ORALS

SESSION: GlassTuePM3-R10	Durán International Symposium on Sustainable Glass Processing and Applications
Tue. 28 Nov. 2023 / Room: Boardroom
Session Chairs: Andreas Diegeler; Session Monitor: TBA

17:55: [GlassTuePM313] OS
DIGITAL MATERIAL DATA-BASED GLASS SCREENING FOR THE SYSTEMATIC DEVELOPMENT OF NEW GLASSES
Andreas Diegeler¹ ; Martin Kilo² ; Ralf Müller³ ; Altair Contreras-Jaimez¹ ; Tina Waurischk⁴ ; Stefan Reinsch⁵ ; ¹Fraunhofer ISC, Wertheim, Germany; ²Fraunhofer ISC, Senior Scientist, Wertheim, Germany; ³BAM Berlin, Head of Division Glass, Berlin, Germany; ⁴Scientist, BAM Berlin, Berlin, Germany; ⁵Senior Scientist, BAM Berlin, Berlin, Germany;
Paper Id: 113 [Abstract]

Glass development works traditionally iteratively by melting series of samples, investigating their properties, and then melting more samples with modified composition. The whole process might be pretty long and can take several months, up to one year in special cases. Fraunhofer ISC has developed a rapid-screening systems during the last years, which is currently being optimized in collaboration with the BAM in Berlin.The robotic glass melting systems currently running in Berlin allows the melting of 20 samples during 24 hours and is backed up with high throughput RFA, LIBS and DSC devices for chemical composition, glass transition and crystallization characterization. As an additional option, the system can be extended with a robotic in-line characterization module for fundamental glass properties like viscosity, thermal expansion and crystallization behavior. Therefore, the method TOM - Thermo-Optical-Measurement method" was developed at Fraunhofer ISC to characterize physical and chemical parameter of glass.Recent advances include the preparation of larger samples with masses of up to 200 g, an optimized cooling process and a better batch as well as glass melt homogenization system.Further advancements of the system towards the development of glass-ceramics as well as enamel systems are discussed. 

References:
[1] Scholze, H. Glass: Nature, Structure, and Properties. Springer. Göttingen 1991, p. 30
[2] Fluegel, A., Earl, D.A., Varshneya, A.K., Oksoy, D.: Statistical analysis of viscosity, electrical resistivity, and further glass melt properties. In: High temperature glass melt property database for process modeling. Editors: Seward III, TP, Vascott, T. The American Ceramic Society, Westerville, Ohio, 2005, p. 187-256
[3] Yang, Y., Han, J., Zhai, H., 2022. Prediction and screening of glass properties based on high-throughput molecular dynamics simulations and machine learning. In: Journal of Non-Crystalline Solids 597(1-3):121927
[4] Bødker, ML, Bauchy, M., Du, T., Mauro, JC, Smedskjaer, MM.. 2022. Predicting glass structure by physics-informed machine learning. In: npj Computational Materials, Volume 192, p. 1-9
[5] Raether, F., Meinhardt, J., Schulze-Horn, P. 2007. TOM - A versatile thermooptical measuring system for the optimization of heat treatments. In: Ceramic Forum International 84(4), p. E18-E21