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Toolpath optimization of metal-based additive manufacturing processes iscurrently hampered by the high-dimensionality of its design space. In thiswork, a reinforcement learning platform is proposed that dynamically learnstoolpath strategies to build an arbitrary part. To this end, three prominentmodel-free reinforcement learning formulations are investigated to designadditive manufacturing toolpaths and demonstrated for two cases of dense andsparse reward structures. The results indicate that this learning-basedtoolpath design approach achieves high scores, especially when a dense rewardstructure is present.

Toolpath design for additive manufacturing using deep reinforcement learning