Uncertainty-Aware Neurosymbolic Root-Cause Analysis for Surface-Mount Assembly

Root-cause analysis (RCA) in surface-mount assembly must support rapid containment and verification while remaining auditable under imperfect evidence. We present an uncertainty-aware neurosymbolic pipeline in which a neural evidence layer converts per-board observations into probabilities for defect class, stage-wise mechanism, and parameter risk, and a deterministic semantic layer assembles and ranks defect–mechanism–parameter-violation hypotheses over an explicit causal mapping. The output is