Study On Imaging Of Defects In Carbon Fiber Reinforced Polymer Using Ultrasonic Phased Array Technique Based On Compressed Sensing

Carbon Fiber Reinforced Plastic(CFRP)has the advantages of low relative density,high specific strength and specific modulus,and is widely used in aerospace,automotive and other fields.CFRP are prone to delamination defects during production and service,which will seriously affect their mechanical properties.Ultrasonic phased array detection technology is used to detect delamination defects in CFRP due to its advantages such as fast detection speed and high accuracy.However,there are problems in the application of large processing data and high hardware requirements.In view of this,this paper,based on the theory of compressed sensing,combined with the characteristics of ultrasonic phased array imaging,carried out the research on the ultrasonic phased array imaging method of CFRP layered defects based on compressed sensing.The main research work and innovations are as follows:Firstly,the theory of ultrasonic phased array imaging is studied,an ultrasonic phased array detection experimental system is built,and the method of sparse and reconstructed ultrasonic signal of CFRP layered defects is studied.By choosing different sparse basis,observation matrix and orthogonal matching pursuit algorithm(OMP),the ultrasonic signal is compressed and reconstructed under different compression ratios,and the mean square error(MSE),mean absolute percentage error(MAPE),The peak signal-to-noise ratio(PSNR),structural similarity(SSIM)and reconstruction time are used to objectively evaluate different compressed sensing combinations.The experimental results show that the compressed sensing algorithm can significantly reduce the ultrasonic signal sampling rate,and under the OMP algorithm,the selection of discrete cosine sparse basis and Gaussian random observation matrix can better reconstruct the ultrasonic signal of CFRP layered defects.Secondly,in order to deal with the problem of low reconstruction efficiency of compressed sensing technology in processing large-scale images or signals with large data volume,a sampling rate adaptive block-based compressed sensing method(SIG-BCS)based on salient features is proposed.First select the ultrasound C-scan image signal to verify the feasibility and optimization effect of the block compression method(BCS).The results show that,when the compression ratio is constant,the higher the resolution,the better the quality of reconstruction,and the longer the reconstruction time.In order to optimize the BCS,the SIG-BCS algorithm is proposed,which classifies image sub-blocks according to the difference in the salient characteristics of different image sub-blocks,and then assigns different sampling rates to the classification set.Experimental results show that compared with BCS,the SIG-BCS algorithm maintains the advantage of spending less reconstruction time,and its reconstruction quality is significantly improved in terms of PSNR and SSIM.Finally,the greedy reconstruction algorithm is studied.First,analyze the difference in the reconstruction effect of the four greedy iterative reconstruction algorithms on ultrasound signals.The results show that the reconstruction quality of the OMP algorithm is better when the compression ratio is unchanged,but the reconstruction time required is longer.Aiming at the lack of pre-determined sparsity in the OMP algorithm,a variable step sparsity adaptive matching pursuit algorithm(Vs SAMP)is proposed.This algorithm is based on the idea of "large step size fast approach,small step size slow approach" to achieve the approximation of the true sparsity,and design experiments to verify.The experimental results show that,compared with the OMP algorithm,the reconstruction results of the Vs SAMP algorithm are improved in terms of root mean square error,peak signal-to-noise ratio,and defect contour characteristics.The research results in this paper provide a reference basis for ultrasonic phased array imaging of delamination defects in CFRP based on compressed sensing,and have certain theoretical and application values.The paper has 63 pictures,6 tables,and 85 references.