  Process optimization for metal additive manufacturing (AM) is crucial to
ensure repeatability, control microstructure, and minimize defects. Despite
efforts to address this via the traditional design of experiments and
statistical process mapping, there is limited insight on an on-the-fly
optimization framework that can be integrated into a metal AM system.
Additionally, most of these methods, being data-intensive, cannot be supported
by a metal AM alloy or system due to budget restrictions. To tackle this issue,
the article introduces a Reinforcement Learning (RL) methodology transformed
into an optimization problem in the realm of metal AM. An off-policy RL
framework based on Q-learning is proposed to find optimal laser power ($P$) -
scan velocity ($v$) combinations with the objective of maintaining steady-state
melt pool depth. For this, an experimentally validated Eagar-Tsai formulation
is used to emulate the Laser-Directed Energy Deposition environment, where the
laser operates as the agent across the $P-v$ space such that it maximizes
rewards for a melt pool depth closer to the optimum. The culmination of the
training process yields a Q-table where the state ($P,v$) with the highest
Q-value corresponds to the optimized process parameter. The resultant melt pool
depths and the mapping of Q-values to the $P-v$ space show congruence with
experimental observations. The framework, therefore, provides a model-free
approach to learning without any prior.
