Study On Characterization Of Delamination Defects In CFRP Using Ultrasonic Phased Array

Carbon fiber reinforced plastics(CFRP)composite materials are widely used in a variety of technical applications such as aerospace and automobile due to low density,strength-to-weight ratio and modulus rate.However,CFRP tends to suffer from delamination defects which would significantly degrade mechanical property of CFRP.For carbon fiber composite delamination defects damage to explore a kind of reliable and high resolution of detection method is particularly important.Its main works and innovations are summarized as follows:First of all,constructing the single line array element model,the simulation to study the width of element and wavelength ratio on the acoustic field directivity.Through the constructing the array beam model,simulation research the probe frequency,the number of array element,the array size,array element spacing,center distance of side lobe and the influence of the grating lobe.Discusses the beam steering and focusing on the rule of delay.The results of the study for the phased array provides the theory basis for design and optimization of test parameters.Then,study on characterization of delamination defects in CFRP using ultrasonic phased array.For this part investigates ultrasonic phased array based method for characterization of delamination defects in CFRP.After setting a gate in an A-scan signal,the amplitude and time delay of the gated signal are extracted to determine the position and profile of delamination defects.C-scan images were developed with amplitude and time delay features.The results demonstrate that the obtained C-scan images can be to quantitatively identify delamination defects in CFRP,which validates the feasibility of ultrasonic phased array technique for characterization of CFRP.Finally,study on Carbon fiber composite materials in delamination defects automatic identification method.Characterize the first application of KPCA extracting defect position and the size of the defect signals,then built using neural network and support vector classifier,using the particle swarm algorithm to optimize the parameters of support vector machines(SVM).The experimental results show that the research of feature extraction and classification algorithm can accurately identify the size and location of the delamination defects.