5 Werkstofftechnik
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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.
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.
The process of viscous flow sintering is a phenomenon that is closely linked to the surface properties of the glass particles. In this work, we studied the extreme case of acid-leaching of soda-lime-silicate glass beads of two different particle size distributions and its effects on non-isothermal viscous sintering of powder compacts. Depth profiling of the chemical composition after leaching revealed a near-surface layer depleted in alkali and alkaline earth ions, associated with concurrent hydration as mass loss was detected by thermogravimetry. Heating microscopy showed that acid treatment of glasses shifted the sinter curves to higher temperatures with increasing leaching time. Modelling of the shrinkage with the cluster model predicted a higher viscosity of the altered surface layer, while analysis of the time scales of mass transport of mobile species (Na+, Ca2+ and H2O) during isochronous sintering revealed that diffusion of Na+ can compensate for concentration gradients before sintering begins. Also, exchanged water species can diffuse out of the altered layer, but the depletion of Ca2+ in the altered surface layer persists during the sinter interval, resulting in a glass with higher viscosity, which causes sintering to slow down.
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.
Glasses in the systems Me2O-ZnO-B2O3 with Me = Li, Na, K, Rb (MeZB), Na2O-ZnO-CuO-B2O3 (NZCuB), CaO-ZnO-B2O3 (CaZB), and Li2O-PbO-B2O3 (LPbB) as a reference, were studied by differential thermal analysis, dilatometry, rotational viscometry, and heating microscopy. A decrease of viscosity and sintering range was found with decreasing number of fourfold coordinated boron. The viscosity of the alkali zinc borate glasses varies only slightly. LPbB and CaZB stand out by their reduced and increased viscosities, respectively. Sodium, potassium, and calcium zinc borate glasses possess a fragility above 76. All glasses were sintered to full density before crystallization. Mostly binary zinc borate phases govern crystallization. A ternary crystalline phase was detected only in the potassium containing sample. The Weinberg glass stability parameter ranges between 0.07 and 0.12. This is caused by the presence of several crystalline phases and varying melting points of even the same crystalline phase in different glass matrices.
We present an easy-to-apply method to predict structural trends in the internal nucleation tendency of oxide glasses. The approach is based on calculated crystal fracture surface energies derived from easily accessible diatomic bond energy and crystal lattice data. The applicability of the method is demonstrated on literature nucleation data for isochemically crystallizing oxide glasses.