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The achievable spatial resolution of active thermographic testing is inherently limited by the diffusive nature of heat conduction in solids. This degradation of the achievable spatial resolution for a semi-infinite body acting on a defect signal can be approximated by spatial convolution with the Green’s function of the heat PDE. As the degradation in spatial resolution is dependent on the depth 𝐿, a common rule of thumb specifies that for proper detection, any defect should feature a spatial extension greater or equal to the depth it is located at. However, as the exact shape of a defect can have a large impact on its severity, at best a proper reconstruction of the defect shape should be performed, which therefore must also deal with the aforementioned adverse effects of heat conduction. One recent method to overcome the spatial resolution limit of thermographic testing is the photothermal super resolution reconstruction method. It is based on performing multiple active thermographic measurements on the same region of interest (ROI) with varying spatially structured heating and subsequent numerical reconstruction of the measured defect signals by solving a severely ill-posed inverse reconstruction problem relying on heavy regularization. By extending the experimental implementation of the method to make use of random-pixel patterns projected onto the ROI using a laser-coupled DLP-projector, defect reconstructions can now be performed within a reasonable time frame (~15 min per ROI) at high accuracy. Compared to conventional thermographic testing methods, the photothermal super resolution reconstruction stands out by resulting in a sparse representation of the defect structure of the ROI, making it especially well-suited to further automatic defect classification and quality assurance measures in an Industry 4.0 context.
Laser powder bed fusion is one of the most promising additive manufacturing techniques for printing complex-shaped metal components. However, the formation of subsurface porosity poses a significant risk to the service lifetime of the printed parts. In-situ monitoring offers the possibility to detect porosity already during manufacturing. Thereby, process feedback control or a manual process interruption to cut financial losses is enabled.
Short-wave infrared thermography can monitor the thermal history of manufactured parts which is closely connected to the probability of porosity formation. Artificial intelligence methods are increasingly used for porosity prediction from the obtained large amounts of complex monitoring data. In this study, we aim to identify the potential and the challenges of deep-learning-assisted porosity prediction based on thermographic in-situ monitoring.
Therefore, the porosity prediction task is studied in detail using an exemplary dataset from the manufacturing of two Haynes282 cuboid components. Our trained 1D convolutional neural network model shows high performance (R² score of 0.90) for the prediction of local porosity in discrete sub-volumes with dimensions of (700 x 700 x 40) μm³.
It could be demonstrated that the regressor correctly predicts layer-wise porosity changes but presumably has limited capability to predict differences in local porosity. Furthermore, there is a need to study the significance of the used thermogram feature inputs to streamline the model and to adjust the monitoring hardware. Moreover, we identified multiple sources of data uncertainty resulting from the in-situ monitoring setup, the registration with the ground truth X-ray-computed tomography data and the used pre-processing workflow that might influence the model’s performance detrimentally.
The evaluation of technical components and materials in terms of condition and quality with the aid of non-destructive testing methods plays an outstanding role both in industrial serial production and in the individual assessment of components. The ultrasonic echo method is used for a wide variety of testing tasks, such as measuring the thickness of a component. At the Bundesanstalt für Materialforschung und -prüfung (BAM) in Berlin, test methods are developed and their performance is demonstrated. For this purpose, references such as materials, test procedures or data sets are generated, which are used to evaluate these developed test methods. An important component of these references are data sets that are created according to the BAM Data Policy and made available to the scientific community and users of test methods in practice on the basis of the FAIR principles (Findability, Accessibility, Interoperability, and Reuse).
The development of more powerful and more efficient lithium-ion batteries (LIBs) is a key area in battery research, aiming to support the ever-increasing demand for energy storage systems. To better understand the causes and mechanisms of degradation, and thus the diminishing cycling performance and lifetime often observed in LIBs, in operando techniques are essential, because battery chemistry can be monitored non-invasively, in real time. Moreover, there is increasing interest in developing new battery chemistries. Beyond LIBs, sodium ion batteries (NIBs) have gained increasing interest in recent years, as they are a promising candidate to complement LIBs, owing to their improved sustainability and lower cost, while still maintaining high energy density.[1] Initial phases of NIB commercialisation have occurred in the past year. However, for the widespread commercialisation of NIBs, there are still challenges that need to be overcome in developing optimized electrode materials and electrolytes. For the development of such materials and greater understanding of sodium storage mechanisms, solid electrolyte interface (SEI) formation and stability, and degradation processes, in operando methodologies are crucial.
Among the techniques available for in operando analysis, nuclear magnetic resonance spectroscopy (NMR) and imaging (MRI) are becoming increasingly used to characterize the chemical composition of battery materials, study the growth and distribution of dendrites, and investigate battery storage and degradation mechanisms. In situ and in operando 1H, 7Li and 23Na NMR and MRI have recently been used to study LIBs and NIBs, identifying chemical changes in Li and Na species respectively, in metallic, quasimetallic and electrolytic environment as well as directly and indirectly studying dendrite formation in both systems.[2-4] The ability of NMR and MRI to probe battery systems across multiple environments can further be complemented by the enhanced spatial resolution of micro-computed X-ray tomography (μ-CT) which can provide insight into battery material microstructure and defect distribution.
Here, we report in operando 1H and 7Li NMR and MRI experiments that investigate LIB performance, and the identification of changes in the Li signal during charge cycling, as well as the observation of signals in both 1H and 7Li NMR spectra that we attribute to diminishing battery performance, capacity loss and degradation. Additionally, recent operando methodology are adapted and implemented to study Sn based anodes in NIBs. 23Na spectroscopy is performed to monitor the formation and evolution of peaks assigned to stages of Na insertion into Sn, while 1H MRI is used to indirectly visualize the volume expansion of Sn anodes during charge cycling. Battery operation and degradation is further explored in these NIBs, using μ-CT, where the anode is directly visualized to a higher resolution and the loss of electrolyte in the cell, during cycling is observed
Almost all building materials in civil engineering have an open porosity and interact with or are affected by the environmental conditions. Structures might suffer from effects such as moisture adsorption, carbonation, corrosion, penetration of salt ions and chemical substances, etc. In the hygroscopic range, these processes are mostly driven by diffusion. Due to the confinement of small pores (less than1 µm), the Knudsen effect reduces the molecular diffusion. This reduction can become more significant in case of temporal changing pore systems because of physisorption of water vapor, carbonation, or chemisorption.
In this study, unstabilised earth blocks and earth masonry are investigated. In a first step, the pore size distribution of the blocks is measured and sorption isotherms are recorded in experiments. Besides the ordinary physisorption, the involved clay minerals undergo swelling or shrinking due to chemisorption. The following two effects must be considered: first, the reduction of the available pore space by the adsorbed water layer. For this, the Hillerborg sorption theory is used, which is a combination of the well-known Brunauer-Emmett-Teller sorption theory and the Kelvin equation. This allows the computation of adsorbed water layers even in curved pore geometries. Second, the variation of the initial pore size distribution due to chemisorption needs to be modelled. Based on these two models, the effective diffusion coefficient can be predicted. For validation, arrays of relative humidity sensors were embedded into a free-standing earth masonry wall. This monitoring was carried out over more than a year to have a broad variety of environmental conditions and was located in Berlin, Germany.
The prediction of the effective diffusion coefficient can also be transferred to other processes and allows the investigation of materials having temporarily changing pore systems. Examples are the carbonation of cementitious materials, alkali silica reaction, calcium leaching of long-lasting structures, etc. These effects are prominent in the meso-pore range and might significantly alter the effective diffusion coefficient.
Digital Calibration Certificates: Transforming Efficiency and Safety in Hydrogen Refuelling Station
(2023)
Digital Certificates have emerged as a pivotal element in automation and digitalisation. This presentation highlights the added value of a digitalised metrology, its impact on the workflows on the calibration service providing and receiving side as well as their significance in enhancing the quality infrastructure. An overview of digital calibration certificates (DCC) for temperature sensors, including their structure and role in establishing trust in hydrogen refuelling stations (HRS), will be discussed. Additionally, it explores the impact of DCC on optimising efficiency in the hydrogen refuelling process.
Exploring the paradigm shift brought about by Industry 4.0, where machines possess the ability to autonomously interpret digital certificate data, leads to streamlined safety checks and reduced human intervention. We will discuss how automated verification of machine-readable certificates contributes to maintaining and elevating safety standards over human-readable certificates. Furthermore, we will take a deep dive into the application of DCCs in HRS, showcasing how they enhance operational efficiency, accuracy, and maintenance by enabling real-time monitoring and adjustment of process data.
By exploring the interdependent relationship between digital certificates, machine-readable environments, and HRS optimisation, this presentation will provide valuable insights into harnessing cutting-edge technologies to create a safer, more efficient, and technologically empowered hydrogen refuelling process.
When reassessing existing concrete bridges, the challenge is often to obtain missing or incomplete information on the internal structure. In particular, the number and position of the existing reinforcement as well as the geometric dimensions of the components are of interest. Non-destructive testing methods, like radar or ultrasound, which work on the basis of the pulse-echo method, have been established for this purpose, as they only require access to the component from one side. The measurement data recorded on the structure require pre-processing to be able to reproduce the internal structure geometrically accurately. Besides different steps of data processing, the geometrical reconstruction of the measured data based on the Synthetic Aperture Focusing Technique (SAFT) is state of the art today. In this paper, the technical possibilities of the ultrasonic echo method are presented based on measurements in the laboratory and on a real bridge structure. The precision of the reconstruction and its limitations are shown. In addition to the state of the art SAFT technique, open questions and the latest research approaches, such as imaging by reverse time migration (RTM) including initial results are discussed.
The focus of the presentation focus will be on 3D imaging by means of X-ray Computed Tomography (XCT) at the lab and at synchrotron, and the non-destructive residual stress (RS) characterization by diffraction of different kind of materials in FB8.5 Micro-NDT BAM. For instance, the manufacturing defects and high RS are inherent of additively manufacturing techniques and affect structural integrity of the components. Using XCT the defects size and shape distribution as well as geometrical deviations can be characterized, allowing the further optimization of the manufacturing process. Diffraction-based RS analysis methods using neutron and synchrotron X-rays at large scale facilities offer the possibility to non-destructively spatially resolve both surface and bulk RS in complex components and track their changes following applied thermal or mechanical loads.
When reassessing existing concrete bridges, the challenge is often to obtain missing or incomplete information on the internal structure. In particular, the number and position of the existing reinforcement as well as the geometric dimensions of the components are of interest. Non-destructive testing methods, like radar or ultrasound, which work on the basis of the pulse-echo method, have been established for this purpose, as they only require access to the component from one side. The measurement data recorded on the structure require pre-processing to be able to reproduce the internal structure geometrically accurately. Besides different steps of data processing, the geometrical reconstruction of the measured data based on the Synthetic Aperture Focusing Technique (SAFT) is state of the art today. In this paper, the technical possibilities of the ultrasonic echo method are presented based on measurements in the laboratory and on a real bridge structure. The precision of the reconstruction and its limitations are shown. In addition to the state of the art SAFT technique, open questions and the latest research approaches, such as imaging by reverse time migration (RTM) including initial results are discussed.
A major advantage in the reassessment of existing structures is the possibility of including measured data that describe the actual properties and the current condition of the structure to be reassessed. Currently, the incorporation of such measured information is mostly unregulated. However, the use of measurement results is vitally important, since a measured data-based improvement of the computation models level of approximation can lead at least to more meaningful results, possibly to extended remaining life times of the structure and in the best case to a saving of resources. Conversely, not appreciating well measurable and relevant information can be equated with a waste of resources. In this paper, a concept for the comparable use of non-destructively measured data as basic variables in probabilistic reliability assessments is outlined and examined using a typical prestressed concrete road bridge as a case-study. An essential requirement is the calculation of measurement uncertainties in order to evaluate the quality of the measurement results comparably. In conclusion, the example of ultrasonic and radar measurement data is used to demonstrate the effects that the incorporation of the measured information has on the reliability of the structure.
In this study, the wavefield radiated from a building to its surroundings is identified and extracted from M4.6 earthquake recordings collected by sensors installed in a building and on the nearby athletic field in Matera (Italy) using a new approach for soil-structure interaction assessment. The proposed approach for earthquake data analysis combines in an innovative way two methods already used in seismology and engineering seismology: deconvolution and polarization analysis. The approach enables the identification, reconstruction, and characterization of the wavefield radiated from a vibrating building into its surroundings, and the estimation of the amount of energy associated with it. The approach consists of four steps: estimation of the resonant frequencies of the building, deconvolution of the earthquake recordings from a building and its surroundings, identification of the seismic phases, reconstruction of the signal transmitted from the building to its surroundings, and estimation of its energy, and polarization analysis. Analysis of recordings of the M4.6 event highlighted that the motion related to the wavefield radiated from the building to the ground was mostly linearly polarized in the radial and transverse planes, while a clear ellipticity was observed only in the horizontal plane. The wavefield radiated from the building might be dominated by unconventionally polarized surface waves, i.e., quasi-Rayleigh waves or a combination of quasi-Rayleigh and quasi-Love waves. The results indicated that the energy transmitted from the analyzed vibrating building to its surroundings was significant and decreased the ground motion shaking due to the out-of-phase motion.
This presentation summarizes recent developments within the scope of the national pre-standardization project “ZfPStatik”, which aims to prepare a guideline about NDT-supported structural analyses. The focus is on the purposeful and explicit utilization of geometrical tendon and reinforcement bar positions measured on-site in reliability analysis — shown by means of a prestressed concrete bridge as case study.
Reliability assessment of existing concrete bridges with geometrical NDT results - Case studies
(2023)
The results of and the validity in reliability assessment of existing bridges essentially depend on the information available about the considered system. Information about the actual condition as well as structural and material characteristics can be observed on-site to refine the computation models used in assessment. Non-destructive testing (NDT) methods for concrete structures are capable of reconstructing missing, questioned, or inconsistent as-built plans. This contribution summarizes recent developments within the scope of the national pre-standardization project “ZfPStatik”, which aims to prepare a guideline about NDT-supported structural analyses. The focus is on the purposeful and explicit utilization of geometrical tendon and reinforcement bar positions (measured on-site using the ultrasound echo and ground penetrating radar (GPR) techniques) in probabilistic reliability analyses — shown by means of real case studies. The well-established first order reliability method is applied to different concrete bridges, which are typical for the German road bridge stock, to demonstrate the utility of incorporating quality-evaluated NDT-results in terms of changes in structural reliability.
The present work is part of the AIFRI project (Artificial Intelligence For Rail Inspection), where we and our project partners train a neural network for defect detection and classification. Our goal at BAM is to generate artificial ultrasound and eddy current training data for the A.I. This paper has an exploratory nature, where we focus on the simulation of eddy current signals for head check cracks, one of the most important rail surface defects. The goal of this paper is twofold. On the one hand, we present our general simulation setup. This includes geometric models for head check cracks with features like branching and direction change, a model for the HC10 rail testing probe, and the configuration of the Faraday simulation software.
On the other hand, we use the Faraday software to simulate eddy current testing signals with a strong focus on the influence of the damage depth on the signal, while differentiating between different crack geometries. Here, we observe an early saturation effect of the test signal at a damage depth of 2 mm (at a crack angle of 25◦ to the surface). That is about 2 mm earlier than we would expect from measurements at a crack angle of 90◦. This behavior will be investigated further in a future paper. Finally, we interpolate the simulated signals in a two-step curve fitting process. With these interpolations we may generate eddy current test signals for any damage depth within the simulated range.
The present work is part of the AIFRI project (Artificial Intelligence For Rail Inspection), where we and our project partners train a neural network for defect detection and classification. Our goal at BAM is to generate artificial ultrasound and eddy current training data for the A.I. This paper has an exploratory nature, where we focus on the simulation of eddy current signals for head check cracks, one of the most important rail surface defects. The goal of this paper is twofold. On the one hand, we present our general simulation setup. This includes geometric models for head check cracks with features like branching and direction change, a model for the HC10 rail testing probe, and the configuration of the Faraday simulation software.
On the other hand, we use the Faraday software to simulate eddy current testing signals with a strong focus on the influence of the damage depth on the signal, while differentiating between different crack geometries. Here, we observe an early saturation effect of the test signal at a damage depth of 2 mm (at a crack angle of 25◦ to the surface). That is about 2 mm earlier than we would expect from measurements at a crack angle of 90◦. This behavior will be investigated further in a future paper. Finally, we interpolate the simulated signals in a two-step curve fitting process. With these interpolations we may generate eddy current test signals for any damage depth within the simulated range.
Additive manufacturing of concrete structures is a novel and emerging technology. Free contouring in civil engineering, which allows for entirely new designs, is a significant advantage. In the future, lower construction costs are expected with increased construction speeds and decreasing required materials and workers. However, architects and civil engineers rely on a certain quality of execution to fulfil construction standards. Although several techniques and approaches demonstrate the advantages, quality control during printing is highly challenging and rarely applied. Due to the continuous mixing process commonly used in 3D concrete printing, it is impossible to exclude variations in the dry mixture or water content, and a test sample cannot be taken as a representative sample for the whole structure. Although mortar properties vary only locally, a defect in one layer during printing could affect the entire integrity of the whole structure . Therefore, real-time process monitoring is required to record and document the printing process. At the Bundesanstalt für Materialforschung und -prüfung (BAM) a new test rig for the additive manufacturing of concrete is built. The primary purpose is measuring and monitoring the properties of a mortar during the printing process. The following study investigates an approach for calculating yield stress and plastic viscosity based on experimentally recorded pressure data. The calculations assume that fresh mortar behaves as a Bingham fluid and that the Buckingham-Reiner-equation is applicable. A test setup consisting of rigid pipes with integrated pressure sensors at different positions is utilized. Monitoring the printing process with different sensors is crucial for the quality control of an ongoing process.
Defects are still common in metal components built with Additive Manufacturing (AM). Process monitoring methods for laser powder bed fusion (PBF-LB/M) are used in industry, but relationships between monitoring data and defect formation are not fully understood yet. Additionally, defects and deformations may develop with a time delay to the laser energy input. Thus, currently, the component quality is only determinable after the finished process.
Here, active laser thermography, a non-destructive testing method, is adapted to PBF-LB/M, using the defocused process laser as heat source. The testing can be performed layer by layer throughout the manufacturing process. The results of the defect detection using infrared cameras are presented for a custom research PBF-LB/M machine. Our work enables a shift from post-process testing of components towards in-situ testing during the AM process. The actual component quality is evaluated in the process chamber and defects can be detected between layers.
The focus of the presentation focus will be on 3D imaging by means of X-ray Computed Tomography (XCT) at the lab and at synchrotron, and the non-destructive residual stress (RS) characterization by diffraction of additively manufactured (AM) materials in BAM (Berlin, Germany). The manufacturing defects and high RS are inherent of AM techniques and affect structural integrity of the components. Using XCT the defects size and shape distribution as well as geometrical deviations can be characterized, allowing the further optimization of the manufacturing process. Diffraction-based RS analysis methods using neutron and synchrotron X-rays at large scale facilities offer the possibility to non-destructively spatially resolve both surface and bulk RS in complex components and track their changes following applied thermal or mechanical loads.
In recent years, additive manufacturing technologies have gained in importance. Laser powder bed fusion can be used for complex functional components or the production of workpieces in small quantities. High safety requirements, e.g. in aerospace, demand comprehensive quality control. Therefore, non-destructive offline inspection methods such as computed tomography are used after production. Recently, online non-destructive testing methods such as optical tomography have been developed to improve profitability and practicality. In this presentation, the applicability of eddy current inspection using GMR sensors for online inspection of PBF-LB/M parts is demonstrated. Eddy current testing is performed for each layer during the production process at frequencies uo to 1.2 MHz. Despite the use of high-resolution arrays with 128 elements, the testing time is kept low by an adapted hardware. Thus, the measurement can be performed during the manufacturing process without significantly slowing down the production process. In addition to the approach, the results of an online eddy current test of a step-shaped test specimen made of Haynes282 are presented.
The ultrasonic echo technique is frequently used in non-destructive testing (NDT) of concrete structures for thickness measurements, geometry determinations as well as localization of built-in components. To improve ultrasonic imaging of complex structures in concrete, we transferred a geophysical imaging technique, the reverse time migration (RTM), to NDT in civil engineering. In contrast to the conventionally used synthetic aperture focusing technique (SAFT) algorithms, RTM is a wavefield continuation method in time and uses the full wave equation. Thus, RTM can handle complicated wave propagations in any direction without dip limitation. In this paper, we focused on the application and evaluation of a two-dimensional (2D) elastic RTM algorithm considering compressional waves, vertically polarized shear waves, and Rayleigh waves. We tested the elastic RTM routine on synthetic ultrasonic echo data generated with a 2D concrete model consisting of several steps and circular air inclusions. As these complex structures can often be found in real-world NDT use cases, their imaging is especially important. By using elastic RTM, we were able to clearly reproduce vertical reflectors and lower edges of circular air voids inside our numerical concrete model. Such structures cannot be imaged with conventional SAFT algorithms. Furthermore, the used elastic RTM approach also yielded a better reconstruction of a horizontal reflector and upper boundaries of circular air inclusions. Our encouraging results demonstrate that elastic RTM has the potential to significantly improve the imaging of complex concrete structures and, thus, is a step forward for detailed, high-quality ultrasonic NDT in civil engineering.
The Boltzmann plot method is widely used to determine the temperature of laser induced plasma. It involves the use of individual lines that are not easy to find in complex spectra and/or in the spectral range available. If the number of such lines is not enough to build a reliable Boltzmann plot, overlapping lines are often used, which are separated by software. However, line separation is a rather imprecise procedure, which, in addition, requires significant computational costs. This study proposes an extension of the Boltzmann plot method that allows a specific group of unresolved lines to be included in a Boltzmann plot without the need to separate them. This group of lines are multiplets, lines of the same element with similar upper and lower transition states. The multiplet lines along with the individual lines are included in the algorithm, which also includes a correction for self-absorption and is used to determine the plasma temperature. The algorithm is tested on synthetic spectra which are consistent with the model of a homogeneous isothermal plasma in local thermodynamic equilibrium and is shown to be superior to the standard Boltzmann plot method both in more accurate determination of the plasma temperature and in a significant reduction in the computational time. The advantages and disadvantages of the method are discussed in the context of its applications in laser induced breakdown spectroscopy.
Ultrasonic coda wave interferometry can detect small changes in scattering materials like concrete. We embedded ultrasonic transducers in the Gänstorbrücke Ulm, a monitored road bridge in Germany, to test the methodology. Since fall 2020, we've been monitoring parts of the bridge and comparing the results to commercial monitoring systems. We calculate signal and volumetric velocity changes using coda waves, and long-term measurements show that the influence of temperature on strains and ultrasound velocity changes can be monitored. Velocity change maps indicate that different parts of the bridge react differently to environmental temperature changes, revealing local material property differences. A load experiment with trucks allows calibration to improve detectability of possibly damaging events. Our work focuses on measurement reliability, potential use of and distinction from temperature effects, combination with complementary sensing systems, and converting measured values to information for damage and life cycle assessment.
Ultrasonic coda wave interferometry can detect small changes in scattering materials like concrete. We embedded ultrasonic transducers in the Gänstorbrücke Ulm, a monitored road bridge in Germany, to test the methodology. Since fall 2020, we've been monitoring parts of the bridge and comparing the results to commercial monitoring systems. We calculate signal and volumetric velocity changes using coda waves, and long-term measurements show that the influence of temperature on strains and ultrasound velocity changes can be monitored. Velocity change maps indicate that different parts of the bridge react differently to environmental temperature changes, revealing local material property differences. A load experiment with trucks allows calibration to improve detectability of possibly damaging events. Our work focuses on measurement reliability, potential use of and distinction from temperature effects, combination with complementary sensing systems, and converting measured values to information for damage and life cycle assessment.
Wasserstoff als flexibel einsetzbarer und leicht transportierbarer Energieträger bildet eine Schlüsselkomponente auf dem Weg zu einer klimaneutralen Energiewende. Zur Speicherung von gasförmigem Wasserstoff kommen bei stationären und auch mobilen Anwendungen meist Druckbehälter aus Verbundwerkstoffen zum Einsatz. Dabei ist für den Betrieb der Behälter die Gewährleistung der Sicherheit von großer Relevanz. Structural Health Monitoring (SHM) bietet einen innovativen Ansatz, um sowohl die Sicherheit als auch die Zuverlässigkeit der Druckbehälter zu gewährleisten und kritische Versagensfälle zu vermeiden.
Zur kontinuierlichen Überwachung und Bewertung des Zustands eines Wasserstoffdruckbehälters wird in dem vorliegenden Beitrag eine aktive Methode präsentiert, die auf geführten Ultraschallwellen basiert. Dabei kommt das Pitch-Catch Verfahren zwischen Sender und Empfängern zur Überwachung der strukturellen Integrität zum Einsatz. Auf Grundlage des untersuchten Wellenausbreitungsverhaltens im Druckbehälter sowie der Geometrieeigenschaften wird ein Sensornetzwerk aus piezoelektrischen Flächenwandlern (PZT) zur Abdeckung des zu überwachenden Bereichs entworfen. Ziel ist es mit Hilfe eines Optimierungsalgorithmus eine größtmögliche Abdeckung bei gleichzeitiger Minimierung der Anzahl der Sensoren zu erreichen. Zudem soll eine gleichmäßige und platzsparende Verteilung im Vordergrund stehen, so dass zukünftig weitere Messmethoden auf dem Druckbehälter angewendet werden können (z. B. durch akustische oder faseroptische Sensoren).
Das für die Anwendung optimierte Sensornetzwerk wird anschließend hinsichtlich der Robustheit bei der Schadenserkennung und -lokalisierung durch Aufbringen von künstlichen Schäden evaluiert. Die Ergebnisse zeigen, dass die künstlichen Schäden am Wasserstoffdruckbehälter nachgewiesen werden können.
Guided wave ultrasonic features in composite overwrapped pressure vessels towards digital twin
(2023)
The digitalization of quality control processes and the underlying data infrastructures for safety relevant components, such as hydrogen pressure vessels, plays a significant role in the transition towards Industry 4.0. In the current safety regulations for hydrogen pressure vessels, there is no established concept for structural health monitoring. The development of a reliable structural health monitoring methodology for monitoring the structural integrity of pressure vessels enables a fast-forward transition from personnel- and costintensive recurring inspections, a.k.a. periodic maintenance, to predictive maintenance. In the work presented; we investigated the application of ultrasonic guided wave propagation to monitor and assess the condition of Type IV composite overwrapped pressure vessel (COPV). A sensor network of fifteen piezo-electric wafers is placed on the carbon fibre reinforced composite cylinder. Five different artificial damage configurations are created by gluing two different weight blocks on three different locations. The database containing measured guided wave data sets is enriched by two different boundary conditions. We utilized an open-source software, openBIS labnotebook, to store and analyse experimental datasets. The guided wave ultrasonic signals were investigated and analysed by using commonly used ultrasonic features (e.g., amplitude, frequency, time of flight) as well as non-traditional time-series features (kurtosis, skewness, variance). The features were used to calculate damage index and the detection performance for the results has been evaluated. The results suggest that both traditional and non-traditional features assume significant importance in artificial damage detection. The future works will additionally involve the impacts of operational conditions, such as periodic pressure variations temperature loadings as well as material degradations.
Human factors (HF) are little understood, and particularly in non-destructive testing (NDT) experimental data is rare, samples are often small, and statistical methods are rarely used to evaluate results. HF have been widely implicated in major occurrences of technical failure, for example at North Anna Power Plant and on United Airlines Flight 232. Understanding HF is vital for reliable detection and prevention of failures. Reliability assessments, though known to be affected by intrinsic capability, application factors, and HF, have thus far only concentrated to a sufficient degree on intrinsic capability. The addition of HF to that assessment has proven difficult due to the lack of a method of quantifying HF. This paper presents the first attempt from a psychological perspective to quantify HF from qualitative data.
HF data was derived from qualitative human-oriented Failure Modes and Effects Analysis (Human-FMEA) workshops for visual inspection of tunnels by laser scanning and for ultrasonic testing of welds. Data was collected on human failure modes, causes, consequences and preventive measures, as well as eliciting a risk priority number (RPN). Using this data, a system of quantitative weightings was created to allocate errors to inductively derived HF categories for further allocation to existing HF categorisation models.
This weighting model proved useful for creating quantitative summaries of HF, informing and validating qualitative FMEA results, and comparing existing HF categorisation models. Further potential lies within a planned interface to quantitative reliability assessment methods such as POD (Probability of Detection). While providing quantifications, the method retains a qualitative and holistic nature, can, thus, bridge the gap between psychological and engineering concerns of HF and reliability, contributing to future interdisciplinary work.
Capillary active interior insulation materials are an important approach to minimize energy losses of historical buildings. A key factor for their performance is a high liquid conductivity, which enables redistribution of liquid moisture within the material. We set up an experiment to investigate the development of moisture profiles within two different interior insulation materials, calcium-silicate (CaSi) and expanded perlite (EP), under constant boundary conditions. The moisture profiles were determined by two different methods: simple destructive sample slicing with subsequent thermogravimetric drying as well as non-destructive NMR measurements with high spatial resolution. The moisture profiles obtained from both methods show good agreement, when compared at the low spatial resolution of sample slicing, which demonstrates the reliability of this method. Moreover, the measured T2- relaxation-time distributions across the sample depth were measured, which may give further insight into the saturation degree of the different pore sizes. In order to explain differences in the moisture profiles between CaSi and EP, we determined their pore-size distribution with different methods: conversion of the NMR T2 relaxationtime distribution at full saturation, mercury intrusion porosimetry and indirect determination from pressure plate measurements. CaSi shows a unimodal distribution at small pore diameters, while in EP, a bi-modal or wider distribution was found. We assume that the smaller pore diameters of CaSi lead to a higher capillary conductivity, which causes a more distributed moisture profile in comparison with that of EP.
Capillary active interior insulation materials are an important approach to minimize energy losses of historical buildings. A key factor for their performance is a high liquid conductivity, which enables redistribution of liquid moisture within the material. We set up an experiment to investigate the development of moisture profiles within two different interior insulation materials, calcium-silicate (CaSi) and expanded perlite (EP), under constant boundary conditions. The moisture profiles were determined by two different methods: simple destructive sample slicing with subsequent thermogravimetric drying as well as non-destructive NMR measurements with high spatial resolution. The moisture profiles obtained from both methods show good agreement, when compared at the low spatial resolution of sample slicing, which demonstrates the reliability of this method. Moreover, the measured T2-relaxation-time distributions across the sample depth were measured, which may give further insight into the saturation degree of the different pore sizes. In order to explain differences in the moisture profiles between CaSi and EP, we determined their pore-size distribution with different methods: conversion of the NMR T2 relaxationtime distribution at full saturation, mercury intrusion porosimetry and indirect determination from pressure plate measurements. CaSi shows a unimodal distribution at small pore diameters, while in EP, a bi-modal or wider distribution was found. We assume that the smaller pore diameters of CaSi lead to a higher capillary conductivity, which causes a more distributed moisture profile in comparison with that of EP.
Regardless of the experimental care practiced in acquiring X-ray computed tomography (XCT) data, artifacts might still exist, such as noise and blur. This is typical for fast XCT data acquisitions (e.g., in-situ investigations), or low-dose XCT. Such artifacts can complicate subsequent analysis of the data. Digital filters can moderately cure extensive artifacts. The selection of filter type, intensity, and order of application is not always straight forward. To tackle these problems, a complete sequential multilevel, multi-scale framework: BAM SynthCOND, employing newly designed deep convolutional neural networks (DCNNs), was formulated. Although data conditioning with neural networks is not uncommon, the main complication is that completely artifact-free XCT data for training do not exist. Thus, training data were acquired from an in-house developed library (BAM SynthMAT) capable of generating synthetic XCT material microstructures. Some novel DCNN architectures were introduced (2D/3D ACEnet_Denoise, 2D/3D ACEnet_Deblur) along with the concept of Assertive Contrast Enhancement (ACE) training, which boosts the performance of neural networks trained with continuous loss functions. The proposed methodology accomplished very good generalization from low resemblance synthetic training data. Indeed, denoising, sharpening (deblurring), and even ring artifact removal performance were achieved on experimental post-CT scans of challenging multiphase Al-Si Metal Matrix Composite (MMC) microstructures. The conditioning efficiencies were: 92% for combined denoising/sharpening, 99% for standalone denoising, and 95% for standalone sharpening. The results proved to be independent of the artifact intensity. We believe that the novel concepts and methodology developed in this work can be directly applied on the CT projections prior to reconstruction, or easily be extended to other imaging techniques such as: Microscopy, Neutron Tomography, Ultrasonics, etc.
For a long time, the rule of thumb for active thermography as a non-destructive testing method was that the resolution of internal defects/inhomogeneities is limited to a ratio of defect depth/defect size ≤ 1. This is due to the diffusive nature of thermal conduction in solids. So-called super resolution approaches have recently allowed this physical limit to be overcome many times over. This offers the attractive possibility of developing thermography from a purely near surface-sensitive testing method to one with improved depth range. How far this development can be pushed is the subject of current research.
We have already been able to show that this classical limitation for one- and two-dimensional defect geometries can be overcome by illuminating the test object sequentially in a structured manner with individual laser spots and thus subsequently calculating a defect map from the resulting measurement data by applying photothermal super resolution reconstruction, which allows significantly improved separation of individual closely spaced defects. As a result, this method benefits strongly from the combination of sequential spatially structured illumination and modern numerical optimization methods, which come at the expense of higher experimental complexity. This leads to long measurement times, large data sets, and tedious numerical analysis, in contrast to the application of established standard thermographic methods with homogeneous illumination.
In this work, we report on the application of full-area spatially structured two-dimensional illumination patterns, which, by applying state-of-the-art laser projector technology in conjunction with a high-power laser, makes it possible to achieve an efficient implementation of photothermal super-resolution reconstruction even for larger test areas in the first place.
Although layer-based additive manufacturing methods such as laser powder bed fusion (PBF-LB) offer an immense geometrical freedom in design, they are typically subject to a build-up of internal stress (i.e. thermal stress) during manufacturing. As a consequence, significant residual stress (RS) is retained in the final part as a footprint of these internal stresses. Furthermore, localized melting and solidification inherently induce columnar-type grain growth accompanied by crystallographic texture. Although diffraction-based methods are commonly used to determine the RS distribution in PBF-LB parts, such features pose metrological challenges in their application. In theory, preferred grain orientation invalidates the hypothesis of isotropic material behavior underlying the common methods to determine RS. In this work, more refined methods are employed to determine RS in PBF-LB/M/IN718 prisms, based on crystallographic texture data. In fact, the employment of direction-dependent elastic constants (i.e. stress factors) for the calculation of RS results in insignificant differences from conventional approaches based on the hypothesis of isotropic mechanical properties. It can be concluded that this result is directly linked to the fact that the {311} lattice planes typically used for RS analysis in nickel-based alloys have high multiplicity and less strong texture intensities compared with other lattice planes. It is also found that the length of the laser scan vectors determines the surface RS distribution in prisms prior to their removal from the baseplate. On removal from the baseplate the surface RS considerably relaxes and/or redistributes; a combination of the geometry and the scanning strategy dictates the sub-surface RS distribution.
With increasing demand and environmental concerns, researchers are exploring new materials that can perform as well or better than traditional materials while reducing environmental impact. The BAMline, a real-life sample materials research beamline, provides unique insights into materials’ electronic and chemical structure at different time and length scales. The beamline specializes in x-ray absorption spectroscopy, x-ray fluorescence spectroscopy, and tomography experiments. This enables real-time optimization of material properties and performance for various applications, such as energy transfer, energy storage, catalysis, and corrosion resistance. This paper gives an overview of the analytical methods and sample environments of the BAMline, which cover non-destructive testing experiments in materials science, chemistry, biology, medicine, and cultural heritage. We also present our own synthesis methods, processes, and equipment developed specifically for the BAMline, and we give examples of synthesized materials and their potential applications. Finally, this article discusses the future perspectives of the BAMline and its potential for further advances in sustainable materials research.
Synchrotron X-ray computed tomography (SXCT) at BAMline has been paired with in-situ tensile loading to monitor damage evolution in LPBF Metal Matrix Composite (MMC) 7017 Aluminium alloy + 3 wt% Zr + 0.5 wt% TiC. The processing of the material leads formation to different categories of Zr-rich inclusions, precipitates and defects. In-situ SXCT test disclosed the critical role of the pre-cracks in the reinforcement phases in the failure mechanisms of LPBF MMC. The damage was initiated from lack-of-fusion defects and cracks propagated through coalescence with other defects.
Moisture testing and mapping
(2023)
In this work, we report on our progress for investigating a new experimental approach for thermographic detection of internal defects by performing 2D photothermal super resolution reconstruction. We use modern high-power laser projector technology to repeatedly excite the sample surface photothermally with varying spatially structured 2D pixel patterns. In the subsequent (blind) numerical reconstruction, multiple measurements are combined by exploiting the joint-sparse nature of the defects within the specimen using nonlinear convex optimization methods. As a result, a 2D-sparse defect/inhomogeneity map can be obtained. Using such spatially structured heating combined with compressed sensing and computational imaging methods allows to significantly reduce the experimental complexity and to study larger test surfaces as compared to the one-dimensional approach reported earlier.
In this work, the limits of using spatially structured heating combined with subsequent photothermal super resolution reconstruction for the thermographic detection and resolution of internal defects are investigated. The approach is based on the application of modern high-power laser projector technology, which is used to repeatedly project varying spatially structured 2D pixel patterns to photothermally heat the object under test. After processing the generated thermographic data using nonlinear convex optimisation in conjunction with exploiting the joint-sparse nature of the defect signals within the individual measurements, a high-resolution 2D-sparse defect/inhomogeneity map is obtained. The main focus of the investigation is set on the influence of the number of individual measurements on the achievable reconstruction quality. Using numerical simulations based on an analytical representation of the forward solution to the underlying inverse problem, the convergence rate over performed measurements of the achievable reconstruction quality is determined. Finally, all findings are experimentally validated by reconstructing a set of internal defects in an additively manufactured sample. In this work, it is shown that for a variety of different defect separation distances, the projection of 50 different pixel patterns allows for a good trade-off between experimental complexity and reconstruction quality.
It can be difficult to efficiently model ultrasonic waves in 3D structures, especially when the computational model needs to account for complex geometries. This contribution presents a solution based on the Scaled Boundary Finite Element Method (SBFEM). It is a numerical tool suitable for elastodynamic problems. A space-tree discretisation, namely quad-trees, is used. This technique allows the decomposition of an image into quadrilaterals or quads, which are extruded to generate the 3D plate geometry. In particular, small quads resolve regions with discontinuities, allowing them to represent fine details in the structure. Moreover, this meshing technique allows for exploiting cell similarities, making the calculation procedure more efficient. The space-tree discretisations are generated from a high-resolution image containing all the information about damaged regions or boundary conditions. The resulting SBFEM polyhedral domains employ transition elements to ensure correct coupling between cells of different sizes. The analytical solution of a cylindrical scatterer serves as a reference to validate the proposed approach. Other examples also demonstrate the validity of the methodology and its flexibility.
Functionally graded porous scaffolds (FGPS) constructed with pores of different size arranged as spatially continuous structure based on sheet-based gyroid with three different scaling factors of 0.05, 0.1 and 0.2 were produced by electron beam powder bed fusion. The pore dimensions of the obtained scaffolds satisfy the values required for optimal bone tissue ingrowth. Agglomerates of residual powder were found inside all structures, which required post-manufacturing treatment. Using X-ray Computed Tomography powder agglomerations were visualized and average wall thickness, wall-to-wall distances, micro- and macro-porosities were evaluated. The initial cleaning by powder recovery system (PRS) was insufficient for complete powder removal. Additional treatment by dry ultrasonic vibration (USV) was applied and was found successful for gyroids with the scaling factors of 0.05 and 0.1. Mechanical properties of the samples, including quasi-elastic gradients and first maximum compressive strengths of the structures before and after USV were evaluated to prove that additional treatment does not produce structural damage. The estimated quasi-elastic gradients for gyroids with different scaling factors lie in a range between 2.5 and 2.9 GPa, while the first maximum compressive strength vary from 52.5 for to 59.8 MPa, compressive offset stress vary from 46.2 for to 53.2 MPa.
In this study, we present an enhanced deep learning framework for the prediction of porosity based on thermographic in-situ monitoring data of laser powder bed fusion processes. The manufacturing of two cuboid specimens from Haynes 282 (Ni-based alloy) powder was monitored by a short-wave infrared camera. We use thermogram feature data and x-ray computed tomography data to train a convolutional neural network classifier. The classifier is used to perform a multi-class prediction of the spatially resolved porosity level in small sub-volumes of the specimen bulk.
In this study, we present an enhanced deep learning framework for the prediction of porosity based on thermographic in-situ monitoring data of laser powder bed fusion processes. The manufacturing of two cuboid specimens from Haynes 282 (Ni-based alloy) powder was monitored by a short-wave infrared camera. We use thermogram feature data and x-ray computed tomography data to train a convolutional neural network classifier. The classifier is used to perform a multi-class prediction of the spatially resolved porosity level in small sub-volumes of the specimen bulk.
Robot-assisted laser thermography for surface breaking crack detection on complex shaped components
(2023)
Laser thermography using a focused (spot or line) beam has proved to be effective for detection of surface breaking cracks on planar samples. In this work, we use the same principle, but applied to complex shaped components, like a rail cross-section, a gear, and a gas turbine blade. We use a six-axis robot to move the sample in-front of our thermographic setup. Several scanning-path and thermographic parameters are explored: scanning speed, density of points in each scanning slice, laser power, camera framerate. Additionally, we explore semi-automatic evaluation algorithms for crack detection, as well as 2D-to-3D registration of the found indications.
The application and benefits of Semantic Web Technologies (SWT) for managing, sharing, and (re-)using of research data are demonstrated in implementations in the field of Materials Science and Engineering (MSE). However, a compilation and classification are needed to fully recognize the scattered published works with its unique added values. Here, the primary use of SWT at the interface with MSE is identified using specifically created categories. This overview highlights promising opportunities for the application of SWT to MSE, such as enhancing the quality of experimental processes, enriching data with contextual information in knowledge graphs, or using ontologies to perform specific queries on semantically structured data. While interdisciplinary work between the two fields is still in its early stages, a great need is identified to facilitate access for nonexperts and develop and provide user-friendly tools and workflows. The full potential of SWT can best be achieved in the long term by the broad acceptance and active participation of the MSE community. In perspective, these technological solutions will advance the field of MSE by making data FAIR. Data-driven approaches will benefit from these data structures and their connections to catalyze knowledge generation in MSE.
The dataset contains raw data gathered with the ultrasonic pulse-echo method on concrete specimens. The surfaces of the measuring objects were automatically scanned point by point. Pulse-echo measurements were performed at each of these measuring points. The test specimens represent two typical testing tasks in construction industry: the detection of objects and the determination of dimensions to describe the geometry of components. By automating the measurement process, the different test scenarios are examined with a high repeatability, precision and measuring point density. Longitu- dinal and transversal waves were used and the geometrical aperture of the testing system was varied. The low-frequency probes operate in a range of up to approximately 150 kHz. In addition to the specification of the geometrical dimensions of the individual probes, the directivity pattern and the sound field characteristics are provided. The raw data are stored in a universally readable format. The length of each time signal (A-scan) is two milliseconds and the sampling rate is two mega-samples per second. The provided data can be used for comparative studies in signal analysis, imag- ing and interpretation as well as for evaluation pur- poses in different, practically relevant testing scenarios.
The prediction of porosity is a crucial task for metal based additive manufacturing techniques such as laser powder bed fusion. Short wave infrared thermography as an in-situ monitoring tool enables the measurement of the surface radiosity during the laser exposure. Based on the thermogram data, the thermal history of the component can be reconstructed which is closely related to the resulting mechanical properties and to the formation of porosity in the part. In this study, we present a novel framework for the local prediction of porosity based on extracted features from thermogram data. The framework consists of a data pre-processing workflow and a supervised deep learning classifier architecture. The data pre-processing
workflow generates samples from thermogram feature data by including feature information from multiple subsequent layers.
Thereby, the prediction of the occurrence of complex process phenomena such as keyhole pores is enabled. A custom convolutional neural network model is used for classification. Themodel is trained and tested on a dataset from thermographic in-situ monitoring of the manufacturing of an AISI 316L stainless steel test component. The impact of the pre-processing parameters and the local void distribution on the classification performance is studied in detail. The presented model achieves an accuracy of 0.96 and an f1-Score of 0.86 for predicting keyhole porosity in small sub-volumes with a dimension of (700 × 700 × 50) μm3. Furthermore, we show that pre-processing parameters such as the porosity threshold for sample
labeling and the number of included subsequent layers are influential for the model performance. Moreover, the model prediction is shown to be sensitive to local porosity changes although it is trained on binary labeled data that disregards the actual sample porosity.
This data set contains three different data types obtained from concrete specimens. For each specimen, the rebound numbers, ultrasonic data (ultrasonic velocity, time of flight), and destructive concrete strength are given. Two kind of specimen geometries were tested: cubes and drilled cores. The files are labeled according to the specimen geometry as "cube" or "core" and the type of measurement data as "compressive_strength", "rn_R" and "rn_Q" for rebound numbers as well as "us" for ultrasonic data. The ultrasonic data were generated by six independent laboratories, the rebound numbers by five independent laboratories and the destructive tests by one laboratory. The designation of each specimen establishes the relationship between the different data types.
We propose a novel iterative segmentation algorithm (i.S.Sy.Da.T.A: Iterative Segmentation Synthetic Data Training Algorithm) employing Deep Convolutional Neural Networks and synthetic training data for X-ray tomographic reconstructions of complex microstructures. In our method, we reinforce the synthetic training data with experimental XCT datasets that were automatically segmented in the previous iteration. This strategy produces better segmentations in successive iterations. We test our algorithm with experimental XCT re constructions of a 6-phase Al-Si Matrix Composite reinforced with ceramic fibers and particles. We perform the analysis in 3D with a special network architecture that demonstrates good generalization with synthetic training data. We show that our iterative algorithm returns better segmentations compared to the standard single training approach. More specifically, phases possessing similar attenuation coefficients can be better segmented: for Al2O3 fibers, SiC particles, and Intermetallics, we see an increase of the Dice score with respect to the classic approach: from 0.49 to 0.54, from 0.66 to 0.72, and from 0.55 to 0.66 respectively. Furthermore, the overall Dice score increases from 0.77 to 0.79. The methods presented in this work are also applicable to other materials and imaging techniques.
The corner echo is a well-known effect in ultrasonic testing, which allows detection of surface breaking cracks with predominantly perpendicular orientation to the surface as, for example, corrosion cracks in metal pipes or shafts. This echo is formed by two planes, the surface of the crack and the surface which the crack breaks. It can also be classified as a half-skip method, since a reflection of the pulse occurs on the backwall before the reflection at the defect takes place. In combination with the diffraction from the crack tip, the corner echo also allows crack sizing. As shown in this paper, the corner reflection can be used in civil engineering for nondestructive inspection of concrete. Commercially available low frequency ultrasonic arrays with dry point contact sources generate SH transversal waves with sufficient divergence of the sound field in order to detect corner reflections. Ultrasonic line-scans and area-scans were acquired with a linear array on flat concrete specimens, and the data were reconstructed by the Synthetic aperture focusing technique. If the angles and the area of reconstruction are chosen accordingly, the corner echo reflection can be distinguished from other ultrasonic information. The corner echo can thus be used as a method for deciding whether a crack is a partial-depth crack or a full-depth crack and thus for obtaining a statement about crack depth. This paper presents corresponding experimental results obtained on concrete specimens with artificial test defects and cracks induced under controlled conditions.
Two test series were examined using nondestructive measuring methods by six independent laboratories before determining their compressive strength. The nondestructive test methods used were the rebound hammer and ultrasonic pulse velocity measurement. Two types of geometries were investigated: drilled cores and cubes. The measurement procedure for each of these datasets is conditioned to the geometry and is therefore different.
The first series consists of 20 drilled cores (approximately diameter/height = 10 cm/20 cm) from the 55-year-old Lahntal Viaduct near Limburg, Germany. After preparation in the first laboratory, the lateral surface of the drilled cores was tested with the rebound hammer using a given pattern. Every laboratory tested every drilled core at different locations.
Ultrasonic measurements in transmission were performed repeatedly at predefined points on the flat surfaces of the specimen.
The second series consisted of 25 newly manufactured concrete cubes of a mix with a target concrete strength class of C30/37. The edge length was 15 cm. Each laboratory received five specimens of this test series. Thus, contrary to the first series, each specimen was tested by only one laboratory. Two side faces of each cube were tested with the rebound hammer. In addition, ultrasonic measurements were performed by one laboratory. The time of flight was measured between the tested side faces of the rebound hammer at different positions.
For both series, rebound hammers were used to determine the R-value as well as the Q-value. The rebound hammer models within the laboratories were always the same, while they differed between the laboratories. The ultrasonic measurements took place with different measurement systems and couplants. Finally, both specimen series were tested destructively for compressive strength.
The dataset contains the raw data summarized in tabular form. In addition, relevant calculated data are included in some cases. For the ultrasonic measurements, the time of flight has already been converted into the ultrasonic velocity. Besides, in addition to the raw data of the compressive strength test (force, weight, and geometry values), the calculated compressive strengths and densities are also provided.
The microstructure of an apatite-wollastonite (code name AP40) glass-ceramic is analyzed in this study by combining 2D microscopy, phase analysis, X-ray absorption and synchrotron X-ray refraction computed tomography (XCT and SXRCT, respectively). It is shown that this combination provides a useful toolbox to characterize the global microstructure in a wide scale range, from sub-micrometer to millimeter. The material displays a complex microstructure comprising a glassy matrix with embedded fluorapatite and wollastonite small crystals. In this matrix, large (up to 200 μm) spike-shaped structures are distributed. Such microstructural features are oriented around a central sphere, thereby forming a structure resembling a sea urchin. A unique feature of SXRCT, in contrast to XCT, is that internal interfaces are visualized; this allows one to show the 3D distribution of these urchins with exceptionally good contrast. Furthermore, it is revealed that the spike-shaped structures are not single crystals, but rather composed of sub-micrometric crystals, which are identified as fluorapatite and diopside phases by SEM-EDX analysis.
This contribution attempts to shed light on the application of POD analyses and measurement uncertainty calculations in nondestructive testing of concrete structures to provide useful and quality-assured information for reliability assessments of our built environment - with an emphasis on ground penetrating radar. It is shown that signal processing techniques affect POD analysis results to varying degrees, that measurement uncertainties are useful to validate PODs, and that the utilization of both POD analysis and measurement uncertainty calculations can pave the way to NDT-based reliability assessment, which will be exemplarily demonstrated in conclusion – generally aiming to derive those maintenance strategies and actions that are in line with actual demands.