In this paper we apply guided policy search (GPS) based reinforcement learning framework for a high dimensional optimal control problem arising in an additive manufacturing process. The problem comprises of controlling the process parameters so that layer-wise deposition of material leads to desired geometric characteristics of the resulting part surface while minimizing the material deposited. A realistic simulation model of the deposition process along with carefully selected set of guiding distributions generated based on iterative Linear Quadratic Regulator is used to train a neural network policy using GPS. A closed loop control based on the trained policy and in-situ measurement of the deposition profile is tested experimentally, and shows promising performance.
@article{arxiv.2009.05838,
title = {Guided Policy Search Based Control of a High Dimensional Advanced Manufacturing Process},
author = {Amit Surana and Kishore Reddy and Matthew Siopis},
journal= {arXiv preprint arXiv:2009.05838},
year = {2020}
}