Forecasting Repair Accuracy in Industrial Equipment Workshops with Augmented Maintenance Tools

Abstract

The integration of augmented maintenance tools within industrial equipment workshops represents a significant advancement in the paradigm of Industry 4.0, offering unprecedented opportunities to enhance operational efficiency and reduce catastrophic machinery failures. This paper investigates the feasibility and methodology of predicting repair accuracy based on telemetry and interaction data derived from augmented reality maintenance devices. Through a comprehensive experimental analysis conducted in a controlled industrial workshop environment, we examine how human technicians interact with augmented overlays, spatial computing prompts, and real-time diagnostic feedback during complex repair tasks. By capturing granular data on tool trajectory, gaze fixation, procedural hesitation, and task completion latency, we developed predictive frameworks capable of forecasting the likelihood of a successful and accurate repair before the maintenance cycle is fully concluded. The findings indicate that continuous monitoring of user interaction with augmented interfaces yields highly predictive digital biomarkers of cognitive load and situational awareness, which are directly correlated with mechanical repair precision. This research provides a foundational understanding of human-computer synergy in modern manufacturing settings and proposes actionable strategies for deploying adaptive augmented tools that dynamically adjust their instructional interventions based on real-time predictive accuracy assessments.