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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.
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. This includes software tools for data mining as well as property and process modelling. The presentation provides an overview of how all these tools merge into a digital infrastructure and illustrates their usability using examples.
All infrastructural parts were developed by a consortium consisting of 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) as part of a joint project of the German research initiative MaterialDigital.
Im Projekt GlasDigital sollen digitale Werkzeuge für die Hochdurchsatzentwicklung neuartiger Glaswerkstoffe erarbeitet werden. Dies soll durch die Kombination robotischer Syntheseverfahren mit selbstlernenden Maschinen und ihre Einbindung in eine Ontologie-basierte digitale Infrastruktur realisiert werden.
Silicate glass fracture surface energy calculated from crystal structure and bond-energy data
(2023)
We present a novel method to predict the fracture surface energy, γ, of isochemically crystallizing silicate glasses using readily available crystallographic structure data of their crystalline counterpart and tabled diatomic chemical bond energies, D0. The method assumes that γ equals the fracture surface energy of the most likely cleavage plane of the crystal. Calculated values were in excellent agreement with those calculated from glass density, network connectivity and D0 data in earlier work. This finding demonstrates a remarkable equivalence between crystal cleavage planes and glass fracture surfaces.
GlasDigital
(2023)
Der aktuelle Stand des MateriaDigital Projektes GlasDigital wird vorgestellt. Hierbei wird allgemein die Problem- und Zielstellung präsentiert, als auch auf 2 separaten Postern die Ergebnisse. Diese beinhalten zum Einen die smarte Gestaltung der robotergestützten Glasschmelzanlage der BAM inkl. Analytik und zum Anderen die Digitalisierungsbestrebungen im Bereich Glas, d.h. ML-gestützte C-S-P-Simulation, Ontologie für den Werkstoff Glas, Digitaler Zwilling des Gießprozesses.
GlasDigital
(2023)
Although the slow crack growth in glass is dominated by stress-corrosion phenomena, it also should reflect the underlaying intrinsic fracture behavior controlled by glass chemistry and structure. To investigate such underlaying phenomena, crack growth velocity in alkali silicate glasses was measured in vacuum across 10 orders of magnitude with double cantilever beam technique. Measured and literature crack growth data were compared with calculated intrinsic fracture toughness data obtained from Young’s moduli and theoretical fracture surface energy. Data analysis reveals slight correlation with the packing density and significant deviations from the intrinsic brittle fracture behavior. These deviations do not follow simple compositional trends. Two opposing processes may explain this finding: a decrease in the apparent fracture surface energy due to stress-induced chemical changes at the crack tip and its increase due to energy dissipation during fracture.
As part of a joint project involving the Fraunhofer Institute for Silicate Research (ISC), the Friedrich Schiller University of Jena, the Clausthal University of Technology and the Federal Institute for Materials Research and Testing (BAM), digital tools are to be created for the development of new types of glass materials. Current processes for the production of glasses with improved properties are usually very cost- and energy-intensive due to the low degree of automation and are subject to long development cycles. The use of robotic synthesis processes in combination with self-learning machines is intended to overcome these problems in the long term. The development of new types of glass can then not only be accelerated considerably, but also be achieved with much less effort.
In this talk, data generation via a robotic high-throughput glass melting system is presented, which should be the experimental basis for the ontology developed within the project GlasDigital.
A double cantilever beam technique in air equipped with ultrasound modulation was used to measure the crack velocity v in borate and silicate glasses. In all glasses v and the stress intensity KI followed the empirical correlation v ~ KIn. Indicated by its smallest KI at v = 1 μm s − 1, KI* = 0.27 MPa m0.5, the silicoborate glass containing 70 mol% B2O3 was found most susceptible to stress-corrosion enhanced crack growth. Contrarily, the sodium calcium magnesium silicate glass appeared least susceptible with KI* = 0.57 MPa m0.5. No clear correlation is evident between KI*, reflecting the stress-corrosion susceptibility, and the hydrolytic resistance for all glasses under study, but values of n obtained from the present study and taken from previous literature for 35 glasses tend to decrease with increasing network modifier ion fraction. Energy dissipation during stress-corrosion enhanced crack propagation is assumed to cause this trend.