Guided Policy Search Based Control of a High Dimensional Advanced Manufacturing Process
This addresses a high-dimensional optimal control problem in additive manufacturing, though it appears incremental as it applies an existing GPS framework to a new domain.
The paper tackles controlling additive manufacturing process parameters to achieve desired part surface geometry while minimizing material usage, using guided policy search reinforcement learning with a realistic simulation model and iterative Linear Quadratic Regulator guidance. Experimental closed-loop control with in-situ measurements showed promising performance.
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.