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.
@article{arxiv.2009.14365,
title = {Toolpath design for additive manufacturing using deep reinforcement learning},
author = {Mojtaba Mozaffar and Ablodghani Ebrahimi and Jian Cao},
journal= {arXiv preprint arXiv:2009.14365},
year = {2020}
}