Realtime Control-Oriented Modeling and Disturbance Parameterization for Smart and Reliable Powder Bed Fusion Additive Manufacturing
The vision of sustainable mass customization calls for additive manufacturing (AM) processes that are resilient to process variations and interruptions. This work targets to pioneer a system-theoretical approach towards such a smart and reliable AM. The approach is based on control-oriented modeling of the process variations and on closed-loop model-based controls that facilitate in-situ adjustment of the part quality. Specifically, one focused example is laser-aided powder bed fusion. Building on the in-layer precision heating and solidification, together with layer-by-layer iterations of the energy source, feedstock, and toolpath, we discuss mathematical abstractions of process imperfections that will not only understand the intricate thermomechanical interactions but are also tractable under realtime computation budgets. In particular, we develop and validate a surrogate modeling of in-process disturbances induced by the periodic in- and cross-layer thermomechanical interactions. This control-oriented disturbance modeling allows for the adoption of high-performance control algorithms to advance AM quality in a closed loop, and we show a first-instance study on the effect of repetitive controls in reducing melt-pool variations in the periodic energy deposition.