  Selective laser melting (SLM) is one of emerging processes for effective
metal additive manufacturing. Due to complex heat exchange and material phase
changes, it is challenging to accurately model the SLM dynamics and design
robust control of SLM process. In this paper, we first present a data-driven
Gaussian process based dynamic model for SLM process and then design a model
predictive control to regulate the melt pool size. Physical and process
constraints are considered in the controller design. The learning model and
control design are tested and validated with high-fidelity finite element
simulation. The comparison results with other control design demonstrate the
efficacy of the control design.
