Study On Sub-surface Cracks Identification Technology In Laser Ultrasonic Based On Split-spectrum Processing

With the rapid development of ultrasonic inspection applications, Laser ultrasound gradually become the key detection technology in the non-destructive testing field, because of its non-contact, high-precision, at high temperature, high pressure, radiation occasions, work and so on. In this paper, laser ultrasonic echo signals under different crack depth are processed by split-spectrum processing, features are extracted by time domain analysis method, using the radial basis function(RBF) neural network quantitatively recognizes samples microcracks.First, the laser ultrasonic testing system and the mechanism of laser ultrasonic are introduced, detailing herein experimental work steps, on the basis of the collected ultrasonic echo signals in time domain analysis. This paper describes methods for laser ultrasonic surface damage detection signal while comparing these signal processing methods.Secondly, a study on Laser ultrasonic flaw signal feature extraction, the basic theory of Split-spectrum processing is introduced. Simulation signals including noise are processed by five commonly used split-spectrum processing technology. After the processed signal to noise ratio is analyzed, Polar threshold algorithm(PT) is best fit to process such signals. The research results provide a new method for laser ultrasonic signal processing. On the basis of Split-spectrum processing technology to improve signal to noise ratio, time domain analysis method is employed to extract features from laser ultrasonic flaw signals. The features including time-domain waveform features, time domain statistical features and time domain parameters features. It laid the foundation for micro-cracks defect recognitionFinally, pattern classification and neural network-related content are described briefly, by comparison, the final choice of RBF neural network to identify microcrack defects. Experiments show that the proposed laser ultrasonic microcracks flaw detection and identification method is feasible and effective, and has great potential for development.