Decision Stability in Operator-Dependent Dimensional Inspection: A Process-Oriented Framework for Data-Driven Quality Control

Operator-dependent dimensional measurements can produce inconsistent categorical quality decisions, particularly near classification boundaries. This study presents an integrated framework combining measurement-system analysis, inter-operator agreement, analytical-threshold sensitivity, and machine learning to characterize decision instability. Six operators each measured the width and height of 25 Charpy V-notch specimens three times using one digital micrometer (900 measurements). Four exploratory symmetric thresholds around 10 mm generated 1200 operator–specimen–dimension–threshold decisions; disagreement was defined relative to the consensus of the other five operators, not certified dimensional ground truth. Overall, 114 decisions (9.5%) disagreed with consensus, with higher disagreement for height (93/600; 15.5%) than width (21/600; 3.5%). Under nested specimen-grouped cross-validation, the extended Random Forest model achieved AUROC 0.908 (95% specimen-cluster bootstrap confidence interval, 0.878–0.940) and PR-AUC 0.565 (0.448–0.668); a transparent boundary-distance baseline achieved 0.889 and 0.445, respectively. Feature-ablation analysis indicated additional predictive information beyond categorical decision and boundary distance, although target-related dependence remained. Lowering the model-output cutoff from 0.50 to 0.15 increased recall from 0.439 to 0.851 at the cost of more verification warnings. Findings support exploratory risk-based screening, while small specimen numbers, shared instrumentation, and lack of independent reference dimensions limit claims about error detection and industrial transferability.