A Digital Twin-Driven Real-Time Quality Control Framework for Gear Machining Workshops

Abstract

Digital twin-based real-time quality control in gear machining lacks closed-loop feedback, classification without visual recognition, and low-latency I/O synchronization. This paper proposes a digital twin-driven intelligent quality control framework built upon a four-layer cyber–physical architecture comprising three tightly coupled subsystems: a parent-object assignment mechanism that enables precise workpiece type tracking and routing through hierarchical container queries, eliminating the need for computationally expensive visual type classification while maintaining near-perfect tracking accuracy; a rhythm-adaptive multi-robot behavioral control scheme that adjusts production cadence via a global rhythm coefficient without altering spatial trajectories; and a lightweight in-memory key-value store-based I/O synchronization mechanism that achieves architecturally bounded signal update latency (communication over a dedicated localhost TCP/IP path) well below the critical process cycle. Validation on a physical gear hub production line with six operations and three robots demonstrates a single-piece inspection cycle of 11.5 s, collision-free operation, and average error rates of 0.4% for inline quality inspection. The framework provides a low-cost, low-latency, and formally grounded solution for real-time quality control in gear machining workshops.