Scalable control of extraneously induced defects in in-field additive manufacturing

Abstract

In-field Additive Manufacturing (AM) is exposed to irregular variations in process conditions (externalities) that affect defect dynamics. These externalities are invariable in conventional in-factory AM. Stoppage-free and real-time mitigation of part defects induced by these externality variations is necessary for timely delivery of quality parts in in-field AM. But existing solutions either require explicit knowledge of externality variations which is typically unavailable or they render the part unusable due to infeasibly slow defect mitigation. This work addresses this issue by establishing a novel Conditional Reinforcement Learning (ConRL) approach for rapid and real-time data-driven defect mitigation based on an implicit consideration of externality variations. Validation within a smart manufacturing pipeline on a Fused Filament Fabrication testbed reveals the unprecedented ability to mitigate defects at 10× greater speed via a single control action and within the same printed line. A hitherto unreported degree of scalability is observed, i.e., it is possible to mitigate defects induced by untrained-for, unknown and unmeasured externality variations without any retraining of the policy. The results also reveal new insight into the significance of conditionality in ConRL and of real-time defect quantification. The implications for wider adoption of ConRL to other in-field AM processes is discussed.