This course is part of the module: Fundamentals of Materials Science (EN), Fundamentals of Materials Science (GES), Introduction to Materials Science and Engineering (EN)
After the lecture you should be able to (lecture objectives):
Understand the basic structure of polymers and ceramics
Know how to characterize the properties of polymeric and ceramic materials
Know how to analyze the microstructure of ceramic and polymeric materials
Identify and understand the main fabrication techniques to process ceramics and polymers
Ceramic materials
Introduction to material science and engineering What is material science and materials engineering? What is the relevance of them for other engineering disciplines?
Introduction to ceramic materials what are ceramic materials and where are they used? Overview of potential applications.
Crystal structure of ceramics Different crystal structures of ceramics; influence of bonding type on properties; influence of crystal structure on properties; phase transition.
Ceramic powder preparation and shaping mixing, comminution, separation and granulation of powders; dry and wet routes for processing; shaping methods: die and isostatic pressing, slip casting, tape casting, robocasting (3D printing), extrusion, injection molding.
Sintering and microstructures Driving force and mechanism of sintering; types of sintering processes; sintering stages; resulting microstructures: what is a microstructure? how to analyze it? why analyze it?
Characterization of ceramics Overview of the main techniques used to characterize ceramic materials.
Functionalproperties of ceramics Overview of the different applications ofceramics according to their functional properties.
Polymeric materials
Polymers in engineering Development ofpolymers; A worldofpolymers; Properties ofpolymersand lighweight; Recycling and productlifecycles
Synthesis and structure of the macromolecule Synthesis and structureofpolymers; Structureand bonds;
Hund, P. (2021). Modellierung eines elektrischen Netzes zur Demonstration des Einflusses von virtueller Trägheit durch umrichterbasierte Energieanlagen.
Hund, P. (2021). Koordinierte Bereitstellung von virtueller Trägheit durch erneuerbare umrichterbasierte Energieanlagen in Verteilnetzen mithilfe von künstlicher Intelligenz.
Möller, P. (2021). Erfassung der Knotenspannung in Niederspannungsnetzen auf Basis von dezentralen Messeinrichtungen mithilfe von Machine learning.
Plant, R. (2021). Estimation of Power System Inertia in an Inverter-Dominated Distribution Grid Using Machine Learning.
2020
Dressel, M. (2020). Modellierung der Zustandsschätzung eines elektrischen Netzes mit Hilfe von Graph neuronalen Netzen.
Schmidt, M. (2020). Vorhersage von zuverlässig bereitstellbarer Regelleistung aus Erneuerbaren Energien mithilfe von neuronalen Netzen.