Research On Characteristic Analysis And Quantitative Characterization Of Laser Ultrasonic Surface Wave

In the manufacture and use of precision instruments such as aircraft engine main shafts,micro-circuit boards,and important components,the generation and growth of micro-cracks on the surface of the material directly affect the characteristics of the material and the final performance of the product to a large extent.Here,based on K-wave,this article establishes a laser ultrasonic metal aluminum plate surface defect detection model,studies the interaction process of the surface wave excited by the excitation source and surface defects,and then processes the collected signals with various analysis methods to extract the depth of the defect Quantitative relationship with ultrasound sensitive features.In this paper,ultrasonic control equations,k-space algorithm,etc.are used to elaborate the modeling process of surface defects of metal materials,and the excitation source excitation ultrasonic model is established.Wave waveform characteristics and comparative analysis;time and frequency domain analysis of reflected and transmitted wave signals at different defect depths;the mechanism of the oscillation signal generated by the interaction of ultrasonic waves and the defect groove boundary and the size of the defect to the oscillation Influence of signal propagation characteristics.Aiming at the problem of difficult feature extraction caused by the low signal-to-noise ratio of the surface defects of metal aluminum plates,this paper uses the EMD algorithm and the wavelet threshold(hard threshold,soft threshold,semi-soft threshold)method to perform noise reduction pretreatment on the reflected echo collected by simulation Based on the signal-to-noise ratio(SNR)and root mean square error(RMSE),the denoising performance of these methods is evaluated.The results show that the wavelet semi-soft threshold denoising method obtains a high signal-to-noise ratio and minimum rms error,thus achieving the best noise reduction effect.It is applied to experimental data to verify the effectiveness of wavelet semi-soft threshold noise reduction.In order to solve the problems of low feature extraction accuracy and low time-frequency resolution,this paper performs wavelet decomposition and spectral energy analysis on the reflected and transmitted waves measured from the experimental data after noise reduction,and reflects the waves at different defect depths according to the surface wave Crossing the frequency of the transmitted wave,the threshold frequency index that characterizes the depth of the defect is extracted.The results show that the relationship between the wavelength calculated by the threshold frequency and the defect depth(ie?(28)4h)is in good agreement with the theoretical data analysis results.The research results in this paper provide a reference for the study of the characteristics of laser ultrasonic surface waves,the noise reduction technology of defect signals,and the quantitative extraction and characterization technology of defect sensitive features.