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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 397 records · Page 22
All sourcesNASA NTRSDOE OSTI
NASA NTRS2010

High-Temperature Surface-Acoustic-Wave Transducer

Aircraft-engine rotating equipment usually operates at high temperature and stress. Non-invasive inspection of microcracks in those components poses a challenge for the non-destructive evaluation community. A low-profile ultrasonic guided wave sensor can detect cracks in situ. The key feature of the sensor is that it should withstand high temperatures and excite strong surface wave energy to inspect surface/subsurface cracks. As far as the innovators know at the time of this reporting, there is no existing sensor that is mounted to the rotor disks for crack inspection; the most often used technology includes fluorescent penetrant inspection or eddy-current probes for disassembled part inspection. An efficient, high-temperature, low-profile surface acoustic wave transducer design has been identified and tested for nondestructive evaluation of structures or materials. The development is a Sol-Gel bismuth titanate-based surface-acoustic-wave (SAW) sensor that can generate efficient surface acoustic waves for crack inspection. The produced sensor is very thin (submillimeter), and can generate surface waves up to 540 C. Finite element analysis of the SAW transducer design was performed to predict the sensor behavior, and experimental studies confirmed the results. One major uniqueness of the Sol-Gel bismuth titanate SAW sensor is that it is easy to implement to structures of various shapes. With a spray coating process, the sensor can be applied to surfaces of large curvatures. Second, the sensor is very thin (as a coating) and has very minimal effect on airflow or rotating equipment imbalance. Third, it can withstand temperatures up to 530 C, which is very useful for engine applications where high temperature is an issue.

Zhao, Xiaoliang↗
NASA NTRSDate not supplied

NASA's Office Safety Mission Assurance Efforts to Improve Non-destructive Evaluation Methods for Additive Manufacturing and In-Space Inspection

National Aeronautics and Space Administration (NASA) continues to push the boundary of manned flight. Current mission profiles include flights to the Moon and Mars. Many of these missions require advance manufacturing and planned off planet resource utilization. As NASA continues to develop highly advance materials and manufacturing techniques to meet these mission profiles, inspection of these increasingly complex parts has had to evolve as well. Additive manufacturing is coming to the forefront as a primary manufacturing technique but offers unique challenges when inspection and certification are required. NASA has been working diligently to be a leader in developing and utilizing inspection techniques capable of inspecting these highly advanced parts. Additionally additive manufacturing techniques can also be used to build parts in space and provides a vehicle for in-situ resource utilization for off world missions. As NASA and its commercial crew partners move forward, inspection techniques will need to continue to advance to ensure safe manned space flight. In this talk NASA will highlight many of the advanced nondestructive evaluation efforts going on across the agency, as well as providing highlights for points of infusion for helping NASA meet its future mission goals.

NDE↗
NASA NTRS2008

Characterization of Fatigue Damage for Bonded Composite Skin/Stringer Configurations

The fatigue damage was characterized in specimens which consisted of a tapered composite flange bonded onto a composite skin. Quasi-static tension tests were performed first to determine the failure load. Subsequently, tension fatigue tests were performed at 40%, 50%, 60% and 70% of the failure load to evaluate the debonding mechanisms. For four specimens, the cycling loading was stopped at intervals. Photographs of the polished specimen edges were taken under a light microscope to document the damage. At two diagonally opposite corners of the flange, a delamination appeared to initiate at the flange tip from a matrix crack in the top 45deg skin ply and propagated at the top 45deg/-45deg skin ply interface. At the other two diagonally opposite corners, a delamination running in the bondline initiated from a matrix crack in the adhesive pocket. In addition, two specimens were cut longitudinally into several sections. Micrographs revealed a more complex pattern inside the specimen where the two delamination patterns observed at the edges are present simultaneously across most of the width of the specimen. The observations suggest that a more sophisticated nondestructive evaluation technique is required to capture the complex damage pattern of matrix cracking and multi-level delaminations.

Paris, Isabelle↗
DOE OSTI2021

Negative Index Metamaterial Lens for Subwavelength Microwave Detection

Metamaterials are engineered periodic structures designed to have unique properties not encountered in naturally occurring materials. One such unusual property of metamaterials is the ability to exhibit negative refractive index over a prescribed range of frequencies. A lens made of negative refractive index metamaterials can achieve resolution beyond the diffraction limit. This paper presents the design of a metamaterial lens and its use in far-field microwave imaging for subwavelength defect detection in nondestructive evaluation (NDE). Theoretical formulation and numerical studies of the metamaterial lens design are presented followed by experimental demonstration and characterization of metamaterial behavior. Finally, a microwave homodyne receiver-based system is used in conjunction with the metamaterial lens to develop a far-field microwave NDE sensor system. A subwavelength focal spot of size 0.82λ was obtained. The system is shown to be sensitive to a defect of size 0.17λ × 0.06λ in a Teflon sample. Consecutive positions of the defect with a separation of 0.23λ was resolvable using the proposed system.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

In-Sodium Testing of a Prototype Thermoacoustic Power Sensor for Sodium-Cooled Fast Reactors

The ultimate goal of this project is to develop and demonstrate a thermoacoustic power sensor (TAPS) for Sodium-Cooled Fast Reactors (SFRs), with potential application also envisioned to other nuclear technologies such as Lead-Cooled Fast Reactors (LFRs), Molten Salt Reactors (MSRs), in addition to Light Water Reactors (LWRs). The project was led by Westinghouse Electric Company, LLC (Westinghouse) and carried out in collaboration with Argonne National Laboratory (ANL) and the University of Pittsburgh. A TAPS is a passive (self-powered), non-invasive (wireless) sensor envisioned for measuring key parameters, such as local temperature and neutron flux, in a nuclear reactor core. The sensor generates pressure waves (i.e., sound waves) with a frequency and amplitude dependent upon nuclear operating conditions (coolant temperature or power changes). The acoustic waves are able to travel through the core and associated structures, and reach to the sensor network placed outside and/or inside of the reactor vessel. These sensors require a very small amount of power which, during loss of power events, can be provided, for example, by harvesting gamma radiation energy, thus resulting in a monitoring system that can function both during normal operation and during loss of power events. Westinghouse and the University of Pittsburgh designed and fabricated TAPS prototypes for Argonne National Laboratory (ANL) to carry out in-sodium testing to evaluate the effects of sodium on the TAPS and the performance of the TAPS technique in sodium. Argonne received a TAPS prototype from Westinghouse, and the prototype was modified such that it can be installed into a test vessel and function in sodium at elevated temperature without potentially leaking. A water mockup test apparatus was constructed to validate proper working of the prototype. An instrumentation and control (I&C) system, running on the National Instruments (NI) LabView platform, was developed to: 1) operate both the water mockup test and the in-sodium test facility; and 2) process and analyze the received acoustic signals from an array of accelerometers and the Argonne sodium-submersible high-temperature acoustic sensor. The prototype was successfully tested in a water bath at different temperatures. Water mockup tests demonstrated that the TAPS prototype is working properly and its resonance frequency changes linearly with the coolant (water) temperature. A TAPS test apparatus was constructed and integrated with the upgraded Under-Sodium Viewing (USV) sodium test facility. The integrated USV-TAPS sodium test facility has been operational. The TAPS prototype and a high-temperature sodium-submersible acoustic sensor (SSAS) developed by Argonne were both installed inside the TAPS test vessel. Being operated within argon cover gas under ambient conditions, the TAPS prototype demonstrated that it was functioning properly with a resonance frequency at 1407.2 Hz, which was successfully detected by the accelerometers mounted on the external wall of the vessel and the high-temperature SSAS installed inside the vessel. After successfully transferring sodium into the vessel, in-sodium tests of the prototype were conducted. Tests of the TAPS prototype demonstrated that the resonance frequency of the TAPS changes linearly with respect to the temperature difference between the interior of the TAPS and bulk sodium. The early tests showed that the TAPS prototype could not establish a continuous and consistent resonance in sodium. The resonance diminished before the prototype reached its operating temperature. A signal postprocessor was added to the DAQ module latterly to isolate interferences, enhance signal conditioning, improve peak detection, and generate resonance frequency versus temperature plots. After testing in molten sodium and immersion at higher temperature for several weeks, the TAPS prototype was able to establish a continuous resonance. Performance evaluation of the TAPS prototype was then conducted in sodium. The tests included the investigation of 1) the effects of the temperature difference between the TAPS and bulk sodium, 2) the effects of sodium flowrate; and 3) the performance of the different sensor-receiver systems positioned inside or outside the vessel. Results of a test demonstrated that, with limited sodium circulation, a continuous and consistent resonance of the TAPS prototype was established occasionally. The tests also demonstrated that, because of the nature of detection principles and mounting methods, the high temperature SSAS is more affected by acoustic noise, while accelerometers are more affected by vibrations, in the test environment. It is unknown why the TAPS prototype only occasionally established a continuous and consistent resonance when the TAPS temperature reached its operating temperature in molten sodium, and why it ultimately failed to resonate at all. A failure modes assessment was conducted and a few potential causes of failure were identified. Different post in-sodium tests were conducted to obtain additional information potentially relevant to the cause of the failure. Nondestructive evaluation techniques are suggested to examine the internal integrity as well as the gas mixture of the prototype. If they prove inconclusive, the prototype should be cut open to conduct a thorough inspection of its internal integrity and determine the state of the gas mixture.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
NASA NTRS2016

Benchmarking of Computational Models for NDE and SHM of Composites

Ultrasonic wave phenomena constitute the leading physical mechanism for nondestructive evaluation (NDE) and structural health monitoring (SHM) of solid composite materials such as carbon-fiber-reinforced polymer (CFRP) laminates. Computational models of ultrasonic guided-wave excitation, propagation, scattering, and detection in quasi-isotropic laminates can be extremely valuable in designing practically realizable NDE and SHM hardware and software with desired accuracy, reliability, efficiency, and coverage. This paper presents comparisons of guided-wave simulations for CFRP composites implemented using three different simulation codes: two commercial finite-element analysis packages, COMSOL and ABAQUS, and a custom code implementing the Elastodynamic Finite Integration Technique (EFIT). Comparisons are also made to experimental laser Doppler vibrometry data and theoretical dispersion curves.

composite materials↗
NASA NTRS1996

Welding rework data acquisition and automation

Aluminum-Lithium is a modern material that NASA MSFC is evaluating as an option for the aluminum alloys and other aerospace metals presently in use. The importance of aluminum-lithium is in it's superior weight to strength characteristics. However, aluminum-lithium has produced many challenges in regards to manufacturing and maintenance. The solution to these problems are vital to the future uses of the shuttle for delivering larger payloads into earth orbit and are equally important to future commercial applications of aluminum-lithium. The Metals Processes Branch at MSFC is conducting extensive tests on aluminum-lithium which includes the collection of large amounts of data. This report discusses the automation and data acquisition for two processes: the initial weld and the repair. The new approach reduces the time required to collect the data, increases the accuracy of the data, and eliminates several types of human errors during data collection and entry. The same material properties that enhance the weight to strength characteristics of aluminum-lithium contribute to the problems with cracks occurring during welding, especially during the repair/rework process. The repairs are required to remove flaws or defects discovered in the initial weld, either discovered by x-ray, visual inspection, or some other type of nondestructive evaluation. It has been observed that cracks typically appear as a result of or beyond the second repair. MSFC scientists have determined that residual mechanical stress introduced by the welding process is a primary cause of the cracking. Two obvious solutions are to either prevent or minimize the stress introduced during the welding process, or remove or reduce the stress after the welding process and MSFC is investigating both of these.

Romine, Peter L.↗
DOE OSTI2026

Nondestructive Modular Leak Detection in 3D Printed 316L Stainless Steel Pipes via Laser Powder Bed Fusion

This research investigates the leak detection features of 316L Stainless Steel pipe structures manufactured via Laser Powder Bed Fusion (LPBF). This work involves the design of a modular sensor system integrating nondestructive evaluation (NDE) methods, including thermal imaging and ultrasonic frequency detection to detect and characterize leaks in components. This aims to improve leak detection sensitivity within medium-pressure gas systems, during continuous operation without halting flow or introducing safety risks. The system could be adaptable for use on unmanned aerial vehicles (UAVs), enabling remote leak detection in active environments. A custom pneumatic system incorporating temperature and pressure sensors was assembled to detect leaks in LPBF-printed 316L SS tee pipes. Experimental results and simulations confirm the system’s effectiveness in leak detection and material evaluation. This research program also integrated a Python-based image recognition platform based on a metallography and optical microscopy to assess the porosity and complement the leak detection data on the printed structures. This allows a detailed analysis of pore distribution and internal leak paths, which could compromise structural integrity, critical for quality control during manufacturing. Findings suggest that the investigated approach holds potential for enhancing leak detection technologies and adapt them for advanced manufactured parts.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Generation of selective single-mode guided waves by d 36 type piezoelectric wafer

In general, mixed modes and dispersion of guided wave occur at any excitation frequency, while single modes are needed for effective nondestructive evaluation of structures. In this work, we present an approach to generate a selective single-mode guided wave in plate-like structures by exploiting the unique directionality of the d 36 type piezoelectric wafer and the symmetry of fundamental guided wave modes. Specifically, we devise a unified fundamental shear horizontal (SH 0 ) wave/fundamental antisymmetric mode (A 0 ) wave directional transducer pair by attaching the d 36 type piezoelectric wafers back to back on the opposite sides of the plate to provide selective, directional single-mode guided waves for actuation and sensing. Originally, the d 36 -type piezoelectric wafer, poled and cut from the lead magnesium niobate-lead titanate crystal, produces a mixture of the in-plane SH 0 (symmetric) mode and out-of-plane A 0 (asymmetric) and S 0 (symmetric) modes. By applying the in-phase or out-of-phase applied electrical field to the coupled d 36 -type piezoelectric wafer pair, our devised approach generates selective single modes, SH 0 or A 0 , respectively, to propagate in the structure. We describe the theoretical development of our approach and conduct both numerical simulations and laboratory experiments for validations.

42 ENGINEERING↗
DOE OSTI2025

Combining Deep Learning and scatterControl for High-Throughput X-ray CT Based Non-Destructive Characterization of Large-Scale Casted Metallic Components

X-ray computed tomography (XCT) is essential for nondestructive evaluation and quality control of large-scale metal components. XCT imaging, however, faces significant challenges from metal artifacts, particularly those caused by Compton scattering, which degrade image quality and obscure critical details. Hardware-based solutions (e.g. scatterControl) offer advancements by intercepting scattered photons and reducing artifacts, but they can be time-consuming and require additional processing. Here, we propose modifying and leveraging a novel deep learning (DL) framework, Simurgh, to enhance and accelerate scatter correction in XCT. By combining scatterControl with DL-based artifact removal, we demonstrate significant reduction in scan time while producing high-quality reconstructions. Through extensive evaluation on industrial XCT data, we show that our methods reduce scan time by up to more than 10 x while preserving flaw detectability. Quantitative analysis across multiple segmentation techniques confirms that Simurgh-based reconstructions consistently outperform traditional Feldkamp-Davis-Kress, model-based iterative reconstruction, and commercial DL models in both pixel-level and task-specific evaluations, enabling scalable, high-throughput XCT workflows for characterization of large scale components in applications such as casting and metal additive manufacturing.

Complex metal parts↗
DOE OSTI2022

A System-Level Model for Estimating Residual Strain and Life of Nuclear Reactor Coolant System Components Under Connected-System-Thermal–Mechanical Boundary Conditions

Background: Environmental-assisted fatigue (EAF) is a major issue for the long-term survival of nuclear power plant fleets in the U.S. and worldwide. Multi-material welded regions (e.g., nozzles) and other high-stress regions of reactor coolant system (RCS) components are prone to EAF-related damage. Objective: The discussed work describes a system-level finite element (FE) model of RCS components of a pressurized water reactor (PWR). This is with the goal of predicting the stress hotspots, strain residuals, strain amplitudes and the resulting fatigue lives. Methods: Herein, the FE model was developed considering system-level loading conditions (under connected system thermal-mechanical boundary conditions). Thermal-mechanical stress analysis was performed considering thermal stratification and a design-basis reactor loading cycle. Based on the FE model results, the strain residuals, strain amplitudes and resulting fatigue lives of RCS components were predicted. Results: The results show that some of the RCS components can have significantly different strain amplitudes, residual strain, and fatigue lives, despite having similar geometry and material. Higher residual strain can lead to accelerated cyclic hardening of material and the associated effect of EAF. The simulated component-level strain profile (under realistic multi-axial-multi-physics loading cycle) can guide the selection of appropriate test inputs for conducting laboratory-scale EAF tests, which is a focus of future works. Conclusions: Despite similar geometry and material the RCS component can have significantly different strain profiles and resulting fatigue lives. The discussed approach can help to identify and prioritize the RCS components for conducting expensive nondestructive evaluation (NDE) inspections.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

High Speed Impact Testing of UHMWPE Composite Using Orthogonal Arrays

Abstract Background Ultra high molecular weight polyethylene composites are fiber based composites used in armor applications. While some characterization has been conducted experimentally, this study varies multiple parameters simultaneously to investigate material response under a wide range of conditions. Objective This work focuses on characterizing the response of Dyneema ® HB26 hard laminate composites under high-speed impacts to examine the influence of plate diameter, clamping pressure, and plate spacing on target performance. Additionally, micro Computer Tomography scans are used to nondestructively evaluate the damage evolution in the targets. Methods These scan results are used in concert with more traditional armor performance metrics to evaluate the effect of various parameters using the method of orthogonal array analysis. This technique allows for multiple variables to be investigated in the same test series, saving time and budget while still providing quality results across a range of variables and variable values. Results We conclude that of the parameters investigated, the plate spacing parameter has the largest effect on performance, followed by the plate diameter. Bolt torque was found to not have a significant impact on results, indicating that an edge clamping pressure is not critical to material response. Additionally, by examining the high resolution scans, we can quantify the damage with an effective damage angle and that this angle is a good predictor of performance. Conclusion Finally a damage theory involving the effective bending strength of the plates is discussed as an explanation for all of the results observed in this test series.

Hannah, T. (ORCID:0000000230261362)↗
DOE OSTI2020

Quality Control of Additively Manufactured Metallic Structures with Machine Learning of Thermography Images

Additive manufacturing (AM) of high-strength metals is currently based on the laser powder bed fusion (LPBF) process, which can introduce internal material flaws, such as pores and anisotropy. Quality control (QC) requires nondestructive evaluation of actual AM structures. Flash thermography is a potentially promising QC technique because it is scalable to arbitrary structure size. However, the detection sensitivity of this method is limited by noise. We investigate separation of signal from noise in thermography images using several machine learning (ML) methods, including new spatial–temporal blind source separation and spatial–temporal sparse dictionary learning methods. Performance of the ML methods is benchmarked using thermography data obtained from imaging stainless steel 316L and Inconel 718 specimens produced by the LPBF method with imprinted calibrated porosity defects. The ML methods are ranked by F-score and execution runtime. Finally, the ML methods with higher accuracy require a longer runtime. However, this runtime is sufficiently short to perform QC within a realistic time frame.

36 MATERIALS SCIENCE↗
DOE OSTI2025

A Robust Data-Driven Approach for Mechanical Serial Sectioning

Mechanical serial sectioning (MSS) provides detailed microstructural information across large length scales. By repeatedly removing thin layers of material and imaging the exposed surface, a 3D representation of a specimen’s internal structure can be constructed, enabling failure analysis and feature identification that are otherwise inaccessible via conventional 2D or nondestructive evaluation techniques. Achieving consistent and accurate material removal can be challenging due to system variability, requiring an experienced operator to manually adjust parameters, prolonging data collection times and necessitating post-processing routines to standardize the data. Here, to address these challenges, this paper presents the employment of a one-step model predictive control (MPC) framework tailored to a run-to-run (R2R) controller. The R2R-MPC controller automates the parameter selection process, improving the consistency of material removal through iterative feedback for disturbance rejection and accurate tracking of the target removal rate. Using a data-driven approach, the controller robustly adapts to changing material characteristics. The effectiveness of the R2R-MPC controller is demonstrated through simulation and experimental results and compared to previous data collection procedures.

3D Materials Science↗
DOE OSTI2023

Internal defect detection and characterization of samarium-cobalt sintered magnets by ultrasonic testing technique

Excessive quantities of samarium-cobalt (Sm-Co) magnet material are being scrapped needlessly due to a lack of understanding of inhomogeneity distribution and unacceptable internal defects. If there is a way to identify, locate, characterize and when needed separate the defective portions of magnet material, utilization can be increased and product quality improved. Further, the magnets’ magnetic and mechanical performance can be improved by reducing the occurrence of internal defects. This paper reports on a cost-effective and efficient nondestructive evaluation method based on an ultrasonic testing (UT) technique applied for detecting and characterizing internal defects in Sm-Co sintered magnets. Applying the UT technique will allow users to comprehensively analyze internal defects, such as inclusions, porosity, microcracks, and other structural irregularities, check for homogeneity and anomalous regions and give the locations of these internal anomalies and defects within the Sm-Co sintered magnets. The UT technique can also be applied to other rare-earth permanent magnets, such as sintered or die-upset neodymium-iron-boron (Nd-Fe-B) magnets. The UT technique can effectively guide quality control and acceptable product selection, in addition to optimizing the magnet alloy design and production processes. Therefore, it can facilitate the improvement of magnet manufacturing efficiency and machinability, reduce scrap, prolong service life, increase the use of what would be post-production waste, and enhance product reuse and recycling at end-of-life disposition.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Improved guided-wave acoustic defect detection and localization in pipes under varying temperature conditions using deep learning

Early defect detection in pipelines is critical across industries, particularly in the oil and gas sector, where failures result in significant maintenance costs and operational disruptions. Acoustic guided-wave techniques are widely used for nondestructive evaluation of pipeline defects due to their long-distance propagation capability. However, environmental variations, sensitivity limitations, and complex signal interpretation challenges limit the effectiveness of traditional signal processing approaches with guided-wave signals. Recent advances in deep learning methods have demonstrated remarkable success in solving complex real-world problems in many fields. In particular, deep-learning-based signal processing holds substantial promise to overcome limitations and challenges of conventional signal processing. This study presents a deep learning framework for pipeline inspection using acoustic guided-wave signals under temperature varying environments. The proposed framework employs a dual-path one-dimensional convolutional autoencoder that combines defect detection, localization, and temperature prediction functions. The proposed system utilizes multi-mode and broadband acoustic waves with an optimized number of sensors that provide high accuracy while retaining practical simplicity. Experimental validation is performed on a carbon steel pipe. The results indicate exceptional defect detection accuracy and precise defect localization with a mean absolute error of 66 mm. The proposed technique also predicts the effective average temperature of the pipe with a mean absolute error of 0.2°C. Comparative analysis shows superior performance of the proposed method over a traditional method previously developed by the authors' team. These results highlight the potential of integrating deep learning methods into guided-wave pipeline inspection systems to improve reliability under varying environmental conditions.

42 ENGINEERING↗
DOE OSTI2024

Unsupervised learning-enabled pulsed infrared thermographic microscopy of subsurface defects in stainless steel

Metallic structures produced with laser powder bed fusion (LPBF) additive manufacturing method (AM) frequently contain microscopic porosity defects, with typical approximate size distribution from one to 100 microns. Presence of such defects could lead to premature failure of the structure. In principle, structural integrity assessment of LPBF metals can be accomplished with nondestructive evaluation (NDE). Pulsed infrared thermography (PIT) is a non-contact, one-sided NDE method that allows for imaging of internal defects in arbitrary size and shape metallic structures using heat transfer. PIT imaging is performed using compact instrumentation consisting of a flash lamp for deposition of a heat pulse, and a fast frame infrared (IR) camera for measuring surface temperature transients. However, limitations of imaging resolution with PIT include blurring due to heat diffusion, sensitivity limit of the IR camera. We demonstrate enhancement of PIT imaging capability with unsupervised learning (UL), which enables PIT microscopy of subsurface defects in high strength corrosion resistant stainless steel 316 alloy. PIT images were processed with UL spatial–temporal separation-based clustering segmentation (STSCS) algorithm, refined by morphology image processing methods to enhance visibility of defects. The STSCS algorithm starts with wavelet decomposition to spatially de-noise thermograms, followed by UL principal component analysis (PCA), fine-tuning optimization, and neural learning-based independent component analysis (ICA) algorithms to temporally compress de-noised thermograms. The compressed thermograms were further processed with UL-based graph thresholding K-means clustering algorithm for defects segmentation. The STSCS algorithm also includes online learning feature for efficient re-training of the model with new data. For this study, metallic specimens with calibrated microscopic flat bottom hole defects, with diameters in the range from 203 to 76 µm, were produced using electro discharge machining (EDM) drilling. While the raw thermograms do not show any material defects, using STSCS algorithm to process PIT images reveals defects as small as 101 µm in diameter. To the best of our knowledge, this is the smallest reported size of a sub-surface defect in a metal imaged with PIT, which demonstrates the PIT capability of detecting defects in the size range relevant to quality control requirements of LPBF-printed high-strength metals.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Thermal tomography 3D imaging of additively manufactured metallic structures

Thermal tomography is a computational method for heat diffusion-based imaging of solids, which provides 3D visualization of data from flash thermography measurements. We investigate thermal tomography imaging and nondestructive evaluation of stainless steel and nickel super alloy metallic structures produced with the laser powder bed fusion (LPBF) additive manufacturing (AM) process. Metallic structures produced with LPBF contain defects, and there are limited capabilities to evaluate these structures non-destructively. Thermal tomography reconstruction of 3D apparent spatial effusivity provides information about AM structure geometry and internal material flaws. We study performance of thermal tomography in imaging of metallic structures through COMSOL computer simulations of transient heat transfer and through reconstruction of data obtained from experimental measurements. Thermal tomography reconstructions of structure shape and dimensions are shown for the Inconel 718 AM structure which has variations in the horizontal plane but is uniform along the depth dimension. Reconstruction of internal defects is investigated using a stainless steel 316L specimen with flat bottom hole (FBH) indentations, and the Inconel 718 plate is produced with the LPBF method, which contains imprinted hemispherical shape low density regions containing non-sintered metallic powder. The FBHs have the same sizes as the imprinted defects in the LPBF specimens but offer better imaging contrast. Thermal tomography reconstructions provide visualizations of internal defects and allow for estimation of their sizes and locations. Results of this study demonstrate that thermal tomography can be used for visualization and quality control in AM.

36 MATERIALS SCIENCE↗