Material and strain sensing uncertainties quantification for the shape sensing of a composite wing box

The shape sensing, i.e. the reconstruction of the displacement field of a structure from discrete strain measures, is a key tool for the support and development of the modern Structural Health Monitoring frameworks and has received a huge attention in the last few decades. The parallel increase in the use of composite materials in the aerospace industry has consequently generated the need to investigate the applicability of the shape sensing methods to this peculiar kind of materials. In fact, the manufacturing complexity of the composite materials can result in a significant variability in the characteristics of the material. Therefore, a study on the propagation of this kind of uncertainty on the performance of the shape sensing methods is paramount. The uncertainties in the strain measurements can influence the shape sensing results and must be also considered. This paper, for the first time, investigates the propagation of these two sources of inputs' uncertainty on the performance of three shape sensing methods, the inverse Finite Element Method (iFEM), the Modal Method (MM) and the Ko's Displacement theory. Using the Monte Carlo Simulation (MCS) with Latin Hypercube Sampling (LHS), the robustness of the three methods with respect to the inputs' variability is evaluated on the reconstruction of the displacement field of a composite wing box. The MM shows a significant robustness and the iFEM, although more affected by the uncertainties, is the method that achieves the best accuracy. The Ko's displacement theory, on the other hand, is the less accurate and the less robust. (C) 2021 Elsevier Ltd. All rights reserved.

Automated summary

Shape sensing is crucial for modern Structural Health Monitoring frameworks. This study assesses the impact of uncertainties on the performance of three shape sensing methods, with Modal Method showing the most robustness and iFEM achieving the highest accuracy. Ko's Displacement theory, on the other hand, is the least accurate and robust among the three methods.