Material Evaluation Using X-ray Computed Tomography
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Time-Domain Terahertz Computed Axial Tomography NDE System
NASA has identified the need for advanced non-destructive evaluation (NDE) methods to characterize aging and durability in aircraft materials to improve the safety of the nation's airline fleet. 3D THz tomography can play a major role in detection and characterization of flaws and degradation in aircraft materials, including Kevlar-based composites and Kevlar and Zylon fabric covers for soft-shell fan containment where aging and durability issues are critical. A prototype computed tomography (CT) time-domain (TD) THz imaging system has been used to generate 3D images of several test objects including a TUFI tile (a thermal protection system tile used on the Space Shuttle and possibly the Orion or similar capsules). This TUFI tile had simulated impact damage that was located and the depth of damage determined. The CT motion control gan try was designed and constructed, and then integrated with a T-Ray 4000 control unit and motion controller to create a complete CT TD-THz imaging system prototype. A data collection software script was developed that takes multiple z-axis slices in sequence and saves the data for batch processing. The data collection software was integrated with the ability to batch process the slice data with the CT TD-THz image reconstruction software. The time required to take a single CT slice was decreased from six minutes to approximately one minute by replacing the 320 ps, 100-Hz waveform acquisition system with an 80 ps, 1,000-Hz waveform acquisition system. The TD-THZ computed tomography system was built from pre-existing commercial off-the-shelf subsystems. A CT motion control gantry was constructed from COTS components that can handle larger samples. The motion control gantry allows inspection of sample sizes of up to approximately one cubic foot (.0.03 cubic meters). The system reduced to practice a CT-TDTHz system incorporating a COTS 80- ps/l-kHz waveform scanner. The incorporation of this scanner in the system allows acquisition of 3D slice data with better signal-to-noise using a COTS scanner rather than the gchirped h scanner. The system also reduced to practice a prototype for commercial CT systems for insulating materials where safety concerns cannot accommodate x-ray. A software script was written to automate the COTS software to collect and process TD-THz CT data.
Zimdars, David↗
NDE of Fiber Reinforced Foam Composite Structures for Future Aerospace Vehicles
This slide presentation reviews the complexities of non-destructive evaluation (NDE) of fiber reinforced foam composite structures to be used for aerospace vehicles in the future.Various views of fiber reinforced foam materials are shown and described. Conventional methods of NDE for composites are reviewed such as Micro-computed X-Ray Tomography, Thermography, Shearography, and Phased Array Ultrasonics (PAUT). These meth0ods appear to work well on the face sheet and face sheet ot core bond, they do not provide adequate coverage for the webs. There is a need for additional methods that will examine the webs and web to foam core bond.
Walker, james↗
Computed tomography for non-destructive evaluation of composites: Applications and correlations
The state-of-the-art fabrication techniques for composite materials are such that stringent species-specific acceptance criteria must be generated to insure product reliability. Non-destructive evaluation techniques including computed tomography (CT), X-ray radiography (RT), and ultrasonic scanning (UT) are investigated and compared to determine their applicability and limitations to graphite epoxy, carbon-carbon, and carbon-phenolic materials. While the techniques appear complementary, CT is shown to provide significant, heretofore unattainable data. Finally, a correlation of NDE techniques to destructive analysis is presented.
Goldberg, B.↗
Automatic Incorporation of Flaws into Cad and FEA Models Based On Measured NDE Data
Automatic incorporation of non-destructive evaluation (NDE) data into CAD and finite element (FE) models for structural analysis is essential for accurate residual-life and strength analyses of composite structures. Currently, two significant but independent challenges exist for incorporating NDE data in such analyses: interpretation of the NDE signals to locate and characterize the structural defects and the incorporation of that information into structural analysis tools. The first challenge will be addressed through linear inversion of calculated synthetic responses generated by a simulation engine to evaluate flaw size, location, and other characteristics. The second challenge will be addressed by building on prior and parallel defect modeling efforts to integrate suitable flaw models into the CAD/finite-element representation of a specimen. Ultrasonic NDE information is usually represented using C-Scans of time-of-flight or intensity information. Other modalities such as thermography and computed tomography (CT) could also be used to characterize the structure. This information will first be projected into material coordinates and then aligned with the specimen’s finite element mesh. The NDE data can then be incorporated by inserting flaw geometry into the CAD and FE analysis model and automatic modification of local material properties and boundary conditions. This process will enable the use of structural analysis tools even for practical modalities such as ultrasound and thermography which may not directly give accurate-enough geometric information to fully define the flaw. The full flaw characteristics will be inferred using prior expert and modeling knowledge to categorize flaw signatures. Outputs for this project will include demonstrated methods for creating structural analysis geometric models that incorporate flaw characterization information; these models can then be used directly in popular commercial FEA software. In addition, the primary deliverable for the project will include a software that will be able to automatically incorporate different kinds of flaws into an existing FE models. The models will be generated in standard file formats to enable direct import into the analysis software.
Adarsh Krishnamurthy↗
Scalable in situ non-destructive evaluation of additively manufactured components using process monitoring, sensor fusion, and machine learning
Laser Powder Bed Fusion (L-PBF) Additive Manufacturing (AM) is among the metal 3D printing technologies most broadly adopted by the manufacturing industry. However, the current industry qualification paradigm for critical-application L-PBF parts relies heavily on expensive non-destructive inspection techniques, which significantly limits the use-cases of L-PBF. In situ monitoring of the process promises a less expensive alternative to ex situ testing, but existing sensor technologies and data analysis techniques struggle to detect sub-surface flaws (e.g., porosity and cracking) on production-scale L-PBF printers. In this work, an in situ NDE (INDE) system was engineered to detect subsurface flaws detected in X-Ray Computed Tomography (XCT) directly from process monitoring data. A multilayer, multimodal data input allowed the INDE system to detect numerous subsurface flaws in the size range of 200–1000µm using a novel human-in-the-loop annotation procedure. Furthermore, a framework was established for generating probability-of-detection (POD) and probability-of-false-alarm (PFA) curves compliant with NDE standards by systematically comparing instances of detected subsurface flaws to post-build XCT data. Here, we also introduce for the first time in the AM in situ sensing literature the a 90/95 – the flaw size corresponding to a 90% detection rate on the lower 95% confidence interval of the POD curve. The INDE system successfully demonstrated POD capabilities commensurate with traditional NDE methods. Traditional ML performance metrics were also shown to be inadequate for assessing the ability of the INDE system’s flaw detection performance. It is the hope of the authors that future studies will adopt the POD and PFA approach outlined here to provide better insight into the utility of process monitoring for AM.
36 MATERIALS SCIENCE↗
Research Performed within the Non-Destructive Evaluation Team at NASA Glenn Research Center
Non-destructive testing is essential in many fields of manufacturing and research in order to perform reliable examination of potentially damaged materials and parts without destroying the inherent structure of the materials. Thus, the Non-Destructive Evaluation (NDE) Team at NASA Glenn Research Center partakes in various projects to improve materials testing equipment as well as analyze materials, material defects, and material deficiencies. Due to the array of projects within the NDE Team at this time, five research aims were supplemental to some current projects. A literature survey of "DE and testing methodologies as related to rocks was performed. Also, Mars Expedition Rover technology was assessed to understand the requirements for instrumentation in harsh space environments (e.g. temperature). Potential instrumentation and technologies were also considered and documented. The literature survey provided background and potential sources for a proposal to acquire funding for ultrasonic instrumentation on board a future Mars expedition. The laboratory uses a Santec Systems AcousticScope AS200 acoustography system. Labview code was written within the current program in order to improve the current performance of the acoustography system. A sample of Reinforced Carbon/Carbon (RCC) material from the leading edge of the space shuttle underwent various non-destructive tests (guided wave scanning, thermography, computed tomography, real time x-ray, etc.) in order to characterize its structure and examine possible defects. Guided wave scan data of a ceramic matrix composite (CMC) panel was reanalyzed utilizing image correlations and signal processing variables. Additional guided wave scans and thermography were also performed on the CMC panel. These reevaluated data and images will be used in future presentations and publications. An additional axis for the guided wave scanner was designed, constructed, and implemented. This additional axis allowed incremental spacing of the previously fixed transducers for ultrasonic velocity measurements.
Burns, Erin A.↗
ASTM International Conference on Additive Manufacturing – 2022
The In-Situ Project: Correlating In-situ monitoring data and Non-Destructive Evaluation (NDE) methods to characterize defect populations in Laser Powder Bed Fusion (L-PBF) material NDE is critical to enable the safe use of additive manufactured (AM) parts in aerospace applications. However, traditional NDE methods are not fully adaptable to complex AM geometries to appropriately screen for critical defects, and L-PBF processes inherently generate defects. In-situ monitoring techniques can give information about the quality of the build process and indicate process variations that could potentially create defects. Proven, causal correlations between in-situ monitoring indications and resulting defects are needed to inform about the final quality of the part. Thus, the development of a new approach is necessary. NASA started a two-phase study that would investigate techniques for characterizing defect populations in L-PBF material, first in material with induced defects, then in a nominal L-PBF process, by correlating in-situ, NDE, and serial sectioning data. Objectives also include the evaluation of seeded defect methodologies for realistic defect creation, creating a baseline of defect populations in a Qualified Material Process (QMP), characterizing the effect of heat treatment, and correlating defect populations with tensile and fatigue properties. By comparing computed tomography images, in-situ monitoring images, and metallography images, the team has been able to draw preliminary conclusions. They include a better understanding of the limitation of the NDE tools, learning about the “healing” of the samples observed during layer building and a comparison study on different laser power variation samples. Those preliminary conclusions will help dictate the follow-up work that will be presented at the conference.
additive manufacturing↗
In-Space Inspection Needs: Opportunities for advanced NDE tools such as x-ray CT for additively manufactured parts, in-situ resource utilization, geological applications, and more
It is now 50 years since the first human presence on the surface of the Moon and as we strive to return with women and men in the next few years, we embrace new technical challenges, goals, and innovative solutions to address 21st century objectives. These new ambitions carry fresh challenges and risks, with the field of NDE playing an increasingly more relevant role towards meeting these essential goals. In recent years, more advanced NDE tools have triggered a rapid expansion of applications for the space industry. In particular, x-ray Computed Tomography (CT) has proven to be a trusted and powerful asset for spaceflight hardware inspection, as well as applied geotechnical analysis for natural materials (e.g., rocks, soils) for NASA and across industry. However, such methods have yet to be extended to “deep space” applications such as those that are now part of the US National Space Policy Directive (SPD-1) and the accelerated push to return humans to the Moon (i.e., Artemis). For this reason, advancing these powerful Earth-based laboratory methods via new technologies, integrated computational solutions, and creative engineering approaches is directly aligned with national space policies, as well as with multiple NASA Strategic Plan priorities. The use of x-ray CT at scales as fine as a few microns or smaller can identify spacecraft part failure modes relevant to quality assurance for flight hardware and AM parts such as those recently developed for ISS. This technology could also identify valuable metallic phases within geological materials (i.e., rocks or drill cores), enabling resource-relevant triage of samples for In-Situ Resource Utilization (ISRU) and high science value sample return to Earth laboratories. There is also significant application for 3D imaging tools for medical use such as inspecting protective gear as well as bone density degradation studies which are critical in establishing a sustained presence in space. Timing for development of these tools for space use is advantageous as we prepare for new opportunities in the next few years and recognize recent commercial technology advancements which make it feasible. Moreover, as NASA strives to take full advantage of developments in AM technologies, including In-Space Manufacturing (ISM), it is widely recognized that NDE tools such as CT will play an essential role in acceptance of these parts for widespread use. New in-space 3D inspection tools with complimentary technology such as AI-based automated feature recognition (accelerated by machine learning), rapid compositional analysis, and advanced sample manipulation, would be a game-changing step toward a new class of crew-based laboratory sensors once human outposts on the Moon are established.
In-Space Non-Destructive Evaluation↗
Machine Learning Enabled Sensor Fusion for In-Situ Defect Detection in Laser Powder Bed Fusion
Laser Powder Bed Fusion (L-PBF) Additive Manufacturing (AM) is among the metal 3D printing technologies most broadly adopted by the manufacturing industry. The current industry qualification paradigm for critical-application L-PBF parts relies heavily on expensive non-destructive inspection techniques such as X-Ray Computed Tomography (XCT), which significantly limits the use-cases of L-PBF. In situ monitoring of the process promises a less expensive alternative to ex situ testing, but existing sensor technologies and data analysis techniques struggle to detect sub-surface flaws (e.g., porosity and cracking) on production-scale L-PBF printers. RTX Technologies Research Center (RTRC) has licensed ORNL’s Peregrine software package – a printer- and camera-agnostic data analytics tool designed specifically for detecting process anomalies using in situ data collected during powder bed printing. The goal of this project was to feed temporally rich, multi-modal sensor data, including visible light, integrated near infrared (NIR), and spatially mapped co-axial melt pool thermal emission data into Peregrine to enable detection of subsurface flaws. XCT data was used as ground truth training data to allow Peregrine’s deep learning algorithms to recognize anomalies in these complex data streams in both test artifacts and industrially relevant geometries. Completion of this program has seen the successful implementation of multi-modal, multi-layer sensor data footprints for training of machine learning models in Peregrine. Flaws detected in XCT data have been successfully detected directly from this in situ data footprint, and initial analyses of the in situ probability-of-detection has been conducted, showing performance levels commensurate with traditional non-destructive evaluation (NDE) methods. The in situ monitoring methodology was then applied to an industrially relevant component that was using post-build NDE, highlighting the utility of the proposed method for hard-to-inspect AM components. As a direct result of this program, two journal manuscripts [1], [2] have been published in Additive Manufacturing, with additional manuscripts planned following program completion.
36 MATERIALS SCIENCE↗
Pulsed Thermal Tomography Nondestructive Examination of Additively Manufactured Reactor Materials (Second Annual Progress Report)
Additive manufacturing (AM) is an emerging method for cost-efficient fabrication of nuclear reactor parts. AM of metallic structures for nuclear energy applications is currently based on laser powder bed fusion (LPBF) process, which can introduce internal material flaws, such as pores and anisotropy. Integrity of AM structures needs to be evaluated nondestructively because material flaws could lead to premature failures due to exposure to high temperature, radiation and corrosive environment in a nuclear reactor. Thermal tomography (TT) provides a capability for non-destructive evaluation of sub-surface defects in arbitrary size 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 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. Reconstruction of internal defects is investigated using a stainless steel 316L specimen with flat bottom hole (FBH) indentations, and Inconel 718 plate produced with laser powder bed fusion (LPBF) method, which contains imprinted hemispherical shape low density regions containing non-sintered metallic powder. The FBH’s 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. Detection sensitivity of TT is limited by noises. We investigate separation of signal from noise in thermography images using several machine learning (ML) methods, including new spatio-temporal blind source separation (STBSS) and spatio-temporal sparse dictionary learning (STSDL) methods. Performance of the ML methods is benchmarked using thermography data obtained from imaging stainless steel 316L and Inconel 718 specimens produced LPBF method with imprinted calibrated porosity defects. The ML methods are ranked by F-score and execution runtime. Finally, we investigate TT of AM stainless steel 316L specimen with imprinted internal porosity defects using relatively low-cost, small form factor infrared (IR) camera based on uncooled micro bolometer detector. Sparse coding related K-means singular value decomposition (SVD) machine learning, image processing algorithms are developed to improve quality of TT images through removal of Additive white Gaussian noise without blurring the images. Following initial qualification of an AM component for deployment in a nuclear reactor, a compact TT can also be used for in-service nondestructive evaluation (NDE). With capability to perform in-service NDE of the AM component lifecycle, TT data can be used for development of a component digital twin.
22 GENERAL STUDIES OF NUCLEAR REACTORS↗
Use of Computed Tomography for Characterizing Materials Grown Terrestrially and in Microgravity
Computed Tomography (CT) has advanced considerably since being responsible for such dramatic advances in diagnostics within the medical field. It has become a major tool in non destructive evaluation (NDE), and is used in many fields as diverse as coal-mining to metal solidification to examination of rock cores. A review of industrial applications has been written by Dennis. It is only recently that the technique has been seriously used to determine composition through precise measurement of density. While such applications are restricted to cases when there is no ambiguity in the relationship of composition to density. Thus alloy solid solutions lend themselves to compositional analysis provided there is a large change in density with composition. The technique is most useful when rapid non-destructive evaluation is needed. Such cases will occur with samples returning from the International Space Station (ISS) when knowledge of the results could affect future strategies for processing of on-board samples. Experiments from those Principal Investigators (PI) that are most likely to benefit from early CT scanning are discussed. With a dearth of samples, the major emphasis in the first part of this project has been on preparing suitable standards, optimizing the CT technique for these applications, and using the CT system to determine density variations with temperature. An interesting application of CT has been in the examination of meteorites, which can be classified as space-grown materials and will certainly have solidified in a low gravity environment.
Gillies, Donald C.↗