Project Details
Projekt
Design of process-informed models for NC milling processes
Applicants
Professorin Dr.-Ing. Gisela Lanza; Professor Dr.-Ing. Florian Stamer; 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 effective use of data-driven models for the optimization of machining processes requires a comprehensive database. However, the high number of experiments necessary for this purpose and the associated challenges to reliably measure relevant process characteristics contradict the idea of a model-based, resource-efficient design of manufacturing processes. In this context, the main goal of this project is to develop a methodology for developing a new class of reusable models based on an efficiently acquired database. These base models are intended to be adapted into process-informed models by incorporating a limited number of new, process-specific data and contextualized within specific machining configurations and individual machine tools. In this regard, developing an experimental concept for efficient data acquisition and evaluation is necessary. Particular focus is placed on researching an appropriate measurement concept and its qualification to generate a comprehensive, high-quality database. To achieve this, an explicit assessment of measurement and model uncertainties is included. The aim of modeling is the resource-efficient prediction of process stability during milling to enable a productivity-enhancing process optimization. To significantly extend the limits of previous predictions and to ensure a robust process design, the consideration and integration of process-, machine-, tool-, and workpiece-specific variables is necessary. The data will be combined in this project and transferred into a database consisting of experimental and simulation-based data. Thus, it represents the central project within FOR in which the data and models of all subprojects will be combined to develop a methodology for a resource-efficient model generation without a significant reduction in predictive accuracy.
DFG Programme
Research Units
