Sousa, JoaoJoaoSousaDarabi, RoyaRoyaDarabiSousa, ArmandoArmandoSousaReis, LuísLuísReisBrückner, FrankFrankBrücknerReis, Ana IsabelAna IsabelReisSá, José César deJosé César deSá2024-03-042024-03-042023https://publica.fraunhofer.de/handle/publica/46275810.1115/IMECE2023-1136292-s2.0-85185390387Directed Energy Deposition (DED) is crucial in additive manufacturing for various industries like aerospace, automotive, and biomedical. Precise temperature control is essential due to high-power lasers and dynamic environmental changes. Employing Reinforcement Learning (RL) can help with temperature control, but challenges arise from standardization and sample efficiency. In this study, a model-based Reinforcement Learning (MBRL) approach is used to train a DED model, improving control and efficiency. Computational models evaluate melt pool geometry and temporal characteristics during the process. The study employs the Allen-Cahn phase field (AC-PF) model using the Finite Element Method (FEM) with the Multi-physics Object-Oriented Simulation Environment (MOOSE). MBRL, specifically Dyna-Q+, outperforms traditional Q-learning, requiring fewer samples. Insights from this research aid in advancing RL techniques for laser metal additive manufacturing.enMOOSEDEDreinforcement learningmodel-basedQ-learningDyna-Qconference paper