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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 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Real-Time Defect Correction in Large-Scale Polymer Additive Manufacturing via Thermal Imaging and Laser Profilometer

Defects can result in a failed part and are costly in terms of time and material. This cost is even greater in the context of large-scale additive manufacturing where the objects can be very large. As a result, a great deal of research has focused on defect identification and mitigation. To address defects during object construction, researchers at Oak Ridge National Laboratory’s Manufacturing Demonstration Facility investigated an in-situ control system comprised of two sensors: a thermal camera and laser profilometer. This control system adjusted material flow and build speed to mitigate three types of defects: low layer times, underfill, and overfill. Several test objects were constructed. The control system was found to adjust build parameters to handle low layer times of approximately 15 seconds and height deviations from -100% underfill (the absence of a layer) to 50% overfill. Within two layers, height deviations could be returned to within 10% of the expected layer height. Further, preliminary results suggest the system can compensate for uneven build surfaces.

Borish, Michael↗
DOE OSTI2021

Local prediction of Laser Powder Bed Fusion porosity by short-wave infrared imaging thermal feature porosity probability maps

We report that local thermal history can significantly vary in parts during metal Additive Manufacturing (AM), leading to local defects. However, the sequential layer-by-layer nature of AM facilitates in-situ part voxelmetric observations that can be used to detect and correct these defects for part qualification and quality control. The challenge is to relate this local radiometric data with local defect information to estimate process error likelihood in future builds. This paper uses a Short-Wave Infrared (SWIR) camera to record the temperature history for parts manufactured with Laser Powder Bed Fusion (LPBF) processes. The porosity from a cylindrical specimen is measured by ex-situ micro-computed tomography (μCT). Specimen data from the SWIR camera, combined with the μCT data, are used to generate thermal feature-based porosity probability maps. The porosity predictions made by various SWIR thermal feature-porosity probability maps of a specimen with a complex geometry are scored against the true porosity obtained via μCT. The receiver operating characteristic curves constructed from the predictions for the complex sample demonstrate the porosity probability mapping methodology’s potential for in-situ based porosity detection.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Thermal images collected during large-scale 3D printing with Ingersoll MasterPrint

This data set contains images produced to test the performance of anomaly and fault detection methods in the context of additive manufacturing. Two print jobs were executed using the Ingersoll MasterPrint with the Model 30 Strangpresse extruder. The material used was Techmer compounded polylactic acid (PLA) with wood flour as a filler (80/20 PLA/WF by weight). The first print job consists of the 3D printing of a hexagonal cylinder with a two-bead wall. This print was sliced at a gantry velocity of 3000 millimeter per minute, and an extruder screw speed of 68.14 rotations per minute. This are considered the normal operating conditions. A second hexagonal cylinder was printed using a reduced extruder screw speed, 15% lower than under normal operating conditions. The images were collected with a Teledyne FLIR Lepton 3.5 infra-red camera, a small form factor radiometric long-wave infrared camera with a spectral range of 8 µm to 14 µm. Sensor resolution was 160x120 pixels, with a pixel size of 12 µm, a temperature range of -10 - 450°C, and an accuracy of +/- 10°C in its low gain configuration. Images in this data set were collected with cameras oriented at the printer nozzle. The nozzle camera setup consisted of two cameras located 12.5cm from the nozzle center. These were mounted directly to the print head, so that the camera positions relative to the print direction would remain constant as it rotated around its C-axis to follow the print path. One camera was placed ahead of the nozzle to capture the previous layer immediately before being covered by the new layer of material, while the second camera was placed behind the nozzle and captured the freshly extruded bead. Images are collecting during each phase of the print: (a) idle (i.e., no material deposited), (b) extrusion (i.e., to prime the extruder), and (c) printing (deposition of material to manufacture the hexagonal cylinder).

36 MATERIALS SCIENCE↗
DOE OSTI2026

Thermal images collected during 3D printing with Cincinnati BAAM

This data set contains images produced to test the performance of anomaly and fault detection methods in the context of additive manufacturing. A total of 10 print jobs were executed using a Cincinnati BAAM (model 606) with a Cincinnati medium compression screw. The material used was compounded polylactic acid (PLA) with wood flour as a filler produced by Jabil (80% NatureWorks Ingeo 6060D amorphous PLA, % 100 mesh pine flour from American Wood Fiber). Print sheets were 1/4in polycarbonate.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Two-color high speed thermal imaging system for laser-based additive manufacturing process monitoring

Monitoring melt pool temperature in laser powder bed fusion by providing a build laser that produces a laser beam that is directed onto the melt pool and produces an incandescence that emanates from the melt pool, receiving the incandescence and producing a first image having a first spectral band and a second image having a second spectral band, and determining the ratio of said first image having a first spectral band and said second image having a second spectral band to monitor the melt pool temperature.

Calta, Nicholas P.↗
DOE OSTI2023

Microscale Imaging of Thermal Conductivity Suppression at Grain Boundaries

Abstract Grain‐boundary engineering is an effective strategy to tune the thermal conductivity of materials, leading to improved performance in thermoelectric, thermal‐barrier coatings, and thermal management applications. Despite the central importance to thermal transport, a clear understanding of how grain boundaries modulate the microscale heat flow is missing, owing to the scarcity of local investigations. Here, thermal imaging of individual grain boundaries is demonstrated in thermoelectric SnTe via spatially resolved frequency‐domain thermoreflectance. Measurements with microscale resolution reveal local suppressions in thermal conductivity at grain boundaries. Also, the grain‐boundary thermal resistance – extracted by employing a Gibbs excess approach – is found to be correlated with the grain‐boundary misorientation angle. Extracting thermal properties, including thermal boundary resistances, from microscale imaging can provide comprehensive understanding of how microstructure affects heat transport, crucially impacting the materials design of high‐performance thermal‐management and energy‐conversion devices.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Imaging of Calibrated Defects in Additively Manufactured Materials: Pulsed Thermal Tomography Nondestructive Examination of Additively Manufactured Reactor Materials and Components

Additive manufacturing (AM, or 3D printing) for commercial nuclear energy applications is an emerging method for cost-efficient manufacturing aimed at replacing aging nuclear reactor parts and reducing costs for new construction. Known material flaws in AM include low-density regions consisting of non-sintered powder, which have to be detected to ensure the safety of long-term performance nuclear reactor components. Currently, limited options are available for nondestructive evaluation (NDE), either during or post manufacturing. As a solution to NDE of AM, we are developing pulsed thermal imaging which is non-contact, one-sided, and scalable to arbitrary size and shape of the AM parts. Pulsed thermography (PT) system utilizes a high intensity flash lamp to rapidly heat surface of sample, and a high sensitivity fast-frame megapixel infrared (IR) camera to capture data of surface temperature variations. The acquired data cube consists of a stack of surface temperature images taken at different times. Information about material internal defects is extracted by analyzing the data cube. This report provides results of preliminary performance evaluation of pulsed thermal imaging capability in detection of imprinted flaws in AM metallic structures. The flaws were introduced into AM parts as imprinted hemispherical low density regions, consisting of trapped un-sintered metallic powder. Specimens for developed for this study consisted of AM stainless steel 316 and Inconel 718 plates. The diameters of imprinted defects varied from 1mm to 8mm, and their depths below the plate flat surface varied between 1mm and 6mm. Pulsed thermal tomography (PTT) processes the measured data cube to obtain 3D reconstructions of material effusivity using a unique inversion algorithm developed at Argonne. PTT has been previously used in imaging of similar size flat bottom hole (FBH) simulated defects in stainless steel 316 and Inconel 718 specimens. In the study involving AM specimens, PTT imaging results have shown that 1mm-diameter defects located 1mm and 2mm below the surface of specimens were detectable. Larger size defects were detectable at greater depth. We also explored an alternative approach to detection of material flaws in PT data cube, which is using neural learning-based approach to blind source separation. Detection of small material defects requires finding features in the data cube which have signal contrast levels approaching sensitivity limit of IR camera. In this study, an optimized Neural Learning based Blind Source Separation (NLBSS) algorithm, including Principal Component Analysis (PCA), and Independent Component Analysis (ICA) is demonstrated to automatically extract principal temporal and spatial features of thermography frames to enhance flaw detection. By using the NLBSS algorithm, material internal defects can be automatically detected. Furthermore, this processing approach compensates for experimental thermal imaging artifacts, such as noise and uneven heating. By merging artificial intelligence with phtotothermics, the NDE system detects internal calibrated defects of various sizes and depths in AM nuclear-grade metallic alloys.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Online thermal profile prediction for large format additive manufacturing: A hybrid CNN-LSTM based approach

Large format additive manufacturing (LFAM) is an advanced 3D printing technique that efficiently fabricates large-scale components through a layer-by-layer extrusion and deposition process. Accurate surface layer temperature monitoring is essential to prevent manufacturing failures and ensure final product quality. Traditional physics-based offline approaches for simulating thermal behavior are often inefficient and complex, posing challenges on real-time, in-situ monitoring. Here, to address this, we propose a data-driven hybrid CNN-LSTM model to predict sequential thermal images of arbitrary length using real-time infrared thermal imaging. In this approach, a Convolutional Neural Networks (CNN) is trained offline to capture spatial features, reduce dimensional complexity, and enhance time efficiency, while a stacked Long Short-Term Memory (LSTM) is applied online to capture temporal information for improved prediction of future thermal behavior in subsequent printing layers. Model performance is evaluated using MSE, SSIM, and PSNR metrics and is benchmarked against stacked LSTM and convolutional LSTM models, demonstrating superior accuracy and applicability. Additionally, to mitigate noise from moving extruders and gantry backgrounds in thermal images, a fine-tuned semantic segmentation model is implemented offline to extract printing geometry, enabling precise temperature tracking along the tool path for further thermal analysis. The frameworks developed in this study significantly advance temperature monitoring, thermal analysis, and in-situ manufacturing control for LFAM, bridging the gap between theoretical modeling and practical application.

Geometry extraction↗
DOE OSTI2025

A mechanistic study on environment gas in laser powder bed fusion

A variety of protective or reactive environmental gases have recently gained growing attention in laser-based metal additive manufacturing (AM) technologies due to their unique thermophysical properties and the potential improvements they can bring to the build processes. However, much remains unclear regarding the effects of different gas environments on critical phenomena in laser AM, such as rapid cooling, energy coupling, and defect generation. Through simultaneous high-speed synchrotron x-ray imaging and thermal imaging, we identify distinct effects of two environmental gases in laser AM and gained a deeper understanding of the underlying mechanisms. Compared to the commonly used protective gas, argon, it is found that helium has a negligible effect on cooling the part. However, helium can suppress unstable keyholes by decreasing effective energy absorption, thus mitigating keyhole porosity generation and reducing pore size under certain processing conditions. In conclusion, these observations provide guidelines for the strategic use of environmental gases in laser AM to produce parts with improved quality.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Machine learning–aided real-time detection of keyhole pore generation in laser powder bed fusion

Porosity defects are currently a major factor that hinders the widespread adoption of laser-based metal additive manufacturing technologies. One common porosity occurs when an unstable vapor depression zone (keyhole) forms because of excess laser energy input. With simultaneous high-speed synchrotron x-ray imaging and thermal imaging, coupled with multiphysics simulations, we discovered two types of keyhole oscillation in laser powder bed fusion of Ti-6Al-4V. Amplifying this understanding with machine learning, we developed an approach for detecting the stochastic keyhole porosity generation events with submillisecond temporal resolution and near-perfect prediction rate. Finally, the highly accurate data labeling enabled by operando x-ray imaging allowed us to demonstrate a facile and practical way to adopt our approach in commercial systems.

42 ENGINEERING↗
DOE OSTI2024

Exploring thermal runaway propagation in Li-ion batteries through high-speed X-ray imaging and thermal analysis: Impact of cell chemistry and electrical connections

Battery safety design is important to consider from the individual Li-ion cell to the level of the macro-system. On the macro-level, failure in one single cell can lead to propagation of the thermal runaway and rapidly set a whole battery pack on fire. Factors that can impact the propagation outcome, such as cell model/chemistry and electrical connection are here investigated using a combination of measurements. Several abusive tests were conducted, combining two different cell models (Molicel P42A and LG M50, both 21700s) in series and parallel connections (16 tests per configuration). Overall, a propagation outcome of 56% was measured from the 32 conducted tests, a minimum temperature of 150 °C was required to initiate propagation, and the fastest propagation occurred in 123 s. Temperature measurements were higher in series connected cells, initiating the discussion of cell chemistry and internal resistance on this effect. The difference in current-flow during thermal runaway in series and parallel connections, and how this can affect the temperature evolution is further discussed. Spatio-temporal mapping of X-ray radiography allowed us to derive the speed of thermal runaway evolution inside the battery and has shown that series connected cells, in particular P42A, occur faster. It was further observed that deviant sidewall behaviors such as temperature-induced breaches and pressure-induced ruptures occurred in P42As only respective nail-penetrated cells only.

25 ENERGY STORAGE↗
DOE OSTI2020

Device-Level Multidimensional Thermal Dynamics With Implications for Current and Future Wide Bandgap Electronics

Researchers have been extensively studying wide-bandgap (WBG) semiconductor materials such as gallium nitride (GaN) with an aim to accomplish an improvement in size, weight, and power of power electronics beyond current devices based on silicon (Si). However, the increased operating power densities and reduced areal footprints of WBG device technologies result in significant levels of self-heating that can ultimately restrict device operation through performance degradation, reliability issues, and failure. Typically, self-heating in WBG devices is studied using a single measurement technique while operating the device under steady-state direct current measurement conditions. However, for switching applications, this steady-state thermal characterization may lose significance since the high power dissipation occurs during fast transient switching events. Therefore, it can be useful to probe the WBG devices under transient measurement conditions in order to better understand the thermal dynamics of these systems in practical applications. In this work, the transient thermal dynamics of an AlGaN/GaN high electron mobility transistor (HEMT) were studied using thermoreflectance thermal imaging and Raman thermometry. Also, the proper use of iterative pulsed measurement schemes such as thermoreflectance thermal imaging to determine the steady-state operating temperature of devices is discussed. These studies are followed with subsequent transient thermal characterization to accurately probe the self-heating from steady-state down to submicrosecond pulse conditions using both thermoreflectance thermal imaging and Raman thermometry with temporal resolutions down to 15 ns.

42 ENGINEERING↗
DOE OSTI2026

Thermal neutron imaging detector with wavelength shifting fiber and digital readout

Here, we developed a large area, digital thermal neutron imaging detector. The detector uses a 6 LiF/ZnS(Ag) neutron-sensitive scintillator combined with wavelength shifting fiber technology. The signals from fiber channels are amplified, integrated, and digitized using individual analog-to-digital converters for each channel. The neutron position is determined from the digitized signal using a least-squares gaussian-fitting algorithm. The detector size was 77 × 38 cm 2 (2926 cm 2 ), it provided 1.3 mm resolution, and its resolution can further be improved. The detector was designed for a powder diffraction neutron scattering beamline and provided substantial improvement of d-space resolution compared with existing detectors. This detector may have broader applications due to its large area and high spatial resolution extended over its large area.

6LiF/ZnS(Ag) scintillator↗
DOE OSTI2022

Classification of computed thermal tomography images with deep learning convolutional neural network

Thermal tomography (TT) is a computational method for the reconstruction of depth profile of the internal material defects from Pulsed Infrared Thermography (PIT) nondestructive evaluation. Here, the PIT method consists of recording material surface temperature transients with a fast frame infrared camera, following thermal pulse deposition on the material surface with a flashlamp and heat diffusion into material bulk. TT algorithm obtains depth reconstructions of thermal effusivity, which has been shown to provide visualization of the subsurface internal defects in metals. In many applications, one needs to determine the defect shape and orientation from reconstructed effusivity images. Interpretation of TT images is non-trivial because of blurring, which increases with depth due to the heat diffusion-based nature of image formation. We have developed a deep learning convolutional neural network (CNN) to classify the size and orientation of subsurface material defects in TT images. CNN was trained with TT images produced with computer simulations of 2D metallic structures (thin plates) containing elliptical subsurface voids. The performance of CNN was investigated using test TT images developed with computer simulations of plates containing elliptical defects, and defects with shapes imported from scanning electron microscopy images. CNN demonstrated the ability to classify radii and angular orientation of elliptical defects in previously unseen test TT images. We have also demonstrated that CNN trained on the TT images of elliptical defects is capable of classifying the shape and orientation of irregular defects.

42 ENGINEERING↗
DOE OSTI2022

Fiber and bundle orientations, matrix rich regions, and mechanical properties of fiber reinforced composites using thermal digital image correlation

Methods for assessing fiber and bundle orientations and mechanical properties of fiber reinforced composite materials using Thermal Digital Image Correlation (TDIC) are disclosed. In some examples, the method comprises exposing the composite material to a temperature change; imaging the composite material at a plurality of time points before, during and/or after the temperature change; and assessing the characteristic of the composite material based on the imaging. In others, temperature changes naturally occur during the cooling process after manufacturing can be employed for this method such as compression molding process, injection molding process, resin transfer molding processes and its variants.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Sublimation and oxidation measurements of graphite and carbon black at high temperatures in a shock tube using absorption imaging and thermal emission

Surface mass loss rates due to sublimation and oxidation at temperatures of 3000–7000 K have been measured in a shock tube for graphite and carbon black (CB) particles. Diagnostics are presented for measuring surface mass loss rates by diffuse backlit illumination extinction imaging and thermal emission. Here, the surface mass loss rate is found by regression fitting extinction and emission signals with an independent spherical primary particle assumption. Measured graphite sublimation and oxidation rates are reported to be an order of magnitude greater than CB sublimation and oxidation rates. It is speculated that the difference between CB and graphite surface mass loss rates is largely due to the primary particle assumption of the presented technique which misrepresents the effective surface area of an aggregate particle where primary particles overlap and shield inner particles. Measured sublimation rates are compared to sublimation models in the literature, and it is seen graphite shows fair agreement with the models while CB underestimates, likely a result of the particle shielding affect not being considered in the sublimation model.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Performance of Pulsed Thermal Tomography Imaging with Machine Learning-Based Classification of Defects in Additively Manufactured Structures

Additive manufacturing (AM) is an emerging method for cost-efficient fabrication of complex topology nuclear reactor parts from high-strength corrosion resistance alloys, such as stainless steel and Inconel. AM of metallic structures for nuclear energy applications is currently based on laser powder bed fusion (LPBF) process. Some of the challenges with using LPBF method for nuclear manufacturing include the possibility of introducing pores into metallic structures. Integrity of AM structures needs to be evaluated nondestructively because material flaws could lead to premature failures in high temperature nuclear reactor environment. Currently, there exist limited capabilities to evaluate actual AM structures non-destructively. Pulsed Thermal Tomography Imaging (PTT) provides a capability for non-destructive evaluation (NDE) of subsurface defects in arbitrary size structures. The PTT method is based on recording material surface temperature transients with infrared (IR) camera following thermal pulse delivered on material surface with flash light. The PTT method has advantages for NDE of actual AM structures because the method involves one-sided non-contact measurements and fast processing of large sample areas captured in one image. Following initial qualification of an AM component for deployment in a nuclear reactor, a PTT system can also be used for in-service nondestructive evaluation (NDE) applications. In this report, we describe recent progress in enhancing PTT capabilities in detecting and visualizing microscopic defects in metallic specimens. The thermal tomography (TT) algorithm obtains depth reconstructions of spatial effusivity from the data cube of sequentially recorded surface temperatures. However, interpretation of TT images is non-trivial because of blurring of images with increasing depth. To address this challenge, we have developed a deep learning convolutional neural network (CNN) to classify size and orientation subsurface defects in simulated TT images. CNN is trained on a database of TT images created for a set of simulated metallic structures with elliptical subsurface voids. Test of CNN performance demonstrate the ability to classify radii and angular orientation of subsurface defects in TT images. In addition, we have shown that CNN trained on elliptical defects is capable of classifying irregular-shaped defects obtained from scanning electron microscopy (SEM) of stainless steel sections printed with LPBF.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗