Imaging and other nondestructive evaluation techniques are commonly used for material characterization and defect recognition in safety critical aerospace applications, with data fusion providing the framework for uncertainty quantification in these contexts. Most commonly, forward physics-based modeling predicts the response conditioned on material properties and defect assumptions, and probabilistic methods are used to infer the hidden state of the subject of the inspection from a combination of prior information, likelihoods, and inspection data. In this paper Bayesian methods are used to estimate bond thickness in lap joints comprised of aluminum adherends using a combination of infrared thermography and ultrasound. The concept of the conflation of probability distributions is applied to combine the posterior distributions derived from thermography and ultrasound and the quality of the fused estimates are compared against the individual estimates against synthetic data that was created to mimic the inspection of a lap joint comprised of aluminum adherends.
An elastic-poroelastic simulation of ultrasound inspection for lithium-metal batteries is presented and compared to empirical reflection spectra measured during battery cycling. Simulated reflection spectra were obtained using a two-dimensional (2D) plane strain model, comprised of dozens of individual microns-thick layers within a Li-metal pouch cell. The simulated reflection spectra were then compared to ultrasonic reflection spectra measurements taken intermittently during cell cycling. A sensitivity analysis and parameter calibration were performed for the pristine pouch cell simulation prior to cycling, providing a baseline to account for difficult to measure poroelastic material parameters. Then, the reduction in solid Li anode thickness and corresponding growth into a mossy lithium layer was modeled to represent aging conditions. Results from both simulations and empirical inspections show similar trends in through-thickness resonance frequencies due to cell aging.
Nondestructive characterization of battery structures is important as both a research tool and as a means for developing reliable prognostics for batteries in service. Local Ultrasonic Resonance Spectroscopy (LURS) is a technique that measures spatially localized through-thickness vibrational resonances in layered materials. In battery cells, LURS measurements can reveal layer spacing and changes in mechanical properties. This study examines changes in structure that occur from fabrication to end of life for batteries cycled under different conditions as a demonstration of the capabilities of the LURS approach. Lithium metal pouch cell batteries were studied in both single- and multi-layer form factors. The cells were electrically cycled under constant current conditions at charge rates ranging from 0.2 C to 2 C, where 1 C is the charge rate (C-rate) required to fully charge a battery in one hour. In addition to varying charge rates, cells were also cycled under different temperatures and loading conditions, leading to a wide variety of electrode structures at end of life. LURS scans were conducted at various points in the battery lifetime to examine how damage developed. Multiple processing methods are shown, which help to reveal information about the internal resonance in each case and the ways in which resonance changes due to cell aging and spatial variation in the layered structure. Scan data in some instances showed evidence of manufacturing defects such as foreign object debris (FOD) on the electrode surface. In other cases, scan data showed spatial variation in degradation of the lithium anode surface that was dependent on the charge rate, loading scenario, or cycling temperature.
Registration techniques play a central role in applications of image processing to computer vision, medical imaging, and automatic target tracking. Feature-based techniques such as scale-invariant feature transform (SIFT) and speeded up robust features (SURF) are commonly used to register images derived from a single modality. However, SIFT and SURF struggle to register images from different modalities because the features tend to manifest rather differently and at sometimes very different length-scales. The most successful methods that have been developed to register multi-modal data use information-theoretic approaches. These methods play a key part in nondestructive evaluation scenarios where data that is collected by sensors of different modalities must be registered to be fused. In this paper, automated registration based on normalized mutual information is applied to align data derived from ultrasonic and radiographic inspections of (i) additively manufactured titanium alloy test coupons, and (ii) thin, lithium metal pouch-cell batteries. The quality of the registration is quantified in terms of computational resources and spatial accuracy. In the first case the X-ray computed tomography (XCT) data is captured on a region corresponding to a small subset of the ultrasonic data, while in the case of the lithium batteries the digital radiography (DR) captures a larger region of interest than the ultrasonic data. In both cases the radiographic data resolution is much higher than for ultrasound, but interestingly, in both cases the accuracy of the registration is approximately equal to two-to-three-pixel lengths in the ultrasonic images.
Lithium metal batteries are prone to subtle defects such as internal dendrites, which can cause internal short circuits and lead to catastrophic ignition. These defects are often undetectable by battery management systems, prompting the need to advance the development of nondestructive evaluation (NDE) techniques for battery applications. Ultrasonic inspection techniques are being evaluated as a means of identifying flaws and irregular lithium plating that can be a precursor to dendrite formation and, ultimately, battery failure. Two ultrasonic approaches were compared in this study to assess their relative merits for battery inspection. The first was local ultrasonic resonance spectroscopy (LURS), which measures the local through-thickness resonances of the battery to detect changes in structure. The second technique was guided wave ultrasound, which was assessed for its potential for in situ monitoring. Guided wave testing was performed via pitch-catch testing using piezoelectric electric wafer active sensors (PWAS), as well as line scans via laser Doppler vibrometry (LDV). Both measurement modes were applied to lithium metal pouch cell batteries seeded with lithium chips emulating localized plating. The results show the ability to detect and monitor the internal structure of batteries for relatively coarse defects and highlight use cases for each of the two inspection modalities.
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