Mapping of stability zones in metal active gas surfacing using current-voltage and acoustic signatures for weld bead quality assessment

This study tests whether defective metal active gas (MAG) surfacing weld beads form distinct mechanism-specific clusters in multimodal sensor space, with implications for machine learning (ML) pipeline design.A synchronized acquisition platform integrating current and voltage signals at 10 kS/s, airborne acoustic sensing at 48-192 kHz, and high-resolution post-bead imaging was developed and validated, then used in a three-phase campaign yielding 105 weld beads on S355 steel.Current-based descriptors strongly separate acceptable and defective beads, with Cohen's |d| = 1.34 for the current coefficient of variation and 1.42 for the spike rate.The arc-stability response to voltage trim is strongly asymmetric, with current variability changing by a factor of 6.6 between trim = -3 V and +3 V. Defective beads form two distinct clusters in PCA and t-SNE projections, corresponding to electrical instability and insufficient material deposition.A baseline ML evaluation across five classifiers reached macro-F1 = 0.735 for the fused configuration.The findings provide quantitative pilot-scale evidence that defective weld beads do not form a homogeneous class, that the borderline class is similarly heterogeneous, and that supervisedlearning pipelines for weld-bead quality assessment should be designed with mechanism-aware labels and sensor coverage rather than aggregated quality categories.The reported baseline macro-F1 = 0.735 should therefore be read as evidence that the extracted descriptors carry useful information for downstream supervised learning, not as a final model ranking.