Physical characterization and detection of process instabilities in wire arc additive manufacturing through arc-cycle feature extraction

Abstract This research concerns the characterization and detection of process instabilities of the wire arc directed energy deposition (WA-DED) additive manufacturing process, popularly known as wire arc additive manufacturing (WAAM), through a multi-sensor approach resorting to an adaptive signal segmentation and a physics-informed feature extraction methodology. The objective is to detect two main types of instabilities in WAAM, namely, wire stubbing and droplet overgrowth through monitoring and machine learning of multi-sensor data streams. To realize this objective, current, voltage, acoustic signatures, and high-speed melt pool imaging data were acquired during processing. Six physically intuitive features were extracted from each of the three fundamental phases of the electric arc, i.e., arcing, short circuit and arc ignition. These features were employed as inputs to a computationally tractable, support vector machine model trained to differentiate between stable and unstable processing states. The approach predicted the onset of process instability in thin walls with statistical accuracy exceeding 95%. An ablation study across the sensing modalities confirmed the complementarity of the acoustic information to the electrical signal characterization. Further transferability analysis was conducted by using the model as-is without calibration for detection of instabilities in complex geometries. While accuracy of detection is maintained, the false positive rates increase to ~4.5% from 2.3% due to transient melt pool conditions. These results demonstrate that physically tractable features allow for partial transferability to new geometries even for small training datasets.