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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.
Glasig-kristalline Werkstoffe für Schlüsseltechnologien - Prozessbegleitende Prüfung und Simulation
(2017)
Eigenschaftsprofile und Kennwertstreuungen keramischer und glaskeramischer Komponenten und Bauteile werden durch ihren Herstellungsprozess wesentlich mitbestimmt. Prozessbegleitende Prüf- und Simulationsmethoden gewinnen so wachsenden Einfluss auf die Erschließung neuer Applikationsfelder dieser Werkstoffe. Eine wichtige Rol¬le spielen hierbei oft thermokinetische Prozesse, die u. a. Wärmetransport- und Diffusionsvorgänge, Phasengrenzflächenreaktionen, das rheologische Verhalten heterogener Systeme sowie deren Gefügeevolution beim Sintern umfassen. Ziel des Vortrages ist es, diesen Trend anhand ausgewählter Bei-spiele aus der Arbeit des Fachbereichs Glas der BAM zu illustrieren.
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.
The viscosity of silica glasses with different water contents was measured by penetration of sapphire microspheres in the range from 1012-1014·7 Pas at ambient pressure. Commercial silica glasses were used together with hydrous silica glasses, which were prepared by melting glass powder plus water in an internally heated pressure vessel at 2-3 kbar and 1350-1450°C. The temperature dependence of the viscosity of silica glasses with total water contents Cw of 5·4×10-4, 7·0×10-4, 1·00×10-2, and 1·55×10-2(Cw in mass fractions) can be described by Arrhenius equations. From the results, the linear dependence of the T12 isokom (K) with the logarithm of the water content was determined as T12=192-409 logCw. The analysis of the compositional dependence of the T12 isokom in the H2O-Na2O-SiO2 system reveals a temperature depression of up to 200 K for mixed water- and sodium-bearing glasses compared to H2O-SiO2 and Na2O-SiO2 glasses with same molar silica content. From these findings we conclude that protons may contribute significantly to the mixed alkali effect in glasses.
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.
Advanced methods for 3D green density characterization like computed tomography and 3D FE sinter modeling can be utilized for increasing the reliability of sintered components. The experimental in situ observation of sintering, however, is currently restricted to silhouette methods, i.e. heating microscopy. For complex shaped samples, in situ shape screening during shrinkage would allow much better validation of 3D sinter simulation models. Further, by revealing temporary sinter warpage, 3D high-temperature shape screening allows to locate potential defects of complex sintered components. Against this background, BAM developed a testing device for in situ 3D high-temperature shape screening for ceramic and glass-ceramic tapes up to 1000°C [1-3]. Current work is focused on dropping this restriction in sample shape and temperature. The poster illustrates the current state of this work and possible applications of the method e.g. in detecting sinter warpage of metallized glass-ceramic LTCC tapes.
Advanced ceramics are promising key components for microsystems
engineering, communication Technology and medical engineering.
Driven by steadiiy increasing demands on dimensional accuracy of
components and Integration density, sintering has to be controlled at
an unprecedented degree of precision.
Being most crucial for components of complex shape, graded or heterogeneous-composed parts like LTCC multilayers for microsystems, precise control of sintering has a decisive influence on the dimensional accuracy, mechanical integrity and reliability of sintered components.
Thus, permanent or temporary shape distortions or warping during sintering may result from spatially heterogeneous microstructure and
temperature distribution, e.g. during the co-firing process of green
multilayer Stacks printed with metallization and other functional pastes.
Whereas permanent warping can be easily measured after sintering, temporary warping during firing is more difficult to measure. Nonetheless, this effect may cause latent failures later, which often limit the applicability of ceramic components.
Hochleistungssinterwerkstoffe spielen eine wachsende Rolle in derzeitigen Schlüsseltechnologien wie der Mikrosystem-, Informations-, Kommunikations- und Medizintechnik.
Dabei steigen die Anforderungen an die präzise Steuerung der Sinterung. Besonders für komplexe, gradierte oder heterogen zusammengesetzte Formkörper wie zum Beispiel keramische LTCC-Multilayer für die Mikrosystemtechnik hat die exakte Beherrschung dieses Prozessschrittes wesentlichen Einfluss auf die Maßhaltigkeit, mechanische Integrität und Zuverlässigkeit der gesinterten Bauteile. Bedingt durch die Heterogenität der mehrlagigen und mit Leit- und anderen Funktionspasten bedruckten Sinterkörper hinsichtlich ihrer sinterphysikalischen oder thermischen Eigenschaften können bleibende
oder temporäre Verwerfungen beim Schrumpfungsprozess auftreten, wobei letztere unter Umständen Ursache latenter Fehler im späteren Bauteil sind.
Es wurde ein Gerät zur In-situ-Kontrolle des Sinterns keramischer Bauteile mit Hilfe der Hochtemperatur-Lasertriangulation aufgebaut. Durch Rotation der Probe und gleichzeitiger Positionierung des Laserabstandssensors erfolgt die dreidimensionale Erfassung der Topografie der Probenoberfläche. Derzeit können flache Proben mit den Maßen < 20 cm x 20 cm x 1 cm bis 1000 °C untersucht werden. Die örtliche Wiederholpräzision des verwendeten Lasersensors beträgt 10 µm bei 20 °C und 15 µm bei 1000 °C. Die lineare Positioniergenauigkeit des Sensors liegt bei 1 µm, die Winkelauflösung der Drehung des Probentellers beträgt 0,0025 °. Daten für ein 3D-Oberflächenprofil werden in etwa 2 bis 3 Minuten erzeugt. Spezielle Linienprofile erhält man bei einer Messzeit von wenigen Sekunden.