A Hong Kong Polytechnic University (PolyU) research team, led by Professor Su Zhongqing and Professor Zhou Limin, has developed unique nanocomposites-inspired sensors that can be sprayed on flat or curved engineering structural surfaces, such as train tracks and airplane structures. The sprayable sensors can be networked, to render real-time information on the health status of the structure under monitoring. Due to its light weight and low fabrication cost, large quantities of sensors can be deployed in a sensor network for detecting hidden flaws of structures, paving the way for faster, greater, and lower-cost ultrasonics-based structural health monitoring.
Currently, the number of conventional ultrasound sensors, such as those made of lead zirconate titanate (PZT), used for in-situ monitoring is usually limited by the factors related to the sensor’s cost and weight. These sensors are usually stiff, introducing remarkable weight and volume penalty to a host structure to which the sensor is to be mounted. The nanocomposite sensors developed by the team, however, can be fabricated in large quantities to form a dense sensor network for structural health monitoring at a much lower fabrication cost and weight than using conventional sensors.
Low Cost, Light Weight
PolyU’s innovative sensing technology encompasses a sensor network with a number of the sprayed nanocomposite sensors and an ultrasound actuator, to actively detect the health condition of the structure to which they are fixed, quickly and accurately showing if there is any damage in the structure. When the ultrasound actuator emits guided ultrasonic waves (GUWs), the sensors will receive and measure the waves. If damage, such as a crack is present in the structure, propagation of GUWs will be interfered by the damage, leading to unique wave scattering phenomena, to be captured by the sensor network. Based on wave scattering, the damage can be characterized quantitatively and accurately via an all-in-one system that was developed by the team.
Compared to the conventional ultrasound sensors which costs over US$10 each and weighs few grams, this new breed of nanocomposite sensor costs only US$0.5 and 0.04g for each. As such more sensors can be adopted in one structure, generating more information for analysis, with less weight added to the structure. In addition, Professor Su’s sensor has excellent flexibility and can adapt to curved structure surfaces. That allows a wide range of practical engineering applications. It is also sprayable on the surface of a moving structure to transmit the structural health information in a real-time manner.
Wider Frequency Range of Response
The sensor developed by the team can measure an ultrasound signal from static to up to 900 kHz yet with ultralow magnitude. The acquisition of wave scattering in an ultrasonic regime allows detection of cracks as small as 1 to 2 mm in most engineering materials. That response frequency is over 400 times more than the highest frequency than nanocomposite sensors that are reportedly available (as reported in international journals).
While conventional ultrasound sensor can measure a wider range of ultrasound waves when compared to those developed by the team, the high cost and weight of the conventional sensors make a large quantity application infeasible, limiting the quantity of data acquired. There are lots of limitations in applying the conventional ultrasound sensors to practical applications, especially in aerospace structures.
New Technology To Cut Cost And Enhance Sensitivity
Made of a hybrid of carbon black (CB), 2D graphene, conductive nano-scale particles, and polyvinylidene fluoride (PVDF), the nanocomposite sensor can be easily and flexibly tailored to different sizes towards various engineering applications. The secret of its high sensitivity to structural change lies in the optimized nanostructure of the hybrid, which endows the sensor to possess an ability to identify the dramatic changes in piezoresistivity of the nanocomposite. To measure and analyse the dramatic change of piezoresistivity, Professor Su and his team tested numerously on the weight ratio of nanofillers in order to optimize the conductivity of the nanocomposite.
Each sensor is connected to a network via a wire printed on the structure. By analyzing and comparing the electrical signals converted from the electric resistivity, the network can spot the defect in a structure, as well as translate the signals into 3D images.
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