Abstract Summary
This study is driven by the ever-growing enthusiasm from industry and academia towards the development of a robust, lean, and autonomous structural health monitoring system. Owing to their lightweight, reduced carbon footprint and new design possibilities composite materials are ubiquitous in various industrial applications, including large-scale safety-critical applications in aerospace, automotive, renewable, marine and construction. However, their multilayered, anisotropic properties not only lead to complex failure modes but also makes it a challenge to detect and monitor damage initiation and growth. Non-intrusive inspection methods are vital to the continuous monitoring regime and to move towards a predictive maintenance regime for these structures. This relies heavily on the capabilities of the onboard monitoring system and its efficacy of signal acquisition and processing on the edge, to extract essential signal features. Such signal features, when mapped to parameterized damage metrics, can characterize damages, and help to realize an automated framework for assessment of structural integrity and maintenance intervention points. This paper presents a systematic ultrasonic guided wave-based active inspection approach integrated with suitable data conditioning and processing operations to identify fundamental wave modes in the recorded signal. An experimental setup consisting of a signal generation/reception system was used to actuate a carbon fiber reinforced composite panel with user-defined guided wave signals over a range of carrier frequencies. A sparse array of sensors attached to this panel was used to record the responses to actuation. The recorded signals were conditioned to extract structural acoustic response in the ultrasonic frequency band and were investigated with techniques to extract frequency components and wave packets in time-frequency domain. The final objective was to filter individual structural wave modes from which experimentally measured dispersion curves could be extracted autonomously and consistently for active ultrasonic interrogation signals introduced by the transducer network. The individual wave velocities and directional properties were compared with a semi-analytical finite element model. Authors highlight their vision of an integrated cyber-physical system with the ability to make intelligent projections and extrapolations upon exploring scenarios that the physical model is yet to encounter. Quantifying the uncertainty associated with these predictions is crucial to realize an autonomous structural health monitoring system and a research area with immense potential.
