Research On Some Key Techniques Of Intelligent Defect Recognition In The Ultrasonic A-scan Testing

As an important NDT(Non-destructive Testing)technology, Ultrasonic Testing has been widely used in modern industry, and the application of ultrasonic A-scan is the most widely. The experience factor play a key role in the Ultrasonic A-scan Testing, so, more and more researches have been carried on the technology of ultrasonic automatic identification in the current, which could reduce the interference of human factors and improve the accuracy of defect discrimination in the Ultrasonic A-scan Testing. In this paper, according to the relationship between the characteristics of dynamic waveform variation and the detection mode of the probe in the Ultrasonic A-scan Testing, the ultrasonic waveform database has been established and the Relative variability Relational Degree Analysis was introduced into the weld ultrasonic flaw identification.This paper presented a waveform matching and recognition algorithm to develop a ultrasonic A-scan intelligent recognition system which has a high recognition rate.This paper analyses the dynamic waveforms of different typical defects under the level and around testing ways of probe, and presents a kind of probe recognition method in ultrasonic flaw detection based on image processing. With the image processing module in MATLAB toolbox, the probe position can be identified and scanning mode can be determined.In this paper the basic principle of wavelet signal processing has been analyzed and the selection method of wavelet parameters has been determined in the ultrasonic signal denoising.By collecting the dynamic waveform of some typical defects with HSD4 Ultrasonic data acquisition card, intercepting several static waveform which can reflect dynamic waveform characteristics, and extracting the amplitude, wave length and wave area three characteristic parameters of the static waveforms which have been dropped noise by wavelet analysis.This paper has analyzed the change regulation of the three characteristic parameters under different testing ways of probe and different typical defects.Typical weld defects test blocks containing pores, slag, lack of penetration, lack of fusion, crack have been made by artificially,and twenty typical defects dynamic waveform data which under the probe level and around testing ways of probe has been collected, and then collecting relative variability of the dynamic waveform’s characteristic parameters and establishing ultrasonic waveform database.Relative variability of unknown defects’ characteristic parameters are done fuzzy association calculation with relative variability of typical defects’characteristic parameters respectively.In order to identify the type of unknown defect the a recognition method e been put forward. Experiments show that it has a highly accurate to recognize the defect type by this way.Base on mixed programming of MATLAB and Visual C++6.0, a ultrasound intelligent recognition system which has a high recognition rate have been developed.