# Toolpath design for additive manufacturing using deep reinforcement learning

arXiv: https://arxiv.org/abs/2009.14365 · HTML: https://mlsanity.com/paper/2009.14365/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Sep 2020.

## Built on by (most cited first, 2 of 2)

- [A Reinforcement Learning Approach for Process Parameter Optimization in Additive Manufacturing](https://mlsanity.com/paper/2211.09545/) (2022)
- [Advancing Additive Manufacturing through Deep Learning: A Comprehensive Review of Current Progress and Future Challenges](https://mlsanity.com/paper/2403.00669/) (2024)

## Abstract (arXiv)

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.