In-situ metrology, inverse analysis and first-principle modelling for the physics- and data-based prediction of highly non-linear material behaviour and failure in manufacturing technology

Severing manufacturing processes such as shear cutting use mechanical mechanisms to break up the material cohesion of work pieces. In this process, highly non-linear deformations and complex thermo-visco-plastic material effects as well as mechanisms of material damage and material failure occur, which are based on the interaction between physical state variables and the material microstructure, which decisively determine the manufacturing process quality. The lack of understanding of the interdependencies between state variables such as temperature, strain and stress field as well as process variables such as cutting speed, cutting clearance and cutting edge geometry has the consequence that all existing modelling approaches cannot be used in a generalizable predictive way and lose their validity outside a local, narrow process window. While the highly non-linear material behaviour typical for the process and the resulting broad spectrum of state variables severely limits the validity of existing local characterisation and modelling approaches, it is precisely this process property that is interpreted by the applicants as an opportunity and source of comprehensive information on process and material behaviour. Specifically, the research project will combine novel probabilistic methods of machine learning for inverse material parameter and material model identification, first-principle modelling approaches based on high-fidelity finite element methods as well as high-resolution in-situ measurement methods in a novel way in order to enable accurate and global predictions of material behaviour, i.e. predictions that are valid in the entire process parameter space and in principle also transferable to other processes. While previous research approaches do not consistently combine modern numerical modelling and analysis methods and innovative experimental measurement and evaluation techniques, but rather consider them predominantly in isolation from each other, only the proposed combined physics- and data-based approach allows to fully exploit potentials with regard to predictive and generalisable material and process modelling.