Toolpath design for additive manufacturing using deep reinforcement learning
This work addresses toolpath design challenges for additive manufacturing practitioners, but it is incremental as it applies existing reinforcement learning methods to this domain.
The paper tackled the high-dimensional toolpath optimization problem in metal-based additive manufacturing by proposing a reinforcement learning platform that dynamically learns strategies to build arbitrary parts, achieving high scores particularly with dense reward structures.
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.