Model predictive force control for multi-axis rough milling

• Applicants: Professor Dr.-Ing. Thomas Bergs; Dr.-Ing. Sebastian Stemmler, since 10/2024
• Subject Area: Metal-Cutting and Abrasive Manufacturing Engineering ; Automation, Mechatronics, Control Systems, Intelligent Technical Systems, Robotics
• Term: since 2021
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 450794086

Force is the most important process variable in the milling process, because it determines productivity (via the feed rate), product quality (via the displacement of the tool) and process safety (with regard to tool breakage). A model predictive control (MPC) uses an explicit process and machine model to determine the future machine behavior for a short time horizon and controls the process accordingly. It enables for a safe, overshoot-free, optimal control at the technological limit of the process. Such a system has been developed as a prototype for the biaxial milling process. In this project, a control system for multi-axis milling is to be developed and systematically examined. For this purpose, the measurement signal of a table dynamometer must be corrected during the process, e.g. of its inertia and of the change in the mass of the workpiece. This introduces additional uncertainties, which should be considered through a continuous calibration ("identification") of the process model. Since the MPC uses the model coefficients as the feedback-loop, this enables for a robust control. The MPC controls the feed along the tool path, but the behavior of the machines varies along this path. This is because different machine axes are involved for a movement - especially in multi-axis milling. This nonlinear behavior must also be taken into account in the control. The aim of the project is to develop and research a closed-loop force control system for multi-axis roughing. It is assumed that the controller is operates a multi-axis milling process at any reference of the force without exceeding it - independent of the condition of the tool. The system is benchmarked to a control method, which is already established in the research of cutting technology.

• DFG Programme: Research Grants
• Co-Investigator: Professorin Dr.-Ing. Heike Vallery
• Ehemaliger Antragsteller: Professor Dr.-Ing. Dirk Abel, until 9/2024