere, and with the presence of powder. During laser scanning of a powder bed surface, we find a significant absorption reduction after 400 mu s due to a dramatic change in the vapor depression aspect ratio-an event known to create porosity. Furthermore, as several industrial scan strate-gies necessitate thousands of these events during a build, their identification and control is of significant practical importance. Lastly, a normalized enthalpy model is demonstrated to be effective in quantifying the relationship between the laser absorption and cavity depth, even under transient conditions. In addi-tion to providing vital quantitative data for simulation calibration, the correlation of melt pool geometry with laser absorption during realistic processing conditions suggests the use of a total backscattered light detection system for real-time process control.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Melt Pool and Heat Treatment Optimization for the Fabrication of High-Strength and High-Toughness Additively Manufactured 4340 Steel

Additively manufactured (AM) components offer superior design flexibility compared to their conventionally manufactured counterparts, and optimizing processing parameters is key to achieving high-quality depositions with desirable and predictable mechanical properties. This study was focused on 4340 steel fabricated using laser powder bed fusion (LPBF), and 42 laser power and scan speed combinations have been systematically investigated to determine an optimized melt pool geometry that would ensure fully-dense parts. The AM material was compared with a wrought 4340 equivalent and studied in two customized heat treated conditions, optimized for strength and toughness, respectively. The microstructures of the as-fabricated and heat treated AM and wrought materials were characterized to assess differences introduced by the layer-by-layer fabrication process and subsequent heat treatment. Tensile properties of both materials were also evaluated and demonstrate that the AM materials offer equal or superior properties compared to the wrought equivalents. Differences in fracture surface morphologies indicate the distinct failure mechanisms associated with the materials’ characteristic microstructures, and the role of inclusions in the failures was studied to elucidate these differences. Complementary to the experimental investigations, the dataset was leveraged to make recommendations for future design of experiments to optimize AM build parameters in other material systems. A statistical Monte Carlo analysis was used to predict the interpolation error produced using reduced datasets and to enable informed processing parameters selection. These findings are discussed to make recommendations for the use of AM materials for high-integrity structural applications.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Cost of Using Laser Powder Bed Fusion to Fabricate a Molten Salt-to-Supercritial Carbon Dioxide Heat Exchanger for Concentrating Solar Power

Advances in manufacturing technologies and materials are crucial to the commercial deployment of energy technologies. We present the case of concentrating solar power (CSP) with molten salt (MS) thermal storage, where low-cost, high-efficiency heat exchangers (HXs) are needed to achieve cost competitiveness. Here, the materials required to tolerate the extreme operating conditions in CSP systems make it difficult or infeasible to produce them using conventional manufacturing processes. Although it is technically possible to produce HXs with adequate performance using additive manufacturing, specifically laser powder bed fusion (LPBF), here we assess whether doing so is cost-effective. We describe a process-based cost model (PBCM) to estimate the cost of fabricating a MS-to-supercritical carbon dioxide HX using LPBF. The PBCM is designed to identify modifications to designs, process choices, and manufacturing innovations that have the greatest effect on manufacturing cost. Our PBCM identified HX design and LPBF process modifications that reduced projected HX cost from $\$750$ per kilo-Watt thermal (kW-th) ($\$8$ /cm 3 ) to $\$350$ /kW-th ($\$6/$ cm 3 ) using currently available LPBF technology, and down to $\$220$ /kW-th ($\$4$ /cm 3 ) with improvements in LPBF technology that are likely to be achieved in the near term. The PBCM also informed a redesign of the HX design that reduced projected costs to $\$140$ –160/kW-th ($\$3$ /cm 3 ).

36 MATERIALS SCIENCE↗
DOE OSTI2023

Single-Phase Jet Impingement Cooling for a Power-Dense Silicon Carbide Power Module

The adoption of silicon carbide (SiC) devices in the electric vehicle (EV) industry is increasing due to their superior performance over silicon devices. SiC devices enable further miniaturization of EV inverters, increasing their power density, which results in thermal management challenges. Here in this paper, the limits of single-phase jet impingement cooling are explored for an automotive SiC power module. We propose embedding pin fins in the direct-bonded-copper (DBC) substrate of the power module package using laser powder bed fusion additive manufacturing. The thermalhydraulic performance of the DBC-embedded pin fins is compared against folded fins that are directly soldered to the DBC substrate. A heat conduction analysis was conducted on a SiC package to determine the target heat transfer coefficient (HTC) for the heat sink. A water-ethylene glycol (WEG) jet impingement on the proposed concepts was studied using unit-cell models to achieve the target HTC. The studied designs put emphasis on the reliability and manufacturability requirements of the automotive industry. The thermal performance of DBC-embedded pin fins outperformed the DBC-soldered folded fins. The performance of the DBC-embedded pin fins is benchmarked against WEG-based cooling systems of commercial EVs. With the proposed cooling solution, we have shown a pathway of reducing the specific thermal resistance by 75% compared to BMW i3 thermal management system without any penalty on pressure drop or parasitic power.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2024

Additively Manufactured Molten Salt to Supercritical Carbon Dioxide Heat Exchanger

Design, fabrication, and experimental characterization of the performance of a molten salt (MS)-to-supercritical carbon dioxide (sCO 2 ) heat exchanger (HE) for concentrating solar power (CSP) applications using laser powder bed fusion (LPBF) additive manufacturing (AM) of nickel-based superalloys is presented in this report. The hypothesis is that such additively manufactured HEs will result in a monolithic, compact, low-pressure drop HE that is durable under cyclic operation at high temperature and high pressure in a corrosive salt environment. Two nickel-based superalloys were considered in the project- Haynes 230 (H230) and Haynes 282 (H282). Process windows for LPBF AM were established to provide dense low porosity parts for both alloys. Creep tests results on heat treated AM H230 and H282 samples showed that the AM fabricated specimens were as robust as the wrought specimens and showed no deficit in creep strength at high temperatures. Haynes 282 was down selected for the HE fabrication based on better dimensional tolerance, better corrosion resistance to chloride salt, and higher creep strength.

14 SOLAR ENERGY↗