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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., Structural Integrity & Composites, Group Rans, Group Benedictus, Group Groves

In this paper, the effect of interface properties on the compressive failure behavior of 3D woven composites (3DWC) is investigated by incorporating a micromechanics-based multiscale damage model (MMDM). The correlation between the mesoscopic stress of yarns and microscopic stress of constituents is established by defining a stress amplification factor. With the microscopic stresses, the fiber breakage and matrix failure can be separately evaluated at the microscale, without assuming the yarns as transversely isotropic homogeneous materials. Especially, the interfacial debonding between yarns and matrix is also a dominant damage mode within 3DWC. Given that there is still a lack of studies on the influence of interfacial properties on the compressive failure behavior of 3DWC, it is meaningful to perform numerical parametric studies to reveal how the interface properties contribute to the damage mechanisms of 3DWC under compressions. The predicted results indicate that with the increase of interface strengths and fracture toughness, the compressive resistance of 3DWC can be significantly improved, resulting in higher strength and failure strain. Additionally, the studied 3DWCs with weak, medium and strong interfaces exhibit different damage development processes., Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Structural Integrity & Composites

Health indicators are indices that act as intermediary links between raw SHM data and prognostic models. An efficient HI should satisfy prognostic requirements such as monotonicity, trendability, and prognosability in such a way that it can be effectively used as an input in a prognostic model for remaining useful life estimation. However, discovering or designing a suitable HI for composite structures is a challenging task due to the inherent complexity of the evolution of damage events in such materials. Previous research has shown that data-driven models are efficient for accomplishing this goal. Large labeled datasets, however, are normally required, and the SHM data can only be labeled, respecting prognostic requirements, after a series of nominally identical structures are tested to failure. In this paper, a semi-supervised learning approach based on implicitly imposing prognostic criteria is adopted to design a novel HI suitable. To this end, single-stiffener composite panels were subjected to compression-compression fatigue loading and monitored using acoustic emission (AE). The AE data after signal processing and feature extraction were fused using a multi-layer LSTM neural network with criteria-based hypothetical targets to generate an intelligent HI. The results confirm the performance of the proposed scenario according to the prognostic criteria., Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Structural Integrity & Composites

Embedding fiber optic sensors (FOSs) within parts for strain measurement is attracting widespread interest due to its great potential in the field of structural health monitoring (SHM). This work proposes a novel method of embedding FOSs using capillaries within solid structures and investigates fiber positions and orientation uncertainties within capillaries of different sizes and their influences on strain measurement accuracies. To investigate how the fiber positions and orientation variations influence strain measurement accuracy, both analytical and numerical models are utilized to predict strain distributions along embedded fibers at different positions and with different orientations within the specimen. To verify the predictions, a group of specimens made of Aluminum 6082 was prepared, and the specimens in each group had capillaries of 2 mm, 4 mm, and 6 mm diameters, respectively. Fibers were embedded within each specimen using the capillaries. Four-point bending static tests were conducted for each specimen with embedded FOSs, performing in situ strain measurement. Subsequently, the specimens were partitioned into several pieces, and the cross sections were observed to know the real positions of the embedded fiber. Finally, the strain predictions at the real locations of the fiber were compared with the measured strain from the embedded FOSs. The predicted strain distributions as a function of the fiber positions alone and as a function of both the fiber positions and orientations were compared to assess the influence of fiber orientation change. The results from a combination of analytical, numerical, and experimental techniques suggest that the fiber position from the capillary center is the main factor that can influence strain measurement accuracies of embedded FOSs, and potential fiber misalignments within the capillary had a negligible influence. The fiber position-induced measured error increases from 10.5% to 18.5% as the capillary diameter increase, Structural Integrity & Composites

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., Structural Integrity & Composites

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., Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Structural Integrity & Composites

A health indicator (HI) is a valuable index demonstrating the health level of an engineering system or structure, which is a direct intermediate connection between raw signals collected by structural health monitoring (SHM) methods and prognostic models for remaining useful life estimation. An appropriate HI should conform to prognostic criteria, i.e., monotonicity, trendability, and prognosability, that are commonly utilized to measure the HI's quality. However, constructing such a HI is challenging, particularly for composite structures due to their vulnerability to complex damage scenarios. Data-driven models and deep learning are powerful mathematical tools that can be employed to achieve this purpose. Yet the availability of a large dataset with labels plays a crucial role in these fields, and the data collected by SHM methods can only be labeled after the structure fails. In this respect, semi-supervised learning can incorporate unlabeled data monitored from structures that have not yet failed. In the present work, a semi-supervised deep neural network is proposed to construct HI by SHM data fusion. For the first time, the prognostic criteria are used as targets of the network rather than employing them only as a measurement tool of HI's quality. In this regard, the acoustic emission method was used to monitor composite panels during fatigue loading, and extracted features were used to construct an intelligent HI. Finally, the proposed roadmap is evaluated by the holdout method, which shows a 77.3% improvement in the HI's quality, and the leave-one-out cross-validation method, which indicates the generalized model has at least an 81.77% score on the prognostic criteria. This study demonstrates that even when the true HI labels are unknown but the qualified HI pattern (according to the prognostic criteria) can be recognized, a model can still be built that provides HIs aligning with the desired degradation behavior., Structural Integrity & Composites

Strain-based structural health monitoring (SHM) relies on high performance strain sensing methods. Gold nanoparticle (NP) doped fibre optic sensors not only have the potential to increase the intensity of the backscattered signal to increase the signal to noise ratio but also have plasmon resonance peaks in the visible light range. The spectral peak shift of the plasmon resonance may be used for strain sensing. In this paper, the spectral peak shift of the plasmon resonance of an optical fibre containing gold NPs under axial strain was analysed. A modified Lorentz-Drude (LD) model with the T-matrix method was used and the spectral peak shifts of spheroidal NPs under strain were calculated. An approximate analytical expression was derived for faster calculation. The modelling presented in this paper shows that the ratio of the change of the peak wavelength to the strain can be related to the refractive index (RI) change of the optical fibre under strain, the shape change of the gold NP, and the RI change of the gold NP. The peak shift was also observed experimentally in an optical adhesive containing gold NPs under compression. The peak shifts were analysed at different RI of the optical fibres, 1.35, 1.45, 1.55 and 1.65 respectively, in order to cover the range of RI of fused silica and some polymer materials. The results confirm experimentally that the applied axial strain can induce the peak wavelength shift by the NPs. By choosing a different optical fibre or the properties of the NPs, the wavelength change ratio has the potential to be tuned, which may be used for highly sensitive strain sensing., Structural Integrity & Composites

Continuous ultrasonic welding is a promising high-speed and energy-efficient joining technique for thermoplastic composite structures. However, in the current state-of-the-art research on the topic numerous deconsolidation voids could be identified at the welding interface, which results in a strength knock-down. The aim of this study is, therefore, to improve the quality of continuous ultrasonically welded joints by adding a consolidation shoe to the welding setup. To determine the required consolidation pressure, the size of the shoe, and its distance from the sonotrode a stepwise approach was followed based on the static ultrasonic welding process. The closest consolidation distance, best representing the static welding conditions, did not improve the weld quality as significant porosity was still found in the weld line and in the adherends. However, for the furthest consolidation distance high-quality continuous welds were obtained with almost no porosity and a high strength., Aerospace Structures & Computational Mechanics, Aerospace Manufacturing Technologies, Structural Integrity & Composites

With the increased use of composites in aircraft, many new successful contributions to the advancement of the structural health monitoring (SHM) field for composite aerospace structures have been achieved. Yet its application is still not often seen in operational conditions in the aircraft industry, mostly due to a gap between research focus and application, which constraints the shift towards improved aircraft maintenance strategies such as condition-based maintenance (CBM). In this work, we identify and highlight two key facets involved in the maturing of the SHM field for composite aircraft structures: (1) the aircraft maintenance engineer who requires a holistic damage assessment for the aircraft’s structural health management, and (2) the upscaling of the SHM application to realistic composite aircraft structures under in-service conditions. Multi-sensor data fusion concepts can aid in addressing these aspects and we formulate its benefits, opportunities, and challenges. Additionally, for demonstration purposes, we show a conceptual design study for a fusion-based SHM system for multi-level damage monitoring of a representative composite aircraft wing structure. In this manner, we present how multi-sensor data fusion concepts can be of benefit to the community in advancing the field of SHM for composite aircraft structures towards an operational CBM application in the aircraft industry., Structural Integrity & Composites