In this paper, we introduce an advanced AI-based solution for predicting structural damage in aircraft laminates. Our innovative approach focuses on detecting and locating fatigue damage within composite structures, thereby enhancing the assessment of aircraft health and usage. By leveraging state-of-the-art ultrasonic guided wave (UGW) inspection simulations of composite laminates integrated with piezoelectric transducers, comprehensive datasets are extracted efficiently. The signals captured by the piezoelectric sensors are utilized to engineer key features sensitive to composite structural damage, which are then used to train a deep neural network (DNN) for accurate structural damage prediction. Our findings demonstrate the significant potential of combining advanced simulation techniques with machine learning to improve the accuracy and reliability of structural health monitoring in aerospace applications.

This work addresses a critical challenge for the implementation of Structural Health Monitoring (SHM) systems in the aerospace sector: the validation of Ultrasonic Guided Wave (UGW) models in curved geometries and under real conditions of fatigue crack growth. The geometric complexity of curved aeronautical composites generates dispersion, attenuation, and mode conversion that complicates UGW signal interpretation. While UGW monitoring is well-established for flat composites and fatigue damage is more commonly tracked via Acoustic Emission (AE), validated models for mixed-mode fatigue delamination in curved components remain scarce. The main objective is to validate a 2D Finite Element Model (FE) developed at Abaqus to simulate the precise interaction between guided waves (A0 and S0 modes) and a Mixed-Mode fatigue delamination in two curved specimens of different materials and thicknesses. A co-simulation model was implemented to replicate the Round-Robin tests with Macro Fiber Composite (MFC) transducers, in which crack propagation was experimentally monitored using UGW through two bonded MFC piezoelectric sensors acting as both actuators and receivers, while a 15 mm PTFE film was inserted during manufacturing to ensure crack growth in a single delamination plane. The delamination simulation was carried out by progressively releasing the nodes of the set of contact nodes between the upper and lower surfaces of the delamination, resulting in a free surface, replicating the Severity and Location of the Damage (SoD and LoD) observed experimentally. Numerical-experimental validation focused on comparing the trends of the same key signal features extracted from the experimental campaign and the FE simulations as the material was damaged. The results confirm a high correlation in the features, demonstrating that the FE model successfully captures the effect of delamination growth and wave-curvature interaction. Finally, this validated model is proposed as the ideal training environment for a Leave-One-Group-Out Cross Validation (LOGO-CV) scheme for material generalization, demonstrating robustness to manufacturing variations.

The safety and service life of next-generation aerospace composite structures depend on the integrity of their complex geometries, such as curved C-sections. This paper presents a comprehensive methodology for Structural Health Monitoring (SHM) focused on the study of Mixed-Mode fracture in two curved composite demonstrators. The main objective is to develop and validate a Digital Twin (DT) framework capable of predicting the growth of delamination in real time under operational loads. The approach is to establish an accurate correlation between the Ultrasonic Guided Wave (UGW) data captured in-situ during fracture tests and the delamination status predicted by high-fidelity numerical models. This correlation is essential for an effective transition from traditional non-destructive testing (NDT) methods to intelligent SHM systems. The methodology is based on the continuous acquisition of UGW signals using a Micro-Fiber Composite (MFC) sensor network. Key UGW signal features are then extracted to estimate the experimental delamination size, which growth is correlated with Finite Element (FE) simulations and Reduced Order Models (ROMs), creating a direct mapping between the experimental features of the UGW signals and the numerical delamination progression. This validated Digital Twin allows the SHM system to use real-time UGW data to estimate remaining life and structural integrity under future load scenarios. In essence, the validation demonstrates the feasibility of an active UGW-based SHM system for continuous monitoring of Mixed-Mode fractures. This methodology, developed within the framework of the Horizon European GENEX Project, represents a significant step towards autonomous maintenance strategies for composite aircraft, improving safety margins and reducing inspection downtime.

Ultrasonic Guided Wave (UGW)-based Structural Health Monitoring (SHM) is a promising strategy for detecting damage to aeronautical structures, although its application is complicated by signal complexity and experimental uncertainty. This work seeks to identify damage-sensitive signal features for integration into Machine Learning (ML) frameworks, offering physics-informed indicators. The study combined experimental monitoring of damage to Carbon Fibre Reinforced Polymer (CFRP) plates and finite element models. To overcome the numerical–experimental mismatch, an ML algorithm predicted experimental characteristics from numerical data. The robustness of the model was validated by extrapolation (prediction of future damage) and generalization (prediction on unseen plates) strategies, confirming that ML can robustly correct for uncertainty. These results validate hybrid strategies that feed Digital Twin approaches to structural diagnosis and real-time forecasting.

Structural Health Monitoring (SHM) in aerospace applications requires accurate detection and characterization of impact events to ensure operational safety and reduce maintenance costs. This work presents an experimental framework for developing physically informed machine learning models that leverage embedded PVDF-TrFE transducers for acoustic emission (AE) monitoring. Representative composite coupons were instrumented with PVDF-TrFE sensors and subjected to controlled impact tests at varying energy levels to generate AE datasets under pristine and damaged conditions. The experimental campaign was designed to capture key features associated with impact occurrence, localization, and energy quantification in critical regions of aerostructures. To enhance model robustness and scalability, experimental data were fused with synthetic datasets derived from high-fidelity numerical simulations, enabling the integration of physical constraints into the learning process. This hybrid approach aims to improve generalization across different geometries and loading scenarios, paving the way for future deployment on full-scale aerospace components. Preliminary results indicate that the proposed methodology can identify impact events and estimate their severity with promising accuracy, while ongoing work focuses on refining localization and energy quantification metrics. The study demonstrates the potential of combining advanced sensing technologies with physics-informed data-driven models for next-generation SHM systems in aerospace applications.