SPP 2422:  Data-driven process modelling in metal forming technology

• Subject Area: Mechanical and Industrial Engineering ; Computer Science, Systems and Electrical Engineering ; Materials Science and Engineering ; Thermal Engineering/Process Engineering
• Term: since 2023
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 500936349

The aim of the priority program is to develop novel methods for using implicit knowledge from process data in combination with expert knowledge and solution spaces from process simulations for the active surface design of forming tools. This is to be achieved through interdisciplinary collaboration between institutes of forming technology, automation and data science working in the fields of process development, artificial intelligence and machine learning. The originality of the research content is highlighted by the fact that already established methods of data science are used for modeling in order to address concrete challenges of forming applications. The interdisciplinary collaboration between the fields of forming technology, automation and data science ensures that mathematically generic representations of forming operations can be defined and modeled. At the same time, it is expected that these can also be concretely validated in the corresponding forming domain in order to generate new knowledge from the derived models. The interconnection and fine-tuning of all the individual projects in the interdisciplinary priority program will be in the responsibility of the coordinator. For this purpose, funds for coordination are proposed.

• DFG Programme: Priority Programmes

Projects

• AI based setup assistance system for multi-stage presses (Applicants Hinz, Lennart ;  Krimm, Richard )
• Data-based die spotting in sheet metal forming (Applicants Ihlenfeldt, Steffen ;  Niggemann, Oliver )
• Data-based identification and prediction of the die surface condition and interactions in sheet bulk metal forming processes from coil (Applicants Merklein, Marion ;  Vogel-Heuser, Birgit )
• Data-Driven Modelling of Metal Bending Processes (Applicants Hammer, Barbara ;  Homberg, Werner ;  Trächtler, Ansgar )
• Data-driven process modeling in stamping technology (Applicants Althoff, Matthias ;  Hartmann, Christoph ;  Volk, Wolfram )
• Data-driven Prozessmodelling in metal forming technology - Coordination proposal (Applicant Liewald, Mathias )
• DatProForge - Data-driven process modelling of closed-die forging processes to increase productivity using adaptive tool design methodology (Applicants Gardill, Markus ;  Härtel, Sebastian )
• Derivation of cause-effect relationships for die design on the basis of data-driven process modeling for fine blanking (Applicants Bergs, Thomas ;  Kröger, Peer ;  Trimpe, Sebastian )
• Design Method for Forming Tools for Rotary Draw Bending of Bend-in-Bend Geometries (Applicants Engel, Bernd ;  van Laerhoven, Kristof )
• Development of a data-driven model for the evaluation and improvement of process robustness in the design of deep-drawing tools (Applicants Ben Khalifa, Noomane ;  Heger, Jens )
• Optimisation of the active surface design of high-speed progressive tools using machine and deep learning algorithms (Applicants Groche, Peter ;  Kersting, Kristian )
• Process data-driven modeling for the robustification of shear-cutting collar-pulling processes using effective tool-knit surface design with consideration of edge-crack sensitivity (Applicants Koschmider, Agnes ;  Kräusel, Verena )
• Robust active surface design for multi-stage sheet metal forming processes based on data- and computation-driven surrogate modelling of springback behaviour (Applicants Liewald, Mathias ;  Weyrich, Michael )
• Transparent AI-Based Process Modelling in Die Forging (Applicants Brunotte, Kai ;  Huber, Marco )

• Spokesperson: Professor Dr.-Ing. Mathias Liewald