<ul><li>This paper presents a Model Predictive Control (MPC) framework using a deep neural network for real-time decision-making in additive manufacturing.</li><li>The framework, named Time-Series Dense Encoder (TiDE), can predict future states within the prediction horizon in one shot, accelerating the MPC process.</li><li>Using Directed Energy Deposition (DED) additive manufacturing as a case study, the MPC achieves precise temperature tracking and melt pool depth control.</li><li>Compared to a PID controller, the MPC provides smoother laser power profiles with competitive or better melt pool temperature control performance.</li></ul>

Real-Time Decision-Making for Digital Twin in Additive Manufacturing with Model Predictive Control using Time-Series Deep Neural Networks

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