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Der Vortrag gibt eine Einführung in die Methode der Vakuumheißextraktion und beschreibt die Anwendungsmöglichkeiten der an der BAMN betriebenen Anlage.
Dissolved water decisively influences numerous thermally activated relaxation phenomena in glasses like stress relaxation, sub-critical crack growth, internal friction, viscosity, sintering, and crystallization. Thermoanalytical methods can essentially help for better understanding of these phenomena. The lecture introduces the Vacuum Hot Extraction method (VHE) and illustrates its possibilities for measuring water content, degassing and mobility. As another thermoanalytical method, the Dynamic Mechanical Themoanalysis (DMA), allowing to study the effect of dissolved water on the internal friction in glasses, is introduced.
Wasser in Silicatglas
(2018)
Glass powders are promising candidates for manufacturing a broad diversity of sintered materials like sintered glass-ceramics, glass matrix composites or glass bonded ceramics with tailored mechanical, thermal, electrical and optical properties and complex shape. Its wide and precise adjustability makes this class of materials a key component for advanced technologies. Processing of glass or composite powders often allow even more flexibility in materials design. At the same time, however, processing can have substantial effects on the glass powder surface and sinterability. Thus, mechanical damage and surface contamination can strongly enhance surface crystallization, which may retard or even fully prevent densification. Whereas sintering and concurrent crystallization have been widely studied, partially as cooperative effort of the TC7 of the ICG, and although glass powder sintering is predominantly applied for glasses of low crystallization tendency, sintering is also limited by gas bubble formation or foaming. The latter phenomenon is much less understood and can occur even for slow crystallizing glass powders. The lecture illustrates possible consequences of glass powder processing on glass sintering, crystallization and foaming.
Glass powders are promising candidates for manufacturing a broad diversity of sintered materials like sintered glass-ceramics, glass matrix composites or glass bonded ceramics with properties and complex shape. Powder processing, however, can substantially affect sinterability, e.g. by promoting surface crystallization. On the other hand, densification can be hindered by gas bubble formation for slow crystallizing glass powders. Against this background, we studied sintering and foaming of silicate glass powders with different crystallization tendency for wet milling and dry milling in air, Ar, N2, and CO2 by means of heating microscopy, DTA, Vacuum Hot Extraction (VHE), SEM, IR spectroscopy, XPS, and ToF-SIMS. In any case, foaming activity increased significantly with progressive milling. For moderately milled glass powders, subsequent storage in air could also promote foaming. Contrarily, foaming could be substantially reduced by milling in water and 10 wt% HCl. Although all powder compacts were uniaxially pressed and sintered in air, foaming was significantly affected by different milling atmosphere and was found most pronounced for milling in CO2 atmosphere. Conformingly, VHE studies revealed that foaming is mainly driven by carbonaceous species, even for powders milled in other gases. Current results of this study thus indicate that foaming is caused by carbonaceous species trapped on the glass powder surface.
Glasses stand out by their wide and continuously tunable chemical composition and large variety of unique shaping techniques making them a key component of modern high technologies. Glass development, however, is still often too cost-, time- and energy-intensive. The use of robotic melting systems embedded in an ontology-based digital environment is intended to overcome these problems in future. As part of the German research initiative MaterialDigital, the joint project GlasDigital takes first steps in this direction. The project consortium involves the Fraunhofer ISC in Würzburg, the Friedrich Schiller University Jena (OSIM), the Clausthal University of Technology (INW), and the Federal Institute for Materials Research and Testing (BAM, Division Glasses) and aims to combine all main basic components required for accelerated data driven glass development. For this purpose, a robotic high throughput glass melting system is equipped with novel inline sensors for process monitoring, machine learning (ML)-based, adaptive algorithms for process monitoring and optimization, novel tools for high throughput glass analysis and ML-based algorithms for glass design, including software tools for data mining as well as property and process modelling. The talk gives an overview how all these tools are interconnected and illustrates their usability with some examples.
Gläser zeichnen sich durch eine breite und kontinuierlich abstimmbare chemische Zusammensetzung sowie einzigartige Formgebungstechniken aus, was sie oft zur Schlüsselkomponente moderner Hochtechnologien macht. Die Glasentwicklung ist jedoch oft noch zu kosten-, zeit- und energieintensiv. Der Einsatz von robotergestützten Schmelzsystemen, eingebettet in eine Ontologie-basierte digitale Umgebung, soll diese Probleme in Zukunft überwinden. Im Rahmen der BMBF Forschungsinitiative MaterialDigital unternimmt das Verbundprojekt GlasDigital „Datengetriebener Workflow für die beschleunigte Entwicklung von Glas“ erste Schritte in diese Richtung. Das Projektkonsortium, an dem das Fraunhofer ISC in Würzburg, die Friedrich-Schiller-Universität Jena (OSIM), die Technische Universität Clausthal (INW) und die Bundesanstalt für Materialforschung und -prüfung (BAM, Fachgruppe Glas) beteiligt sind, will alle wesentlichen Basiskomponenten für eine beschleunigte datengetriebene Glasentwicklung zusammenführen. Zu diesem Zweck wird ein robotergestütztes Hochdurchsatz-Glasschmelzsystem mit neuartigen Inline-Sensoren zur Prozessüberwachung, auf maschinellem Lernen (ML) basierenden adaptiven Algorithmen zur Prozessüberwachung und -optimierung, neuartigen Werkzeugen für die Hochdurchsatz-Glasanalyse sowie ML-basierten Algorithmen zum Glasdesign, Data Mining sowie Eigenschafts- und Prozessmodellierung ausgestattet. Der Vortrag gibt einen Überblick darüber, wie all diese Komponenten miteinander verzahnt sind, und veranschaulicht ihre Nutzbarkeit anhand einiger Beispiele.
lasses stand out by their wide and continuously tunable chemical composition and large variety of unique shaping techniques making them a key component of modern high technologies. Glass development, however, is still often too cost-, time- and energy-intensive. The use of robotic melting systems embedded in an ontology-based digital environment is intended to overcome these problems in future. As part of the German research initiative MaterialDigital, the joint project GlasDigital takes first steps in this direction. The project consortium involves the Fraunhofer ISC in Würzburg, the Friedrich Schiller University Jena (OSIM), the Clausthal University of Technology (INW), and the Federal Institute for Materials Research and Testing (BAM, Division Glasses) and aims to combine all main basic components required for accelerated data driven glass development. For this purpose, a robotic high throughput glass melting system is equipped with novel inline sensors for process monitoring, machine learning (ML)-based, adaptive algorithms for process monitoring and optimization, novel tools for high throughput glass analysis and ML-based algorithms for glass design, including software tools for data mining as well as property and process modelling. The talk gives an overview how all these tools are interconnected and illustrates their usability with some examples.