  Powder-based additive manufacturing techniques provide tools to construct
intricate structures that are difficult to manufacture using conventional
methods. In Laser Powder Bed Fusion, components are built by selectively
melting specific areas of the powder bed, to form the two-dimensional
cross-section of the specific part. However, the high occurrence of defects
impacts the adoption of this method for precision applications. Therefore, a
control policy for dynamically altering process parameters to avoid phenomena
that lead to defect occurrences is necessary. A Deep Reinforcement Learning
(DRL) framework that derives a versatile control strategy for minimizing the
likelihood of these defects is presented. The generated control policy alters
the velocity of the laser during the melting process to ensure the consistency
of the melt pool and reduce overheating in the generated product. The control
policy is trained and validated on efficient simulations of the continuum
temperature distribution of the powder bed layer under various laser
trajectories.
