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Organisationseinheit der BAM
- 5.6 Glas (50) (entfernen)
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.
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.
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 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.
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.
Hydrogen gas diffusivity of fourteen glasses of the Na2O-Al2O3-SiO2 system are studied along the joins quartzalbite-jadeite-nepheline (Qz-Ab-Jd-Np, fully polymerized) and albite-sodium disilicate (Ab-Ds, depolymerized).
Density measurements show that ionic porosity decreases from 54.4% (Qz) to 51.5% (Np) and from 52.4% (Ab) to 50.2% (Ds). Hydrogen diffusivity D follows similar trends but at another scale. D at 523 K decreases from 4×10−12 to 3×10−14m2 s−1 (Qz-Np) and from 4×10−13 to 3×10−15m2 s−1 (Ab-Ds). Charge compensating Na+ acting as a filling agent in fully polymerized network structures leads to up to one order of Magnitude higher diffusivities as depolymerized glass structures of the same SiO2 content where Na+ takes the role of a
modifier ion. Temperature dependence of the diffusivity indicates that both the activation energy involved with the moving H2 molecule as well as the accessible volume in the structure contribute to this compositional trend.
The permeation of hydrogen gas was studied in meta-aluminous (tectosilicate) glass powders of Li2O×Al2O3×SiO2 (LAS), Na2O×Al2O3×SiO2 (NAS) and MgO×Al2O3×SiO2 (MAS) systems by pressure loading and vacuum extraction in the temperatures range 210–310 °C. With this method, both the solubility S and the diffusivity D were determined, while the permeability was given by the product SD. For all glasses, S was found to decrease with temperature, while D increased. Since the activation energy of diffusion of H2 molecules exceeded that of dissolution, permeation increased slightly with temperature. When extrapolated to standard conditions (25 °C), the permeability of tectosilicate glasses was found to be only 10-22–10-24 mol H2 (m s Pa)-1, which is 8–10 magnitudes lower than most polymers. Thin glass liners of these compositions are expected to be the most effective barrier for tanks of pressurised hydrogen.