Research On Nondestructive Testing Of CFRP Materials Based On Planar Capacitance Sensor

Carbon fiber reinforced polymer(CFRP)has been widely used in aerospace field in recent years,and it is very important for aerospace safety to detect its structural integrity.Based on the anisotropic electrical properties of CFRP laminates,a planar capacitance sensor is proposed for structural damage detection.This method has the advantages of low cost,non-intrusion and non-radiation.In this paper,the planar capacitance sensor is used to do the following researches on damage detection of CFRP laminated plates:(1)Four kinds of planar two-electrode capacitance sensors with different shapes are compared and analyzed.Three typical damage models of CFRP laminates are constructed by COMSOL software.The four kinds of capacitance sensors are compared in terms of signal strength,sensitivity,signal-to-noise ratio,measurement dynamic range and correlation coefficient.At the same time,combined with the actual measured data,the detection performances of the four sensors are compared when the surface damage radius of CFRP is 5 mm,2 mm and 1 mm respectively.The results show that the triangle sensor has at least 9.8% higher detection accuracy than other sensors in the correlation coefficient index,which means that it can effectively identify the defects of CFRP laminates.(2)Based on the anisotropic property of CFRP laminates,two planar capacitance sensors of four-electrode plate are designed,and their detection performances are evaluated by evaluation index.Combined with the actual measured data,the detection performances of sensors for different sizes of impact damage and crack damage are compared.The experimental results show that the overall performances of the fan-annular quad-plate sensor are better than those of the triangular quad-plate sensor.(3)A testing system of planar capacitance sensor is established.The system is composed of sensor unit,switching circuit,LCR meter and PC.The system is used to test the damage defection ability for different planar capacitance sensor,and the feasibility of the system is verified.(4)In view of the situation that image orientation cannot accurately identify the damage type,a hybrid network model based on deep confidence network and extreme learning machine is proposed.Besides,in this paper,particle swarm optimization(PSO)algorithm is used to randomly and adaptively select the super parameters of DBN to determine the optimal structure of the network,and finally achieve the classification of multiple damage of CFRP laminates.The experimental results show that the accuracy of the proposed RSAPSO-DBN-ELM algorithm can reach more than 98%,which is better than the other algorithms.It has a good engineering application prospect,and provides an effective scheme for damage detection.