Project Details
Projekt
Coordination Funds
Applicant
Professorin Dr.-Ing. Petra Wiederkehr
Subject Area
Metal-Cutting and Abrasive Manufacturing Engineering
Term
since 2026
Project identifier
Deutsche Forschungsgemeinschaft (DFG) - Project number 546480484
The aim of the proposed research group FOR is to research a new methodology for the development of process-informed models for the efficient design of machining processes. Through a targeted combination of data, knowledge and context based on the integration of basic experiments and complex NC milling as well as the enrichment of real measurement data with simulation results, knowledge extraction will be maximized in order to increase the prediction quality of the models. The vision is to significantly extend the limits of previous predictions by developing transferable models and to achieve a more efficient and robust model-based process design. To reach this objective, a concept will be developed that allows a high-quality and high-volume database for NC milling to be obtained in a technologically resource-efficient manner. In combination with data from experimental and simulative basic experiments (e.g. orthogonal cutting test and chip formation simulation), transferable and adaptable basic models will be developed, which will then be placed in the context of the individual machine tool and the specific material and tool batch used in the milling process. By using machine-integrated sensor technology, data from in-process measurements during NC machining will be continuously obtained and a model adaptation will be carried out (process-informed). Over time, this will result in models that are more robust against uncertainties and disturbance variables. The development of the new methodology will be demonstrated in the first phase on a demonstrator machine with integrated sensor technology using the example of NC milling of the heat-treatable steel 42CrMo4 and the transfer to another machine tool. The detailed feedback of the information from the process into the models and thus the generalizability and robustness against disturbance variables will be researched in the second phase.
DFG Programme
Research Units
