Self-optimizing compensation of surface deviations in 5-axis ball-end milling based on an enhanced description of cutting conditions

This article presents an approach for a self-optimizing compensation of tool load induced surface deviations in 5-axis ball-end milling. In order to predict the surface deviation independently from the workpiece geometry, the tool deflection is modelled as a function of the tool engagement using a machine learning approach. For that purpose, a novel description of the cutting conditions in ball-end milling is introduced. The selected features are derived from a process-parallel simulation. Subsequently, the learning behavior, the transferability of process knowledge to other shapes and the feasible compensation are investigated experimentally. It is shown that the developed approach can reduce the shape error by over 70%.