Adaptive feed rate toolpath generation

People: Teodor-Petre Cioaca, Prof. Dr. Lars Linsen



Description: The goal of this project is to devise a method for automatically computing discrete paths for CNC machining/micro-milling applications where high-speed workpiece processing and accuracy are the main measures of efficiency and quality. Such an optimization task involves monitoring dynamical/machine parameters for individual axis motors, as well as chord error as direct estimator of machining quality. The input data is provided as a discrete set of points in a 5-dimensional space, together with target feed rates. From this input, the derived solution consists of a denser discrete path, which can be then used during a micro-milling process. The most important restrictions come fom the need to ensure bounded axis dynamics and low-tolerance path tracking. The past implementation of such a solution uses a greedy strategy to recover the spindle dynamics and, consequently, possible positions of the tool tip with respect to the workpiece coordinate system. Whenever a constraint is violated, the algorithm employs backtracking by fitting lower feed rate profiles for the problematic regions. As most greedy strategies, this solution is far from being optimal, its performance being also sensitive to the input data sets.