Integration of Digital Twin Technology and Industry 4.0 Principles for Real-Time Structural Health Monitoring in Smart Manufacturing Facilities

Abstract

Within Industry 4.0 manufacturing environments, Structural Health Monitoring (SHM) is recognized as mission-critical; nevertheless, extant Digital Twin (DT) implementations seldom achieve deep fusion with the production layer and consequently struggle to co-optimize structural integrity alongside operational efficiency. This paper therefore introduces, and subsequently validates, an integrated DT framework expressly conceived to close that lacuna. Four objectives guided the inquiry: first, to architect a distributed digital-twin topology underpinned by edge–cloud analytics capable of real-time SHM; second, to operationalize a machine-learning-driven predictive-maintenance regime that causally couples structural response data with both manufacturing process signatures and ambient environmental variables; third, to embed the resultant framework within incumbent MES/ERP ecosystems spanning multiple production facilities; and fourth, to quantify the concomitant reductions in maintenance expenditure, production downtime, and energy utilization. A longitudinal, 24-month, multi-site investigation furnished empirical corroboration. The framework couples a high-fidelity DT to legacy MES/ERP strata through a distributed edge-cloud fabric; an ensemble of machine-learning algorithms—Long Short-Term Memory networks prominent among them—was deployed for predictive anomaly detection. The system attained 96% anomaly-detection accuracy (F1-score: 0.95) and translated this diagnostic precision into demonstrable operational gains: maintenance costs fell by 42.1%, downtime by 31.1%, and energy intensity by 23.2% (p < 0.001). The edge-centric architecture reduced processing latency by 67%, thereby enabling sub-50 ms integration with MES/ERP layers, while inter-site model transfer achieved 94.0% adaptation efficacy. 

Article history: Received (July 9, 2025); Revised (September 25, 2025); Accepted (November 11, 2025); Published online (November 28, 2025)