PSI - Issue 52

Ilias N. Giannakeas et al. / Procedia Structural Integrity 52 (2024) 655–666 Ilias N. Giannakeas/ Structural Integrity Procedia 00 (2022) 000 – 000

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1. Introduction A composite is the combination of at least two distinct materials that aims in attaining mechanical properties that would be impossible if each material was used individually. Contrary to metallic material however, the failure behaviour of composites is not as well understood. In composite materials there are plenty failure mechanisms that can manifest such as fibre breakage, matrix crack and delamination. The complexity of the problem is further magnified by the fact that composites can fail differently under different loading conditions (Giurgiutiu 2015a). Additionally, composites are susceptible to low velocity impacts that can cause Barely Visible Impact Damage (BVID). These types of damages are both hard to detect implementing traditional Non Destructive Evaluation (NDE) approaches and can significantly reduce the reliability of the structure (Aliabadi and Khodaei 2017). Composites have plenty properties that are very attractive for the aerospace industry such as stiffness-to-weight ratio, fatigue performance and corrosion resistance. From a structural point of view, the growing acceptance of composites materials in the aviation industry and the development of advanced processing technologies are leading to the design and development of larger, lightweight, more complex structures. Current damage tolerant design philosophies require that the material should possess adequate residual resistance throughout its lifecycle for an assumed worst case damage. These limits are enforced by the certification regulations for aircraft airworthiness such as FAR25, CS25 and MIL HDBK 17 – 1F (Giannakeas et al. 2023). Combined with the complex failure behaviour of these material, overconservative safety factors are imposed during the design stages. These safety factors lead to oversizing of the structures and underutilization of the material. According to IATA 2021 report, the target in aviation is to achieve net-zero emission by 2050. To achieve such bold targets, significant technological improvements are required that will allow the transition to green aviation. Structural health monitoring (SHM) offers an alternative way to non-destructive evaluation (NDE) to assess the integrity of structures (Giurgiutiu 2015b). A typical SHM system employs sensors that are permanently mounted on a structure and is able to collect, analyse and interpret the measurements to determine its current health status. Integration of SHM in composite structures has the potential to provide continuous and on-demand early warning indications regarding the integrity of the structure without taking the structure out of service which is costly and time consuming. In the aviation industry, maintenance costs contribute around 10% to the overall costs (Giljohann and Klingauf 2014; Minwoo, Larry, and Wenbin 2019). Using this stream of integrity assessment, SHM systems pave the way to transitioning from the current schedule-based maintenance to true condition-based maintenance practices. Furthermore, recent studies have indicated that considering integration of SHM systems during the early design stages of a structure has the potential of allowing the relaxation of the safety factors, reducing the overall weight by up to 5% (Dienel et al. 2019). Therefore, SHM has the potential of significant fuel savings through the weight reduction of the structure as well as offer pathways for novel digitalized maintenance strategies that can both contribute to the sustainability of the sector. This study focus on guided wave based SHM systems (GWSHM) that employ a permanently mounted distributed network of piezoelectric transducers (PZTs) to interrogate the structure over long distances. The operation and the outputs of the system can be categorized into 4 distinct levels: i) detection, ii) localization iii) characterization and iv) residual life estimation (Kralovec and Schagerl 2020). The first three levels correspond to diagnosis of the current health status of the structure while the last level to the prognosis of the future behaviour of the structure (Fig. 1). Plenty contributions have demonstrated the suitability of GWSHM for diagnosis of thin composite structures (Giurgiutiu 2015b; Yue, Khodaei, and Aliabadi 2021; Sharif-Khodaei and Aliabadi 2014; Salmanpour, Sharif Khodaei, and Aliabadi 2017). Prognosis estimations on the other hand are much more challenging due to the complex failure mechanism and degradation phenomena in composites. Although significant advancements have been made in computational mechanics, the prediction of failure in complex systems is still not well understood and in many scenarios prediction models based on physics are limited to specific and simplified cases (Chao et al. 2022). Additionally, these models are deterministic and fail to capture the underlying uncertainties. It is speculated that the advent of SHM and the accompanied stream of health indications will allow the implementation of hybrid approaches where data-driven machine learning models will be used in tandem with physics-based models. Such approaches can leverage the capability to learn complex patterns from the former with the generalization capabilities of the later. After the initial training of these models, they allow making rapid estimations at new inputs making them very popular and suitable for applications in digital twins where a digital representation of the structure is informed by the sensor readings to update the estimations for its current health state (Chao et al. 2022; Milanoski et al. 2023; Li et al. 2021; Millwater, Ocampo, and Crosby 2019).