Experimental studies on the failure mode of composite T-joint specimens, in the presence of adhesive interlaminar layers, are presented. Tests underline the role played by the interaction between delamination and debonding phenomena. Quasi static analyses of the tests are performed by means of explicit FE models. A modelling technique based on a cohesive zone approach is adopted to simulate damage propagation both in the adhesive interface and in the most critical interlaminar layers of laminates. All the numerical results are compared against the experimental evidences in terms of fracture patterns, of load vs. displacement responses and local strain levels

The economic impact of Structural Health Monitoring Systems based on optical fibre sensors is assessed in the development of composite helicopter rotor blades. Hence, the focus of this analysis is on the helicopter’s Beginning Of Life stage. Two applications of the Structural Health Monitoring System are considered in the development of composite blades: curing cycle development and accomplishment of laboratory and flight certification tests. Optical fibre sensors measure the temperature field during the curing cycle and strain field during the laboratory tests and allow load identification during the load survey activity. It was found that Structural Health Monitoring Systems can potentially lead to economic benefits during the development of the blade provide that a reduction in the number of curing cycles and number of blades tested is achieved as a consequence of the improvement of the temperature and strain field quality. Moreover, an economic benefit could be achieved during the load survey activity, needed to complete the certification of the composite blade, avoiding the periodical maintenance of the applied strain gauges acquiring the strains during the flight.

Monitoring the integrity of aeronautical structures is fundamental for safety. Structural Health Monitoring Systems (SHMSs) perform real-time monitoring functions, but their performance must be carefully assessed. This is typically done by introducing artificial damages to the components; however, such a procedure requires the production and testing of a large number of structural elements. In this work, the damage detection performance of a strain-based SHMS was evaluated on a composite helicopter rotor blade root, exploiting a Finite Element (FE) model of the component. The SHMS monitored the bonding between the central core and the surrounding antitorsional layer. A damage detection algorithm was trained through FE analyses. The effects of the load’s variability and of the damage were decoupled by including a load recognition step in the algorithm, which was accomplished either with an Artificial Neural Network (ANN) or a calibration matrix. Anomaly detection, damage assessment, and localization were performed by using an ANN. The results showed a higher load identification and anomaly detection accuracy using an ANN for the load recognition, and the load set was recognized with a satisfactory accuracy, even in damaged blades. This case study was focused on a real-world subcomponent with complex geometrical features and realistic load conditions, which was not investigated in the literature and provided a promising approach to estimate the performance of a strain-based SHMS.

Flight Condition Recognition (FCR) is pivotal for aviation safety, enabling the recognition of the real usage spectrum and thus adapting and optimizing the maintenance schedule defined at design time. However, as the spectrum of flight regimes expands, recognition accuracy becomes complex. With increasing regimes, classifier representations overlap due to synthesized indicators summarizing multivariate time-series behaviors. Neglecting temporal dependencies and dynamic behaviors impairs classifier performance, particularly for specific regimes, limiting the expansion of the recognized set. In this paper, we propose a functional similarity index to address this challenge, aiding analysts in identifying similar regimes and improving classification performance. To define such index, we rely on functional data analysis to capture regime dynamics, enhancing both performance and behavior analysis. The designed similarity index offers dual benefits: it investigates supervised FCR classifier outcomes and guides a closed-loop feature selection procedure to enhance recognition performance. This process eliminates the need for direct supervised classifier retraining, using the similarity index as a gauge to estimate the classifier's capability to distinguish critical regimes given a specific features set. When applied to extensive data collected during load survey flights performed by two Leonardo helicopter models, our method provides insights into results assessed by a state-of-the-art FCR supervised classifier. It identifies critical regimes and identifies informative features to include in the set to enhance recognition. This versatile framework has the potential to significantly enhance operational efficiency and aviation system safety, thereby fortifying overall operational capabilities.

International Measurement Confederation (IMEKO)