8.0 Abteilungsleitung und andere
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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.