Chemie und Prozesstechnik
Filtern
Dokumenttyp
- Vortrag (455)
- Zeitschriftenartikel (265)
- Beitrag zu einem Tagungsband (204)
- Posterpräsentation (123)
- Sonstiges (23)
- Forschungsdatensatz (15)
- Buchkapitel (10)
- Dissertation (9)
- Beitrag zu einem Sammelband (4)
- Forschungsbericht (3)
Sprache
- Englisch (773)
- Deutsch (335)
- Russisch (4)
- Mehrsprachig (3)
Schlagworte
- Concrete (87)
- Ultrasound (75)
- NDT (71)
- LIBS (64)
- Non-destructive testing (62)
- Thermography (53)
- Zerstörungsfreie Prüfung (53)
- Ultraschall (42)
- Additive manufacturing (38)
- Super resolution (34)
Organisationseinheit der BAM
- 8 Zerstörungsfreie Prüfung (1115) (entfernen)
Paper des Monats
- ja (6)
Eingeladener Vortrag
- nein (455)
This paper explores combining a gimbal-mounted tunable diode laser absorption spectroscopy (TDLAS) sensor and a video camera to create a virtual gas camera for methane leak detection. This provides a low-to-zero-cost extension of typical TDLAS gas tomography systems. A prototype setup mounted on a ground robot is evaluated. Results acquired using a simulated methane leak show the feasibility of the virtual gas camera, accurately detecting methane leaks by overlaying concentrations onto a visual image. While the acquisition time is significantly longer than for traditional gas cameras, potential enhancements are discussed. The study concludes that the virtual gas camera is feasible and useful, despite its longer acquisition time. It serves as a valuable software-only addition to typical TDLAS gas tomography systems, offering quickly-available on-site data augmentation for visual leak assessment at low-to-zero cost.
This paper outlines significant advancements in our previously developed aerial gas tomography system, now optimized to reconstruct 2D tomographic slices of gas plumes with enhanced precision in outdoor environments. The core of our system is an aerial robot equipped with a custom-built 3-axis aerial gimbal, a Tunable Diode Laser Absorption Spectroscopy (TDLAS) sensor for CH4 measurements, a laser rangefinder, and a wide-angle camera, combined with a state-of-the-art gas tomography algorithm. In real-world experiments, we sent the aerial robot along gate-shaped flight patterns over a semi-controlled environment with a static-like gas plume, providing a welldefined ground truth for system evaluation. The reconstructed cross-sectional 2D images closely matched the known ground truth concentration, confirming the system’s high accuracy and reliability. The demonstrated system’s capabilities open doors for potential applications in environmental monitoring and industrial safety, though further testing is planned to ascertain the system’s operational boundaries fully.
This paper explores combining a gimbal-mounted tunable diode laser absorption spectroscopy (TDLAS) sensor and a video camera to create a virtual gas camera for methane leak detection. This provides a low-to-zero-cost extension of typical TDLAS gas tomography systems. A prototype setup mounted on a ground robot is evaluated. Results acquired using a simulated methane leak show the feasibility of the virtual gas camera, accurately detecting methane leaks by overlaying concentrations onto a visual image. While the acquisition time is significantly longer than for traditional gas cameras, potential enhancements are discussed. The study concludes that the virtual gas camera is feasible and useful, despite its longer acquisition time. It serves as a valuable software-only addition to typical TDLAS gas tomography systems, offering quickly-available on-site data augmentation for visual leak assessment at low-to-zero cost.
This paper outlines significant advancements in our previously developed aerial gas tomography system, now optimized to reconstruct 2D tomographic slices of gas plumes with enhanced precision in outdoor environments. The core of our system is an aerial robot equipped with a custom-built 3-axis aerial gimbal, a Tunable Diode Laser Absorption Spectroscopy (TDLAS) sensor for CH4 measurements, a laser rangefinder, and a wide-angle camera, combined with a state-of-the-art gas tomography algorithm. In real-world experiments, we sent the aerial robot along gate-shaped flight patterns over a semi-controlled environment with a static-like gas plume, providing a welldefined ground truth for system evaluation. The reconstructed cross-sectional 2D images closely matched the known ground truth concentration, confirming the system’s high accuracy and reliability. The demonstrated system’s capabilities open doors for potential applications in environmental monitoring and industrial safety, though further testing is planned to ascertain the system’s operational boundaries fully.
Normung und Standardisierung in der zerstörungsfreien Prüfung finden auf nationaler, europäischer und internationaler Ebene statt. Die Übernahme von Leitungsfunktionen in diesen Gremien erzeugt die Möglichkeit der Steuerung von Themen und Inhalte der relevanten Normen. Das persönliche Engagement der Fachexpert*innen in den nationalen Gremien und den europäischen und internationalen Arbeitsgruppen ist ein zusätzlicher Faktor für die inhaltliche Korrektheit und die erwünschte inhaltliche Ausrichtung der Norm. Denn wer nicht normt wird genormt und muss letztlich Regeln verwenden, die andere entsprechend ihrer eigenen Interessen gestalten. Voraussetzung für die Teilnahme in europäischen und internationalen Normungsgremien ist die Mitwirkung in den entsprechenden DIN-Spiegelgremien, welche die europäischen (CEN) und internationalen (ISO) Aktivitäten spiegeln und die Fachexperten für die Mitarbeit autorisieren. Auf internationaler Ebene findet die Normungsarbeit in den technischen Komitees ISO/TC 135 “Non-destructive testing” und ISO/TC 44/SC 5 “Testing and inspection of welds” und ihren Arbeitsgruppen statt. Das europäische Pendant ist das CEN/TC 138 “Zerstörungsfreie Prüfverfahren” mit seinen Arbeitsgremien. Die DIN-Spiegelgremien sind für die deutsche Mitarbeit in diesen Gremien und die inhaltliche Bewertung der Norm-Entwürfe zuständig.
Normung und Standardisierung in der zerstörungsfreien Prüfung finden auf nationaler, europäischer und internationaler Ebene statt. Die Übernahme von Leitungsfunktionen in diesen Gremien erzeugt die Möglichkeit der Steuerung von Themen und Inhalten der relevanten Normen. Das persönliche Engagement der Fachexpert*innen in den nationalen Gremien und den europäischen und internationalen Arbeitsgruppen ist ein zusätzlicher Faktor für die inhaltliche Korrektheit und die inhaltliche Ausrichtung der Norm. Denn wer nicht normt, wird genormt und muss letztlich Normen anwenden, die andere entsprechend ihrer eigenen Interessen gestaltet haben. Voraussetzung für die Teilnahme in europäischen und internationalen Normungsgremien ist die Mitwirkung in den entsprechenden DINSpiegelgremien, welche die europäischen (CEN) und internationalen (ISO) Aktivitäten spiegeln und die Fachexpert*innen für die Mitarbeit autorisieren.
Auf internationaler Ebene findet die Normungsarbeit in den technischen Komitees ISO/TC 135 “Non-destructive testing” und ISO/TC 44/SC 5 “Testing and inspection of welds” und ihren Arbeitsgruppen statt. Das europäische Pendant ist das CEN/TC 138 “Zerstörungsfreie Prüfverfahren” mit seinen Arbeitsgremien. Die DINSpiegelgremien sind für die deutsche Mitarbeit in diesen Gremien und die inhaltliche Bewertung der Norm-Entwürfe zuständig.
Bei der zerstörungsfreien Prüfung verlegter Eisenbahnschienen werden die Rohdaten derzeit in proprietären Datenformaten gespeichert und auf Datenträgern zwischen den Prüfzügen und den auswertenden Stellen versendet. Die proprietären Datenformate sind in der Regel nur den Herstellern der Prüfsysteme bekannt und deren Dokumentation nicht allgemein zugänglich.
Die „Standard Practice for Digital Imaging and Communication in Nondestructive Evaluation“ (DICONDE), basierend auf dem medizinischen Standard „Digital Imaging and Communication in Medicine“ (DICOM), ermöglicht es, sowohl Prüfdaten als auch Prüfergebnisse und Streckeninformationen in einem standardisierten Format zu speichern und zwischen verschiedenen Endpunkten zu übertragen.
Das Poster gibt zunächst einen kurzen Überblick über die hierarchische Struktur von DICONDE und zeigt dann, wie DICONDE bei der Prüfung verlegter Eisenbahnschienen verwendet werden kann. Die geometrischen Besonderheiten (mehrere Kilometer Länge pro Prüffahrt, kurviger Streckenverlauf) stellen dabei eine besondere Herausforderung dar. Im Rahmen des mFUND-geförderten Projektes „Arteficial Intelligence for Railway Inspection (AIFRI)“, Förderkennzeichen 19FS2014C, wurde ein Vorschlag für eine Erweiterung des DICONDE-Standards für die Schienenprüfung erarbeitet und bei der ASTM eingereicht.
Bei der zerstörungsfreien Prüfung verlegter Eisenbahnschienen werden die Rohdaten derzeit in proprietären Datenformaten gespeichert und auf Datenträgern zwischen den Prüfzügen und den auswertenden Stellen versendet. Die proprietären Datenformate sind in der Regel nur den Herstellern der Prüfsysteme bekannt und deren Dokumentation nicht allgemein zugänglich.
Die „Standard Practice for Digital Imaging and Communication in Nondestructive Evaluation“ (DICONDE), basierend auf dem medizinischen Standard „Digital Imaging and Communication in Medicine“ (DICOM), ermöglicht es, sowohl Prüfdaten als auch Prüfergebnisse und Streckeninformationen in einem standardisierten Format zu speichern und zwischen verschiedenen Endpunkten zu übertragen.
Das Poster gibt zunächst einen kurzen Überblick über die hierarchische Struktur von DICONDE und zeigt dann, wie DICONDE bei der Prüfung verlegter Eisenbahnschienen verwendet werden kann. Die geometrischen Besonderheiten (mehrere Kilometer Länge pro Prüffahrt, kurviger Streckenverlauf) stellen dabei eine besondere Herausforderung dar. Im Rahmen des mFUND-geförderten Projektes „Arteficial Intelligence for Railway Inspection (AIFRI)“, Förderkennzeichen 19FS2014C, wurde ein Vorschlag für eine Erweiterung des DICONDE-Standards für die Schienenprüfung erarbeitet und bei der ASTM eingereicht.
Bei der Erhaltung von Stahlbetonbauwerken ist der Chloridgehalt im Beton ein wichtiger Parameter zur Festlegung geeigneter Instandsetzungsmaßnahmen. Die bisher etablierten Verfahren basieren auf der nasschemischen Analyse einer Probe, Bohrmehl oder aufgemahlener Segmente eines Bohrkerns. Das Verfahren liefert einen Chloridgehalt je Tiefensegment, was mit einer starken Homogenisierung der Probe einhergeht, wodurch detaillierte Informationen zu Chloridunterschieden im Millimeterbereich verloren gehen. Eine alternative Methode ist die laserinduzierte Plasmaspektroskopie (LIBS), die nicht nur die quantitative ortsaufgelöste Bestimmung des Chloridgehalts im Bezug zur Zementmasse ermöglicht, sondern auch simultan detaillierte Informationen über die Verteilung vieler anderer chemischer Elemente liefert. Die räumliche Auflösung liegt in der Regel im Bereich von einigen Millimetern, kann aber bei Bedarf auf 0,1 mm oder weniger erhöht werden. Das neue Merkblatt B14 „Quantifizierung von Chlorid in Beton mit der laserinduzierten Plasmaspektroskopie (LIBS)“ der Deutschen Gesellschaft für Zerstörungsfreie Prüfungen (DGZfP) regelt und vereinheitlicht erstmals die zuverlässige und reproduzierbare Ermittlung des Chloridgehalts mit LIBS. In diesem Beitrag wird das Merkblatt vorgestellt und anhand praktischer Anwendungsbeispiele das Potenzial von LIBS für Fragestellungen in der Betonerhaltung verdeutlicht.
This technical note highlights the fact that CF-LIBS algorithms work in mole fractions, while results of spectrochemical analysis are usually reported in mass fractions or mass percent. Ignoring this difference and not converting mole fractions to mass fractions can lead to errors in reported concentrations determined by the CF-LIBS method and inadequate comparison of these concentrations with certified concentrations. Here, the key points of the CF-LIBS algorithm are reproduced and the formulae for converting a mole fraction to a mass fraction and vice versa are given. Several numerical examples are also given, which show that the greater the difference between the molar mass of an individual element in a sample and the average molar mass, the greater the discrepancy between the mole and mass fractions.
An observation of the fracture process in front of the crack tip inside a dentin sample by means of ex-situ X-ray computed tomography after uniaxial compression at different deformation values was carried out in this work. This ex-situ approach allowed the microstructure and fracturing process of human dentin to be observed during loading. No cracks are observed up to the middle part of the irreversible deformation in the samples at least visible at 0.4μm resolution. First cracks appeared before the mechanical stress reached the compression strength. The growth of the cracks is realized by connecting the main cracks with satellite cracks that lie ahead of the main crack tip and parallel its trajectory. When under the stress load the deformation in the sample exceeds the deformation at the compression strength of dentin, an appearance of micro-cracks in front of the main cracks is observed. The micro-cracks are inclined (~60°) to the trajectory of the main cracks. The further growth of the main cracks is not realized due to the junction with the micro-cracks; we assume that the micro-cracks dissipate the energy of the main crack and suppressed its growth. These micro-cracks serve as additional stress accommodations, therefore the samples do not break apart after the compression test, as it is usually observed under bending and tension tests.
AbstractAir‐coupled ultrasonic testing (ACU) is a pioneering technique in non‐destructive testing (NDT). While contact testing and fluid immersion testing are standard methods in many applications, the adoption of ACU is progressing slowly, especially in the low ultrasonic frequency range. A main reason for this development is the difficulty of generating high amplitude ultrasonic bursts with equipment that is robust enough to be applied outside a laboratory environment. This paper presents the fluidic ultrasonic transducer as a solution to this challenge. This novel aeroacoustic source uses the flow instability of a sonic jet in a bistable fluidic switch to generate ultrasonic bursts up to 60 kHz with a mean peak pressure of 320 Pa. The robust design allows operation in adverse environments, independent of the operating fluid. Non‐contact through‐transmission experiments are conducted on four materials and compared with the results of conventional transducers. For the first time, it is shown that the novel fluidic ultrasonic transducer provides a suitable acoustic signal for NDT tasks and has potential of furthering the implementation of ACU in industrial applications.This article is protected by copyright. All rights reserved
In ultrasonic testing, material and structural properties of a specimen can be derived from the time-of-flight (ToF). Using signal features, such as the first peak or envelope maximum, to calculate the ToF is error-prone in multipath arrangements or dispersive and attenuating materials, which is not the case for the signal onset. Borrowing from seismology, researchers used the Akaike information criterion (AIC) picker to automatically determine onset times. The most commonly used formulation, Maeda's AIC picker, is reassessed and found to be based on inappropriate assumptions for signals often used in ultrasonic testing and dependent on arbitrary parameters. Consequently, an onset picker for ultrasonic through-transmission measurements is proposed, based on a spectral entropy criterion (SEC) to model the signal using the AIC framework. This SEC picker takes into account the spectral properties of the ultrasonic signal and is virtually free of arbitrary parameters. Synthetic and experimental data are used to compare the performance of SEC and AIC pickers. It is shown that the accuracy of onset picking is improved for densely sampled data.
AbstractHigh-strength aluminum alloys used in aerospace and automotive applications obtain their strength through precipitation hardening. Achieving the desired mechanical properties requires precise control over the nanometer-sized precipitates. However, the microstructure of these alloys changes over time due to aging, leading to a deterioration in strength. Typically, the size, number, and distribution of precipitates for a quantitative assessment of microstructural changes are determined by manual analysis, which is subjective and time-consuming. In our work, we introduce a progressive and automatable approach that enables a more efficient, objective, and reproducible analysis of precipitates. The method involves several sequential steps using an image repository containing dark-field transmission electron microscopy (DF-TEM) images depicting various aging states of an aluminum alloy. During the process, precipitation contours are generated and quantitatively evaluated, and the results are comprehensibly transferred into semantic data structures. The use and deployment of Jupyter Notebooks, along with the beneficial implementation of Semantic Web technologies, significantly enhances the reproducibility and comparability of the findings. This work serves as an exemplar of FAIR image and research data management.
A semi-automatic thermographic procedure for the assessment of the welded area of resistance projection welded joints has been developed. Currently, to assess the quality of RPW joints destructive tests are used and the more commonly used non-destructive technique is the ultrasonic one. The possibility for a quantitative evaluation of the welded area by thermographic technique has been proved by means of an innovative procedure applied on steel RPW joints with ‘as it’ surface conditions. Measurements obtained by thermography and ultrasound have been compared, to verify the developed procedure.
Simulation-Assisted Augmentation of Missing Wedge and Region-of-Interest Computed Tomography Data
(2024)
This study reports a strategy to use sophisticated, realistic X-ray Computed Tomography (CT) simulations to reduce Missing Wedge (MW) and Region-of-Interest (RoI) artifacts in FBP (Filtered Back-Projection) reconstructions. A 3D model of the object is used to simulate the projections that include the missing information inside the MW and outside the RoI. Such information augments the experimental projections, thereby drastically improving the reconstruction results. An X-ray CT dataset of a selected object is modified to mimic various degrees of RoI and MW problems. The results are evaluated in comparison to a standard FBP reconstruction of the complete dataset. In all cases, the reconstruction quality is significantly improved. Small inclusions present in the scanned object are better localized and quantified. The proposed method has the potential to improve the results of any CT reconstruction algorithm.
Until the 1980s radiography was used to inspect civil structures in case of special demands and showed a much better resolution than other NDT techniques. However, due to safety concerns and cost issues, this method is almost never used anymore. Meanwhile, non-destructive techniques such as ultrasound or
radar have found regular, successful practical application but sometimes suffer from limited resolution and accuracy, imaging artefacts or restrictions in detecting certain features when applied to reinforced or prestressed concrete inspection.
Muon tomography has received much attention recently. Muons are particles generated naturally by cosmic rays in the upper atmosphere and pose no risk to humans. Novel detectors and tomographic imaging algorithms have opened new fields of application, mainly in the nuclear sector, but also in spectacular cases such as the Egyptian pyramids.
As a first step towards practical application in civil engineering and as a proof of concept we used an existing system to image the interior of a reference reinforced 600 kg concrete block. Even with a yet not optimized setup for this kind of investigation, the muon imaging results have been at least of similar quality compared to ultrasonic and radar imaging, potentially even better. Recently, the research was expanded to more realistic testing problems such as the detection of voids in certain structural elements. However, before practical implementation, more robust, mobile, and affordable detectors would be required as well as user-Friendly imaging and simulation software.
The built infrastructure ages and requires regular inspection and, when in doubt, monitoring. To ensure that older concrete bridges showing signs of deterioration can be used safely, several innovative monitoring tools have been introduced, including but not limited to optical, fiber-optic, or acoustic emission techniques. However, there are gaps in the portfolio. A sensing technique that covers a wide range of damage scenarios and larger volumes, while still being sensitive and specific, would be beneficial.
For about 15 years, research has been conducted on ultrasonic monitoring of concrete structures that goes beyond the traditional ultrasonic pulse velocity test (PV test), mostly using a very sensitive data evaluation technique called coda wave interferometry. At BAM we have developed sensors and instrumentation specifically for this method.
We have instrumented a 70-year-old, severely damaged prestressed concrete bridge in Germany in addition to a commercial monitoring system. We have now collected data for almost 3 years. We can show that we can provide information about the stress distribution in the bridge. We have also been able to confirm that there has been no significant additional damage to the bridge since the installation.
X-rays without X-rays: Can muon tomography provide pictures from within concrete and other objects?
(2024)
Until the 1980s radiography was used to inspect civil structures in case of special demands and showed a much better resolution than other NDT techniques. However, due to safety concerns and cost issues, this method is almost never used anymore. Meanwhile, non-destructive techniques such as ultrasound or
radar have found regular, successful practical application but sometimes suffer from limited resolution and accuracy, imaging artefacts or restrictions in detecting certain features when applied to reinforced or prestressed concrete inspection.
Muon tomography has received much attention recently. Muons are particles generated naturally by cosmic rays in the upper atmosphere and pose no risk to humans. Novel detectors and tomographic imaging algorithms have opened new fields of application, mainly in the nuclear sector, but also in spectacular cases such as the Egyptian pyramids.
As a first step towards practical application in civil engineering and as a proof of concept we used an existing system to image the interior of a reference reinforced 600 kg concrete block. Even with a yet not optimized setup for this kind of investigation, the muon imaging results have been at least of similar quality compared to ultrasonic and radar imaging, potentially even better. Recently, the research was expanded to more realistic testing problems such as the detection of voids in certain structural elements. However, before practical implementation, more robust, mobile, and affordable detectors would be required as well as user-Friendly imaging and simulation software.
The talk also discusses other applications , such as volcanology, mining and geothermal exploration.
Manufacturing austenitic stainless steels (ASSs) using additive manufacturing is of great interest for cryogenic applications. Here, the mechanical and microstructural responses of a 316L ASS built by laser powder bed fusion were revealed by performing in situ neutron diffraction tensile tests at the low-temperature range (from 373 to 10 K). The stacking fault energy almost linearly decreased from 29.2 ± 3.1 mJm⁻² at 373 K to 7.5 ± 1.7 mJm⁻² at 10 K, with a slope of 0.06 mJm⁻²K⁻¹, leading to the transition of the dominant deformation mechanism from strain-induced twinning to martensite formation. As a result, excellent combinations of strength and ductility were achieved at the low-temperature range.
Die Thermografie ist trotz ihrer ausgereiften wissenschaftlichen und technologischen Grundlagen ein noch relativ junges Mitglied in der Familie der zerstörungsfreien Prüfverfahren. Sie erschließt sich aufgrund einer Reihe von Vorzügen eine wachsende Anwendungsgemeinde. Für eine weitere Verbreitung insbesondere im industriellen Kontext spielen Normen, Standards und technische Regeln eine wichtige Rolle. In diesem Beitrag wird der aktuelle Stand der Normung in Deutschland vorgestellt. Wir zeigen, welche Normen und technischen Regeln es für die Thermografie in Deutschland und international gibt und wir wagen einen Blick in die Zukunft. Darüber hinaus lebt auch die Normierungsarbeit von der Beteiligung durch interessierte Kreise. Dies können industrielle und akademische Anwender*innen, Hersteller*innen von Geräten, Forschungseinrichtungen oder Dienstleistungsunternehmen sein. Sie können gern Ihre Bedarfe bezüglich Normierungsprojekten mitbringen und/oder direkt an die Autoren senden.
Die Thermografie ist trotz ihrer ausgereiften wissenschaftlichen und technologischen Grundlagen ein noch relativ junges Mitglied in der Familie der zerstörungsfreien Prüfverfahren. Sie erschließt sich aufgrund einer Reihe von Vorzügen eine wachsende Anwendungsgemeinde. Für eine weitere Verbreitung insbesondere im industriellen Kontext spielen Normen, Standards und technische Regeln eine wichtige Rolle. In diesem Beitrag wird der aktuelle Stand der Normung in Deutschland vorgestellt. Wir zeigen, welche Normen und technischen Regeln es für die Thermografie in Deutschland und international gibt und wir wagen einen Blick in die Zukunft. Darüber hinaus lebt auch die Normierungsarbeit von der Beteiligung durch interessierte Kreise. Dies können industrielle und akademische Anwender*innen, Hersteller*innen von Geräten, Forschungseinrichtungen oder Dienstleistungsunternehmen sein. Sie können gern Ihre Bedarfe bezüglich Normierungsprojekten mitbringen und/oder direkt an die Autoren senden.
To evaluate the durability of new alternative cement compositions, it is important to examine the internal transport of moisture through these materials. For this purpose, mortars were prepared from different types of cement and capillary suction experiments were carried out. The moisture transport was studied with an NMR tomograph and compared with weight measurements. With the tomograph, the total moisture input could be determined, as well as the moisture content within the samples non-destructively and spatially resolved. This allows precise observation of the moisture fronts. The tomograph was also used to determine the capillary transport coefficient.
The European Commission has identified Advanced Manufacturing and Advanced Materials as two of six Key Enabling Technologies (KETs). By fully utilizing these KETs, advanced and sustainable economies will be created. It is considered that Metrology is a key enabler for the advancement of these KETs. EURAMET, the association of metrology institutes in Europe, has strengthened the role of Metrology for these KETs by enabling the creation of a European Metrology Network for Advanced Manufacturing. The EMN is made up of National Metrology Institutes (NMIs) and Designated Institutes (DIs) from across Europe and was formally established in October 2021. The EMN aims to provide a high-level coordination of European metrology activities for the Advanced Materials and Advanced Manufacturing community.
The EMN itself is organized in three sections representing the major stages of the manufacturing chain: 1) Advanced Materials, 2) Smart Manufacturing Systems, and 3) Manufactured Components & Products. The EMN for Advanced Manufacturing is engaging with stakeholders in the field of Advanced Manufacturing and Advanced Materials (Large companies & SMEs, industry organisations, existing networks, and academia), as well as the wider metrology community (including TCs) to provide input for the preparation of a Strategic Research Agenda (SRA) for Metrology for Advanced Manufacturing.
This presentation will describe the progress in the development of the SRA by the EMN for Advanced Manufacturing. The metrology challenges identified across the various key industrial sectors, which utilise Advanced Materials and Advanced Manufacturing will be presented.
The EMN for Advanced Manufacturing is supported by the project JNP 19NET01 AdvManuNet.
The European Commission has identified Advanced Manufacturing and Advanced Materials as two of six Key Enabling Technologies (KETs). It is considered that Metrology is a key enabler for the advancement of these KETs. Consequently, EURAMET, the association of metrology institutes in Europe, has strengthened the role of Metrology for these KETs by enabling the creation of a European Metrology Network (EMN) for Advanced Manufacturing. The EMN is comprised of National Metrology Institutes (NMIs) and Designated Institutes (DIs) from across Europe and was formally established in October 2021. The aim of the EMN is to provide a high-level coordination of European metrology activities for the Advanced Manufacturing community.
The EMN itself is organized in three sections representing the major stages of the manufacturing chain: 1) Advanced Materials, 2) Smart Manufacturing Systems, and 3) Manufactured Components & Products. The EMN for Advanced Manufacturing is engaging with stakeholders in the field of Advanced Manufacturing (large companies & SMEs, industry organisations, existing networks, and academia), as well as the wider Metrology community, including Technical Committees, to provide input for the Strategic Research Agenda (SRA) on Metrology for Advanced Manufacturing.
This contribution will give an overview about the first version of the SRA prepared by the EMN for Advanced Manufacturing
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 European Commission has identified Advanced Manufacturing and Advanced Materials as two of six Key Enabling Technologies (KETs). It is considered that Metrology is a key enabler for the advancement of these KETs. Consequently, EURAMET, the association of metrology institutes in Europe, has strengthened the role of Metrology for these KETs by enabling the creation of a European Metrology Network (EMN) for Advanced Manufacturing. The EMN is comprised of National Metrology Institutes (NMIs) and Designated Institutes (DIs) from across Europe and was formally established in October 2021. The aim of the EMN is to provide a high-level coordination of European metrology activities for the Advanced Manufacturing community.
The EMN itself is organized in three sections representing the major stages of the manufacturing chain: 1) Advanced Materials, 2) Smart Manufacturing Systems, and 3) Manufactured Components & Products. The EMN for Advanced Manufacturing is engaging with stakeholders in the field of Advanced Manufacturing (large companies & SMEs, industry organisations, existing networks, and academia), as well as the wider Metrology community, including Technical Committees, to provide input for the Strategic Research Agenda (SRA) on Metrology for Advanced Manufacturing.
This contribution will give an overview about the first version of the SRA prepared by the EMN for Advanced Manufacturing.
Human factors (HFs) are a frequently mentioned topic when talking about the reliability of non-destructive testing (NDT). However, probability of detection (POD), the commonly used measure of NDT reliability, only looks at the technical capability of an NDT system to detect a defect.
After several decades of research on the influence of HFs on NDT reliability, there is still no commonly accepted approach to rendering HFs visible in reliability assessment. This paper provides an overview of possible quantitative and qualitative methods for integrating HFs into the reliability assessment. It is concluded that reliability assessment is best carried out using both quantifiable and non-quantifiable approaches to HFs.
The through-life management of our constantly ageing infrastructure is a basic requirement in order to ensure their structural safety and serviceability. Each structure experiences deterioration processes with time leading to a decrease of structural safety and serviceability. The design of new structures considers the expected deterioration for a defined period, the design service life. However, a frequent survey of structural safety controlling structural condition should be mandatory and a maintenance plan should be an integral part of the design. In addition, many structures have exceeded their design service life already or are very close to it leading to an increasing demand for condition assessment. On the one hand, assumptions made during design are not valid any more due to change of the loads, e.g., increasing traffic loads in terms of number and weights. On the other hand, design codes evolved over time in such a way that existing structures do not comply with today’s standards. In all these cases, the through-life management is an important tool to maintain the accessibility of existing structures with known reliability.
In line with the new Model Code for Concrete Structures, which includes guidance for both – design of new structures and assessment of existing structures, the Task Group 3.3 focused on the compilation of a state-of-the-art guideline for the through-life management of existing concrete structures, including:
Data acquisition by testing and monitoring techniques;
Condition assessment for the evaluation of existing structures;
Performance prediction using advanced methods;
Decision-making procedures to perform a complete assessment of existing structure.
The overall objective of the through-life management is the assessment of the current condition and the estimation of the remaining service life under consideration of all boundary conditions.
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.
The application of composite overwrapped pressure vessels (COPV) to store hydrogen and other compressed gases, especially when operating at high pressures, imposed the need for an innovative and reliable approach to ensure the safe operation of the system. Continuous structural health monitoring (SHM) based on ultrasonic guided waves (GWs) is a promising approach due to the ability of the wave to propagate for long distances and go around complex structures, moreover the high sensitivity to various failure modes such as delamination, matrix cracking and debonding. In this study, we scrutinize the behavior of (GWs) within the COPV using a network of PZT elements that are used for excitation and sensing. A laser doppler vibrometer (LDV) was also used to scan the surface of the vessel in various directions. L(0,1) and L(0,2) were observed in the captured signal. The L(0, 2) appeared to be a dominant mode in the COPV and was capable of propagating along the entire length and maintaining a good signal-to-noise ratio. The L(0,2) mode maintained the same phase velocity when it is captured at various excitation angles (0, 45, and 90 degrees). The reduced effect of the orthotropy of the materials on the propagating waves is an important result as it will reduce the complexity in data processing when performing damage identification.
In the event of moisture deterioration, rapid detection and localization is particularly important to prevent further deterioration and costs. For building floors, the layered structure poses a challenging obstacle for most moisture measurement methods. But especially here, layer-specific information on the depth of the water is crucial for efficient and effective repairs. Ground Penetrating Radar (GPR) shows the potential to generate such depth information. Therefore, the present work investigates the suitability of GPR in combination with machine learning methods for the automated classification of the typical deterioration cases (i) dry, (ii) wet insulation, and (iii) wet screed.
First, a literature review was conducted to identify the most common methods for detecting moisture in building materials using GPR. Here, it especially became clear that all publications only investigated individual time-, amplitude- or frequency features separately, without combining them. This was seen as a potential aspect for innovation, as the multivariate application of several signal features can help to overcome individual weaknesses and limitations.
Preliminary investigations carried out on drying screed samples confirmed the profitable use of multivariate evaluations. In addition to the general suitability and dependencies of various features, first limitations due to possible interference between the direct wave and the reflection wave could be identified. This is particularly evident with thin or dry materials, for which the two-way travel times of the reflected radar signals become shorter.
An extensive laboratory experiment was carried out, for which a modular test specimen was designed to enable the variation of the material type and thickness of screed and insulation, as well as the simulation of moisture deteriorations. The data collected revealed clear differences between dry and deteriored structures within measured B-scans. These deviations were to be detected with the newly introduced B-scan features, which evaluate the statistical deviation of A-scan features within a survey line. In this way, deteriorations to unknown floor structures are recognized, regardless of the material parameters present. In a subsequent training and cross-validation process of different classifiers, accuracies of over 88 \% of the 504 recorded measurements (252 different experimental setups) were achieved. For that, the combination of amplitude and frequency features, which covered all relevant reflections of the radar signals, was particularly beneficial. Furthermore, the data set showed only small differences between dry floors and deteriored screeds for the B-scan features, which could be attributed to a homogeneous distribution of the added water in the screeds. The successfully separation of these similar feature distributions raised the suspicion of overfitting, which was examined in more detail by means of a validation with on-site data.
For this purpose, investigations were carried out at five different locations in Germany, using the identical measurement method like in the laboratory. By extracting drilling cores, it was possible to determine the deterioration case for each measurement point and thus generate a corresponding reference. However, numerous data had to be sorted out before classification, since disturbances due to underfloor heating, screed reinforcements, steel beams or missing insulation prevented comparability with the laboratory experiments. Validation of the remaining data (72 B-scans) achieved only low accuracy with 53 \% correctly classified deterioration cases. Here, the previously suspected overfitting of the small decision boundary between dry setups and deteriored screeds within the laboratory proved to be a problem. The generally larger deviations within (also dry) on-site B-scans were thus frequently misclassified as screed deterioration. In addition, there were sometimes strongly varying layer thicknesses or changing cases of deterioration within a survey line, which caused additional errors due to the local limitation of the drilling core reference. Nevertheless, individual on-site examples also showed the promising potential of the applied signal features and the GPR method in general, which partly allowed a profound interpretation of the measurements. However, this interpretation still requires the experience of trained personnel and could not be automated using machine learning with the available database. Nevertheless, such experience and knowledge can be enriched by the findings of this work, which provide the basis for further research.
Future work should aim at building an open GPR data base of on-site moisture measurements on floors to provide a meaningful basis for applying machine learning. Here, referencing is a crucial point, whose limitations with respect to the moisture present and its distribution can easily reduce the potential of such efforts. The combination of several reference methods might help to overcome such limitations. Similarly, a focus on monitoring approaches can also help to reduce numerous unknown variables in moisture measurements and increase confidence in the detection of different deterioration cases.
We employ in-house generated synthetic Al-Si matrix composite XCT data for training deep convolutional neural networks for XCT data conditioning and automatic segmentation. We propose an in-house multilevel deep conditioning framework capable of rectifying noise and blur in corrupted XCT data sequentially. Furthermore, for automatic segmentation, we utilize a special in-house network coupled with a novel iterative segmentation algorithm capable of generalized learning from synthetic data. We report a consistent SSIM efficiency of 92%, 99%, and 95% for the combined denoising/deblurring, standalone denoising, and standalone deblurring, respectively. The overall segmentation precision was over 85% according to the Dice coefficient. We used experimental XCT data from various scans of Al-Si matrix composites reinforced with ceramic particles and fibers.
It is shown that preconditioning of experimental X‐ray computed tomography (XCT) data is critical to achieve high‐precision segmentation scores. The challenging experimental XCT datasets and deep convolutional neural networks (DCNNs) are used that are trained with low‐resemblance synthetic XCT data. The material used is a 6‐phase Al–Si metal matrix composite‐reinforced with ceramic fibers and particles. To achieve generalization, in our past studies, specific data augmentation techniques were proposed for the synthetic XCT training data. In addition, two toolsets are devised: (1) special 3D DCNN architecture (3D Triple_UNet), slicing the experimental XCT data from multiple views (MultiView Forwarding), the i.S.Sy.Da.T.A. iterative segmentation algorithm, and (2) nonlocal means (NLM) conditioning (filtering) for the experimental XCT data. This results in good segmentation Dice scores across all phases compared to more standard approaches (i.e., standard UNet architecture, single view slicing, standard single training, and NLM conditioning). Herein, the NLM filter is replaced with the deep conditioning framework BAM SynthCOND introduced in a previous publication, which can be trained with synthetic XCT data. This leads to a significant segmentation precision increase for all phases. The proposed methods are potentially applicable to other materials and imaging techniques.
The applications of composite overwrapped pressure vessels (COPVs) in extreme conditions, such as storing hydrogen gases at very high pressure, impose new requirements related to the system's integrity and safety. The development of a structural health monitoring (SHM) system that allows for continuous monitoring of the COPVs provides rich information about the structural integrity of the component. Furthermore, the collected data can be used for different purposes such as increasing the periodic inspection intervals, providing a remaining lifetime prognosis, and also ensuring optimal operating conditions. Ultimately this information can be complementary to the development of the envisioned digital twin of the monitored COPVs. Guided waves (GWs) are preferred to be used in continuous SHM given their ability to travel in complex structures for long distances. However, obtained GW signals are complex and require advanced processing techniques. Machine learning (ML) is increasingly utilized as the main part of the processing pipeline to automatically detect anomalies in the system's integrity. Hence, in this study, we are scrutinizing the potential of using ML to provide continuous monitoring of COPVs based on ultrasonic GW data. Data is collected from a network of sensors consisting of fifteen Piezoelectric (PZT) wafers that were surface mounted on the COPV. Two ML algorithms are used in the automated evaluation procedure (i) a long short-term memory (LSTM) autoencoder for anomaly detection (defects/impact), and (ii) a convolutional neural network (CNN) model for feature extraction and classification of the artificial damage sizes and locations. Additional data augmentation steps are introduced such as modification and addition of random noise to original signals to enhance the model's robustness to uncertainties. Overall, it was shown that the ML algorithms used were able to detect and classify the simulated damage with high accuracy.
Im Rahmen des Forschungsprojekts "Artificial Intelligence for Rail Inspection" (AIFRI) wird ein KI-Algorithmus entwickelt, um die Fehlererkennung bei der Auswertung von Schienenprüfungen zu verbessern. Der Prozess der mechanisierten Schienenprüfung wird analysiert und die Schienenfehler sowie Artefakte werden in einem digitalen Zwilling abgebildet, um in einem weiteren Schritt die automatische Fehlererkennung und Klassifizierung mit KI-Algorithmen trainieren zu können. Zu diesem Zweck werden Ultraschalldatensätze auf der Grundlage der Regelwerke und Informationen aus der Instandhaltung mit einer Simulationssoftware erstellt, die Anzeigen der verschiedenen Schienenschädigungen und Artefakte enthalten.
Die Schienenfehler werden bei der Auswertung in Fehlerklassen eingeordnet, für das KI-Training priorisiert und auf Basis der von der DB Netz AG ausgewählten Informationen untersucht. Hierfür werden die Schienenfehler nach den für das KI-Training relevanten Merkmalen zerlegt und die Konfiguration der Parameter der Simulation entsprechend abgestimmt.
Für die Grundstruktur des Datensatzes wird ein Schienenmodell mit einer Länge von einem Meter für die Simulation eingesetzt, auf dessen Basis alle bei der Schienenprüfung zu verwendenden Prüfköpfe für den jeweiligen Reflektortyp betrachtet werden. Die simulierten Daten werden auf einer Testschiene im Labormaßstab validiert. Mögliche Einflussparameter wie z. B. der Signal-Rausch-Abstand sowie die Fahrgeschwindigkeit werden in den Datensätzen herangezogen. Die Zusammenstellung eines Testdatensatzes mit lokal veränderlichen Einflussgrößen erfolgt aus den simulierten Daten unter Verwendung der skriptbasierten Programmierumgebung Python und Matlab.
Das Projekt AIFRI wird im Rahmen der Innovationsinitiative mFUND unter dem Förderkennzeichen 19FS2014 durch das Bundesministerium für Digitales und Verkehr gefördert.
The BAMline at the synchrotron X-ray source BESSY II (Berlin, Germany) is supporting researchers especially in materials science. As a non-destructive characterization method, synchrotron X-ray imaging, especially tomography with hard X-Rays, plays an important role in structural 3D characterization. The imaging capabilities allow for in-situ and operando experiments. In this presentation the data handling pipeline is presented.
In this paper, we demonstrate the value of 1H NMR relaxometry for studying the hydration of clinker-reduced, more climate-friendly cementitious binders. The results were obtained on typical CEM I cements and sister samples containing two different reactive agricultural ashes as well as non-reactive biochars as supplementary cementitious materials. The findings prove that time-resolved NMR measurements provide valuable additional information when combined with classical heat flow calorimetry.
The lack of traceability to meter of X-ray Computed Tomography (CT) measurements still hinders a more extensive acceptance of CT in coordinate metrology and industry. To ensure traceable, reliable, and accurate measurements, the determination of the task-specific measurement uncertainty is necessary. The German guideline VDI/VDE 2630 part 2.1 describes a procedure to determine the measurement uncertainty for CT experimentally by conducting several repeated measurements with a calibrated test specimen. However, this experimental procedure is cost and effort intensive. Therefore, the simulation of dimensional measurement tasks conducted with X-ray computed tomography can close these drawbacks. Additionally, recent developments towards a resource and cost-efficient production (“smart factory”) motivate the need for a corresponding numerical model of a CT system (“digital twin”) as well. As there is no standardized procedure to determine the measurement uncertainty of a CT system by simulation at the moment, the project series CTSimU was initiated, aiming at this gap. Concretely, the goal is the development of a procedure to determine the measurement uncertainty numerically by radiographic simulation. The first project (2019-2022), "Radiographic Computed Tomography Simulation for Measurement Uncertainty Evaluation - CTSimU" developed a framework to qualify a radiographic simulation software concerning the correct simulation of physical laws and functionalities. The most important outcome was a draft for a new guideline VDI/VDE 2630 part 2.2, which is currently under discussion in the VDI/VDE committee. The follow-up project CTSimU2 "Realistic Simulation of real CT systems with a basic-qualified Simulation Software" will deal with building and characterizing a digital replica of a specific real-world CT system. The two main targets of this project will be a toolbox including methods and procedures to configure a realistic CT system simulation and to develop tests to check if this replica is sufficient enough. The result will be a draft for a follow-up VDI/VDE guideline proposing standardized procedures to determine a CT system's corresponding characteristics and test the simulation (copy) of a real-world CT system which we call a "digital twin".
The project series CTSimU was initiated with the goal to develop a set of procedures to enable the determination of the task-specific measurement uncertainty of a CT system numerically by radiographic simulation. The first project (2019-2022) “Radiographic Computed Tomography Simulation for Measurement Uncertainty Evaluation - CTSimU” was focused on the sufficient physical correctness of the radiographic simulation and created as a result a test framework for simulation softwares and a draft of a VDI standard in the series VDI/VDE 2630 for this application. However, for the realistic simulation of a CT system in a simulation software (i.e. a digital twin), not only the correctness of the simulation software itself is crucial, but also the quality of the parameterization of the CT system in the simulation software - this represents the starting point of the 2nd project “Realistic Simulation of real CT systems with a basic-qualified Simulation Software - CTSimU2” (2022-2024).
The parameterization of a CT system in a simulation software can be divided into four steps: after the data acquisition at the real CT system (step 1) follows the evaluation of the acquired data for the generation of general parameter specifications (step 2). It follows the transfer of the parameters into the specific simulation software (step 3) and the validation of the resulting simulation parameters by a suitable test (step 4). The intended result of the project CTSimU2 is a draft VDI standard (for VDI/VDE 2630) for this test, which contains an informative annex on the state of the art regarding the possibilities for parameter determination.
Die Entwicklung von Werkzeugen zur realitätsnahen Nachbildung eines industriellen CT-Systems in einer Simulationssoftware ist derzeit Hauptaufgabe des WIPANO Forschungsprojektes CTSimU2 Realistische Simulation realer Röntgencomputertomografie - Systeme mit basisqualifizierter Simulationssoftware. Als Voraussetzung dienen dabei Simulationssoftwares, die durch das Testframework aus dem Vorprojekt CTSimU1 basisqualifiziert wurden. Das Testframework testet die hinreichende physikalische Korrektheit und Funktionalität einer Simulationssoftware (Basisqualifizierung der Software). Für eine realitätsnahe Nachbildung ist nicht nur die Güte der Simulationssoftware, sondern insbesondere die Güte der Parametrisierung des realen CT-Systems in der Simulationssoftware ausschlaggebend. Dabei kann das Vorgehen der Parametrisierung in vier Schritte unterteilt werden: die Datenaufnahme am realen CT-System (Schritt 1), die Auswertung der aufgenommenen Daten für die Generierung allgemeiner Parameterangaben (Schritt 2), die Übertragung der Parameter in die spezifischen Simulationssoftwares (Schritt 3) und die Validierung der resultierenden Simulationsparameter durch einen geeigneten Test (Schritt 4). Ziel des Projektes ist es daher neben der Erarbeitung eines Werkzeugkastens mit allgemeinen Methoden zur Datenaufnahme und Auswertung der Daten, die Entwicklung eines Tests, auf dessen Basis die ausreichend korrekte Simulation einer realen Anlage beurteilt werden kann. Die erarbeiteten Ergebnisse sollen wie bereits im Vorprojekt CTSimU1 in einen Richtlinienentwurf für die Richtlinienreihe VDI/VDE 2630 übertragen werden. Dieser Beitrag soll einen Überblick über das Projekt und die ersten Ergebnisse geben.
Virtual CT with aRTist
(2023)
The software aRTist is a simulation tool for the generation of realistic radiographs of virtual radiographic superstructures.
With radiographic simulations, virtual component models can be scanned as in a computer tomograph.
Industrial X-ray computed tomography (CT) enables the non-destructive detection of internal and external surfaces as well as inhomogeneities of technical objects. Virtual CT offers new possibilities for the investigation of parameter influences of this complex testing and measuring technique. In addition to the option of switching physical effects on and off, scanning movements can also be tested before their technical realization.
The virtual CT generates projection images from different directions for the subsequent reconstruction of a volume model of the examined object. The reconstruction of the simulated scans is carried out with the algorithms and programs for real scans. Tomographic scans consist of a large number of projections, which practically cannot be generated individually by the user of a simulation. The software offers various options for the automated simulation of tomographic scans. These range from standard CT to scans on free trajectories or with individual projection matrices.
Laser-based active thermography is a contactless non-destructive testing method to detect material defects by heating the object and measuring its temperature increase with an infrared camera. Systematic deviations from predicted behavior provide insight into the inner structure of the object. However, its resolution in resolving internal structures is limited due to the diffusive nature of heat diffusion. Thermographic super resolution (SR) methods aim to overcome this limitation by combining multiple thermographic measurements and mathematical optimization algorithms to improve the defect reconstruction.
Thermographic SR reconstruction methods involve measuring the temperature change in an object under test (OuT) heated with multiple different spatially structured illuminations. Subsequently, these measurements are inputted into a severely ill-posed and heavily regularized inverse problem, producing a sparse map of the OuT’s internal defect structure. Solving this inverse problem relies on limited priors, such as defect-sparsity, and iterative numerical minimization techniques. Previously mostly experimentally limited to one-dimensional regions of interest (ROIs), this thesis aims to extend the method to the reconstruction of two-dimensionalROIs with arbitrary defect distributions while maintaining reasonable experimental complexity. Ultimately, the goal of this thesis is to make the method suitable for a technology transfer to industrial applications by advancing its technology readiness level (TRL).
In order to achieve the aforementioned goal, this thesis discusses the numerical expansion of a thermographic SR reconstruction method and introduces two novel algorithms to invert the underlying inverse problem. Furthermore, a forward solution to the inverse problem in terms of the applied SR reconstruction model is set up. In conjunction with an additionally proposed algorithm for the automated determination of a set of (optimal) regularization parameters, both create the possibility to conduct analytical simulations to characterize the influence of the experimental parameters on the achievable reconstruction quality. On the experimental side, the method is upgraded to deal with two-dimensional ROIs, and multiple measurement campaigns are performed to validate the proposed inversion algorithms, forward solution and two exemplary analytical studies. For the experimental implementation of the method, the use of a laser-coupled DLP-projector is introduced, which allows projecting binary pixel
patterns that cover the whole ROI, reducing the number of necessary measurements per ROI significantly (up to 20x).
Finally, the achieved reconstruction of the internal defect structure of a purpose-made OuT is qualitatively and qualitatively benchmarked against well-established thermographic testing methods based on homogeneous illumination of the ROI. Here, the background-noise-free two-dimensional photothermal SR reconstruction results show to outclass all defect reconstructions by the considered reference methods.
The overview of the activity of Federal Institute for Material Research and Testing (BAM, Belin, Germany) in the field of additively manufacturing material characterization will be presented. The research of our group is focused on the 3D imaging of AM materials by means of X-ray Computed Tomography at the lab and at synchrotron, and the residual stress characterization by diffraction (nondestructive technique). Also, two successful research project in collaboration with CAM2, Sweden are presented.
Detaillierte Kenntnisse der elastischen Materialeigenschaften sind in vielen ingenieurtechnischen Bereichen von grundlegender Bedeutung. Insbesondere für die Anwendung von Predictive Maintenance und Structural- Health-Monitoring Methoden mit Ultraschall ist die genaue Kenntnis der elastischen Materialkonstanten eine Grundvoraussetzung. Die von den Herstellern zur Verfügung gestellten Angaben zu den elastischen Materialkonstanten, insbesondere für Polymere und faserverstärkte Kunststoffe, sind jedoch oft unzureichend, da diese vom Produktionsprozess abhängig sind und sich zusätzlich aufgrund von Materialabbauprozessen oder Ermüdung ändern können. In der Praxis liegen polymere Werkstoffe, faserverstärkte Kunststoffe und Metalle oft als dünne, plattenförmige Strukturen vor, in welchen sich geführte Ultraschallwellen (UGWs) ausbreiten können. In der aktuellen Forschung sind bereits verschiedene Neuronale Modelle zur Bestimmung der elastischen Konstanten und der Materialcharakterisierung mittels UGWs bekannt. Ein einfaches neuronales Netz, mit aus Dispersionsbildern extrahierten Werten für Frequenz und Wellenzahl ausbeutungsfähiger Moden als Eingabe, zur Vorhersage der elastischen Konstanten wird in verwendet. Ein rekurrentes Neuronales Netz mit einem Zeit-Frequenz Vektor als Eingabe wird in angewandt, während in ein 1D- Convolutional-Neuronal-Networks (CNN) unter Verwendung der zeitlichen Auslenkung der Grundmoden und in ein 2D-CNN unter Verwendung einer polaren Gruppengeschwindigkeitsdarstellung zur Bestimmung der elastischen Konstanten verwendet wird. In diesem Vortrag wird ein Ansatz zur Bestimmung der isotropen elastischen Konstanten von dünnen Platten auf der Grundlage von UGWs unter Verwendung von Dispersionsbildern und 2D-CNNs vorgestellt. Dispersionsabbildungen aus numerischen Simulationen werden mithilfe verschiedener Methoden vorverarbeitet, um realistische Messdaten zu simulieren. Mit den modifizierten Daten wird das Modell trainiert und die Architektur optimiert. Anschließend wird die Genauigkeit des erzeugten Modells mit realen Messdaten validiert. Es wird gezeigt, dass 2D-CNNs in der Lage sind, die isotropen elastischen Konstanten anhand multimodaler Merkmale aus Dispersionsbildern vorherzusagen, ohne dass eine anfängliche Schätzung der Parameter oder manuelle Merkmalsextraktion erforderlich ist.