Early In-Process Prediction of GMA Weld Quality from Laser Doppler Vibrometer Signals

Post-process weld quality assurance detects a defect only after the joint has been completed and therefore cannot intervene while an unstable weld is still being produced. The question addressed here is how much of a seam must be observed before its final quality state can be recognized. This study investigates whether the final quality state of a gas metal arc (GMA) weld can be recognized from the initial segment of the seam using only a laser Doppler vibrometer as the diagnostic sensor. Thirty-two bead-on-plate GMA welds were produced under stable and deliberately destabilized conditions. The welding current channel was used only for offline arc segmentation and independent reference, whereas classification used vibration-derived time, frequency, envelope, and wavelet features extracted in 0.10 s windows. Causal prefix features were evaluated with leave-one-weld-out validation and weld-level bootstrap confidence intervals, and a streaming variant updated the probability of a defective weld window by window. The final process-stability state was distinguishable after approximately 5 mm of seam (AUC = 0.94), with high discrimination through the early-to-mid seam. As an online alarm, the aggregate prefix rule produced no false alarms among six acceptable welds, detected 95% of the defective welds, and achieved a median recognition lead of ~3.6 s. However, these specificity and lead-time estimates are coarse because only six acceptable welds were available, and with a single exception, the defective welds were unstable throughout; thus, the results mainly reflect early recognition of an established state. Decision latency was nearly constant in time (0.15-0.25 s), so the spatial decision position and remaining lead time depended mainly on welding speed. The results support non-contact early recognition of unstable GMA welding, while larger balanced datasets with mid-weld transitions are needed to validate true defect-onset anticipation.