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.

无论超声断层扫描 (UT) 在损伤评估方面的普及程度和已证明的有效性如何，只有使用密集换能器网络才能保证重建精确的断层扫描图像。然而，使用诸如压电晶片之类的传感器与被检查结构集成的网络不可避免地会降低局部材料强度，从而降低结构完整性。出于这个动机，开发了一种可植入的、受纳米复合材料启发的压阻传感器网络，用于对碳纤维增强聚合物 (CFRP) 层压板实施基于 UT 的原位结构健康监测。网络中的单个传感器由石墨烯纳米片和聚乙烯吡咯烷酮制成，使用喷涂沉积工艺制造，并通过高导电性碳纳米管纤维作为导线连接，以形成密集的传感器网络。传感器忠实地响应兆赫兹的超声波信号。由于对宿主复合材料的侵入可忽略不计，植入的传感器网络结合 UT 方法，该方法通过改进后的增强用于损伤概率检查的重建算法基于成像算法，已经证明能够准确地对 CFRP 层压板中的异常进行成像并持续监测结构健康状态，同时不以牺牲复合材料的原始完整性为代价。