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Large-scale additive manufacturing involves fabricating parts by joint printing of materials layer upon layer. The product quality and process efficiency are yet to be addressed to guarantee the process viability in practice. The print surface temperature has a significant impact on both of these elements and can be controlled by properly scheduling the material depositions on the surface. The thermal infrared images captured in real-time are processed, and the extracted thermal profiles are translated into a nonlinear profile model describing the heat dissipation on the surface. A real-time layer time control model is formulated to determine the best time to print the next layer. Furthermore, exploiting the maneuverability characteristics of the printer head while considering its mechanical constraints, a real-time printer head speed control model is formulated as a nonlinear mixed-integer program. Following the deterministic finite-state optimal control and shortest path problem paradigm, a novel algorithm is developed to decide the optimal printing speed trajectory for each layer. The proposed approach was tested by two case studies, including a thin wall specimen and a car lower chassis. The results showed that the method can capture the thermodynamics of the process and achieve simultaneous improvement in both quality and efficiency.

Dynamic material deposition control for large-scale additive manufacturing