Designing health indicators (HIs) for aerospace composite structures that demonstrate their health comprehensively, including all types of damage that can be adaptively updated, is challenging, especially under complex conditions like impact and compression-fatigue loadings. This paper introduces a new AI-based approach to designing reliable HIs (fulfilling requirements—monotonicity, prognosability, and trendability—referred to as ’Fitness’) for single-stiffener composite panels under fatigue loading using acoustic emission sensors. It incorporates complete ensemble empirical mode decomposition with adaptive noise for feature extraction, semi-supervised base deep learner models made of long short-term memory layers for information fusion, and a semi-supervised paradigm to simulate labels inspired by the physics of progressive damage. In this way, nondifferentiable prognostic criteria are implicitly implemented into the learning process. Ensemble learning, especially using a semi-supervised network built with bidirectional long short-term memory, improves HI quality while reducing deep learning randomness. The Fitness function equation has been modified to provide a more trustworthy foundation for comparison and enhance the practical reliability of the standard in prognostics and health management. Ablation experiments are conducted, including variations in dataset division and leave-one-out cross-validation, confirming the generalizability of the approach.

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Developing comprehensive health indicators (HIs) for composite structures encompassing various damage types is challenging due to the stochastic nature of damage accumulation and uncertain events (like impact) during operation. This complexity is amplified when striving for HIs independent of historical data. This paper introduces an AI-driven approach, the Hilbert transform-convolutional neural network under a semi-supervised learning paradigm, to designing reliable HIs (fulfilling requirements, referred to as 'fitness'). It exclusively utilizes current guided wave data, eliminating the need for historical information. Ensemble learning techniques were also used to enhance HI quality while reducing deep learning randomness. The fitness equation is refined for dependable comparisons and practicality. The methodology is validated through investigations on T-single stiffener CFRP panels under compression-fatigue and dogbone CFRP specimens under tension-fatigue loadings, showing high performance of up to 93% and 81%, respectively, in prognostic criteria.

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Acousto-ultrasonic composite transducers (AUCTs), comprising piezoceramic materials in a reinforced polymeric matrix, show promise for structural health monitoring in composite structures. Challenges arise when integrating AUCTs onto highly loaded thermoplastic composites, especially low-surface-energy materials like polyaryletherketone composites. To address this, the study explores the viability of attaching AUCTs to low-melting polyaryletherketone carbon fiber-reinforced thermoplastic composite structures using ultrasonic welding. This welding technique forms a joint where the interface material fuses with the AUCT embedment and the structure matrix, providing a reliable and automatable process. The investigation includes a comparative analysis of an ultrasonic welded joint with an external energy director and a reference AUCT system integrated using a vacuum bagging oven procedure. Results highlight the potential of AUCT configurations integrated by ultrasonic welding as an alternative solution, acknowledging challenges that persist for further development and increased reliability in structural health monitoring applications.

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In this research, a generalized machine learning (ML) framework is proposed to estimate the fatigue life of epoxy polymers and additively manufactured AlSi10Mg alloy materials, leveraging their failure surface void characteristics. An extreme gradient boosting algorithm-based ML framework encompassing Synthetic Minority Over-sampling TEchnique (SMOTE), categorical data encoding, and external loop cross-validation is developed to evaluate the fatigue life across materials. The influence of different training strategies based on materials, input features, encoding method, and data standardization on the model performance is explored. Additionally, the importance of anti-data-leakage and anti-overfitting measures over the ML model performance is addressed. The result shows that the data-leakage-free, external loop cross-validated model can estimate the fatigue life of selective epoxy polymers and metal alloys with an average R² of 0.71 ± 0.06 using a mere 12 to 27 experimental data points per material category. Whereas the model trained with data-leakage and overfitting results in high R² of 0.9.

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RFID (Radio Frequency Identification) is commonly used to monitor goods along the supply chain. The feasibility of the application of a RFID tag to monitor CFRP components was demonstrated in a previous work by some of the authors. This work is aimed at evaluating how the integration of a RFID tag inside a CFRP laminate affects the quasi-static strength and the failure mode. Finite element modelling is used to design a specimen with an embedded tag, that may be representative of the tensile state of stress in a larger laminate. Tensile testing is performed both up to failure and by interrupting tests at different load levels in order to inspect the specimen by C-scan. Acoustic Emission (AE) and Digital Image Correlation (DIC) are used as additional non-destructive monitoring techniques. Some interrupted tests were selected to extract micrographic sections that illustrate damage development in the laminate. The extensive destructive and non-destructive characterization allowed to quantify the effect of embedding a RFID tag in the laminate in terms of strength decrease and failure mode.

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The prognostic of the Remaining Useful Life (RUL) of composite structures remains a critical challenge as it involves understanding complex degradation behaviors while it is emerging for maintaining the safety and reliability of aerospace structures. As damage accumulation is the primary degradation indicator from the structural integrity point of view, a methodology that enables monitoring the damage mechanisms contributing to the structure's failure may facilitate a reliable and effective RUL prognosis. Therefore, in this study, an integrated methodology has been introduced by targeting the RUL and progressive delamination state via Deep Neural Network (DNN) trained with Guided wave-based damage indicators (GW-DIs). These GW-DIs are obtained via signal processing, Hilbert transform, and Continuous Wavelet Transform. This work uses GW-DIs to train and test the proposed model within two frameworks: one focusing on individual sample analysis to explore path dependency in RUL and delamination prognosis and another on an ensembled dataset to propose a generic model across varying stress scenarios. Results from the study indicate that proposed DNN frameworks are capable of encapsulating fast and slow degradation scenarios to evaluate the RUL prediction with associated delamination progress, which could contribute to ensuring the integrity and longevity of critical life-safe structures.

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In a wide range of disciplines, such as aeronautical, automotive, and structural engineering, infrared thermography (IRT) has demonstrated promising performance for inspecting and monitoring structures. This chapter provides a survey of IRT as a nondestructive assessment method for composite materials subjected to impact loads that cause critical damage scenarios inside the structure. The chapter starts with a general overview of the principles of IRT and its historical evolution. Various methodologies of thermography inspection, such as optically, mechanically, and inductively stimulated thermography, are described for the evaluation of impact damage in composite structures. In the heating waveforms and data processing section, the relevant processing techniques and data analysis algorithms for each type of the above-mentioned thermography methodologies based on the applied waveform are presented. Case studies of composite specimens suffering from impact damage are provided at the end of the chapter to demonstrate the applicability and performance of IRT inspection.@en

In recent years, prognostics gained attention in various industries by optimizing maintenance, boosting operational efficiency, and preventing costly downtime. Central to prognostics is the Remaining Useful Life (RUL), representing the critical time before system failure. Deep learning advancements facilitate RUL forecasting by extracting features from diverse data formats such as time series, images, or sequences thereof, in one, two, or three dimensions, respectively. Yet, predicting RUL from image sequences often relies heavily on resource-intensive techniques like digital image correlation, complicating data acquisition. To address challenges with high-dimensional data and unreliable models, this study introduces ISTRUST, an innovative Transformer-based architecture. ISTRUST (Interpretable Spatiotemporal TRansformer for Understanding STructures) tackles the dual challenges posed by high-dimensional data and the black-box nature of existing models. Leveraging Transformers’ attention mechanism, ISTRUST breaks down the spatiotemporal domain, effectively realizing interpretable RUL predictions under uncertainty using only sparse raw image sequences as input. Evaluated on fatigue-loaded composite samples showcasing crack propagation, ISTRUST interprets the relation between cracks and RUL via the attention mechanism. The results substantiate its capacity to interpret and clarify instances in which predictions may exhibit variability in accuracy. Through the attention mechanism, a strong correlation between the model’s spatiotemporal focus and the RUL predictions is established, making it, to the best of our knowledge, the first model to provide interpretable stochastic RUL predictions directly from sequential images of this nature.@en

Fatigue damage prognosis always requires a degradation model describing the damage evolution with time; thus, the prognostic performance highly depends on the selection of such a model. The best model should probably be case specific, calling for the fusion of multiple degradation models for a robust prognosis. In this context, this paper proposes a scheme of online fusing multiple models in a particle filter (PF)-based damage prognosis framework. First, each prognostic model has its process equation built through a physics-based or data-driven degradation model and has its measurement equation linking the damage state and the measurement. Second, each model is independently processed through one PF to provide one group of particles. Then, the particles from all models are adopted for remaining useful life prediction. Finally, the particles from each PF are fused with those from all the other PFs to improve their particle diversity, and consequently, to provide better estimation and prognostic performance. The feasibility and robustness of the proposed method are validated by an experimental study, where an aluminum lug structure subject to fatigue crack growth is monitored by a guided wave measurement system.

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Wind turbine blades carry the risk of impact damage during transportation, installation, and operation. Such impacts can cause levels of damage that can propagate throughout the structure compromising performance and safety. In this study, the effect of impact damage on fatigue damage propagation in test specimens representative of a spar cap-shear web adhesively-bonded connection of a wind turbine blade was investigated. In addition, the effectiveness of using acoustic emissions to detect early impact-induced fatigue damage was studied. Three impact tests with increasing levels of energy were investigated. The results showed that for an impact test with an average energy of 16.32 J, the fatigue damage accumulation process was not influenced by the size and location of the impact damage. But for impact tests with an average energy of 23.68 J and 32.13 J, greater crack density and accelerated de-lamination and de-bonding of the adhesive from the laminate could be seen in the impact zone. Acoustic emission was shown to identify the position of the damage zone for the higher energy impact tests. It was also effective in showing the progressive accumulation of fatigue damage in this zone during the fatigue test.

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Continuous monitoring of spray velocity during the cold spray process would be desirable to support quality control, as spray velocity is the key process parameter determining the deposit quality. This study explores the feasibility of utilising Airborne Acoustic Emission (AAE) for real-time monitoring of spray velocity. Six spray tests were conducted, varying pressure and temperature to achieve different velocities. Optical means were used to measure velocity; while, the signal from the AAE was captured during deposition via a microphone. Features demonstrating a strong correlation with velocity were extracted from the acoustic signals. Both rule-based and machine learning models were employed to identify the moments where the nozzle was engaged with the substrate and diagnose the velocity. The results indicate that monitoring the spray velocity of the cold spray process using AAE is feasible.

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The present work aims at understanding the stochastic matrix crack evolution in CFRP cross-ply laminates under tension–tension fatigue loading. An experimental campaign was carried out on twenty-three specimens at different stress levels, while two optical techniques were used for the in-situ monitoring of the accumulation of transverse matrix cracks. The results showed a significant scatter in crack evolution among specimens. This stochastic behaviour was further investigated using image analysis and numerical modelling. It was found that transverse matrix cracks can be classified into the independent and dependent cracks based on a critical crack spacing. Furthermore, the severity of interaction among cracks was quantified by introducing a dependent crack ratio. Finally, a strength-based probabilistic model was proposed to describe the scattering regime of the crack evolution. The agreement between model and test results indicates that local strength variations of 90 plies are the dominant scattering source governing the initial fatigue resistance to cracking and determining the accumulation of transverse matrix cracks among specimens. These results may provide a new insight into the stochastic nature of matrix cracking in composite laminates and aid in the design of fatigue resistance properties.

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To move towards a condition-based maintenance practice for aircraft structures, design of reliable health management methodologies is required. Development of diagnostic methodologies is commonly realised on simplified sample structures with assumptions that methodologies can be adapted for application to realistic aircraft structures under in-service conditions. Yet such actual applications are not conducted. In this work, we study the development of diagnostic methodologies to training structures and their application to dissimilar testing structures. A heterogeneous population is considered, consisting of single-stiffener composite panels for methodology development and training and a multi-stiffener composite panel for application and testing. Characteristics as its composite material, lay-up, and temperature condition are constant while topologies and applied loads differ between the dissimilar structures. Damage in the structural panels is monitored on multiple diagnostic levels using a variety of structural health monitoring (SHM) techniques, including acoustic emission and distributed strain sensing. Specifically, we develop diagnostic methods for localising and monitoring disbond growth after impact using strain data collected during fatigue testing of multiple single-stiffener panels and apply these for disbond monitoring in an upscaled version of a multi-stiffener panel. In this manner, this study aids in the maturement and application of SHM methodologies to realistic aircraft structures.

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The present study proposes a comprehensive integrity assessment approach for a full-scale adhesively-bonded bi-material joint for maritime applications. The joint represents a cross-section of the bond-line connection of a ship with a steel hull and a sandwich composite superstructure. The full-scale joint consists of a sandwich composite core adhesively bonded to two U-shaped steel brackets. The joint was subjected to a quasi-static loading profile including 6 load cycles up to the final failure. Each load cycle was followed by a dwell segment holding the joint at the maximum displacement for 30 s and then unloading to 50% of the maximum displacement. Three Structural Health Monitoring (SHM) techniques including Acoustic Emission (AE), Fiber Optic Sensor (FOS), and Digital Image Correlation (DIC) were employed during the test to assess the damage state of the joint. Moreover, a Finite Element Model (FEM) was developed to simulate the evolution behavior of different damage mechanisms in the joint and the FE results were compared against the experimental findings. The obtained results showed that the integration of all the employed techniques could successfully detect the damage initiation, assess the severity of the damage, localize the critical regions of the joint, and distinguish the different damage mechanisms.

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Optical fiber sensors (OFSs) represent an efficient sensing solution in various structural health monitoring (SHM) applications. However, a well-defined methodology is still missing to quantify their damage detection performance, preventing their certification and full deployment in SHM. In a recent study, the authors proposed an experimental methodology to qualify distributed OFSs using the concept of probability of detection (POD). Nevertheless, POD curves require considerable testing, which is often not feasible. This study takes a step forward, presenting a model-assisted POD (MAPOD) approach for the first time applied to distributed OFSs (DOFSs). The new MAPOD framework applied to DOFSs is validated through previous experimental results, considering the mode I delamination monitoring of a double-cantilever beam (DCB) specimen under quasi-static loading conditions. The results show how strain transfer, loading conditions, human factors, interrogator resolution, and noise can alter the damage detection capabilities of DOFSs. This MAPOD approach represents a tool to study the effects of varying environmental and operational conditions on SHM systems based on DOFSs and for the design optimization of the monitoring system.@en

Acousto-ultrasonic composite transducers (AUCT), which are made of piezoceramic materials embedded in a reinforced polymeric matrix, are promising for the health monitoring of composite structures. However, when they are integrated into highly loaded thermoplastic composite structures, ensuring proper joining properties is a challenge. The conventional approach of attaching the AUCT using adhesive may not be sufficiently reliable in aeronautic applications for low surface energy materials such as polyaryletherketone composites, where surface treatments are needed for adhesion. Welding techniques can be used to create a joint in which the interface material interfuses with the AUCT embedment and the structure matrix, resulting in a homogeneous interface with properties comparable to the host structure matrix throughout its service life. With this in mind, the main objective of the present work is to investigate the viability of attaching AUCT to low-melting polyaryletherketone carbon fiber reinforced thermoplastic composite structures using the ultrasonic welding (UW) procedure and characterize the joint performance. The ultrasonic welded joint using an external energy director in the interface is investigated by comparing the findings to those of a reference AUCT system integrated into the structure with autoclave co-consolidation. Infrared thermography is employed to monitor the process, and a parameter study of the UW process is carried out. The AUCT survivability during the UW process is determined by measuring the capacitance, and C-scan is used to assess joint quality. The results show the challenges of attaching AUCT to thermoplastic composite structures using UW and surviving the procedure.@en

In this paper we execute a complex test campaign to develop a novel methodology for the Remaining Useful Life (RUL) estimation of complex multi-stiffened composite aeronautical panels utilizing Machine Learning models trained with Structural Health Monitoring (SHM) data from hierarchically simpler elements, i.e., single-stiffened panels. Distributed Fiber Optical sensors (DFOS) are employed to monitor the panels’ behavior undergoing variable amplitude compression-compression fatigue after multiple impacts. A data processing methodology is first applied to the DFOS data, to both alleviate the effect of the variable loading conditions on the monitored strain and ease the computational burden. In this upscaling endeavor, an advanced strain-based Health Indicator (HI) based on Genetic algorithms, created and validated on the single-stiffened panel data, is utilized as the prognostic feature for the RUL estimations of the multi-stiffened panels. The HI displays favorable characteristics in terms of monotonicity and prognosability which are highly desirable for more accurate RUL estimations. For the prognostic task, standard machine learning models are trained using the historical degradation data of the single-stiffened panels and a similarity analysis is performed to enhance the accuracy when predicting the RUL of the multi-stiffened panels. Despite the increased structural complexity of the multi-stiffened panels, we demonstrate that the RUL is able to be predicted with reasonable accuracy. The present work paves the road for upscaling and applying prognostic methodologies to more complex structures beyond simple coupons or generic elements.

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Condition-Based Maintenance (CBM) is a policy that uses information about the health condition of systems and structures to identify optimal maintenance interventions over time, increasing the efficiency of maintenance operations. Despite CBM being a well-established concept in academic research, the practical uptake in aviation needs to catch up to expectations. This research aims to identify challenges, limitations, solution directions, and policy implications related to adopting CBM in aviation. We use a generalizable and holistic assessment framework to achieve this aim, following a process-oriented view of CBM development as an aircraft lifecycle management policy. Based on various inputs from industry and academia, we identified several major sets of challenges and suggested three primary solution categories. These address data quantity and quality, CBM implementation, and the integration of CBM with future technologies, highlighting future research and practice directions. @en

In this paper, a new indicator to localize fatigue damage in a fibre glass composite structure, i.e. spar cap to shear web thick adhesive joint of a wind turbine blade, is presented. This indicator is based on the effect of damping on the phase of the mode shapes of the structure. When fatigue damage occurs, damping increases in the defective area and this leads to an increase in the local energy dissipation. This non-uniformity in the energy dissipation throughout the structure causes the structure to vibrate with mode shapes whose structural elements no longer have the same phase creating complex mode shapes. A visco-elastic finite element (FE) vibration model is developed for a thick adhesive joint of a wind turbine blade. The mass, stiffness, and damping matrix extracted from the FE model are used to determine the complex mode shapes. The results show that the damaged area is located where the spatial derivative of the phase of the components of the mode shapes is minimum. Changes in the phase of mode shapes of the structural elements are strongly dependent on the location of damage. In the locations where the strain modal energy is greater, the change in the phase is also higher.

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