  Designing efficient closed-loop control algorithms is a key issue in Additive
Manufacturing (AM), as various aspects of the AM process require continuous
monitoring and regulation, with temperature being a particularly significant
factor. Here we study closed-loop control of a state space temperature model
with a focus on both model-based and data-driven methods. We demonstrate these
approaches using a simulator of the temperature evolution in the extruder of a
Big Area Additive Manufacturing system (BAAM). We perform an in-depth
comparison of the performance of these methods using the simulator. We find
that we can learn an effective controller using solely simulated process data.
Our approach achieves parity in performance compared to model-based controllers
and so lessens the need for estimating a large number of parameters of the
intricate and complicated process model. We believe this result is an important
step towards autonomous intelligent manufacturing.
