Modeling Human–AI Teaming for Fault Prediction: Integrating Process Data and Human Recognition Processes in a Pickling Line Under Time Pressure

Human–AI teaming is increasingly attractive also for safety-critical industrial decision making, where the reliability of the joint system depends not only on the accuracy of the AI but also on the sequence in which the human and the AI contribute to the decision under time pressure. This paper examines three decision-making workflows for weld-crack fault prediction at the entry section of a steel pickling line: a human-only workflow (HO), an AI-first workflow (AIF) in which the prediction is presented before the operator’s judgment, and a human-first workflow (HF) in which the operator decides first and the AI provides feedback only in case of disagreement. The AI component is implemented as a Random-Undersampling-Boosted classification ensemble trained on twelve process parameters from past flash-butt welding operations. The three workflows were deployed in a live three-shift production environment over 40 days, with the active workflow randomly assigned at each shift change, resulting in 6600 evaluated welds. Performance was evaluated separately for the initial inspection of a weld and for repeat inspections, and complemented by a human-reliability analysis that quantifies the contribution of time pressure to the predicted human error probability. The HF workflow yields the best detection performance during the initial inspection and reduces unnecessary repeats compared to HO, whereas the AIF workflow clearly outperforms the others in repeat inspections, where it nearly halves the repeat rate while maintaining a higher specificity. These findings support a stage-dependent allocation of decision authority in human–AI teaming for safety-critical industrial assessment tasks.