论文标题
使用深入增强学习的工具路径设计用于增材制造
Toolpath design for additive manufacturing using deep reinforcement learning
论文作者
论文摘要
目前，其设计空间的高维度妨碍了基于金属的添加剂制造工艺的工具路径优化。在这项工作中，提出了一个强化学习平台，该平台动态学习刀具策略以建立任意部分。为此，研究了三个突出的无模型增强学习公式，以设计增材制造工具路径，并针对两种浓密且稀疏的奖励结构进行了证明。结果表明，这种基于学习的工具路径设计方法可以达到高分，尤其是在存在密集的奖励结构时。
Toolpath optimization of metal-based additive manufacturing processes is currently hampered by the high-dimensionality of its design space. In this work, a reinforcement learning platform is proposed that dynamically learns toolpath strategies to build an arbitrary part. To this end, three prominent model-free reinforcement learning formulations are investigated to design additive manufacturing toolpaths and demonstrated for two cases of dense and sparse reward structures. The results indicate that this learning-based toolpath design approach achieves high scores, especially when a dense reward structure is present.
