Different options that rely on fracture mechanics are currently used in engineering during the design and assessment of components. One of the most important aspects is the time taken for a crack to extend to its critical size. If this time is long enough, a design concept based on inspection intervals can be applied, as is it the case of a railway axle component. To define inspection intervals that ensure the continuous and safe operation of a damage-tolerant railway axle, a reliable estimation of its fatigue crack growth life is required. Due to the uncertainties involved in the fatigue process, inspections must be devised not only considering the uncertainties in the performance of the inspection technique, but also based on a probabilistic lifespan prediction. From this premise, this paper presents a procedure for determination of inspection intervals that uses a conservative fatigue crack growth life estimation based on the lifespan probability distribution. A practical example to illustrate the reliability-based inspection planning methodology in a railway axle under random bending loading is given. The inspection intervals are further assessed in terms of overall probability of detecting cracks in successive inspections and in terms of probability of failure, considering the probability of detection curve of the non-destructive testing technique. The procedure developed provides recommendation for the definition of inspection intervals and associated inspection techniques.

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.

Carbon-fibre reinforced polymer (CFRP) laminates are susceptible to both static and fatigue-driven delamination. Predicting this type of failure in curved composite structures, often referred to as delamination by unfolding, remains a critical challenge. This work presents the development of a Virtual Crack Closure Technique (VCCT)-based computational method for simulating fatigue-driven delamination propagation under non-constant mixed-mode conditions. The fatigue delamination growth model follows a phenomenological approach based on a Paris–Erdogan-based power-law relationship, where the delamination propagation rate depends on the strain energy release rate. This methodology has been implemented as a user-defined subroutine, UMIXMODEFATIGUE, for Abaqus, integrating the effects of load ratio and mode mixity conditions while leveraging the mode separation provided by VCCT. The proposed approach is validated against an experimental case involving a four-point bending test applied to an L-shaped CFRP curved beam specimen with a unidirectional layup. Unlike the existing standard configuration, the proposed test campaign introduces a non-adhesive Teflon foil insert at the bend, placed within the midplane layers to act as a delamination initiator, representing a manufacturing defect. In addition to the testing machine, digital image correlation (DIC) is used to monitor delamination length. The simulation method developed accurately predicts fatigue delamination propagation under varying mode mixity at the delamination front. By improving delamination modelling in composites, this approach supports timely maintenance and helps prevent fatigue failures. Additionally, it deepens the understanding of how the mode mixity influences the delamination propagation process.

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.