Abstract
Cyber physical systems (CPS) are changing the way machine tools function and operate. As the CAD-CAM-CNC tool chain gains intelligence the boundaries of the elements of the tool chain become blurred and new features, based on advancements in artificial intelligence can be integrated. The main task of the CAD-CAM-CNC chain is to generate the cutter trajectories for the manufacturing operation. Driven by sustainability and the need for capacity, the need arises to optimize the paths through this tool chain. In this paper a concept for path optimization with reinforcement learning is proposed, with focus on the reward function, specific to tool path optimization via the channel method.
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Dripke, C., Höhr, S., Csiszar, A., Verl, A. (2017). A Concept for the Application of Reinforcement Learning in the Optimization of CAM-Generated Tool Paths. In: Beyerer, J., Niggemann, O., Kühnert, C. (eds) Machine Learning for Cyber Physical Systems. Technologien für die intelligente Automation. Springer Vieweg, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-53806-7_1
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DOI: https://doi.org/10.1007/978-3-662-53806-7_1
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