Machine learning methods for adaptive process planning of 5-axis milling

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The proposed project aims to research a framework for a learning 5-axis compensation of shape errors in milling processes based on a process-parallel material removal simulation and sophisticated machine learning strategies. Moreover, we aim to investigate the ability of knowledge transfer between different workpiece geometries, milling tools and machine tools for an enhanced process planning. For this purpose, we will establish a framework that encompasses the functionalities needed to support a flexible and real-time-capable filtering, fusion and storage of data streams with different characteristics. Next, fundamental knowledge about the performance of different machine learning algorithms for building up process knowledge and design suitable supervised learning methods is provided. Based on this knowledge a method that identifies novel process situations automatically and decides whether a new model domain is necessary or if existing knowledge can be transferred, is researched. Finally, we plan to develop a compensation strategy for shape errors that combines an adjustment of the toolpath using 5-axis of the machine tool with a local adaption of the feed rate. Since production data is only available to a very limited extent to the scientific community, the experimental data sets and labels are made accessible online to the scientific community. This will allow other research groups to reproduce our findings and evaluate their own methods.