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Adopting X-ray computed tomography (XCT) for ex-situ characterization of battery materials has gained interest in the past decade. The main goal of this paper is to demonstrate the effectiveness of several X-ray computer tomography techniques to study commercial batteries. General guidelines are provided to select the most suitable imaging equipment and parameters for investigations of lithium-ion batteries, spanning the length scales from cell to electrode, down to particle level. Relevantly, such parameters would also be suitable for operando experiments.
Safety mechanisms and manufacturing inconsistencies at cell level as well as defects and inhomogeneity in cathode and anode is illustrated and quantified. Furthermore, relation of beam energy and sample-detector-distance on contrast retrieved from attenuation and phase shift is inspected using Synchrotron XCT.
Direct Laser Writing (DLW) and X-ray computed tomography (XCT) both offer unique possibilities in their respective fields. DLW produces full three-dimensional (3D) polymer structures on the microscale with resolutions below 100 nm. The fabricated structures can be analysed by XCT or X-ray microscopy (XRM), which incorporates additional X-ray lenses, in three dimensions down to a minimal basic spatial resolution of about 500 nm or 50 nm, respectively. In this work, two different DLW structures are analysed via XCT. Internal defects are detected and analysed for the purpose of quality control. Defects and structures with sizes down to 1.5 µm are successfully analysed. A 3D reconstruction and internal, hidden features of the fabricated structures are shown and discussed. In a first-of-its-kind study, we demonstrate the detectability of a single-voxel line inside a fabricated structure that would not be detectable with SEM or light microscopy. Furthermore, the direct fabrication on a PET substrate is shown to overcome the high X-ray absorbance of commonly used glass substrates. Attenuation spectra of SZ2080 and glass substrates are compared to a fabrication route direct on a 170 µm PET foil. The practical aspects of XCT measurements for DLW structures on different substrates will be discussed.
Learned block iterative shrinkage thresholding algorithm for photothermal super resolution imaging
(2022)
Block-sparse regularization is already well known in active thermal imaging and is used for multiple-measurement-based inverse problems. The main bottleneck of this method is the choice of regularization parameters which differs for each experiment. We show the benefits of using a learned block iterative shrinkage thresholding algorithm (LBISTA) that is able to learn the choice of regularization parameters, without the need to manually select them. In addition, LBISTA enables the determination of a suitable weight matrix to solve the underlying inverse problem. Therefore, in this paper we present LBISTA and compare it with state-of-the-art block iterative shrinkage thresholding using synthetically generated and experimental test data from active thermography for defect reconstruction. Our results show that the use of the learned block-sparse optimization approach provides smaller normalized mean square errors for a small fixed number of iterations. Thus, this allows us to improve the convergence speed and only needs a few iterations to generate accurate defect reconstruction in photothermal super-resolution imaging.
Integration of fiber reinforcement in high-performance cementitious materials has become widely applied in many fields of construction. One of the most investigated advantages of steel Fiber reinforced concrete (SFRC) is the deceleration of crack growth and hence its improved sustainability.
Additional benefits are associated with its structural properties, as fibers can significantly increase the ductility and the tensile strength of concrete. In some applications it is even possible to entirely replace the conventional reinforcement, leading to significant logistical and environmental benefits.
Fiber reinforcement can, however, have critical disadvantages and even hinder the Performance of concrete, since it can induce an anisotropic material behavior of the mixture if the fibers are not appropriately oriented. For a safe use of SFRC in the future, reliable non-destructive testing (NDT) methods need to be identified to assess the fibers’ orientation in hardened concrete. In this study,
ultrasonic material testing, electrical impedance testing, and X-ray computed tomography have been investigated for this purpose using specially produced samples with biased or random Fiber orientations. We demonstrate the capabilities of each of these NDT techniques for fiber orientation measurements and draw conclusions based on these results about the most promising areas for future research and development.
To date, the destructive extraction and analysis of drilling cores is the main possibility to obtain depth information about damaging water ingress in building floors. The time- and costintensive procedure constitutes an additional burden for building insurances that already list piped water damage as their largest item. With its high sensitivity for water, a ground-penetrating radar (GPR) could provide important support to approach this problem in a non-destructive way. In this research, we study the influence of moisture damage on GPR signals at different floor constructions. For this purpose, a modular specimen with interchangeable layers is developed to vary the screed and insulation material, as well as the respective layer thickness. The obtained data set is then used to investigate suitable signal features to classify three scenarios: dry, damaged insulation, and damaged screed. It was found that analyzing statistical distributions of A-scan features inside one B-scan allows for accurate classification on unknown floor constructions. Combining the features with multivariate data analysis and machine learning was the key to achieve satisfying results. The developed method provides a basis for upcoming validations on real damage cases.
Pulse-compression thermography is an emerging technique that has shown versatility by combination of pulsed and lock-in thermography. Accordingly, several aspects of this technique are still unexplored, and some others not fully developed yet. Barker codes were widely used in radar applications due to their simplicity and their optimum autocorrelation function. Nevertheless, applications were limited by the amplitude of the sidelobes present in the autocorrelation function and therefore, several filters have been developed which aim to reduce the sidelobes. However, the filters usually depend on empirical parameters which must be determined for each application. A better alternative would improve the applicability of the Barker codes. In this work, we further develop the pulse-compression thermography technique by introducing a 13-bit modified Barker code (mBC): This allows to drastically reduce the sidelobes characteristic of the 13-bit Barker code (BC). Consequently, the thermographic impulse response, obtained by cross-correlation, is almost free of such sidelobes. Deeper defects become easier to detect in comparison with using a 13-bit Barker code. Numerical simulations using the finite element method are used for comparison and experimental measurements are performed in a sample of steel grade St 37 with machined notches of three different depths: 2 mm, 4 mm and 6 mm.
Crack detection in steel bars at high surface temperatures is a critical problem in any manufacturing industry. Surface breaking cracks are the major problems during the billet casting. Many NDT techniques are proven its capability in crack detection at room temperature. Here, we are demonstrating the possibility of exposure of cracks using laser line thermography at higher surface temperatures (up to 600 °C). A continuous-wave (CW) laser is used to excite the sample kept at higher surface temperatures. The temperature distribution over the sample due to the laser line scanning is captured using a temperature calibrated infrared (IR) thermal camera. The response of the sample temperature in crack detection is investigated using a validated FE model. The impact of the oxide layer in crack detection is investigated by using two types of samples; one without any oxide layer and the second is with the oxide layer. The influence of laser power in the detection of defects at high temperatures is studied. 3D numerical models were developed for the cases; when the sample is with oxide layer and without any oxide layer for a better understanding of physics. The surface temperature rise due to laser heating is higher for the scaled sample compared to the no-scale sample. The presence of the oxide layer above the parent metal will reduce the reflectivity of the surface. Lower reflectivity will lead to increased absorption of incident energy so that the surface temperature rise will be higher than the surface with no scale. Thermal contrast linearly depends on laser power, which means higher laser power will increase the defect detectability even at a higher surface temperature.
A composite material is a combination of two or more materials with very different mechanical, thermal and electrical properties. The various forms of composite materials, due to their high material properties, are widely used as structural materials in the aviation, space, marine, automobile, and sports industries. However, some defects like voids, delamination, or inhomogeneous fiber distribution that form during the fabricating processes of composites can seriously affect the mechanical properties of the composite material. In this study, several imaging NDE techniques such as: thermography, high frequency eddy current, ultrasonic, x-ray radiography, x-ray laminography, and high resolution x-ray CT were conducted to characterize the microstructure of carbon fiber composites. Then, a 3D analysis was implemented by the destructive technique of serial sectioning for the same sample tested by the NDE methods. To better analyze the results of this work and extract a clear volume image for all features and defects contained in the composite material, an intensive comparison was conducted among hundreds of 3D-NDE and multi serial sections’ scan images showing the microstructure variation.
Photon counting detectors (PCD) offer new possibilities for x-ray micro computed tomography (CT) in the field of non-destructive testing. For large and/or dense objects with high atomic numbers the problem of scattered radiation and beam hardening severely influences the image quality. This work shows that using an energy discriminating PCD based on CdTe allows to address these problems by intrinsically reducing both the influence of scattering and beam hardening.
Based on 2D-radiographic measurements it is shown that by energy thresholding the influence of scattered radiation can be reduced by up to in case of a PCD compared to a conventional energy-integrating detector (EID).
To demonstrate the capabilities of a PCD in reducing beam hardening, cupping artefacts are analyzed quantitatively. The PCD results show that the higher the energy threshold is set, the lower the cupping effect emerges. But since numerous beam hardening correction algorithms exist, the results of the PCD are compared to EID results corrected by common techniques. Nevertheless, the highest energy thresholds yield lower cupping artefacts than any of the applied correction algorithms.
As an example of a potential industrial CT application, a turbine blade is investigated by CT. The inner structure of the turbine blade allows for comparing the image quality between PCD and EID in terms of absolute contrast, as well as normalized signal-to-noise and contrast-to-noise ratio. Where the absolute contrast can be improved by raising the energy thresholds of the PCD, it is found that due to lower statistics the normalized contrast-to-noise-ratio could not be improved compared to the EID. These results might change to the contrary when discarding pre-filtering of the x-ray spectra and thus allowing more low-energy photons to reach the detectors.
Despite still being in the early phase in technological progress, PCDs already allow to improve CT image quality compared to conventional detectors in terms of scatter and beam hardening reduction.
Defect recognition in CFRP components using various NDT methods within a smart manufacturing process
(2018)
The manufacturing process of carbon fiber reinforced polymer (CFRP) components is gaining a more and more significant role when looking at the increasing amount of CFRPs used in industries today. The monitoring of the manufacturing process and hence the reliability of the manufactured products, is one of the major challenges we need to face in the near future. Common defects which arise during manufacturing process are e.g. porosity and voids which may lead to delaminations during operation and under load. To find irregularities and classify them as possible defects in an early stage of the manufacturing process is of high importance for the safety and reliability of the finished products, as well as of significant impact from an economical point of view. In this study we compare various NDT methods which were applied to similar CFRP laminate samples in order to detect and characterize regions of defective volume. Besides ultrasound, thermography and eddy current, different X-ray methods like radiography, laminography and computed tomography are used to investigate the samples. These methods are compared with the intention to evaluate their capability to reliably detect and characterize defective volume. Beyond the detection and evaluation of defects, we also investigate possibilities to combine various NDT methods within a smart manufacturing process in which the decision which method shall be applied is inherent within the process. Is it possible to design an in-line or at-line testing process which can recognize defects reliably and reduce testing time and costs? This study aims to show up opportunities of designing a smart NDT process synchronized to the production based on the concepts of smart production (Industry 4.0). A set of defective CFRP laminate samples and different NDT methods were used to demonstrate how effective defects are recognized and how communication between interconnected NDT sensors and the manufacturing process could be organized.