This work experimentally investigated the feasibility and complementarity of aeroacoustic and infrared thermography (IRT) techniques for detecting damage in rotating wind turbine blades under controlled wind tunnel conditions. Two representative types of damage were considered: trailing edge cracks and internal shear web delamination, created in the scaled blades manufactured in-house. Experiments were conducted in the open jet facility at Delft University of Technology. Acoustic measurements using a two-dimensional microphone array revealed that trailing edge cracks induce distinct tonal noise modifications, which depend on the effective trailing edge thickness and are captured through spectral analysis and acoustic beamforming. The crack-induced tonal noise peaks at a trailing-edge-thickness-based Strouhal number, (Formula presented), between 0.15 and 0.25. IRT, by contrast, are highly sensitive to internal structural features; delaminated regions exhibited localized temperature variations due to changes in thermal properties. Principal component thermography was applied to further enhance the visualization of the internal shear webs and internal delamination. The results demonstrate that the use of aeroacoustic and IRT methods provides a complementary strategy for detecting both edge and internal damage in wind turbine blades.

Inspection of composite liquid hydrogen tanks (LH2) for aviation is highly challenging using current non-destructive testing techniques. Also, manufacturing of these composite LH2 tanks is challenging due to thermal stresses, making post-manufacturing and in-service inspection essential for safe operations. The external insulating layer and restricted access to the tank’s interior are the reasons for this. To inspect the inner curved surfaces of a carbon fiber-reinforced polymer (CFRP) composite tank, a compact, high-payload-ratio ultrasonic probe-based inspection using an autonomous robotic crawler was developed. The developed device offers a workable solution for challenging LH2 tank inspection situations, as it is designed to function through tank apertures as small as 250–300 mm in diameter. For accurate localization on the uneven inner surfaces of the composite LH2 tank, the robot utilizes wheel encoders and features wheeled locomotion. A phase array-based wheel probe for ultrasonic examination, operating at 5 MHz, is used in conjunction with a spring load to ensure proper coupling with the surface. To perform straight-line motion on a CFRP composite surface under dynamic conditions at various speeds, this work examines proportional-integral-derivative tuning for a crawler, combined with an ultrasonic phased array probe, and determines an optimized speed for composite LH2 inspection. The device demonstrates the detection of possible ply drop-off and surface irregularity damage. The findings are important, as an investigation has been conducted to assess the quality of ultrasonic scans on the curved surface with varying thicknesses of the composite tank.

This study investigates the multifunctional potential of metallized polyphenylene sulfide (PPS) nonwovens integrated as electrically conductive interlayers in glass fiber-reinforced polymer (GFRP) composites. The PPS nonwovens were coated with a nickel–phosphorus layer via electroless plating and embedded between the laminate plies. The system was evaluated both as an electrothermal heating element for de-icing and as a sensing layer for non-destructive testing. For de-icing applications, icing wind tunnel tests were conducted under glaze-ice and mixed-ice conditions. The integrated heating layer enabled complete ice removal within approximately 120 s for both icing regimes, while the distributed Joule-heating mechanism ensured stable and spatially uniform surface temperatures. Furthermore, the metallized nonwovens were successfully applied as an internal thermal excitation source in shearography, allowing clear identification of impact-induced damage, including delamination. The incorporation of the metallized PPS interlayer also enhanced the mechanical performance of the GFRP composite, with flexural strength increasing from 944 MPa (reference) to approximately 1164 MPa. Dynamic mechanical analysis indicated a slight increase in glass transition temperature from 132 °C to 141 °C. These findings demonstrate that metallized thermoplastic nonwovens provide an effective approach to designing multifunctional composites for advanced engineering applications.

A large, full-scale (6 m tall, 2.5 m wide) full-composite ship hull section was subjected to three consecutive impacts with an impact energy level (∼20 kJ) mimicking a realistic heavy crash. The structure was continuously monitored with acoustic emission (AE) sensors during the impacts, which allowed the possible degradation of the composite hull to be assessed. Unsupervised learning was applied to AE data features to enable the categorization of the damage accumulated during the consecutive impacts. The implemented unsupervised learning routine was a combination of automatic Laplacian data feature selection followed by density-based spatial clustering of applications with noise (DBSCAN), for which the hyperparameters were automatically optimized with a silhouette-driven approach. Three predominant damage mechanisms (sandwich-core crushing/cracking, skin-core debonding, and matrix cracking) were identified through the clustering solution (with 0.9 mean silhouette and 6% outliers) and verified by AE feature bands found in the literature. The AE-based damage categorization was validated with impact slow-motion videos and postimpact digital laser shearography inspections. The study highlights the capability of the developed categorization methodology to be deployed for online monitoring, where SHM algorithms are retrained during ship operation to keep improving diagnosis accuracy. The results show that in the online training scenario, where Impacts 1 and 2 were used for training and Impact 3 was used for testing, the categorization performance was comparable to when data from all three impacts had been used for training, with a mean silhouette of 0.884 and only 2% outliers. Altogether, the damage categorization routine demonstrated reliability and stability for handling realistic AE data variability and different data availability scenarios. This study is an important step toward a complete condition diagnosis (comprising detection, localization, categorization, and quantification) of full-scale composite ship structures, which is crucial for estimating their remaining useful life in real time and thereby for enabling their condition-based maintenance.

The objective of this work is to study defect detection efficacy using embedded carbon nanotube (CNT) fibers as heaters for shearography and thermography. For that, GFRP laminates with various amounts of CNT-doped strips embedded at different layers have been manufactured. Impact tests are performed to create realistic damage in the GFRP specimens for inspection. Shearography and thermography non-destructive testing of the GFRP laminates are performed simultaneously with embedded CNT strips as heating sources before and after the impact test. This research was performed as part of the Horizon Europe COMP-ECO project (grant agreement 101079250). In the future, we aim at developing a novel demonstrator of a composite aerospace structure with integrated CNT-doped sensors that support NDT and enable self-sensing and in-situ SHM capabilities.

Hydrogen, a key component of a net-carbon free society, requires precise sensing solutions. This research focuses on the development of metal hydride-coated Fibre Bragg Grating (FBG) based hydrogen sensors, marking a significant step towards the realisation of multipoint hydrogen sensing systems - a growing demand in the industry. The performance of three FBG sensors coated with nanometre-thick tantalum, palladium, and palladium-gold hydrogen sensing metal thin films, deposited via magnetron sputtering, is presented. Among these, the novel tantalum sensor exhibited the best performance, achieving a minimum detection limit of 50 ppm and and an enhanced sensitivity below 0.1% H₂ levels at room temperature.

Non-destructive inspection (NDI) of small-scale defects in fiber-reinforced composites is an urgent challenge to ensure the structural integrity of safety–critical components. Shearography is a non-contact and full-field optical NDI method that can be used to characterize surface strain components under loading. Thermal loading is widely used in shearography because of the advantages of being non-contact and convenient for in-service inspection. Shearography has received considerable industrial acceptance for the inspection of aerospace and marine composite structures, however its efficacies in detecting small mm and sub-mm defects have not been fully characterised. Besides, one major issue regarding shearography with thermal loading is fiber-related deformation or fiber noise, which can affect the efficacy of defect detection significantly, especially when detecting small and deep defects. In this study, the novel shearography pair method is proposed and developed to reduce fiber noise for reliable inspection of small mm and sub-mm defects in composites. The defect detection capabilities of the proposed method have been studied and compared with conventional shearography practice and with fast Fourier transform (FFT) and principal component analysis (PCA) based signal processing algorithms. The results demonstrate that the proposed shearography pair method has the advantages of less fiber noise, improved inspection results, and being faster with reduced number of datasets. It enables the detection of mm and sub-mm defects (down to 0.6–0.8 mm in diameter) in composites; these inspection results are one of the smallest defect sizes detected with shearography and reported in literature.

This study demonstrates Phase Measuring Deflectometry (PMD) as an optical technique for visualizing heat flow and detecting thermal anomalies in metallic materials. Heat conducted from a thermally loaded sample into an adjacent transparent medium induces spatial refractive index gradients, which distort a projected fringe pattern. These distortions are captured and analysed using PMD, enabling emissivity-independent visualization of the heat flow field. Experimental results on aluminium and structural steel slabs reveal distinct phase signatures linked to thermal conductivity differences. The method offers a compact, non-invasive, and surface-independent alternative to conventional thermography, well-suited for engineering diagnostics and material evaluation.

The development of reliable hydrogen sensing materials for subzero environments is crucial for aviation, cryogenic storage, and hydrogen infrastructure applications. In this study, we investigate tetragonal β-tantalum (β-Ta) thin films at −60 °C to assess their potential for optical hydrogen sensing. In situ X-ray diffraction (XRD) measurements reveal a reversible lattice expansion upon hydrogen exposure, with β-Ta exhibiting a smaller volumetric expansion compared to α-Ta, indicating lower hydrogen solubility. Optical transmission measurements demonstrate a monotonic and fully reversible optical response across a range of hydrogen pressures, free of any hysteresis. However, β-Ta exhibits prolonged response times at low temperatures due to diffusion-limited kinetics, as confirmed by power-law response rate analysis and direct diffusion front measurements. Although β-Ta offers a temperature-independent resolution and structural robustness, its slower response time suggests the need for further microstructural optimizations to enhance hydrogen diffusion.

Assessing the structural integrity of cultural heritage objects is of great importance for their structural conservation and long-term preservation. This paper focuses on the development of a non-destructive inspection (NDI) approach using 3D shearography to evaluate the structural integrity of wax-resin lined paintings, specifically for The Night Watch (1642), a large-format 17th-century canvas painting by Rembrandt van Rijn (1606–1669) that is on display in the Rijksmuseum, Amsterdam. The Night Watch has a complex treatment history that has many old repairs of structural defects and damages (holes, tears, etc.) and three wax-resin relinings. In 2021, before a new structural intervention involving retensioning of the canvas support, it was vital to evaluate the structural integrity of the painting, specifically the condition of the treatment carried out in 1975–76 when, among other actions, several long cuts in the area of Captain Frans Banninck Cocq's breeches were repaired and an old canvas insert in the drum was replaced. To assess the structural condition, we applied 3D shearography to quantitatively analyse the in- and out-of-plane surface strains with controlled thermal loading. First, a safe loading procedure was developed by inspecting a representative wax-resin lined test painting where reference delaminations and structural repairs to canvas supports were reliably identified with 3D shearography by raising the temperature with 1-2 °C. As part of Operation Night Watch, in November 2021 an in-situ investigation was carried out in the Rijksmuseum gallery. Two areas of interest in The Night Watch, the restored slashes in the Captain's breeches (0.5 × 1 m) and the canvas insert in the drum (0.2 × 0.5 m), were inspected from the reverse of the painting. Results revealed no critical structural problems associated with the repaired slashes, nor with adhesion of the lining. For the patched canvas in the drum, it showed higher in- and out-of-plane strain variations. Overall, 3D shearography provided valuable non-destructive inspection results for assurances regarding the structural integrity of the 1975 repairs and the adhesion of the lining canvas in The Night Watch.

With the rapid development of artificial intelligence (AI) technologies, deep learning-based structural health monitoring (DeepSHM) methods have gained significant attention. However, their black box nature often limits interpretability and trust. The field of Explainable AI (XAI) aims to address this by enhancing model transparency and reliability through human-comprehensible explanations. This study investigates the use of XAI algorithms in interpreting a 1D convolutional neural network (1D CNN) developed for Lamb wave monitoring of bolt-loosening detection in multi-bolted double-layer aluminum plates under varying temperatures. Four existing XAI algorithms were employed, including Sensitivity Analysis, Deep Taylor, Gradient-weighted Class Activation Mapping (Grad CAM) and Guided Grad CAM. In addition, this paper introduces two new XAI methods, Smooth Simple Taylor and Deep Grad CAM as an enhancement of the Simple Taylor and Grad CAM methods, respectively. These six XAI algorithms were used to establish the relation between the 1D CNN model parameters and the input vector. The results were evaluated for their effectiveness in comparison to the physical insights of the input vector using two proposed methods, namely the Correlation Coefficient with Residual Signal and the Residual Signal Weighted Importance Score Ratio. The results of the evaluation methods, in conjunction with Infidelity, Sense sum, and Sanity check, were utilized to rank the performance of the six XAI algorithms. The rankings were consistent in both simulation and experiment data sets, and the newly proposed XAI algorithm, Smooth Simple Taylor, appeared to be the best in both data sets. Overall, this research establishes a novel approach to using XAI algorithms to enhance the explainability of AI in practical engineering applications.

This research presents a set of methods for obtaining measurable data on yarns in historical textiles, addressing a gap in conservation and conservation science. A systematic analysis was conducted on 26 specimens, primarily from historical paintings of known provenance, all including a selvedge. Techniques for measuring crimp, twist, yarn width and yarn thickness were developed. Methods for the measurement of thread count, fabric thickness, weight, and pH are also discussed. By quantifying these characteristics, this study enhances our understanding of traditional textile production. Numerical data enable direct comparisons between different fabric structures and allow correlations with the tensile properties of historical textiles. Correlations have been established between the measured characteristics of the interlaced yarns and the warp and weft directions, which appear to be uncontroversial within this group of samples. This improves the ability to distinguish warp and weft in a textile when a selvedge is not available. The set of methods is largely non-destructive, as only a few yarns need to be extracted to measure their crimp and thickness. The data needed for textile engineering research are made available for historical woven structures, providing new opportunities for their analysis and for predictive digital simulation. The next steps in this ongoing research are to explore correlations between the measured characteristics and the tensile response of the analysed textiles, and to extend the study to a wider range of historical fabrics to obtain more broadly representative data.

Monitoring fatigue damage in mechanical connections is essential for maintaining the safety and structural integrity of offshore wind turbines (OWTs), particularly during the early stage of crack initiation. Recently, the C1 wedge connection (C1-WC) has emerged as a promising innovation for use in OWTs. Acoustic emission (AE) monitoring is a widely used real-time technique for detecting fatigue cracks. The space limitations of the lower segment holes in the C1-WC presents challenges for detecting surface cracks with conventional AE sensors. Thin Piezoelectric Wafer Active Sensors (PWAS), while small and lightweight, face limitations due to their poor signal-to-noise ratio. In this study, we propose a baseline-based approach to enhance the effectiveness of PWAS for accurate AE monitoring in confined spaces. A benchmark model correlating the damage state of specimens is created by breaking pencil leads. Multivariate feature vectors are extracted and then mapped to the Mahalanobis distance for damage identification. The proposed method is validated through testing on compact specimens and C1-WC specimens. To enhance the AE detection results, supplementary monitoring techniques, including digital image correlation, crack propagation gauges, and distributed optical fiber sensors, are employed. The experimental setup, signal acquisition, and detection efficiency of these techniques are briefly outlined. This study demonstrates that the proposed approach is highly effective in detecting early damage in C1-WC specimens using AE monitoring with PWAS.

Inspection of composite liquid hydrogen tanks (LH2) for aviation is very challenging using existing non-destructive testing (NDT) technology. This is due to the external insulation layer and limited access to the inside of the tank. New inspection technology is urgently needed for the LH2 tank inspection and maintenance of these LH2 tanks. This work presents the development of a compact high-payload ratio ultrasonic inspection automatic robotic crawler designed for the inspection of the inner curved surfaces of a carbon fiber reinforced polymer (CFRP) composite. The robot is equipped with wheeled locomotion and uses wheel encoders for precise localization on the irregular inner surfaces of the composite LH2 tank. The crawler carries an ultrasonic flaw detector payload housed in a specially designed enclosure, which securely holds a 5 MHz wheel probe for ultrasonic inspection. This study investigates Proportional-Integral-Derivative (PID) tuning for a crawler integrated with an ultrasonic phased array probe, executing straight-line motion on a CFRP composite surface under dynamic conditions at various speeds. The system is designed to operate through tank openings as small as 250-300 mm in diameter, providing a practical solution for challenging inspection scenarios of the LH2 tank.

Hydrogen is a cornerstone of the emerging net-zero carbon economy, and its widespread deployment demands sensitive, stable, and scalable detection technologies. In this study, we present a comparative performance analysis of Fibre Bragg Grating (FBG) sensors coated with nanometre-thick metal hydride-forming layers—tantalum (Ta), tantalum-palladium alloy (Ta_0.88 Pd_0.12), palladium (Pd), and palladium-gold alloy (Pd _0.6 Au_0.4)—for optical hydrogen sensing. The integration of Ta and Ta _0.88 Pd_0.12, two tantalum-based metal hydrides, with FBG sensors is introduced here for the first time, offering a promising alternative to conventional Pd-based materials. All coatings were deposited via magnetron sputtering and tested under controlled hydrogen exposure across concentrations ranging from 0.001% to 100% H₂. The Ta-based FBGs exhibited outstanding performance, showing a remarkably linear relative wavelength shift over the full tested range (0.001% to 100% H₂), with sensitivity detectable down to 10 ppm—the lowest concentration achievable in the current setup. Both Ta and Ta_0.88 Pd_0.12 sensors exhibited fully reversible and hysteresis-free response characteristics, with rapid response and recovery. Among them, the Ta_0.88 Pd_0.12 sensor with a 100 nm coating demonstrated the highest logarithmic sensitivity of ∼9 pm/decade(%H₂), corresponding to a 9 pm wavelength shift for every tenfold increase in hydrogen concentration between 0.001% and 100% H₂. In contrast, Pd and Pd _0.6 Au_0.4 sensors showed degraded performance at low concentrations and greater signal hysteresis. These results underscore the potential of Ta and Ta _0.88 Pd_0.12 coatings as robust and high-performance alternatives to conventional Pd-based materials for next-generation distributed fibre-optic hydrogen sensing systems.

Carbon fibre reinforced plastic (CFRP) exhibits complex optical behaviour due to its anisotropy and highly scattering surface. These optical characteristics pose significant challenges for the automated laser-based inspection systems used in CFRP manufacturing, as they lead to variations in light interaction with the material, affecting the accuracy and reliability of inspections. To investigate this complex optical behaviour, an inverse optical model based on the Multi-Gaussian method has been developed. Laser speckle patterns from the CFRP surface are decomposed into multiple Gaussian components to model the material's optical properties. A greedy optimisation algorithm is employed to estimate the optimal coefficients for the Gaussian sets, which are further refined by introducing negative amplitude Gaussian components. These enhancements improve the optimisation, resulting in a better correlation between the Multi-Gaussian model and actual laser speckle measurements.

Shearography and electronic speckle pattern interferometry (ESPI) have historically been developed in limited collaboration. Both techniques have a significant entry barrier for new researchers to get reliable results. The situation is even worse regarding data and code availability: only three documented and publicly available shearography datasets and very limited open software realisations exist. The data sharing aspect gets more critical. First, AI developments are well reported, while only two datasets were published. Second, developments in phase processing are reported without publicly available code. This limits reproducing and validating the results. Following an example from open data challenges in digital image correlation (DIC), this presentation highlights the Open Science issues and proposes three shearography datasets with inspection of composites. This presentation intends to initiate a discussion in the field that could lead to better practices on data and code sharing.

Twist and crimp values are of paramount importance to the textile industry in understanding the properties and performance of a textile, and their quantification has been a subject of study since the early 20th century. Twist and crimp are the result of how the fibers have been modified from the original bundle to shape the textile, so the industrial methods used to measure them are based on mechanically reversing such deformations. The same information is needed to study the mechanics of historical fabrics such as canvas paintings supports and historical textiles, but they are more difficult to obtain because these are often brittle and impregnated with foreign materials, less homogeneous and very limited in availability for sampling. Therefore, such fundamental parameters are usually unavailable for conservation studies.

This paper examines the protocols used in the textile industry and proposes new methods, developed from previous research, for the reliable measurement of twist and crimp in historical textiles. The twist measurement method is non-destructive as it is based on observing the textile and the fibers on the surface of the yarn. Crimp is the undulation of the interlaced yarns and its measurement is an invasive examination of the internal structure of the textile, as it requires the observation of individual yarns. Both methods, applied here to a group of historical textiles, provide data in accordance with the current parameters of the textile industry, and their use is relatively simple and inexpensive.

Machine Learning (ML) has revolutionized various fields, enabling the development of intelligent systems capable of solving complex problems. However, the process of manually designing and optimizing ML models is often time-consuming, labor-intensive, and requires specialized expertise. To address these challenges, Automatic Machine Learning (AutoML) has emerged as a promising approach that automates the process of selecting and optimizing ML models. Within the realm of AutoML, Neural Architecture Search (NAS) has emerged as a powerful technique that automates the design of neural network architectures, the core components of ML models. It has recently gained significant attraction due to its capability to discover novel and efficient architectures that surpass human-designed counterparts. This manuscript aims to present a systematic review of the literature on this topic published between 2017 and 2023 to identify, analyze, and classify the different types of algorithms developed for NAS. The methodology follows the guidelines of Systematic Literature Review (SLR) methods. Consequently, this study identified 160 articles that provide a comprehensive overview of the field of NAS, encompassing discussion on current works, their purposes, conclusions, and predictions of the direction of this science branch in its main core pillars: Search Space (SSp), Search Strategy (SSt), and Validation Strategy (VSt). Subsequently, the key milestones and advancements that have shaped the field are highlighted. Moreover, we discuss the challenges and open issues that remain in the field. We envision that NAS will continue to play a pivotal role in the advancement of ML, enabling the development of more intelligent and efficient ML models for a wide range of applications.

Hydrogen, which serves as a major driver of sustainable aviation, requires precise sensing methods. This study focuses on the development of a metal hydride-coated tilted fibre Bragg grating (TFBG) based sensor for hydrogen detection, with tantalum as a novel sensing material for fibre optic hydrogen sensing. Magnetron sputtering has been employed to deposit nanometer-scale metal films onto the optical fibre surface of the TFBG structure. In this proof of concept work, changes in both amplitude and the centre wavelength of cladding resonances of the TFBG transmission spectrum were observed in the hydrogen concentration range from 0.01% to 4% at room temperature.