8.0 Abteilungsleitung und andere
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- 2022 (72) (entfernen)
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- Vortrag (47)
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Schlagworte
- Thermography (14)
- Additive Manufacturing (11)
- Thermografie (9)
- LIBS (8)
- NDT (8)
- Laser Powder Bed Fusion (7)
- Super resolution (7)
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- DMD (5)
Organisationseinheit der BAM
- 8 Zerstörungsfreie Prüfung (72)
- 8.0 Abteilungsleitung und andere (72)
- 8.2 Zerstörungsfreie Prüfmethoden für das Bauwesen (7)
- 9 Komponentensicherheit (6)
- 9.6 Additive Fertigung metallischer Komponenten (5)
- 8.5 Röntgenbildgebung (4)
- 3 Gefahrgutumschließungen; Energiespeicher (3)
- 3.3 Sicherheit von Transportbehältern (3)
- 3.5 Sicherheit von Gasspeichern (3)
- 8.4 Akustische und elektromagnetische Verfahren (3)
Eingeladener Vortrag
- nein (47)
Capillary active interior insulation materials are an important approach to minimize energy losses of historical buildings. A key factor for their performance is a high liquid conductivity, which enables redistribution of liquid moisture within the material. We set up an experiment to investigate the development of moisture profiles within two different interior insulation materials, calcium-silicate (CaSi) and expanded perlite (EP), under constant boundary conditions. The moisture profiles were determined by two different methods: simple destructive sample slicing with subsequent thermogravimetric drying as well as non-destructive NMR measurements with high spatial resolution. The moisture profiles obtained from both methods show good agreement, when compared at the low spatial resolution of sample slicing, which demonstrates the reliability of this method. Moreover, the measured T2- relaxation-time distributions across the sample depth were measured, which may give further insight into the saturation degree of the different pore sizes. In order to explain differences in the moisture profiles between CaSi and EP, we determined their pore-size distribution with different methods: conversion of the NMR T2 relaxationtime distribution at full saturation, mercury intrusion porosimetry and indirect determination from pressure plate measurements. CaSi shows a unimodal distribution at small pore diameters, while in EP, a bi-modal or wider distribution was found. We assume that the smaller pore diameters of CaSi lead to a higher capillary conductivity, which causes a more distributed moisture profile in comparison with that of EP.
Capillary active interior insulation materials are an important approach to minimize energy losses of historical buildings. A key factor for their performance is a high liquid conductivity, which enables redistribution of liquid moisture within the material. We set up an experiment to investigate the development of moisture profiles within two different interior insulation materials, calcium-silicate (CaSi) and expanded perlite (EP), under constant boundary conditions. The moisture profiles were determined by two different methods: simple destructive sample slicing with subsequent thermogravimetric drying as well as non-destructive NMR measurements with high spatial resolution. The moisture profiles obtained from both methods show good agreement, when compared at the low spatial resolution of sample slicing, which demonstrates the reliability of this method. Moreover, the measured T2-relaxation-time distributions across the sample depth were measured, which may give further insight into the saturation degree of the different pore sizes. In order to explain differences in the moisture profiles between CaSi and EP, we determined their pore-size distribution with different methods: conversion of the NMR T2 relaxationtime distribution at full saturation, mercury intrusion porosimetry and indirect determination from pressure plate measurements. CaSi shows a unimodal distribution at small pore diameters, while in EP, a bi-modal or wider distribution was found. We assume that the smaller pore diameters of CaSi lead to a higher capillary conductivity, which causes a more distributed moisture profile in comparison with that of EP.
In this work, we report on our progress for investigating a new experimental approach for thermographic detection of internal defects by performing 2D photothermal super resolution reconstruction. We use modern high-power laser projector technology to repeatedly excite the sample surface photothermally with varying spatially structured 2D pixel patterns. In the subsequent (blind) numerical reconstruction, multiple measurements are combined by exploiting the joint-sparse nature of the defects within the specimen using nonlinear convex optimization methods. As a result, a 2D-sparse defect/inhomogeneity map can be obtained. Using such spatially structured heating combined with compressed sensing and computational imaging methods allows to significantly reduce the experimental complexity and to study larger test surfaces as compared to the one-dimensional approach reported earlier.
Self-healing agents have the potential to restore mechanical properties and extend service life of composite materials. Thermoplastic healing agents have been extensively investigated for this purpose in epoxy matrix composites due to their strong adhesion to epoxy and their ability to fill in microcracks. One of the most
investigated thermoplastic additives for this purpose is poly(ethylene-co-methacrylic acid) (EMAA). Despite the ability of thermoplastic healing agents to restore mechanical properties, it is important to assess how the addition of thermoplastic healing agents affect properties of the original epoxy material. In this work, EMAA was added to epoxy resin and the effect of the additive on fracture toughness of epoxy was evaluated. Results indicate that although added in low concentrations, EMAA can affect fracture toughness.
Evaluation of passive Thermography for the inspection of wind turbine blades. Comparison of passive thermography from the ground with drone-supported images and active thermography. Better understand the influence of weather conditions through field measurements. Development of an inspection planning tool that incorporates weather forecasts. Use FEM simulations to predict thermal contrasts of different damages under different environmental conditions.
Die additive Fertigung von metallischen Bauteilen (Additive Manufacturing - AM; auch 3D-Druck genannt) bietet eine Vielzahl an Vorteilen gegenüber konventionellen Fertigungsmethoden. Durch den schichtweisen Auftrag und das selektive Aufschmelzen von Metallpulver im Laser Powder Bed Fusion Prozess (L-PBF) sind u.a. optimierte und flexibel anpassbare Designs und die Nutzung von neuartigen Materialien möglich. Aufgrund der Komplexität des AM-Prozesses und der Menge an Einflussfaktoren ist eine Qualitätssicherung der gefertigten Bauteile unabdingbar. Verschiedene in-situ Monitoringansätze werden bereits angewendet, jedoch findet eine dedizierte Prüfung erst im Nachgang der Fertigung ex-situ statt. Der Grund dafür ist, dass die Entstehung von geometrischen Abweichungen und Defekten auch zeitversetzt zum eigentlichen Materialauftrag und damit auch zum Monitoring stattfinden kann. Die Notwendigkeit geeigneter in-situ Prüfmethoden für L-PBF, um die Erforderlichkeit einer Nacharbeitung frühzeitig festzustellen und Ausschuss zu vermeiden ist angesichts kostenintensiver Ausgangsstoffe und einer oftmals mehrstündigen bis mehrtägigen Prozessdauer besonders hoch.
Daraus motiviert wird im Rahmen des Projektes ATLAMP die Möglichkeit der aktiven Laserthermografie mit Hilfe des defokussierten Fertigungslasers untersucht. Damit ist, bei vergleichsweise geringer Laserleistung, eine zerstörungsfreie Prüfung mittels Flying Spot Thermografie möglich. Diese findet jeweils anschließend an die Fertigung einer Schicht statt, womit der reale Status des Bauteils im Verlauf des AM-Prozesses geprüft wird.
Als Grundlage dafür werden im Rahmen dieser Arbeit mit AM gefertigte, defektbehaftete Probekörper zunächst losgelöst vom Fertigungsprozess untersucht. Damit werden die Grundlagen für den neuartigen Ansatz der aktiven in-situ Laserthermografie im L-PBF-Prozess mittels des Fertigungslasers geschaffen. Auf diese Weise lassen sich auch zeitversetzt auftretende Defekte zerstörungsfrei im Prozessverlauf feststellen und eine aussagekräftige Qualitätssicherung des Ist-Zustands des Bauteils erreichen.
WEBSLAMD
(2022)
The objective of SLAMD is to accelerate materials research in the wet lab through AI. Currently, the focus is on sustainable concrete and binder formulations, but it can be extended to other material classes in the future.
1. Summary
Leverage the Digital Lab and AI optimization to discover exciting new materials Represent resources and processes and their socio-economic impact.
Calculate complex compositions and enrich them with detailed material knowledge. Integrate laboratory data and apply it to novel formulations. Tailor materials to the purpose to achieve the best solution.
Workflow
Digital Lab
Specify resources: From base materials to manufacturing processes – "Base" enables a detailed and consistent description of existing resources
Combine resources: The combination of base materials and processes offers an almost infinite optimization potential. "Blend" makes it easier to design complex configurations.
Digital Formulations: With "Formulations" you can effortlessly convert your resources into the entire spectrum of possible concrete formulations. This automatically generates a detailed set of data for AI optimization.
AI-Optimization
Materials Discovery: Integrate data from the "Digital Lab" or upload your own material data. Enrich the data with lab results and adopt the knowledge to new recipes via artificial intelligence. Leverage socio-economic metrics to identify recipes tailored to your requirements.
In our current research project „Reincarnate“ we aim to anchor the idea of the circular economy in the European construction industry and significantly extend the life cycle of buildings, construction products and materials through innovative solutions. On the long term, this is an approach reduce construction waste by 80 percent and the CO2 footprint of the construction sector by 70 percent."
This project has received funding from the European Union’s Horizon Europe research and innovation programme and will take you on a tour on what are the drivers, what is the goal, who are the partners and how we want to make the world a better place!
With 8% of man-made CO2 emissions, cement production is an important driver of the climate crisis. By using alkali-activated binders, part of the energy-intensive clinker production process can be dispensed. However, as numerous raw materials are involved in the manufacturing process here, the complexity of the materials increases by orders of magnitude. Finding a properly balanced binder formulation is like looking for a needle in a haystack. We have shown for the first time that artificial intelligence (AI)-based optimization of alkali-activated binder formulations can significantly accelerate research.
The "Sequential Learning App for Materials Discovery" (SLAMD) aims to accelerate practice transfer. With SLAMD, materials scientists have low-threshold access to AI through interactive and intuitive user interfaces. The value added by AI can be determined directly. For example, the CO2 emissions saved per ton of cement can be determined for each development cycle: the more efficient the AI optimization, the greater the savings.
Our material database already includes more than 120,000 data points of alternative binders and is constantly being expanded with new parameters. We are currently driving the enrichment of the data with a life cycle analysis of the building materials.
Based on a case study we show how intuitive access to AI can drive the adoption of techniques that make a real contribution to the development of resource-efficient and sustainable building materials of the future and make it easy to identify when classical experiments are more efficient.