Toolpath design for additive manufacturing using deep reinforcement learning

Mojtaba Mozaffar; Ablodghani Ebrahimi; Jian Cao

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arXiv · 2009.14365

Toolpath design for additive manufacturing using deep reinforcement learning

Mojtaba Mozaffar·Ablodghani Ebrahimi·Jian Cao

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Abstract

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.

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Mojtaba Mozaffar, Ablodghani Ebrahimi, Jian Cao. 2020-09-30. Toolpath design for additive manufacturing using deep reinforcement learning. https://arxiv.org/abs/2009.14365

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