Study On Immersion Ultrasonic Non-destructive Testing And Evaluation Of Metal Alloy

With the rapid development of science and technology in recent years,the quality requirements of metal alloy equipment and components are increasing in the aerospace,nuclear industry and other high-end fields.The existence of internal defects seriously affects the mechanical properties of the material.In order to ensure the safety and reliability of equipment,this paper carries out the research on immersion ultrasonic non-destructivetestingand evaluationof metal alloy.As an effective and reliable nondestructive testing method,ultrasonic testing technology has been widely used in the detection of metal alloy materials,which is mainly divided into the detection of macroscopic defects and the non-destructive evaluation of microstructure in metal alloys.Ultrasonic C-scan imaging is usually used to detect the macroscopic defects of metal alloy,which has the limitations of low contrast,blurred edge and incomplete image segmentation.Based on the overlapping characteristic of sound beams between sampling points in single crystal probe C-scan imaging,an ultrasonic image segmentation algorithm based on statistical texture plotter is proposed.Through the feature analysis of ultrasonic beams,the sound beams overlapping data are selected from each subwindow of the ultrasonic image.Then the characteristic information of the defects in the sampling areas are described by the correlation texture measure value of the area descriptors which substitute for the traditional amplitude characteristic value,so as to increase the resolution and contrast of ultrasonic imaging.Finally,the defect recognition rate is further improved by digital morphological noise reduction method.The algorithm is verified by the instance of ultrasonic C-scan imaging of the 304 stainless steel standard with circular blind holes on the bottom of it.The segmentation results of the proposed algorithm and the traditional adaptive threshold algorithm are compared quantitatively.Experimental results show that the algorithm in this paper can obtain clear and complete defect segmentation regions,and is more accurate and reliable than the traditional segmentation algorithm,especiallyfor small size defects whichareeasyto be neglected.The traditional ultrasonic testing is more used to detect the macroscopic defects,and the nondestructive evaluation of the microstructure of the materials is relatively few.The mechanical properties and quality reliability of metal alloy materials are greatly affected by the microstructure such as porosity,and lacks effective and reliable evaluation methods.This paper investigated the correlation between ultrasonic attenuation and porosity and proposed a novel method for characterizing the porosity of metal alloys.Based on the principle of scattering attenuation,the scattering attenuation characteristics of pores in metal alloy materials are explained.The correlation between scattering attenuation and wavelength was discussed,and the attenuation coefficients of different frequency components were calculated.Wavelet packet transformation was used to obtain the characteristic information of ultrasonic signal over time and frequency.Then the optimal components were selected to reconstruct the new signal and obtain the energy attenuation coefficient.Finally a novel evaluation model of porosity was established and compared with the traditional attenuation model.These results show that the evaluation valueerrors of theproposed model are betweentheabsolute errorrange(±0.2%).