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Learned block iterative shrinkage thresholding algorithm for photothermal super resolution imaging
(2020)
Block-sparse regularization is already well-known in active thermal imaging and is used for multiple measurement based inverse problems. The main bottleneck of this method is the choice of regularization parameters which differs for each experiment. To avoid time-consuming manually selected regularization parameters, we propose a learned block-sparse optimization approach using an iterative algorithm unfolded into a deep neural network. More precisely, we show the benefits of using a learned block iterative shrinkage thresholding algorithm that is able to learn the choice of regularization parameters. In addition, this algorithm enables the determination of a suitable weight matrix to solve the underlying inverse problem. Therefore, in this paper we present the algorithm and compare it with state of the art block iterative shrinkage thresholding using synthetically generated test data and experimental test data from active thermography for defect reconstruction. Our results show that the use of the learned block-sparse optimization approach provides smaller normalized mean square errors for a small fixed number of iterations than without learning. Thus, this new approach allows to improve the convergence speed and only needs a few iterations to generate accurate defect reconstruction in photothermal super resolution imaging.
In this presentation we discuss the online monitoring of metallic AM parts produced by the Laser Powder Bed Fusion (LPBF) process by using optical, thermographic and electromagnetic methods. In a first approach we present the detection of defects generated during the process and discuss how to improve these methods for the optimization of design and production of metallic AM parts.
Thermography is one on the most promising techniques for in-situ monitoring of metal additive manufacturing processes. Especially in laser powder bed fusion processes, the high process dynamics and the strong focus of the laser beam cause a very complex thermal history within the produced specimens, such as multiple heating cycles within single layer expositions. This complicates data interpretation, e.g., in terms of cooling rates. A quantity that is easily calculated is the time a specific area of the specimen is at a temperature above a chosen threshold value (TOT). Here, we discuss variations occurring in time-over-threshold-maps during manufacturing of an almost defect free cuboid specimen.
Diese Richtlinie definiert Kenngrößen und Messverfahren, um Terahertzsysteme für die Zeitbereichsspektroskopie (TDS)zu spezifizieren. So können Hersteller ihre Systeme übereinstimmend beschreiben und Anwender einfach Systeme vergleichen. Systeme für die Terahertz-Zeitbereichsspektroskopie sind aktive Systeme und nutzen verschiedene Techniken sowohl zur Erzeugung als auch zur zeitaufgelösten Messung von breitbandigen Terahertzimpulsen. Die dazugehörigen Terahertzspektren und -Phasen werden nach einer zeitlichen Abtastung über eine Fourier-Transformation berechnet. In dieser Richtlinie wird ein Überblick über die Funktionsweisen von Zeitbereichsspektrometern gegeben, die eingesetzten Messverfahren näher erläutert sowie die benötigten Begriffe und Kenngrößen definiert. Die Richtlinie konzentriert sich dabei auf Systeme, die auf sogenannten Abtastmessverfahren (Sampling-Messverfahren) basieren.
Online quality control of security relevant parts manufactured by Laser Powder Bed Fusion (LPBF) remains to be a challenge due to the highly complex process conditions. Furthermore, the influence of characteristic scan strategy parameters is not sufficiently clarified yet due to the commonly used method of single-track investigations. In this contribution, this topic is addressed by observing large 316L volume sections using in-situ melt pool monitoring by thermography in high temporal and spatial resolution. In detail, the influence of the scan angle on the melt pool geometry is investigated on. Characteristic melt pool features are extracted from the image data and analyzed using statistical methods data for altering scan angles. The results show significant changes in the melt pool dimensions and temperature distribution over the scan angle rotation. A first explanation approach is presented that connects the observed changes to phenomena of beam attenuation by metal vapor plume.
A powerful tool to understand, demonstrate and explain the limits of the pulsed technique in terms of detectability and localizability of AM keyhole pores has been assessed by comparing the active thermographic approach (both experimental and FEM simulations) to Computed Tomography results;
✓ µCT results demonstrate that the intended defect geometry is not achieved; indeed a network of voids (microdefects consisting of small sharp-edged hollows with a complicated, almost fractal, inner surface) was found;
✓ both Exp-PT and FEM results explains clearly why no indication of defect related to the thermal contrasts could be found during the investigation of an uncoated surface. However, the application of further data evaluations focusing on the thermal behavior and emissivity evaluation (PPT post data processing) enable the detection of some defects;
✓ coating facilitates a closer inspection of inner defects, but inhomogeneities of the coating could impair the spatial resolution and lead to the emergence of hotspots (the FEM simulation reached its limit with this extreme geometry where a 25 µm thin disc is considered at a 1 cm thick specimen in millisecond time resolution);
✓ both Exp-PT and FEM results allow the conclusion that very short pulses of 200 ms or shorter should be sufficient to detect these defects below, but near the surface; besides a short duration of the thermal phenomenon it should be emphasized, about 0.04 s (high frame rate camera);
The fourth industrial revolution (also known as “Industry 4.0”) promises to relieve people from manual and physical work by developing cyber-physical systems and smart automation and thereby achieving a more efficient, reliable and safe performance. This will change people’s work from being operators of the system into becoming strategical decision makers and flexible problem solvers. This shift will require a new division of work between people and technical systems, a new division of responsibilities and new ways of interacting with technology, which in turn will pose new demands on people. People will require different physical and cognitive capabilities, different competencies and new strategies to cope with the upcoming challenges. The focus of Industry 4.0 is therefore on designing systems with human in the centre: systems that will extend people’s capabilities, that will allow collaborative work and take advantage of performances in which either people or technology excel. This presentation will focus on the benefits of consideration of human-machine interaction in the context of industry 4.0.
The aim of this presentation is twofold: first, to present the field of psychology including its research methods and, second, to show on an example how psychology can be used in material research. The example is of the study of human factors in non-destructive testing (NDT), in which the author has more than 10 years of experience. The conclusion is that psychological methods can be used to identify human-related problems during different tasks and to generate mitigation strategies that can be used to decrease human error and enhance safety.
This presentation offers a holistic view on the assessment of reliability of NDT including the intrinsic reliability (typically expressed in terms of probability of detection (POD) curves), application, human and organisational factors. In addition to POD, advanced methods such as multiparameter POD, volume POD and POD for combined data are presented. Human and organisational factors in NDT require a systematic approach, i.e. it is not just the individual that determines how the inspections are carried out but also the interactions of individuals with the technology, team, organisation and the extra-organisational environment. Lessons learned from the literature as well as from own studies are presented.
Im Zeitalter von Industrie 4.0 muss die zerstörungsfreie Prüfung (ZfP) mit den Anforderungen Schritt halten. Die erfolgreiche Umsetzung von Digitalisierung, Automatisierung, komplexer Vernetzung, künstlicher Intelligenz, Assistenzsystemen, intelligenten Sensortechnologien usw. hängt stark mit einer erfolgreichen und optimalen Mensch-Maschine-Interaktion (MMI) zusammen. In der ZfP 4.0 wird sich die Rolle des Prüfers zum flexiblen Problemlöser und Entscheider verändern. Dieser Wandel wird andere Anforderungen an die Prüfer stellen und eine andere Organisation der Prüfung, Ausbildung etc. erfordern. Der Mensch wird also weiterhin im Mittelpunkt der ZfP stehen. Zu den Herausforderungen, die dieser Wandel mit sich bringen wird, gehören das Vertrauen in automatisierte Systeme und die Akzeptanz neuer Technologien, für die Wege gefunden werden müssen, um sie zu bewältigen.
Preisträgervortrag Georg-Sachs-Preis der DGM
Röntgencomputertomographie (CT) ist heute ein Standardwerkzeug in der Materialcharakterisierung. Im Vortrag zeigen wir ihre Anwendung für die Untersuchung magnetischer Funktionsmaterialien, additiv gefertigter Bauteile und deren Feedstockpulver und stellen erste CT-Ergebnisse biogener Feedstockpulver vor.
Thermomagnetic materials are a new type of magnetic energy materials, which enable the conversion of low temperature waste heat to electricity by three routes: Thermomagnetic motors, generators and microsystems. Taking our recent work on thermomagnetic generators as a starting point, in this talk we analyse the material requirements for a more energy and economic efficient conversion. We will describe the influence of magnetisation change and heat capacity on thermodynamic efficiency, as well as the consequences of thermal conductivity on power density. Our analysis will allow selecting the best thermomagnetic materials in Ashby plots and illustrate the substantial different properties compared to magnetocaloric materials. Supported by DFG, project FA 453/14)
To date, there are only very few technologies available for the conversion of low temperature waste heat to electricity. More than a century ago, thermomagnetic generators were proposed, which are based on a change of magnetization with temperature, switching a magnetic flux, which according to Faraday’s law induces a voltage. In this talk, we first describe the principle of thermomagnetic generators. Then we focus on the impact of topology of the magnetic circuit within thermomagnetic generators. We demonstrate that the key operational parameters strongly depend on the genus, i.e. the number of holes within the magnetic circuit. A pretzel-like topology of the magnetic circuit with genus =3 improves the performance of thermomagnetic generators by orders of magnitude. We will show that this technique is on its way to becoming competitive with thermoelectrics for energy harvesting near room temperature.
Laser excited super resolution thermal imaging for nondestructive inspection of internal defects
(2020)
A photothermal super resolution technique is proposed for an improved inspection of internal defects. To evaluate the potential of the laser-based thermographic technique, an additively manufactured stainless steel specimen with closely spaced internal cavities is used. Four different experimental configurations in transmission, reflection, stepwise and continuous scanning are investigated. The applied image post-processing method is based on compressed sensing and makes use of the block sparsity from multiple measurement events. This concerted approach of experimental measurement strategy and numerical optimization enables the resolution of internal defects and outperforms conventional thermographic inspection techniques.
Laser excited super resolution thermal imaging for nondestructive inspection of internal defects
(2020)
A photothermal super resolution technique is proposed for an improved inspection of internal defects. To evaluate the potential of the laser-based thermographic technique, an additively manufactured stainless steel specimen with closely spaced internal cavities is used. Four different experimental configurations in transmission, reflection, stepwise and continuous scanning are investigated. The applied image post-processing method is based on compressed sensing and makes use of the block sparsity from multiple measurement events. This concerted approach of experimental measurement strategy and numerical optimization enables the resolution of internal defects and outperforms conventional thermographic inspection techniques.
Many PhD students are interested in applying machine learning, AI, data science, etc., and there are many good reasons for this. However, there is a disconnect between mainstream data science and materials science, for example, when it comes to the sheer size of the data. This talk will highlight some of the unique challenges in materials informatics and present some interesting approaches to overcome them. Although the field is large, this talk will focus on cases that have some practical relevance to PhD students at BAM.
Data-driven research is considered the new paradigm in science. In this field, data is the new resource from which knowledge is extracted that is too complex for traditional methods. Several factors such as national funding and advances in information technology, are driving the development. In particular, the creation of databases and the analysis of data with artifical intelligence are playing an important role in establishing the new paradigm. However, there are numerous challenges that must be overcome to realize the full potential of data-driven methods. This talk sets the stage for the upcoming workshop by reviewing some of the historical developments and the current state of data-driven science in NDT and materials science.
Explore and Exploit - Strategische Erweiterung der fraktographischen Datenbank mit Machine Learning
(2020)
In diesem Vortrag stellen wir den aktuellen Stand zu einer Masterarbeit zusammen die sich mit dem Thema beschäftigt wie die Generalisierbarkeit von Datenmodellen auf Basis kleiner Datensätze erhöht werden kann. Wir stellen vor, wie die Datenbasis eines fraktogafischen Bildklassifizierers mit einem statistischen Model strategische erweitert, bzw. an eine Anwendung angepasst werden kann.
Im Bereich der Zerstörungsfreien Prüfung (ZfP) im Bauwesen werden eine Vielzahl von Messdaten erfasst. Obwohl Sie als Grundlage für wissenschaftliche Analysen dienen, gibt es noch keine einheitliche Repräsentation der Daten. Eine Analyse verschiedener verteilter Datensätze über unterschiedliche Prüfobjekte hinweg ist daher kaum möglich.
Wir stellen einen Ansatz für ein integriertes Datenmanagement verteilter Datensätze auf Basis von Semantic-Web Technologien vor. Der Ansatz basiert im Kern auf einem mathematischen Modell – der sogenannten Ontologie – welches das Wissen unserer Domäne ZfPBau repräsentiert. Die von uns entwickelte ZfPBau Ontologie wird mit Datenquellen verknüpft und beschreibt so die semantische Bedeutung der Daten. Darüber hinaus fungiert die Ontologie als zentrales Konzept für den Datenbankzugriff. Domänen-fremde Datenquellen können durch die Verknüpfung mit der ZfPBau Ontologie einfach integriert werden und stehen zur generischen Nutzung im Sinne der Digitalisierung direkt zur Verfügung. Basierend auf einer umfangreichen Literaturrecherche, skizzieren wir die Möglichkeiten die sich daraus für die ZfP im Bauwesen ergeben, wie zum Beispiel Messdaten computergestützt zu sortieren, zu analysieren, Zusammenhänge zu erkennen und zu erklären.
Der erwartete Nutzen dieses Ansatzes der Wissensrepräsentation und des Datenzugriffs für die ZfP-Community ist eine Erweiterung des Wissens durch Datenaustausch in der Forschung (Interoperabilität), die wissenschaftliche Verwertung großer existierender Datenquellen mit datenbasierten Verfahren (wie Bilderkennung, Messunsicherheitsberechnungen, Faktoranaylsen, Materialcharackterisierung) und letztlich ein vereinfachter Transfer von ZFP-Daten in Ingenieurmodelle und somit in die Baupraxis.
Ontologien sind bereits Kern vielzähliger intelligenter Systeme wie Building-Information-Modeling oder Forschungsdatenbanken. Der Beitrag gibt einen Überblick über die Werkzeuge die wir derzeit für die Kommunikation mit ihnen schaffen.
We have arrived in the data age. But why is it so difficult for the NDT community to achieve real breakthroughs with data-driven science? In this seminar, we will take a brief look at the evolution of mainstream data science to understand why the most exciting times are perhaps just ahead. We will give an overview of our activities in the junior research group 8.K which are aimed at enabling the next generation of data science methods in NDT. The seminar addresses the two main work fields of our group: semantic data management and the handling of limited data resources.
The first field addresses the problem that a uniform representation of our data is not yet available. However, knowledge creation in data science - whose main contribution lies in the analysis of distributed resources - requires common data access based on a collective understanding. To achieve this, we present an ontology-based approach. Ontologies are already the core of many intelligent systems such as building information models or research databases. We summarize some of the basic principles of this technology and describe our approach to create an NDT ontology.
The second field ties in with the first and addresses the application of data-based methods in engineering practice. Especially in the field of non-destructive testing many successful applications have been published. In most cases, however, the creation of referenced data is extremely expensive and therefore much sparser than in other research areas. As a result, the available data may cover only one scenario, so that common benchmarks often do not reflect the actual performance of the model in practical applications. Estimates that quantify the transferability from one scenario to another are not only necessary to overcome this challenge - they also prove to be a powerful tool for the strategic expansion of what we consider knowledge.
In the field of non-destructive testing (NDT) in civil engineering, a large number of measurement data are collected. Although they serve as a basis for scientific analyses, there is still no uniform representation of the data. An analysis of various distributed data sets across different test objects is therefore only possible with high manual effort.
We present a system architecture for an integrated data management of distributed data sets based on Semantic Web technologies. The approach is essentially based on a mathematical model - the so-called ontology - which represents the knowledge of our domain NDT. The ontology developed by us is linked to data sources and thus describes the semantic meaning of the data. Furthermore, the ontology acts as a central concept for database access. Non-domain data sources can be easily integrated by linking them to the NDT construction ontology and are directly available for generic use in the sense of digitization. Based on an extensive literature research, we outline the possibilities that this offers for NDT in civil engineering, such as computer-aided sorting, analysis, recognition and explanation of relationships (explainable AI) for several million measurement data.
The expected benefits of this approach of knowledge representation and data access for the NDT community are an expansion of knowledge through data exchange in research (interoperability), the scientific exploitation of large existing data sources with data-based methods (such as image recognition, measurement uncertainty calculations, factor analysis, material characterization) and finally a simplified exchange of NDT data with engineering models and thus with the construction industry.
Ontologies are already the core of numerous intelligent systems such as building information modeling or research databases. This contribution gives an overview of the range of tools we are currently creating to communicate with them.
The knowledge of the cure situation during the production process of
thermosetting material is of enormous importance.
• Mid- and near- infrared (IR) spectroscopy in attenuated total
reflectance (ATR) geometry are promising techniques for curing
studies.
• An IR/THz sensor system was developed to investigate its suitability
for future online monitoring of curing processes in the production line.
Rapid cooling rates and steep temperature gradients are characteristic of additively manufactured parts and important factors for the residual stress formation. This study examined the influence of heat accumulation on the distribution of residual stress in two prisms produced by Laser Powder Bed Fusion (LPBF) of austenitic stainless steel 316L. The layers of the prisms were exposed using two different border fill scan strategies: one scanned from the centre to the perimeter and the other from the perimeter to the centre. The goal was to reveal the effect of different heat inputs on samples featuring the same solidification shrinkage. Residual stress was characterised in one plane perpendicular to the building direction at the mid height using Neutron and Lab X-ray diffraction. Thermography data obtained during the build process were analysed in order to correlate the cooling rates and apparent surface temperatures with the residual stress results. Optical microscopy and micro computed tomography were used to correlate defect populations with the residual stress distribution. The two scanning strategies led to residual stress distributions that were typical for additively manufactured components: compressive stresses in the bulk and tensile stresses at the surface. However, due to the different heat accumulation, the maximum residual stress levels differed. We concluded that solidification shrinkage plays a major role in determining the shape of the residual stress distribution, while the temperature gradient mechanism appears to determine the magnitude of peak residual stresses.
The quality of additively manufactured (AM) parts is determined by the applied process parameters used and the properties of the feedstock powder. The influence of inner gas pores in feedstock particles on the final AM product is a phenomenon which is difficult to investigate since very few non-destructive measurement techniques are accurate enough to resolve the micropores.
3D X-ray computed tomography (XCT) is increasingly applied during the process chain of AM parts as a non-destructive monitoring and quality control tool and it is able to detect most of the pores.
However, XCT is time-consuming and limited to small amounts of feedstock powder, typically a few milligrams. The aim of the presented approach is to investigate digital radiography of AM feedstock particles as a simple and fast quality check with high throughput. 2D digital radiographs were simulated in order to predict the visibility of pores inside metallic particles for different pore and particle diameters. An experimental validation was performed. It was demonstrated numerically and experimentally that typical gas pores above a certain size (here: 3 to 4.4 µm for the selected X-ray setup), which could be found in metallic microparticles, were reliably detected by digital radiography.
Prediction of dynamic effects of reinforced concrete structures under impact loading is a technical challenge. This is a consequence of the great variability of the physical properties resulting from the wide adaptability of reinforced concrete and a consequence of the wide range of impact loading. Experiments and numerical investigations are normally used on a small scale to address the problem. In this paper, impact tests on reinforced conrete plates with the lateral dimensions of 1.5 m x 1.5 m and a thickness of 30 cm are presented. In bending reinforcement, besides the velocity two properties are varied, the diameter and the spatial distribution of the rebars. Experiments are performed at the Otto-Mohr-Laboratory of the Institute of Concrete Structures of the Technische Universit¨at Dresden. Due to the accelerated fall of the impactor the velocity ranges between 20 and 70 m/s. In addition to the measured quantities such as bearing forces, accelerations are also measured at 4 different positions on and under the plate, as well as the deflection at several positions. The measured data are used for the analysis of the damage form and the numerical examinations with the program Ansys Autodyn and the material model after Drucker-Prager.
Numerical investigations support the tests, with detailed analysis of individual effects. These numerical computations and the planar tomographic investigations were carried out at BAM in Berlin. With the help of planar tomographic evaluation, the damaged structure is
made visible and compared with the numerical results. Influences of the bending reinforcement are explained on the basis of damage evaluation in the local area and on selected measured values. In addition to the test evaluation, the tomographic and numerical methods are presented.
Prediction of dynamic effects of reinforced concrete structures under impact loading is a technical challenge. This is a consequence of the great variability of the physical properties resulting from the wide adaptability of reinforced concrete and a consequence of the wide range of impact loading. Experiments and numerical investigations are normally used on a small scale to address the problem. In this paper, impact tests on reinforced conrete plates with the lateral dimensions of 1.5 m x 1.5 m and a thickness of 30 cm are presented. In bending reinforcement, besides the velocity two properties are varied, the diameter and the spatial distribution of the rebars. Experiments are performed at the Otto-Mohr-Laboratory of the Institute of Concrete Structures of the Technische Universit¨at Dresden. Due to the accelerated fall of the impactor the velocity ranges between 20 and 70 m/s. In addition to the measured quantities such as bearing forces, accelerations are also measured at 4 different positions on and under the plate, as well as the deflection at several positions. The measured data are used for the analysis of the damage form and the numerical examinations with the program Ansys Autodyn and the material model after Drucker-Prager. Numerical investigations support the tests, with detailed analysis of individual effects. These numerical computations and the planar tomographic investigations were carried out at BAM in Berlin. With the help of planar tomographic evaluation, the damaged structure is made visible and compared with the numerical results. Influences of the bending reinforcement are explained on the basis of damage evaluation in the local area and on selected measured values. In addition to the test evaluation, the tomographic and numerical methods are presented.
Etalon effects in THz transmittance spectra, either taken in time-domain or frequency-domain, often hamper or even hinder the interpretation of film properties. In this paper we discuss the transferability and applicability of spectroscopic methods usually employed in the near and mid infrared spectral range and exemplarily present the application field.
Novel Technique for On-Line Monitoring of the Curing Process of Fiber Reinforced Polymer Composites
(2020)
A specially designed experimental set up has been integrated into a commercially available FT IR Spectrometer for ATR experiments on Epoxy Systems. Representative data of far infrared spectra measured during the curing process at different temperatures of an epoxy system will be presented in dependency of the curing situation. The experiments and the selected set up are discussed to demonstrate its potential for future monitoring and ageing control applications during a manufacturing process of polymer composites.
Integration of fibre reinforcement in high-performance cementitious materials has become widely applied in many fields of construction. One of the most investigated advantages of steel fibre reinforced concrete (FRC) is the deceleration of crack growth and hence it’s improved sustainability due to e.g. decrease of permeability of concrete by aggressive substances. Additional benefits are associated with the structural properties of FRC, where fibres can significantly increase the ductility and the tensile strength of concrete. In some applications, such as tunnel linings or industrial slabs, it is even possible to entirely replace the conventional reinforcement, leading to significant logistical and environmental benefits. Fibre reinforcement can, however, have critical disadvantages and even hinder the performance of concrete, since it can induce an anisotropic material behaviour of the mixture if the fibres are not appropriately oriented. For a safe use of FRC in the future, reliable non-destructive methods need to be identified to assess the fibres’ orientation in hardened concrete. In this study, ultrasonic material testing, electrical impedance testing, and X-ray computer tomography have been investigated for this purpose using specially produced samples with biased or random fibre orientations. This paper demonstrates the capabilities of each of these NDT techniques for fibre orientation measurements and draws conclusions based on these results about the most promising areas for future research and development using these techniques.
Additive manufacturing offers a range of novel applications. However, the manufacturing process is complex and the production of almost defect-free parts with high reliability and durability is still a challenge. Thermography is a valuable tool for process surveillance, especially in metal additive manufacturing processes. The high process temperatures allow one to use cameras usually operating in the visible spectral range. Here, we compare the results of measurements during the manufacturing process of a commercial laser metal deposition setup using a mid wavelength infrared camera with those from a short wavelength infrared camera and those from a visual spectrum high-speed camera with band pass filter in the near infrared range.
Thermography is one on the most promising techniques for in-situ monitoring for metal additive manufacturing processes. The high process dynamics and the strong focus of the laser beam cause a very complex thermal history within the produced specimens, such as multiple heating cycles within single layer expositions. This complicates data interpretation, e.g., in terms of cooling rates. A quantity that is easily calculated is the time a specific area of the specimen is at a temperature above a chosen threshold value (TOT). Here, we discuss variations occurring in time-over-threshold-maps during manufacturing of a defect free cuboid specimen.
In-situ Prozessüberwachung in der additiven Fertigung von Metallen mittels optischer Verfahren
(2020)
Einer der aussichtsreichsten Ansätze, die Qualität und Sicherheit der gefertigten Teile in der metallbasierten additiven Fertigung (AM) zu erhöhen und die Notwendigkeit aufwändiger und zeitintensiver, zerstörender oder zerstörungsfreier Prüfungen (ZfP) nach der Fertigung zu verringern, liegt in dem Einsatz von in-situ Prozessüberwachungstechniken. Bereits jetzt werden erste Messsysteme zur Kontrolle der Energiequelle, des Bauraums, des Schmelzbades und der Bauteilgeometrie kommerziell angeboten. Weitere ZfP Verfahren, wie z.B. die aktive und passive Thermografie, werden in der Literatur als geeignet für die in-situ Anwendung angesehen, allerdings gibt es noch wenig konkrete praktische Umsetzungen, da die Möglichkeiten und individuellen Grenzen dieser Methoden, angewendet auf AM, noch nicht ausreichend erforscht sind. Aus diesem Grund verfolgt die BAM mit dem Projekt „Process Monitoring of AM“ (ProMoAM) im Themenfeld Material das Ziel, Verfahren des Prozessmonitorings zur in-situ Bewertung der Qualität additiv gefertigter Metallbauteile weiterzuentwickeln.
Im Beitrag wird zunächst das Projekt vorgestellt und anschließend der Fokus auf eine Messserie gelegt, in der Probekörper aus dem austenitischen Edelstahl 316L mit lokal variierenden Prozessparametern mittels selektiven Laserschmelzen (L-PBF) aufgebaut wurden. Der Bauprozess wurde hierbei durch das maschineneigene, koaxial arbeitende Photodiodensystem (Melt-Pool-Monitoring), einer Mittelwellen-Infrarotkamera und einer optischen Tomografiekamera im sichtbaren Wellenlängenbereich (Langzeitbelichtung für die Dauer eines Lagenaufbaus mit einer CMOS-Kamera mit hoher Ortsauflösung) simultan überwacht. Als Referenz für diese Methoden wurden die Probekörper mittels Computertomografie untersucht. Für die dabei anfallenden teils großen Datenmengen wurden Algorithmen für ein effizientes Preprocessing entwickelt. Es wurden Merkmale der Messdaten in Korrelation zu Fehlern und Inhomogenitäten extrahiert, welche für die einzelnen Methoden vergleichend vorgestellt und diskutiert werden.
Recording the temperature distribution of the layer under construction during laser powder bed fusion (L-PBF) is of utmost interest for a deep process understanding as well as for quality assurance and in situ monitoring means. While having a notable number of thermal monitoring approaches in additive manufacturing (AM), attempts at temperature calibration and emissivity determination are relatively rare. This study aims for the experimental temperature adjustment of an off-axis infrared (IR) thermography setup used for in situ thermal data acquisition in L-PBF processes. The temperature adjustment was conducted by means of the so-called contact method using thermocouples at two different surface conditions and two different materials: AISI 316L L-PBF bulk surface, AISI 316L powder surface, and IN718 powder surface. The apparent emissivity values for the particular setup were determined. For the first time, also corrected, closer to real emissivity values of the bulk or powder surface condition are published. In the temperature region from approximately 150 °C to 580 °C, the corrected emissivity was determined in a range from 0.2 to 0.25 for a 316L L-PBF bulk surface, in a range from 0.37 to 0.45 for 316L powder layer, and in a range from 0.37 to 0.4 for IN718 powder layer.
Lichtbogenbasierte Schweißverfahren wie das Metallschutzgasschweißen (MSG) zählen zu den Standardverfahren der Fügetechnik und werden in vielen Industriebereichen automatisiert unter Verwendung von Industrierobotern eingesetzt. Dabei können Schweißnahtabweichungen auftreten, die aus Änderungen der Prozessrandbedingungen und der hohen Prozessdynamik resultieren. Hier ist die Kontrolle von Schmelzbad- und Schweißnahtgeometrie für die Sicherung der Nahtqualität bedeutsam. Durch den Einsatz optischer Sensorsysteme können mit hoher zeitlicher Auflösung in-situ Informationen des Prozesszustands ermittelt werden. Dabei stellen die rauen Prozessbedingungen und die hohe Strahlungsintensität des Lichtbogens eine Herausforderung für die optischen Komponenten dar. Forschungsarbeiten am Institut für Schweißtechnik und Fügetechnik der RWTH Aachen haben gezeigt, dass durch den Einsatz einer HDR-Kamera in Kombination mit einer strukturierten Laserbelichtung gezielt geometrische Informationen des Lichtbogens und des Schmelzbads aus den Prozessaufnahmen gewonnen werden können. In dieser Arbeit wird eine parallele Schweißnaht- und Schmelzbadbeobachtung durchgeführt, wobei geometrische Informationen durch die Anwendung von Bildverarbeitungsalgorithmen extrahiert werden. Dabei wird ein nachlaufendes Sensorsystem eingesetzt, welches aus einer HDR-Kamera und einer Laserbeleuchtung besteht. Es werden diffraktive optische Elemente (DOE) zur Erzeugung von verschiedenen Laserprojektionsmustern verwendet, um sowohl eine unidirektionale als auch eine multidirektionale Prozessbeobachtung durchführen zu können. Aus den geometrischen Informationen werden Kenngrößen berechnet, anhand derer der Prozesszustand beurteilt werden kann. Es zeigt sich, dass anhand der Kenngrößenverläufe Abweichungen in der Schweiß- und Schmelzbadgeometrie und Positionierungsfehler des Roboters identifiziert werden können.
Lichtbogenbasierte Schweißverfahren wie das Metallschutzgasschweißen (MSG) zählen zu den Standardverfahren der Fügetechnik und werden in vielen Industriebereichen automatisiert unter Verwendung von Industrierobotern eingesetzt. Dabei können Schweißnahtabweichungen auftreten, die aus Änderungen der Prozessrandbedingungen und der hohen Prozessdynamik resultieren. Hier ist die Kontrolle von Schmelzbad- und Schweißnahtgeometrie für die Sicherung der Nahtqualität bedeutsam. Durch den Einsatz optischer Sensorsysteme können mit hoher zeitlicher Auflösung in-situ Informationen des Prozesszustands ermittelt werden. Dabei stellen die rauen Prozessbedingungen und die hohe Strahlungsintensität des Lichtbogens eine Herausforderung für die optischen Komponenten dar. Forschungsarbeiten am Institut für Schweißtechnik und Fügetechnik der RWTH Aachen haben gezeigt, dass durch den Einsatz einer HDR-Kamera in Kombination mit einer strukturierten Laserbelichtung gezielt geometrische Informationen des Lichtbogens und des Schmelzbads aus den Prozessaufnahmen gewonnen werden können. In dieser Arbeit wird eine parallele Schweißnaht- und Schmelzbadbeobachtung durchgeführt, wobei geometrische Informationen durch die Anwendung von Bildverarbeitungsalgorithmen extrahiert werden. Dabei wird ein nachlaufendes Sensorsystem eingesetzt, welches aus einer HDR-Kamera und einer Laserbeleuchtung besteht. Es werden diffraktive optische Elemente (DOE) zur Erzeugung von verschiedenen Laserprojektionsmustern verwendet, um sowohl eine unidirektionale als auch eine multidirektionale Prozessbeobachtung durchführen zu können. Aus den geometrischen Informationen werden Kenngrößen berechnet, anhand derer der Prozesszustand beurteilt werden kann. Es zeigt sich, dass anhand der Kenngrößenverläufe Abweichungen in der Schweiß- und Schmelzbadgeometrie und Positionierungsfehler des Roboters identifiziert werden können.
In this ongoing research project, we study the influence of moisture damage on Ground Penetrating Radar (GPR) in different floor constructions. For this purpose, a measurement setup with interchangeable layers is developed to vary the screed material (cement or anhydrite) and insulation material (glass wool, perlite, expanded and extruded polystyrene), as well as the respective layer thickness. The evaluation of the 2 GHz common-offset radar measurements is focused on the extraction of distinctive signal features that can be used to classify the underlying case of damage without any further information about the hidden materials or layer thicknesses. In the collected dataset, we analyze the horizontal distribution of A-scan features in corresponding B-scans to detect water in the insulation layer. Furthermore, possible combinations of these features are investigated with the use of multivariate data analysis and machine learning (logistic regression) in order to evaluate the mutual dependencies. In this study, the combination of an amplitude- and frequency-based feature achieved an accuracy of 93.2 % and performed best to detect a damage in floor insulations.
Crack detection in steel bars at high surface temperatures is a critical problem in any manufacturing industry. Surface breaking cracks are the major problems during the billet casting. Many NDT techniques are proven its capability in crack detection at room temperature. Here, we are demonstrating the possibility of exposure of cracks using laser line thermography at higher surface temperatures (up to 600 °C). A continuous-wave (CW) laser is used to excite the sample kept at higher surface temperatures. The temperature distribution over the sample due to the laser line scanning is captured using a temperature calibrated infrared (IR) thermal camera. The response of the sample temperature in crack detection is investigated using a validated FE model. The impact of the oxide layer in crack detection is investigated by using two types of samples; one without any oxide layer and the second is with the oxide layer. The influence of laser power in the detection of defects at high temperatures is studied. 3D numerical models were developed for the cases; when the sample is with oxide layer and without any oxide layer for a better understanding of physics. The surface temperature rise due to laser heating is higher for the scaled sample compared to the no-scale sample. The presence of the oxide layer above the parent metal will reduce the reflectivity of the surface. Lower reflectivity will lead to increased absorption of incident energy so that the surface temperature rise will be higher than the surface with no scale. Thermal contrast linearly depends on laser power, which means higher laser power will increase the defect detectability even at a higher surface temperature.
Porous ceramic diesel particulate filters (DPFs) are extruded products that possess macroscopic anisotropic mechanical and thermal properties. This anisotropy is caused by both morphologic features (mostly the orientation of porosity) and crystallographic texture. We systematically studied those two aspects in a cordierite and two aluminum titanate (AT) ceramic materials of different porosity using mercury porosimetry, gas adsorption, electron microscopy, X-ray diffraction, and X-ray refraction radiography. We found that a lower porosity in AT content implies a larger isotropy of both the crystal texture and the porous space orientation. We also found that, analogous to cordierite, AT crystallites do align with their axis of negative thermal expansion along the extrusion direction. However, unlike what found for cordierite, the aluminium titanate crystallite form is such that a more pronounced (0 0 2) texture along the extrusion direction implies porosity aligned perpendicular to it.
Magnetocaloric composite wires have been studied by pulsed-field measurements up to μ0ΔH = 10 T with a typical rise time of 13 ms in order to evaluate the evolution of the adiabatic temperature change of the core, ΔTad, and to determine the effective temperature change at the surrounding steel jacket, ΔTeff, during the field pulse. An inverse thermal hysteresis is observed for ΔTad due to the delayed thermal transfer. By numerical simulations of application-relevant sinusoidal magnetic field profiles, it can be stated that for field-frequencies of up to two field cycles per second heat can be efficiently transferred from the core to the outside of the jacket. In addition, intense numerical simulations of the temperature change of the core and jacket were performed by varying different parameters, such as frequency, heat capacity, thermal conductivity and interface resistance in order to shed light on their impact on ΔTeff at the outside of the jacket in comparison to ΔTad provided by the core.
Removal of Etalon Features in the Far-Infrared–Terahertz Transmittance Spectra of Thin Polymer Films
(2020)
Etalon features in infrared spectra of stratified samples, their influence on the interpretation and methods to circumvent their presence in infrared spectra have been in discussion for decades. This paper focuses on the application of a method originally developed to remove interference fringes in the mid-infrared spectra for far-infrared Fourier transform spectroscopy on thin polymer films. We show that the total transmittance-reflectance technique, commonly used for mid-infrared, also works successfully in the far infrared spectral range where other approaches fail. Experimental spectra obtained by such technique are supported by model calculations and reveal the possibility and limits to obtain almost undisturbed far-infrared spectra which are suitable to determine low energy vibrations of ionomer salts under certain sample conditions.
Rapid cooling rates and steep temperature gradients are characteristic of additively manufactured parts and important factors for the residual stress formation. This study examined the influence of heat accumulation on the distribution of residual stress in two prisms produced by Laser Powder Bed Fusion (LPBF) of austenitic stainless steel 316L. The layers of the prisms were exposed using two different border fill scan strategies: one scanned from the centre to the perimeter and the other from the perimeter to the centre. The goal was to reveal the effect of different heat inputs on samples featuring the same solidification shrinkage. Residual stress was characterised in one plane perpendicular to the building direction at the mid height using Neutron and Lab X-ray diffraction. Thermography data obtained during the build process were analysed in order to correlate the cooling rates and apparent surface temperatures with the residual stress results. Optical microscopy and micro computed tomography were used to correlate defect populations with the residual stress distribution. The two scanning strategies led to residual stress distributions that were typical for additively manufactured components: compressive stresses in the bulk and tensile stresses at the surface. However, due to the different heat accumulation, the maximum residual stress levels differed. We concluded that solidification shrinkage plays a major role in determining the shape of the residual stress distribution, while the temperature gradient mechanism appears to determine the magnitude of peak residual stresses.
Material qualification for laser powder bed fusion (L-PBF) processes are often based on results derived from additively manufactured (AM) bulk material or small density cubes, although it is well known that the part geometry has a tremendous influence on the heat flux and, therefore, on the thermal history of an AM component. This study shows experimentally the effect of simple geometrical obstructions to the heat flux on cooling behavior and solidification conditions of 316L stainless steel processed by L-PBF. Additionally, it respects two distinct inter layer times (ILT) as well as the build height of the parts. The cooling behavior of the parts is in-situ traced by infrared (IR) thermography during the built-up. The IR signals reveal significant differences in cooling conditions, which are correlated to differences in melt pool geometries. The acquired data and results can be used for validation of computational models and improvements of quality assurance.
Material qualification for laser powder bed fusion (L-PBF) processes are often based on results derived from additively manufactured (AM) bulk material or small density cubes, although it is well known that the part geometry has a tremendous influence on the heat flux and, therefore, on the thermal history of an AM component. This study shows experimentally the effect of simple geometrical obstructions to the heat flux on cooling behavior and solidification conditions of 316L stainless steel processed by L-PBF. Additionally, it respects two distinct inter layer times (ILT) as well as the build height of the parts. The cooling behavior of the parts is in-situ traced by infrared (IR) thermography during the built-up. The IR signals reveal significant differences in cooling conditions, which are correlated to differences in melt pool geometries. The acquired data and results can be used for validation of computational models and improvements of quality assurance.
Thanks to its good strength/mass ratio, a glass fibre reinforced plastic (GFRP) composite is a common material widely used in aviation, power production, automotive and other industries. In its turn, active infrared (IR) nondestructive testing (NDT) is a common inspection technique for detecting and characterizing structural defects in GFRP. Materials to be tested are typically subjected to optical heating which is supposed to occur on the material surface. However, GFRP composite is semitransparent for optical radiation of both visual and IR spectral bands. Correspondingly, the inspection process represents a certain combination of both optical and thermal phenomena. Therefore, the known characterization algorithms based on pure heat diffusion cannot be applied to semi-transparent materials. In this study, the phenomenon of GFRP semi-transparency has been investigated numerically and experimentally in application to thermal NDT. Both Xenon flash tubes and a laser have been used for thermal stimulation of opaque and semi-transparent test objects. It has been shown that the Penetration of optical heating radiation into composite reduces detectability of shallower defects, and the signal-to-noise ratio can be enhanced by applying the technique of thermographic signal reconstruction (TSR). In the inspection of the semi-transparent GFRP composite, the most efficient has been the laser heating followed by the TSR data processing. The perspectives of defect characterization of semi-transparent materials by using laser heating are discussed. A neural network has been used as a candidate tool for evaluating defect depth in composite materials, but its training should be performed in identical with testing conditions.
Planar tomography and numerical analysis for damage characterization of impact loaded RC plates
(2020)
The damage analysis of reinforced concrete (RC) is of high interest for reasons of effective maintenance and structural safe-ty of buildings. The damage structures of RC plates loaded by an impact were investigated, applying X-ray planar tomogra-phy and finite element method (FEM). Planar tomography allows getting three-dimensional information of the RC elements and the damage including crack, spalling and scabbing. The FEM model validated on the tomography data justifies the appli-cation for further predictions of the damage description. In this study, we investigated concrete plates of three different thick-ness subjected to impacts at different low- and medium-velocity, whereby the used impactor had a flat tip, which resulted in small penetrations on the front side and scabbing on the rear side. In order to quantify the damage, the damage volume and its distribution through the plate were computed and the correlations between degree of damage and impact velocity were found out.
In this paper we propose super resolution measurement and post-processing strategies that can be applied in thermography using laser line scanning. The implementation of these techniques facilitates the separation of two closely spaced defects and avoids the expected deterioration of spatial resolution due to heat diffusion. The experimental studies were performed using a high-power laser as heat source in combination with pulsed thermography measurements (step scanning) or with continuous heating measurements (continuous scanning). Our work shows that laser line step scanning as well as continuous scanning both can be used within our developed super resolution (SR) techniques. Our SR techniques make use of a compressed sensing based algorithm in post- processing, the so-called iterative joint sparsity (IJOSP) approach. The IJOSP method benefits from both - the sparse nature of defects in space as well as from the similarity of each measurement. In addition, we show further methods to improve the reconstruction quality e.g. by simple manipulations in thermal image processing such as by considering the effect of the scanning motion or by using different optimization algorithms within the IJOSP approach. These super resolution image processing methods are discussed so that the advantages and disadvantages of each method can be extracted. Our contribution thus provides new approaches for the implementation of super resolution techniques in laser line scanning thermography and informs about which experimental and post-processing parameters should be chosen to better separate two closely spaced defects.
Relaxation phenomena observed in the electrical low-frequency range (approximately 1 mHz-10 kHz) of natural porous media like sandstones is often assumed to be directly related to the dominant (modal) pore throat sizes measured, for instance, with mercury intrusion porosimetry. Attempts to establish a universally valid relationship between pore size and peak Spectral Induced Polarization (SIP) relaxation time have failed, considering sandstones from very different origins and featuring great variations in textural and chemical compositions as well as in geometrical pore space properties. In addition working with characteristic relaxation times determined in Cole-Cole or Debye decomposition fits to build the relationship have not been successful. In particular, samples with narrow pore throats are often characterized by long SIP relaxation times corresponding to long “characteristic length scales” in these media, assuming that the diffusion coefficients along the electrical double layer were constant. Based on these observations, three different types of SIP relaxation can be distinguished. We present a new way of assessing complex pore spaces of very different sandstones in a multi-methodical approach to combine the benefits of mercury intrusion porosimetry, micro-computed tomography, and nuclear magnetic resonance. In this way, we achieve much deeper insight into the pore space due to the different resolutions and sensitivities of the applied methods to both pore constrictions (throats) and wide pores (pore bodies). We experimentally quantify pore aspect ratios and volume distributions within the two pore regions. We clearly observe systematic differences between three SIP relaxation types identified previously and can attribute the SIP peak relaxation times to measured characteristic length scales within our materials. We highlight selected results for a total of nine sandstones. It seems that SIP relaxation behavior depends on the size difference of the narrow pore throats to the wide pore bodies, which increases from SIP Type 1 to Type 3.
A large amount of data and information is collected in the field of non-destructive testing (NDT) in civil engineering. The weakly structured data are usually evaluated with regard to specific testing tasks (e.g. geometry determination, damage localization, quality assurance). While the data offers great economic potential, i.e. to support construction planning, monitoring and maintenance processes, the evaluation is manual and case-by-case and therefore too inefficient for broader applications. We present recent visions and approaches how these large amounts of data need to be handled in the future and how we aim to make the acquired knowledge accessible to our stakeholders. Building on initiatives in materials research, we stress the importance of further research in the field of semantic data integration particularly motivate why an ontology is needed for the area of NDT in civil engineering.
The one-dimensional thermal quadrupole method is used to evaluate a pulsed thermography measurement at delaminations in a glass-fiber reinforced plastic plate quantitatively. The large-scale delaminations have been induced by tension overload and are air-filled and are usually located at the same depth as the notch bottom of a notch on the rear side. While classical evaluation methods like pulsed phase thermography and thermal Signal reconstruction are focused on the delamination depth only, the thermal quadrupole method determines spatially resolved two parameters for delaminations, delamination depth and local thermal resistance. Interestingly, lateral heat flows do not disturb this kind of depth evaluation.
Using spatial and temporal shaping of laser-induced diffuse thermal wave fields in thermography
(2020)
The diffuse nature of thermal waves is a fun-damental limitation in thermographic nonde-structive testing. In our studies we investigated different approaches by shaping the thermal wave fields which result from heating. We have used high-power laser sources to heat metallic samples. Using these spatial and temporal shaping techniques leads to a higher detection sensitivity in our measurements with the infra-red camera. In this contribution we show our implementation of shaping laser-induced diffuse thermal wave fields and the effect on the defect reconstruction quality.
In this work we focus on our most recent studies to super resolution (SR) laser thermography. The goal of SR nondestructive testing methods is to facilitate the separation of closely spaced defects. We explain how to combine laser scanning with SR techniques. It can be shown that stepwise as well as continuous scanning techniques are applicable. Finally, we discuss the effect of experimental parameters and im-age processing techniques to find the optimal SR technique which leads to the highest reconstruction quality within laser thermography.
Multivariate Auswertung von Radarsignalen zur Bestimmung typischer Feuchteschäden in Fußböden
(2020)
Leitungswasserschäden nahmen im Jahr 2018 mit rund 2,9 Milliarden Euro den größten Posten bei Gebäudeversicherungen ein und verursachten damit mehr Kosten, als Elementar-, Feuer, Sturm- und Hagelschäden zusammen. Neutronensonden helfen hier bereits bei der horizontalen Eingrenzung eines Feuchteschadens, wobei die vertikale Unterscheidung betroffener Schichten nur durch die zerstörende Entnahme von Bohrkernen vorgenommen werden kann. Der parallele Einsatz des Radarverfahrens soll hier mit seiner hohen Sensitivität für Wasser künftig zu einem erhöhten Informationsgehalt beitragen und eine kostengünstigere Schadensdiagnose und Planung der Reparaturmaßnahmen ermöglichen.
In einem laufenden Forschungsvorhaben werden in systematischen Laborstudien gängige Schadensfälle an häufig anzutreffenden Fußbodenaufbauten simuliert und deren Einfluss auf das Radarverfahren untersucht. Die Bewertung der Schadensträchtigkeit zugeführter Wassermengen erfolgt mithilfe von Luftfeuchtesensoren, welche in der Dämmebene platziert sind. Beim Aufbau der Fußbodenstrukturen kommen Zement- und Anhydritestriche, sowie die Dämmmaterialen Styropor, Styrodur, Glaswolle und Perlite-Schüttung mit variierenden Schichtdicken zum Einsatz. Die Robustheit der zu entwickelnden Messmethode gegenüber derartig verschiedenen und in der Praxis häufig unbekannten Schichtstrukturen stellt eine besondere Herausforderung dar und soll durch den Einsatz multivariater Datenauswertung erhöht werden. Diesbezüglich empfehlen bisherige Ergebnisse die Betrachtung der örtlichen Verteilungen qualitativer Signalmerkmale wie Amplituden, Laufzeiten und Frequenzen im Radargramm (B-Scan). Deren Eignung für reale Schadensfälle außerhalb der Laborumgebung wird hierbei stets kritisch hinterfragt und soll weiterführend an Praxiseinsätzen validiert werden.
Da Feuchte insbesondere in Form von Flüssigwasser ein hohes Schadenspotential für Baustoffe und Konstruktionen darstellt, finden zerstörungsfreie Verfahren wie die Kernspinresonanz (NMR) zunehmend Einsatz im Bauwesen. Auch wenn zur qualitativen Lokalisierung möglicher Schad- und Risikostellen bereits zahlreiche zerstörungsfreie Verfahren in der Praxis Anwendung finden, so besteht beispielweise bei der Porenraumcharakterisierung und dem Verständnis von Feuchtetransportmechanismen weiterhin Forschungsbedarf. Eine besondere Herausforderung stellt in diesem Zusammenhang die (ortsaufgelöste) Erfassung und Quantifizierung von Feuchteverteilungen bei Teilsättigung dar.
Mittels 1H-NMR lassen sich der Wassergehalt sowie die Relaxationszeiten der Wasserstoffprotonen in Abhängigkeit von ihrer Bindungsart und der Porenumgebung messen. In vollgesättigten porösen Baustoffen kann somit das NMR-Signal als Porengrößenverteilung interpretiert werden. Da aber in der Praxis nur selten bis nie vollgesättigte Zustände vorliegen, befasst sich eine laufende Forschungsarbeit mit dem Verständnis der NMR-Signale teilgesättigter Porensysteme, die teilweise multimodale Porengrößenverteilungen aufweisen.
Im Rahmen dieser Arbeit werden insgesamt 19 Sandsteine aus Deutschland, Tschechien und Italien sowohl im voll- als auch im teilgesättigten Zustand mit NMR untersucht. Definierte teilgesättigte Zustände werden durch Aufsättigen der Proben in Exsikkatoren mit verschiedenen relativen Luftfeuchten (z. B. 33 %, 75 %, 96%) erzeugt. Die Regulierung der Luftfeuchten erfolgt mit Hilfe von gesättigten Salzlösungen. Als Ergebnis wird die Veränderung der Signalamplitude und der T2-Relaxationszeitenverteilungen in Abhängigkeit von dem Feuchtezustand näher betrachtet. Des Weiteren erfolgt die Berechnung des Flüssigwasseranteils bzw. der adsorbierten Wasserfilmdicke einzelner Poren aus der mit Quecksilberporosimetrie bestimmten Porengrößenverteilung und der Annahme einer Porengeometrie (z.B. Schlitz-, Zylinderpore, etc.). Am Ende dieser Arbeit soll der Zusammenhang des NMR-Signals (Amplitude und Relaxationszeiten) mit dem Porensättigungsgrad in einem Porensystem mit multimodaler Porengrößenverteilung bei Teilsättigung aufgezeigt werden.
Das pulverbettbasierte selektive Laserstrahlschmelzen (engl. laser powder bed fusion, L-PBF) ist die am weitesten verbreitete additive Fertigungstechnologie für die Herstellung metallischer Komponenten. Unter der Vielzahl an Einflussfaktoren auf die Qualität und die mechanischen Eigenschaften von L PBF-Bauteilen hat die Zwischenlagenzeit (engl. inter layer time, ILT) bisher kaum Beachtung in der wissenschaftlichen Literatur gefunden, obwohl sie je nach Bauraumausnutzungsgrad stark variieren kann. In diesem Vortrag werden Ergebnisse einer Studie präsentiert, die den Einfluss der ILT in Kombination mit der Bauteilhöhe und unter Berücksichtigung verschiedener Volumenenergiedichten am Beispiel der austenitischen Stahllegierung AISI 316L untersucht. Die Fertigungsprozesse wurden in situ mittels Thermographiekamera überwacht, sodass Informationen über das Abkühlverhalten der Bauteile während des Prozesses gewonnen werden konnten. Es wurden signifikante Effekte der ILT und der Bauteilhöhe auf Wärmeakkumulation, Mikrostruktur, Schmelzbadgeometrie und Härte festgestellt. Ferner konnte ein Anstieg von Defektdichten bei einem gegenseitigen Wechselspiel aus Bauteilhöhe und ILT aufgezeigt werden. Die Zwischenlagenzeit wurde somit als kritischer Faktor für die L-PBF-Fertigung von Realbauteilen identifiziert.
Industry 4.0 is all about interconnectivity, sensor-enhanced process control, and data-driven systems. Process analytical technology (PAT) such as online nuclear magnetic resonance (NMR) spectroscopy is gaining in importance, as it increasingly contributes to automation and digitalization in production. In many cases up to now, however, a classical evaluation of process data and their transformation into knowledge is not possible or not economical due to the insufficiently large datasets available. When developing an automated method applicable in process control, sometimes only the basic data of a limited number of batch tests from typical product and process development campaigns are available. However, these datasets are not large enough for training machine-supported procedures. In this work, to overcome this limitation, a new procedure was developed, which allows physically motivated multiplication of the available reference data in order to obtain a sufficiently large dataset for training machine learning algorithms. The underlying example chemical synthesis was measured and analyzed with both application-relevant low-field NMR and high-field NMR spectroscopy as reference method. Artificial neural networks (ANNs) have the potential to infer valuable process information already from relatively limited input data. However, in order to predict the concentration at complex conditions (many reactants and wide concentration ranges), larger ANNs and, therefore, a larger Training dataset are required. We demonstrate that a moderately complex problem with four reactants can be addressed using ANNs in combination with the presented PAT method (low-field NMR) and with the proposed approach to generate meaningful training data.
Thermographic reconstruction of defects that lie in the bulk of a sample is a difficult task because entropy production during heat diffusion leads to information loss. To reconstruct defects one has to solve an inverse heat conduction problem. The quality of the reconstruction is closely related to the information content of the observed data set that is reflected by the decreasing ability to spatially resolve a defect with growing defect depth. In this work we show a 2D reconstruction of rectangular slots with different width-to-depth ratios in a metallic sample. For this purpose, we apply the virtual wave concept and incorporate positivity and sparsity as prior information to overcome the diffusion-based information loss partially. The reconstruction is based on simulated and experimental pulse thermography data. In the first reconstruction step, we compute a virtual wave field from the surface temperature data. This allows us, in the second step, to use ultrasonic backpropagation methods for image reconstruction.
Das Neutronensondenverfahren wird bereits seit vielen Jahren erfolgreich zur Eingrenzung und Quantifizierung auftretender Feuchteschäden an Fußböden eingesetzt. Hierzu bedarf es jedoch einer Vielzahl zerstörender Sondierungsbohrungen, welche die gewonnenen Messdaten kalibrieren und eine Tiefenzuordnung des Flüssigwassers zulassen. Dadurch entsteht ein zeitlicher und finanzieller Aufwand, der durch den parallelen Einsatz des elektromagnetischen Radarverfahrens vermieden werden könnte. Mit seiner hohen Sensitivität für Wasser bietet diese Messmethode die Möglichkeit der vertikalen Lokalisierung von Feuchte, was zu einer automatisierten Klassifizierung typischer Schadensfälle beitragen soll.
In einem laufenden Forschungsvorhaben werden in systematischen Laborstudien gängige Schadensfälle an häufig anzutreffenden Fußbodenaufbauten simuliert und deren Einfluss auf die genannten Verfahren untersucht. Hierbei kommen Zement- und Anhydritestriche, sowie unterschiedliche Dämmmaterialen mit variierenden Schichtdicken zum Einsatz. Wesentlicher Bestandteil der Auswertung ist die Extraktion signifikanter Signalmerkmale des Radarverfahrens, welche Rückschlüsse auf den Schadensfall und ggf. die Wassermenge zulassen. Weiterführend sollen die Kombinationsmöglichkeiten der verschiedenen Signalmerkmale und der Neutronensondendaten durch Methoden der multivariaten Datenauswertung und des maschinellen Lernens geprüft werden. Die Unabhängigkeit gegenüber wechselnden Schichtdicken und Materialien steht hierbei besonders im Fokus und soll anhand der erzielten Ergebnisse evaluiert werden.
Das Neutronensondenverfahren wird bereits seit vielen Jahren erfolgreich zur Eingrenzung und Quantifizierung auftretender Feuchteschäden an Fußböden eingesetzt. Hierzu bedarf es jedoch einer Vielzahl zerstörender Sondierungsbohrungen, welche die gewonnenen Messdaten kalibrieren und eine Tiefenzuordnung des Flüssigwassers zulassen. Dadurch entsteht ein zeitlicher und finanzieller Aufwand, der durch den parallelen Einsatz des elektromagnetischen Radarverfahrens vermieden werden könnte. Mit seiner hohen Sensitivität für Wasser bietet diese Messmethode die Möglichkeit der vertikalen Lokalisierung von Feuchte, was zu einer automatisierten Klassifizierung typischer Schadensfälle beitragen soll.
In einem laufenden Forschungsvorhaben werden in systematischen Laborstudien gängige Schadensfälle an häufig anzutreffenden Fußbodenaufbauten simuliert und deren Einfluss auf die genannten Verfahren untersucht. Hierbei kommen Zement- und Anhydritestriche, sowie unterschiedliche Dämmmaterialen mit variierenden Schichtdicken zum Einsatz. Wesentlicher Bestandteil der Auswertung ist die Extraktion signifikanter Signalmerkmale des Radarverfahrens, welche Rückschlüsse auf den Schadensfall und ggf. die Wassermenge zulassen. Weiterführend sollen die Kombinationsmöglichkeiten der verschiedenen Signalmerkmale und der Neutronensondendaten durch Methoden der multivariaten Datenauswertung und des maschinellen Lernens geprüft werden. Die Unabhängigkeit gegenüber wechselnden Schichtdicken und Materialien steht hierbei besonders im Fokus und soll anhand der erzielten Ergebnisse evaluiert werden.
Material defects in fiber reinforced polymers such as delaminations can rapidly degrade the material properties or can lead to the failure of a component. Pulse thermography (PT) has proven to be a valuable tool to identify and quantify such defects in opaque materials. However, quantification of delaminations within semitransparent materials is extremely challenging. We present an approach to quantify delaminations within materials being semitransparent within the wavelength ranges of the optical excitation sources as well as of the infrared (IR) camera. PT experimental data of a glass fiber reinforced polymer with a real delamination within the material were reconstructed by one dimensional (1D) mathematical models. These models describe the heat diffusion within the material and consider semitransparency to the excitation source as well to the IR camera, thermal losses at the samples surfaces and a thermal contact resistance between the two layers describing the delamination. By fitting the models to the PT data, we were able to determine the depth of the delamination very accurately. Additionally, we analyzed synthetic PT data from a 2D simulation with our 1D-models to show how the thermal contact resistance is influenced by lateral heat flow within the material.
Der Frequenzbereich der Terahertzwellen liegt im elektromagnetischen Spektrum zwischen den Mikrowellen und dem infraroten Licht – also etwa im Bereich von 0,1 bis 10 Terahertz (THz). Der VDI-Statusreport „Terahertzsysteme und Anwendungen“ stellt die unterschiedlichen Konzepte für Terahertzsysteme vor und zeigt Anwendungsbeispiele, bei denen die Terahertztechnik erfolgreich eingesetzt wird. Umfangreiche Literaturangaben ermöglichen eine intensivere Beschäftigung mit allen Themen des VDI-Statusreports.
THz and mid IR spectroscopy of high-molecular PE (HMW) and ultra high-molecular PE (UHMW) reveals modifications of the molecular structure. Characteristic absorption bands are changed if the two materials are exposed by γ-Co60 radiation up to 600 kGy and subsequently stored at an annealing temperature of 398 K until for 729 days. UHMW-PE and HMW-PE behave differently during the ageing process because of their molecular weight and inherent structure distinctions. The spectroscopic data offer characteristic absorption bands, which have been used to describe the complete ageing process in more detail. For instance, the integral absorption in the B1u THz-region can be used to describe quantitatively the reduction of crystallinity. The formation of trans vinylene unsaturation and the decay of vinyl during ageing can be observed in detail in the mid IR range.
Generating 2D noise with local, space-varying spectral characteristics is vital where random noise fields with spatially heterogeneous statistical properties are observed and need to be simulated. A realistic, non-stationary noise generator relying on experimental data is presented. That generator is desired in areas such as photography and radiography. For example, before performing actual X-ray imaging in practice, output images are simulated to assess and improve setups. For that purpose, realistic film noise modelling is crucial because noise downgrades the detectability of visual signals. The presented film noise synthesiser improves the realism and value of radiographic simulations significantly, allowing more realistic assessments of radiographic test setups. The method respects space-varying spectral characteristics and probability distributions, locally simulating noise with realistic granularity and contrast. The benefits of this approach are to respect the correlation between noise and image as well as internal correlation, the fast generation of any number of unique noise samples, the exploitation of real experimental data, and its statistical non-stationarity. The combination of these benefits is not available in existing work. Validation of the new technique was undertaken in the field of industrial radiography. While applied to that field here, the technique is general and can also be utilised in any other field where the generation of 2D noise with local, space-varying statistical properties is necessary.
Extracting statistical characteristics from radiographic films is vital for film system classification and contrast sensitivity evaluation and serves as a basis for film noise simulation. A new method for digitising radiographic films in order to extract these characteristics is presented. The method consists of a camera-based setup and image processing procedure to digitise films. Correct optical density values and granularity can be extracted from the digitised images, which are equal to results obtained by standardised measurement procedures. Specific statistical characteristics of film noise are theoretically derived and subsequently verified by the obtained data, including characteristics such as Gaussianity and spatial spectral characteristics of the optical density fluctuations. It is shown that the presented method correctly measures the granularity of film noise and can therefore replace time-consuming microdensitometer measurements traditionally required for film system classifications. Additionally, the inherent unsharpness of film systems was investigated and compared with literature data. This comparison serves as another validation approach of the presented method.
This paper presents different super resolution reconstruction techniques to overcome the spatial resolution limits in thermography. Pseudo-random blind structured illumination from a onedimensional laser array is used as heat source for super resolution thermography. Pulsed thermography measurements using an infrared camera with a high frame rate sampling lead to a huge amount of data. To handle this large data set, thermographic reconstruction techniques are an essential step of the overall reconstruction process. Four different thermographic reconstruction techniques are analyzed based on the Fourier transform amplitude, principal component analysis, virtual wave reconstruction and the maximum thermogram. The application of those methods results in a sparse basis representation of the measured data and serves as input for a compressed sensing based algorithm called iterative joint sparsity (IJOSP). Since the thermographic reconstruction techniques have a high influence on the result of the IJOSP algorithm, this paper Highlights their Advantages and disadvantages.
Signalmerkmale des Radarverfahrens zur Klassifizierung von Feuchteschäden in Fußbodenaufbauten
(2020)
Zur Eingrenzung und Klassifizierung auftretender Feuchteschäden in geschichteten Fußbodenaufbauten wird bereits seit vielen Jahren das Neutronensondenverfahren eingesetzt. Während eine Eingrenzung des Schadens bereits durch die zerstörungsfrei aufgenommenen Messdaten möglich ist, erfordert die Klassifizierung zeit- und kostenintensive Sondierungsbohrungen. Die somit gewonnenen Tiefeninformationen zur Lage des Flüssigwassers tragen maßgeblich zur Planung der erforderlichen Reparaturarbeiten bei, was die Frage nach einer zerstörungsfreien Alternative zur vertikalen Lokalisierung von Feuchte in Fußböden stellt. Das Radarverfahren ist mit seiner hohen Sensitivität für Wasser besonders geeignet und soll künftig zur automatisierten Klassifizierung typischer Schadensfälle beitragen.
In einem laufenden Forschungsvorhaben werden in systematischen Laborstudien gängige Schadensfälle an häufig anzutreffenden Fußbodenaufbauten simuliert und deren Einfluss auf die genannten Verfahren untersucht. Hierbei kommen Zement- und Anhydritestriche, sowie unterschiedliche Dämmmaterialen mit variierenden Schichtdicken zum Einsatz.
Wesentlicher Bestandteil der Auswertung ist die Extraktion signifikanter Signalmerkmale des Radarverfahrens, welche Rückschlüsse auf den Schadensfall und ggf. die Wassermenge zulassen. Die Robustheit gegenüber unterschiedlichsten Aufbauten mit wechselnden Schichtdicken und Materialien steht hierbei besonders im Fokus. Erste Ergebnisse empfehlen die Betrachtung der örtlichen Verteilung bestimmter Signalmerkmale im Radargramm (B-Scan), um eine Klassifizierung der Schadensfälle vornehmen zu können.
Die Porosität sowie die Porengrößenverteilung (PSD) eines Baustoffs sind wichtige petrophysikalische Eigenschaften, die Transportprozesse im Material beeinflussen und im direkten Zusammenhang mit der Schadensanfälligkeit stehen (Frost-Tau-Schäden, Schäden durch Salzeinträge und sauren Regen). Verfahren zur Ermittlung der Porosität stellen die Quecksilberporosimetrie (MIP), das Prinzip der Tauchwägung nach Archimedes (triple weighing method, TWM) und die Nuklear Magnetische Resonanz (NMR) dar.
Das Hauptziel dieser Arbeit ist der Vergleich der drei Methoden für die Porositätsbestimmung. Als Untersuchungsmaterial wurde Sandstein gewählt. Als Baustoff wird Sandstein trotz einer teilweise geringen Festigkeit, aber aufgrund seiner Farbvielfalt häufig für Kulturbauten, Gehwegmaterial und Fassaden eingesetzt. Sandsteine weisen zudem meist eine hohe effektive Porosität (bis zu ca. 30 %) und teilweise breite Porengrößenspektren auf, wodurch Feuchte- und Ioneneinträge begünstigt werden und zu z. B. langfristigen Verfärbungen an Sandsteinfassaden führen können.
Im Rahmen dieser Arbeit werden die Porositäten von 10 verschiedenen Sandsteinen aus Deutschland und Tschechien bestimmt. Für die Untersuchungen mittels TWM und NMR wurden für jede Sandsteinart zwei zylinderförmige Proben unter Unterdruck mit Leitungswasser aufgesättigt. Beim Vergleich der Verfahren liegt der Fokus insbesondere auf NMR und der verwendeten Gerätetechnik, die mit einer minimalen Echozeit von 50 µs ein Wasserstoffsignal in Sandsteinen aus bis zu ca. 2 nm kleine Poren erfassen kann. Darüber hinaus wird das Auflösungsvermögen von MIP und NMR bezüglich der Poren- bzw. Poreneingangsgrößen diskutiert. Für den Vergleich von NMR und MIP werden an ausgewählten Sandsteinproben ebenfalls die zusätzlich ermittelten PSD in Korrelation zueinander gebracht.
Erste Ergebnisse zeigen eine gute Korrelation von NMR- und TWM-Daten, aber teilweise starke Abweichungen zu den mit MIP bestimmten Porositäten. Ergebnisse zu den PSD zeigen teilweise nahezu identische Kurvenverläufe. Jedoch ist die Abschätzung des Umrechnungsfaktors für die NMR-Daten ohne Hinzuziehen von zusätzlichen Referenzverfahren wie Röntgen-Computer-Tomographie oder Spektral Induzierte Polarisation teilweise sehr schwierig.
Durch den großflächigen Einsatz der Computertomographie (CT) in unterschiedlichen Industriebereichen steigen auch die Anforderungen an die quantitative Bildanalyse. Subjektive Bildwahrnehmung muss durch objektive Algorithmen ersetzt werden. In diesem Artikel stellt die Bundesanstalt für Materialforschung und -prüfung (BAM), die seit den 1980er Jahren an der Entwicklung der industriellen CT beteiligt ist, anhand ausgewählter Beispiele den aktuellen Stand ihrer Analysemethoden an verschiedenen Anwendungsbeispielen der CT vor.
Laser powder bed fusion (L-PBF) is the most prominent additive manufacturing (AM) technology for metal part production. Among the high number of factors influencing part quality and mechanical properties, the inter layer time (ILT) between iterative melting of volume elements in subsequent layers is almost completely unappreciated in the relevant literature on L-PBF. This study investigates the effect of ILT with respect to build height and under distinct levels of volumetric energy density (VED) using the example of 316L stainless steel. In-situ thermography is used to gather information on cooling conditions during the process, which is followed by an extensive metallographic analysis. Significant effects of ILT and build height on heat accumulation, sub-grain sizes, melt pool geometries and hardness are presented. Furthermore, the rise of defect densities can be attributed to a mutual interplay of build height and ILT. Hence, ILT has been identified as a crucial factor for L-PBF of real part components especially for those with small cross sections.
Among additive manufacturing (AM) technologies, the laser powder bed fusion (L-PBF) is one of the most important technologies to produce metallic components. The layer-wise build-up of components and the complex process conditions increase the probability of the occurrence of defects. However, due to the iterative nature of its manufacturing process and in contrast to conventional manufacturing technologies such as casting, L-PBF offers unique opportunities for in-situ monitoring. In this study, two cameras were successfully tested simultaneously as a machine manufacturer independent process monitoring setup: a high-frequency infrared camera and a camera for long time exposure, working in the visible and infrared spectrum and equipped with a near infrared filter. An AISI 316L stainless steel specimen with integrated artificial defects has been monitored during the build. The acquired camera data was compared to data obtained by computed tomography. A promising and easy to use examination method for data analysis was developed and correlations between measured signals and defects were identified. Moreover, sources of possible data misinterpretation were specified. Lastly, attempts for automatic data analysis by data Integration are presented.
Machine learning based multi-sensor fusion for the nondestructive testing of corrosion in concrete
(2020)
Half-cell potential mapping (HP) is the most popular non-destructive testing method (NDT) for locating corrosion damage in concrete. It is generally accepted that HP is susceptible to environmental factors caused by salt-related deterioration, such as different moisture and chloride gradients. Additional NDT methods are able to identify distinctive areas but are not yet used to estimate more accurate test results. We present a Supervised Machine Learning (SML) based approach to data fusion of seven different signal features to obtain higher quality information. SMLs are methods that explore (or learn) relationships between different (sensor) data from predefined data labels. To obtain a representative, labelled data set we conducted a comprehensive experiment simulating the deterioration cycle of a chloride exposed device in the laboratory. Our data set consists of 18 measurement campaigns, each containing HP, Ground Penetrating- Radar, Microwave Moisture and Wenner resistivity data. We compare the performance of different ML approaches. Many outperform the best single method, HP. We describe the intrinsic challenges posed by a data-driven approach in NDT and show how future work can help overcome them.