Metal hydrides have been widely studied as hydrogen sensing materials and applied to various optical sensor configurations. With the increasing interest in using hydrogen as an energy source across sectors involving combustion processes, there is a growing demand for reliable hydrogen sensors operating at temperatures above 100 °C. Therefore, it is necessary to evaluate the performance of potential hydrogen sensing materials at elevated temperatures. We conducted experiments to observe the optical response and structural characteristics of palladium, palladium–gold, tantalum, and tantalum-alloy thin films with respect to varying hydrogen concentrations from 28 °C to 270 °C. Our results demonstrate that the optical response of palladium and palladium–gold diminish at 270 °C. However, tantalum provides a remarkable optical response to hydrogen concentrations below 1% for all the observed temperatures and a stable response at 270 °C for 350 cycles. Our measurement results show that tantalum is the most suitable material for detecting hydrogen within the range of 0.01% to 100% at temperatures ranging from 28 °C to 270 °C.

Palladium thin films have been studied as hydrogen sensing materials and applied to variety of optical hydrogen sensors. Recently, tantalum has emerged as an attractive option for hydrogen sensing materials due to its broad sensing range and flexibility in tuning the sensing range by modifying the alloying composition or elements. Following the demand for optical hydrogen sensors for aerospace applications, testing the performance of hydrogen sensing materials is of interest. This work examines the optical response in respect to changing hydrogen concentrations and thermal expansion of palladium-gold (Pd_0.65Au_0.35) and tantalum-ruthenium (Ta_0.97Ru_0.03 and Ta_0.91Ru_0.09) thin films at temperatures similar to a hydrogen combustion engine. Our results suggest that tantalum-ruthenium alloys are suitable for sensing hydrogen from ambient temperatures up to 270^◦C because its low detection limit (0.01% of hydrogen in the atmosphere) is well below the explosive limit of hydrogen (4% of hydrogen in the atmosphere).

With the increasing application of artificial intelligence (AI) techniques in the field of structural health monitoring (SHM), there is a growing interest in explaining the decision-making of the black-box models in deep learning-based SHM methods. In this work, we take explainability a step further by using it to improve the performance of AI models. In this work, the results of explainable artificial intelligence (XAI) algorithms are used to reduce the input size of a one-dimensional convolutional neural network (1D-CNN), hence simplifying the CNN structure. To select the most accurate XAI algorithm for this purpose, we propose a new evaluation method, feature sensitivity (FS). Utilizing XAI and FS, a reduced dimension 1D-CNN regression model (FS-X1D-CNN) is proposed to locate and predict the torque of loose bolts in a 16-bolt connected aluminum plate under varying temperature conditions. The results were compared with 1D CNN with raw input vector (RI-1D-CNN) and deep autoencoders-1D-CNN (DAE-1D-CNN). It is shown that FS-X1D-CNN achieves the highest prediction accuracy with 5.95 mm in localization and 0.54 Nm in torque prediction, and converges 10 times faster than RI-1D-CNN and 15 times faster than DAE-1D-CNN, while only using a single lamb wave signal path.

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.

With the improvements in computational power and advances in chip and sensor technology, the applications of machine learning (ML) technologies in structural health monitoring (SHM) are increasing rapidly. Compared with traditional methods, deep learning based SHM (Deep SHM) methods are more efficient and have a higher accuracy. However, due to the black box nature of deep learning, the trained models are usually difficult to interpret, which blocks their practical application. Therefore, it is of great importance to develop explainable artificial intelligence (XAI) methods to understand the internal decision-making mechanisms of damage classification in Deep SHM. In this paper, a novel XAI algorithm named Deep Gradient-weighted Class Activation Mapping (Deep Grad CAM) is proposed by combining the existing method Grad CAM with the convolutional neural network (CNN) deconvolution mechanism. In this paper, Deep Grad CAM is used to interpret a one-dimensional convolutional neural network trained to detect bolt loosening based on guided wave propagation. The interpretation performance of Deep Grad CAM is compared with Grad CAM, and their performances are quantified using Infidelity. The results show that the Infidelity of Deep Grad CAM is much smaller than that of Grad CAM, indicating significant improvements in explanation accuracy and reliability.

Carbon fiber and glass fiber-reinforced polymer composites are extensively used in many industries such as aerospace, wind energy, and automobile. Nowadays, due to the high interest in manufacturing safe and cost-optimized composite structures, the need for nondestructive-evaluation (NDE) of these structures is rising significantly; either as a part of the manufacturing process or while the structure is in service. Among all NDE techniques, one of the most promising for composite materials is ultrasonic testing (UT). Ultrasonic NDE uses low amplitude acoustic/elastic waves with frequencies higher than about 20kHz to evaluate components without affecting the material properties and their integrity. This technique is extensively employed for the inspection of parts during in-service use, for quality control during the manufacturing process, as well as for material characterizations. The main advantages of the ultrasonic NDE technique are the possibility of high penetration in many commonly used materials, robustness against the conductivity of the material, accuracy in locating and measuring defects, and the ability to be performed with one-sided access. However, there are some drawbacks such as the need for a highly experienced operator and the necessity of maintaining a high degree of coupling between the transducer and the surface of the structure. While conventional ultrasonic NDE using single-element transducers has been commonly used in the past, it has some limitations for the evaluation of thick composite layers, such as the high attenuation and low signal-to-noise ratio. The phased array ultrasonic technique (PAUT) significantly improves the capability of UT for the inspection of composite materials as the ultrasonic waves can be generated and focused at desired energies, angles, and distances. This chapter is organized to provide the essential theoretical background and technical knowledge on ultrasonic NDE of thick composite laminates, starting from the physics of ultrasonics and phased array systems, going up to the signal-processing, considerations on how to deal with the collected data, and finally, the real-world applications.

Acoustic emission (AE) is widely used for identifying source mechanisms and the deformation stage of steel material. The effectiveness of this non-destructive monitoring technique heavily depends on the quality of the measured AE signals. However, the AE signals from deformation are easily contaminated by the signals from noise in a noisy environment. This paper presents a hybrid model for deformation stage identification, which combines a self-adaptive denoising technique and an Artificial neural network (ANN). In pursuit of model generality, AE signals were collected from tensile coupon tests with various steel materials and loading speeds. First, a decomposition-based denoising method is applied based on the singular spectral analysis (SSA) and variational mode decomposition (VMD), which is defined as SSA-VMD. Its effectiveness is demonstrated by simulated signals and experimental results. Following the use of the denoising technique, an ANN is constructed to identify the deformation stage of steel materials with the input of features extracted from the filtered AE signals. The results indicate that the ANN achieves a high prediction accuracy of 0.93 in the test set and 0.87 in unseen data. By applying this denoising method, the ANN-based approach enables accurate correlation of the collected AE signals to deformation stages. The finding can be used as the basis for the creation of new methodologies for monitoring structural health status of in-service steel structures.

Hydrogen, crucial in industrial and environmental realms, demands precise sensing methods. This study focuses on the design of a metal hydride-coated tilted fibre Bragg grating (TFBG) based sensor for hydrogen detection, introducing tantalum as a novel sensing material for fibre optic hydrogen sensor development. To facilitate the ellipsometry inspection of optical constants, magnetron sputtering technique has been employed to deposit nanometer-scale metal films onto a glass substrate. Numerical modeling results are presented for mode analysis of the proposed sensor design, analyzing transverse mode behavior for sensor optimization. The study also provides insights into other TFBG sensor design considerations, suggesting potential hydrogen sensing applications, such as hydrogen-powered aviation and storage solutions.

Nanoparticle- (NP-) doped optical fibres show the potential to increase the signal-to-noise ratio and thus the sensitivity of optical fibre strain detection for structural health monitoring. In this paper, our previous experimental/simulation study is extended to a design study for strain monitoring. 100 nm spherical gold NPs were randomly seeded in the optical fibre core to increase the intensity of backscattered light. Backscattered light spectra were obtained in different wavelength ranges around the infrared C-band and for different gauge lengths. Spectral shift values were obtained by cross-correlation of the spectra before and after strain change. The results showed that the strain accuracy has a positive correlation with the relative spectral sensitivity and that the strain precision decreases with increasing noise. Based on the simulated results, a formula for the sensitivity of the NP-doped optical fibre sensor was obtained using an aerospace case study to provide realistic strain values. An improved method is proposed to increase the accuracy of strain detection based on increasing the relative spectral sensitivity, and the results showed that the error was reduced by about 50%, but at the expense of a reduced strain measurement range and more sensitivity to noise. These results contribute to the better application of NP-doped optical fibres for strain monitoring.

Thick glass fiber-reinforced polymer (GFRP) composites, e.g. thickness of more than 50 mm, are increasingly used in a wide variety of industries, particularly in the marine and wind energy sectors. Defect detection and characterisation in these composites remain appealing challenges due to the material complexity and the presence of various manufacturing and in-service defects. In this study, we propose a novel shearography method with controlled surface temperature (CST) heating for deep defect detection (i.e., 15 mm depth and more) in thick GFRP laminates. The proposed CST heating has been developed based on analytical solutions to control the maximum surface temperature of a test object during shearography inspection. Numerical and experimental studies have been performed to analyse the defect behaviour and defect detection under various heating scenarios, a topic which is rarely reported for thick composites with shearography. Compared with conventional shearography, the CST shearography method maximises heating energy input with a controlled and stable maximum surface temperature for deep defect detection. Results indicate an enhancement of about 27% in defect signal for the defect at 15 mm depth in comparison to conventional heating. The results also provide insight for implementing an efficient inspection in terms of the inspection duration and the number of datasets. This study makes a step towards safe, quantitative and predictable inspection of deep defects in thick composites.

Tilted fiber Bragg grating (TFBG) sensors were demonstrated to simultaneously measure the material thermomechanical and refractometric state in which they are embedded. In this work, for the first time, TFBGs are investigated for three-parameter monitoring of space-qualified NuSil® CV16-2500 silicone operating during high-vacuum ultraviolet (UV) exposure. The first part of the work is focused on the ultraviolet effect on the TFBG spectrum when the sensor is 1) directly exposed to the radiation, 2) covered by a thin cover glass, and with a Kapton layer on top. Successively, the silicone is used as an adhesive in a sandwich structure in which the TFBGs are embedded and exposed under high vacuum to various UV/vacuum UV intensity radiations and durations. The sensors’ spectra were acquired and demodulated to detect the silicone strain-temperature-refractive index variations and correlate the silicone refractometric changes with the equivalent exposure solar hours. The second part of the paper is on silicone degradation state evaluation using the same sensor but during a direct exposure of the adhesive to the radiation. This allowed the UV effects on the silicone to be enhanced but needed a method to compensate for the damaging effect of UV radiation on the TFBG spectrum.

The colour of the ground layers of a painting has an influence on its visual appearance. In addition to the commonly used white ground layers, other colour ground layers have been used, for example, the grey ground layer used in Peter Paul Rubens’s painting Portrait of Clara Serena Rubens helps the colour transition of the skin tones. Understanding the effects caused by the colours of the ground layers is of significance for both technical art history and conservation. Optical non-destructive testing (NDT) techniques are useful tools for the investigation of paintings, for example, optical coherence tomography (OCT) can be used to study the surface and subsurface layers non-destructively. In this work, the interaction of light with paint and ground layers is modelled to supplement OCT measurements of paintings with ground layers. A previously described near-infrared light range OCT system provides high spatial and depth resolution measurements. A four-flux model has been developed for analysing the light interaction in the paint and ground layers. This model considers forwards-propagating collimated light, backwards-propagating collimated light, forwards-propagating diffuse light and backwards-propagating diffuse light. The model uses the optical material properties, including refractive index (RI), absorption and layer thickness, as input. This paper describes the construction of the model and an evaluation of its performance by comparison with OCT data.

The assessment of the structural condition of cultural heritage objects is important for conservation interventions and their long-term preservation. This investigation concerns The Night Watch (1642), a large-format 17^th-century canvas painting by Rembrandt van Rijn that is on display in the Rijksmuseum, Amsterdam. This painting, which has a complex treatment history, has various damaged areas and has undergone three wax-resin relinings. In 1975 the canvas was slashed twelve times with a serrated dinner knife, including several long slashes in the area of Captain Frans Banninck Cocq’s breeches. In 2021, prior to a proposed new structural intervention involving retensioning of the canvas, it was important to evaluate the structural condition of the repaired slashes and of another repair, specifically an old canvas insert in the drum. For this, an in-situ inspection was carried out in the Rijksmuseum as a part of Operation Nightwatch. 3D shearography instrument with thermal loading was used to inspect these two areas of interest on the reverse of The Night Watch. The results showed that the out-of-plane strain in the breeches does not show any large deviations, which alleviated conservators’ concerns about the adhesion of the lining canvas and stability of previous repairs in this region. The patch in the drum showed higher out-of-plane strain variations. This was explained by the lower quality of the patched canvas compared to the repaired slashes in the breeches of Banninck Cocq. Overall, 3D shearography provided valuable inspection results for assurances regarding the structural integrity of the 1975 repairs and the wax-resin lining in The Night Watch, reducing the risks and providing the confidence to proceed with the planned retensioning of the canvas.

The performance of the Acoustic Emission (AE) technique is significantly dependent on the sensors attached to the structural surface. Although conventional commercially AE sensors, like R15a and WSa sensors, have been extensively employed in monitoring many different structures, they are unavailable in restricted-assess areas. In contrast, thin PZT sensors are small, inexpensive and lightweight. These thin PZT sensors have a great potential for passive sensing to detect AE signals. However, their utility in AE monitoring is limited due to their low signal-to-noise ratio and information incompleteness because of their simple construction. This work discusses the issues and possible solutions with regards to the specific selection and application of thin PZT sensors for passive sensing. The compatibility of different thin PZT sensors and conventional bulky sensors is investigated using pencil break lead (PBL) tests. The comparison between the recorded signals is carried out in both the time domain and frequency domain for these sensors. To improve the reliability and performance of the thin PZT sensors, a methodology employing multiple thin PZT sensors of different sizes is proposed based on machine learning techniques and sensor data fusion.

It has been shown that the roots of guided waves in laminate plates produced by the ordinary differential equations (ODE) approach may not hold under to some computational conditions. A particular drawback of the 2D formulation of the ODE approach is the lack of reliability in the case of unidirectional laminates due to the decoupling properties between the SH and Lamb wave modes, which is caused by the unified matrix of roots. Due to this problem, the SH modes disappear from the unified roots of guided modes, then re-emerge with a separate computation of the SH and Lamb wave modes. Initially, we did not notice this computational “bug” in the event of a coupling between the SH and Lamb wave modes. In this context, the Legendre polynomial method is used to illustrate that fact. Results demonstrate how the polynomial method is pre-eminent to handle the laminate modelling over the ODE method for these specific requirements, however, a trade-off between these two methods needs to be considered to obtain stable and robust behavior of guided dispersion curves. This short study ends with conclusions and future perspectives.

The mechanical aspects of canvas painting conservation and the study of the effects of conservation treatments benefit greatly from quantifying the mechanical characteristics of the materials. However, this is seldom possible as only few labs have the necessary equipment. This paper presents the development of a biaxial tester to be used for samples of canvas paintings which exhibit orthotropic behavior under biaxial loads. The machine was built as the first step of ongoing Ph.D. research on the mechanics of canvas paintings. An effort was made to create a system that is easy to assemble, with parts that are easy to source and with an overall cost well below the commercial units available. The control software includes the function of automated pre-tensioning to improve the accuracy of the measurement. Our broader purpose here is to make an easy-to-replicate machine available to help conservators and conservation scientists perform tensile tests to make informed choices in materials science.

The performance of defect detection in composite materials using digital shearography is important for correct decision-making in non-destructive testing. In this work, we compared a high-resolution 24-megapixel digital still camera (DSLR) and a conventional medium-resolution 5-megapixel camera to determine the detectability of blind holes in an aerospace-graded carbon-fiber reinforced polymer (CFRP) sample. The hole diameters ranged from 0.2 to 3 mm with a material thickness of 4 mm and the test sample dimensions of 200×200 mm. The sample was heated and observed from the front (defect-free side) by three halogen lamps for 5 minutes in pulsed heating mode. Speckle interferograms were acquired during the heating and cooling phases from both cameras simultaneously using identical shearing interferometers and shearing distances. Phase maps were calculated using the 4+4 temporal phase step algorithm and then unwrapped. Further, defect-induced deformation (DID) phase maps were obtained by polynomial curve fitting. The DID phase maps obtained from the two cameras were compared. Blind holes with diameters up to 1 mm were detected, which are one of the smallest defects detected with shearography and reported in literature. In addition, the DLSR camera was able to detect holes of 0.8 mm in diameter. We observed that nearly comparable detection capabilities were obtained from both cameras, even though the spatial resolution of the second camera (DLSR) was 5 times higher. Possible reasons of this limitation include effects such as fiber-related deformation in CFRP and speckle noise.

In this research the ageing of a silicone adhesive in a simulated space environment is monitored through an embedded three parameter tilted fibre Bragg grating (TFBG) sensor. Here, the silicone is used as an adhesive between two thin cover glasses, and the space environmental ageing is simulated by thermal cycles in high vacuum conditions (better than 10-5 mbar). These operational conditions can induce variations in the silicone adhesive with respect to its original properties such as dimensional stability, chemical composition, generated contaminants, discoloration and, mechanical or optical degradation. Therefore, surrounded by the adhesive, in the centre of the cover glass sandwich, a weakly tilted FBG sensor was placed to obtain information from its spectra on the state of the polymer during the test. Specifically, the temperature, strain and refractive index (RI) of the silicone can be, simultaneously and separately, measured from the spectrum of a single TFBG from selected resonance peaks. These parameters can be used to evaluate the 'health' state of the silicone during the vacuum thermal cycles. The simultaneous TFBG thermomechanical measurements gave a solution to the non-localized measuring issues when using classical fibre optic or electrical strain-gauges and a thermocouple to compensate the temperature and to better understand the material behaviour. The trends of the measured parameters are reported during the entire testing time, and at the end of the test, the optical fibre sensor measured a negative strain of ∼100 μϵ and a positive RI variation of ∼0.002.

In this paper, to increase the performance of the sparse reconstruction method in real complex engineering structures an adaptive dictionary learning framework is proposed which updates the dictionary matrix, to allow improved compatibility with the complex structure. This proposed framework was developed by combining analytical modeling with training data sets and learning methods. An experimental evaluation of the proposed dictionary learning framework was performed on an anisotropic composite plate with a stiffener. In this experimental evaluation, a moving magnet was used as the artificial damage to capture the training data set, and both artificial damage in several locations and real impact damage was used for detection and location of the target damage. The obtained results confirmed the concept of the proposed dictionary learning framework for the improved health monitoring of complex structures.