Ultrasound tomography for health monitoring of carbon fibre-reinforced polymers using implanted nanocomposite sensor networks and enhanced reconstruction algorithm for the probabilistic inspection of damage imaging

Irrespective of the popularity and demonstrated effectiveness of ultrasound tomography (UT) for damage evaluation, reconstruction of a precise tomographic image can only be guaranteed when a dense transducer network is used. However, a network using transducers such as piezoelectric wafers integrated with the structure under inspection unavoidably lowers local material strength and consequently degrades structural integrity. With this motivation, an implantable, nanocomposite-inspired, piezoresistive sensor network is developed for implementing in situ UT-based structural health monitoring of carbon fibre-reinforced polymer (CFRP) laminates. Individual sensors in the network are formulated with graphene nanosheets and polyvinylpyrrolidone, fabricated using a spray deposition process and circuited via highly conductive carbon nanotube fibres as wires, to form a dense sensor network. Sensors faithfully respond to ultrasound signals of megahertz. With ignorable intrusion to the host composites, the implanted sensor network, in conjunction with a UT approach that is enhanced by a revamped reconstruction algorithm for the probabilistic inspection of damage-based imaging algorithm, has proven capability of accurately imaging anomaly in CFRP laminates and continuously monitoring structural health status, while not at the cost of sacrificing the composites' original integrity.

Automated summary

This study integrates nanocomposite technology into the development of a piezoresistive sensor network, achieving accurate imaging and continuous monitoring of CFRP laminates using an enhanced UT method with a revised reconstruction algorithm.