Project Details
Projekt
Data-Driven Modelling of Metal Bending Processes
Applicants
Professorin Dr. Barbara Hammer; Professor Dr.-Ing. Werner Homberg; Professor Dr.-Ing. Ansgar Trächtler
Subject Area
Primary Shaping and Reshaping Technology, Additive Manufacturing
Term
since 2023
Project identifier
Deutsche Forschungsgemeinschaft (DFG) - Project number 520459685
The quality of complex bended parts depends significantly on the design, coordination and implementation of the multi-stage bending and straightening operations used for this purpose. A particular challenge is the control of cross-stage or quantity-dependent effects. At present, this is often only possible by using expert knowledge and is not always successful due to the complexity of the interactions. In particular, temporally and spatially fluctuating disturbance variables (of a stochastic nature), which affect e.g. the semi-finished product properties, the underlying tribological system or the thermal and mechanical tool and machine behavior, are problematic. A starting point for dramatic improvements in, among other things, the accuracy and repeatability of individual features in complex bent wire parts is the combination of a multi-stage mechatronic straightener with similar bending units to form a complete machine system (mechatronic bending machine (MSA)). However, in order to achieve target-oriented applicability, target-oriented, cross-stage and quantity-dependent modeling is required for the design of such a manufacturing process, machine system and corresponding tools. The aim of the planned research project is therefore the investigation of data-driven, hybrid models with the integration of AI methods and their applicability in such multi-stage and quantity-dependent bending processes. Among other things, this should enable sufficient defect or data tracking across stages and piece numbers. Accordingly, research focuses on the areas of data collection and data bases, relations and interactions between component properties and quality characteristics. A targeted combination of the data collected in this way, using appropriate hybrid AI models that allow the integration of expert knowledge and physical conditions, should enable interactive data analysis through components of explainability. This provides the basis for a key result of the first funding period - initial reference data sets with a data-based representation of the process sequence, i.e. multi-stage straightening and punch bending, with an initial adaptation of machine learning methods in combination with domain knowledge and the learned analysis results.
DFG Programme
Priority Programmes
