The aim of the project is to research a framework for learning 5-axis compensation of form errors in milling processes. This is based on a process-parallel material removal simulation and sophisticated machine learning strategies. Furthermore, we want to investigate the possibility of knowledge transfer between different workpiece geometries, milling tools, and machine tools for improved process planning. To this end, we will establish a framework that supports the necessary functions for flexible and real-time filtering, fusion, and storage of data streams with different characteristics.

Subsequently, basic knowledge about the performance of different machine learning algorithms for building process knowledge is provided and suitable supervised learning methods are designed. Based on this knowledge, a method will be explored that automatically identifies novel process situations and decides whether a new model domain is required or whether existing knowledge can be transferred.

Finally, we plan to develop a compensation strategy for molding errors that combines an adjustment of the tool path using 5 axes of the machine with a local adjustment of the feed rate. Since production data is only available to the scientific community to a very limited extent, the experimental data sets and labeling will be made available online to the scientific community. This allows other research groups to reproduce our results and evaluate their own methods.