Laser power planning in directed energy deposition by deep reinforcement learning

Maintaining the stability of laser melt-pool conditions is critical to ensure consistency in cooling and solidification rates during directed energy deposition (DED) process. This serves to minimize degradation in the built accuracy and can improve overall microstructure homogeneity in DED parts. This work presents a novel laser power planning approach by utilizing a deep neural network (DNN) agent to learn the laser power control policy directly from the melt-pool information predicted by the finite element (FE) model. The agent was trained via the deep Q-learning algorithm using a previously developed FE model as the process simulator. The numerical experiment demonstrates that the trained agent is capable of modulating the laser power during the simulated deposition of a new component and can maintain the fluctuation in melt-pool volume within the defined target range. This work shows the potential of using the numerical modeling and reinforcement learning to generate the required laser power planning and modulation in DED process, without requiring physical sensors and closed-loop in-process monitoring and control.