A Reinforcement Learning Approach for Process Parameter Optimization in Additive Manufacturing. (arXiv:2211.09545v1 [cs.LG])

Process optimization for metal additive manufacturing (AM) is crucial to
ensure repeatability, control microstructure, and minimize defects. Despite
efforts to address this via the traditional design of experiments and
statistical process mapping, there is limited insight on an on-the-fly
optimization framework that can be integrated into a metal AM system.
Additionally, most of these methods, being data-intensive, cannot be supported
by a metal AM alloy or system due to budget restrictions. To tackle this issue,
the article introduces a Reinforcement …

additive manufacturing arxiv manufacturing optimization process reinforcement reinforcement learning