Faculty of Mechanical Engineering involved with two projects in SPP 2402

The focus of the SPP is the combination of the deterministic model world (white box) with a new, data-driven model world (black box) to be researched to form so-called grey box models. On the one hand, these are to represent the temporal changes of cutting tools in use, which cannot be described purely deterministically, up to the end of their service life. On the other hand, it is planned to use the greybox models to predict the onset of failure, the progress of wear, i.e. material degradation, and the time-variant performance of the coated cutting tools. Both teams are looking forward to their collaboration and the exciting insights the new greybox models will provide into structural-mechanical and chemical-physical wear mechanisms.

After the official approval, Prof. Frank Walther (Chair of Materials Testing Technology) and apl. Prof. Andreas Zabel (Institute of Machining Technology) are looking forward to predicting the application behavior of coated cutting tools during turning in the subproject "Greybox model-based prediction of the wear evolution of coated tools by experimental and model-driven identification of relevant load horizons" by combining numerical simulation, machine learning methods and classical experiments much better than is possible today.

The team led by Prof. Dirk Biermann (Institute of Machining Technology), Prof. Wolfgang Tillmann (Chair of Materials Technology) and Dr. Jörg Debus (Experimental Physics 2) will also begin researching the relationships between the coating processes of the tools and their subsequent use behavior in July in order to also achieve a significantly better prediction of use behavior. The subproject "Greybox modeling of the run-in behavior of coated tools in the milling process as a dynamic load collective based on operando, in-situ and ex-situ analyses" makes use of a pool of sophisticated spectroscopic and microscopic methods in addition to simulations and the application of neural networks for data correlation. The bundling of the interdisciplinary expertise of both teams represents a further step in the expansion of the engineering and natural science profile focus of the TU Dortmund.

Back