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 concerns a system-theoretical approach towards such a smart, reliable AM. Specifically, one focused example is laser-aided powder bed fusion for fabricating metallic and high-performance polymeric parts. By capitalizing on the fundamental precision heating and solidification, together with the layer-by-layer iterations of energy source, feedstock, and toolpath, we will discuss mathematical abstractions of process imperfections that understand the intricate thermomechanical interactions and are tractable under realtime computation budgets. A model-based decentralized sensing is then proposed to discard unnecessary information to make full use of data-intensive sensor sources like streaming videos. Finally, we discuss our results of controlling the proper energy deposition to ensure quality and reproducibility in a closed loop. Simulation and experimentation with industrial laser scanning and an in-house built PBF testbed validate the theoretic and algorithmic results.

Conference paper