8.0 Abteilungsleitung und andere
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- 8.0 Abteilungsleitung und andere (75) (entfernen)
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Eingeladener Vortrag
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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.