Rapid Real-Time Defect Mitigation for Hardening In-Field Additive Manufacturing to Unknown Extraneous Disturbances

Abstract In-field Additive Manufacturing (AM) is distinguished from in-factory AM via imposition of extraneous disturbances that induce unknown and unpredictable in-process variations in externalities, i.e., process parameters which are typically assumed as fixed during printing. Real-time mitigation of the resulting part defects is critical for retaining function and delivery of parts, both of which are critical for in-field AM. But the state-of-the-art either needs prior or real-time knowledge of the externality variation, which is often unavailable, or renders the part unusable due to infeasibly slow defect mitigation. This work addresses this issue via a novel Conditional Reinforcement Learning (ConRL) approach for real-time data-driven defect mitigation. ConRL is integrated with existing real-time defect detection methods to create a smart manufacturing framework and is validated for a Fused Filament Fabrication testbed. Orders of magnitude increase in defect mitigation speed within one control step is demonstrated. Further, it is possible to address untrained-for and unknown externality variations that nonlinearly affect the defect dynamics without offline or online retraining of the as-learnt policy. The importance of conditionality, defect quantification, and access to machine firmware, is discussed in light of the experimental observations.