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

该项目旨在研究一种基于过程并行材料去除仿真和复杂机器学习策略的铣削过程中形状误差的学习5轴补偿框架。此外，我们的目标是研究不同的工件几何形状，铣削工具和机床之间的知识转移能力，以增强工艺规划。为此，我们将建立一个框架，其中包括支持具有不同特征的数据流的灵活和实时过滤，融合和存储所需的功能。接下来，提供了关于不同机器学习算法的性能的基础知识，用于建立过程知识和设计合适的监督学习方法。基于这些知识的方法，识别新的过程情况下，自动和决定是否需要一个新的模型域或现有的知识可以转移，进行了研究。最后，我们计划开发一种补偿策略的形状误差相结合的工具路径的调整，使用5轴的机床与局部适应的进给速度。由于科学界只能在非常有限的程度上获得生产数据，因此科学界可以在线获取实验数据集和标签。这将允许其他研究小组复制我们的发现并评估他们自己的方法。

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.