Federated Learning-Enabled Damage Detection in Smart Composite Laminates with Viscoelastic Layers
Keywords:
federated learning, damage detection, smart composite laminates, viscoelastic layers, structural health monitoring, data privacy, edge computingAbstract
Smart composite laminates incorporating viscoelastic layers represent a significant advancement in lightweight engineering structures, offering both load-bearing functionality and intrinsic vibration damping capabilities. Effective damage detection within such heterogeneous multilayer systems is critical for structural integrity and lifecycle management, yet it introduces considerable challenges related to data volume, sensor heterogeneity, privacy of operational data, and the need for continuous model improvement across distributed assets. This paper presents a systems-level investigation into federated learning as an enabling paradigm for collaborative damage detection in smart composite laminates with viscoelastic layers. Departing from centralized machine learning approaches, federated learning allows multiple structural nodes or edge devices to jointly train damage inference models without sharing raw sensor data, thereby addressing data sovereignty concerns inherent in aerospace, automotive, and civil infrastructure applications. The architectural design of federated systems for this domain is examined, encompassing edge-cloud orchestration, secure aggregation protocols, and strategies for managing the highly non-identically distributed data arising from diverse structural configurations and environmental conditions. Data governance frameworks are analyzed, highlighting the intersection of technical privacy guarantees and regulatory compliance. Further, the paper discusses model robustness under adversary scenarios, fairness in diagnostic performance across heterogeneous fleets of composite structures, and the sustainability dimensions of decentralized learning workflows. Through a synthesis of structural health monitoring, composite mechanics, and decentralized artificial intelligence, this work identifies critical trade-offs and proposes governance-aware, resource-efficient deployment pathways. The analysis underscores that federated learning not only facilitates privacy-preserving damage detection but also opens new policy questions regarding liability, certification of continuously updating models, and cross-organizational data collaboration.
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