End–Edge–Cloud Decision Support for Automated Fiber Placement: Field Monitoring and Simulation-Based Predictive Evaluation

Automated fiber placement (AFP) of advanced polymer composites requires timely monitoring of process conditions that influence gaps, overlaps, wrinkles, and inter-ply bonding. This study develops an end–edge–cloud decision-support framework that links read-only sensing, low-latency prediction, risk grading, operator confirmation, and traceable cloud optimization while retaining the numerical-control hard-interlock chain. The edge uses 18 temporal-statistical features for anomaly classification (TF-MLP-C) and temperature–pressure forecasting (TF-MLP-TP), while a separate nine-feature Cutter-RUL MLP estimates cutter life. In 90 days of single-machine operation, the pipeline processed 2.87 × 108 records at 36.9 records/s, supported 80 concurrent users, and achieved 99.6% availability. On fixed-seed parametric simulations, classification accuracy and macro F1 were 91.67% and 90.28%, respectively; mean warning lead time was 5.83 ± 2.54 s. The temperature and pressure MAEs were 0.713 °C and 26.96 N, and cutter-life MAE was 139.52 cuts (R2 = 0.9819). The 90-day field records establish pipeline operation and operator-facing logging, not independent predictive-model accuracy; all reported model scores are simulation-based proof-of-concept estimates, with no evaluation on a held-out labeled AFP production test set. The contribution is a deployable and auditable monitoring workflow that turns heterogeneous AFP data into bounded decision support and provides a reproducible basis for future multi-machine and quality-linked validation.