Open Access

Der aktuelle Stand des Projektes GlasDigital wird in Kurzform präsentiert.

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.

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.

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.

The practical strength of glasses under ambient conditions is substantially lower compared to its intrinsic strength because of sub-critical crack growth (SCCG) from microscopic flaws. While SCCG is related to the humidity of the ambient atmosphere, leading to stress corrosion phenomena, the detailed kinetics are still not fully understood. To get better insight to the contribution of water on the crack-tip, highly water bearing glasses will be investigated by a new device for inert SCCG-measurements using double cantilever beam (DCB) geometry specimens. This device was designed to investigate the stress intensity factor in modus I and crack velocity in vacuum, but different atmospheres can also be introduced. For validation of the new device, first experiments were performed on microscope slides as well as on a soda-lime silicate and a borosilicate crown glass. The results achieved will be presented in comparison to the published results of Wiederhorn.