摘要
A high-precision evaluation of ultrasonic detection sensitivity for a micro-crack can be restricted by a corroded rough surface when the surface microtopography is of the same order of magnitude as the crack depth.In this study,a back-surface micro-crack is considered as a research target.A roughness-modified ultrasonic testing model for micro-cracks is established based on a multi-Gaussian beam model and the principle of phase-screen approximation.The echo signals of micro-cracks and noises corresponding to different rough front surfaces and rough back surfaces are obtained based on a reference reflector signal acquired from a two-dimensional simulation model.Further compari-son between the analytical and numerical models shows that the responses of micro-cracks under the effects of dif-ferent corroded rough surfaces can be accurately predicted.The numerical and analytical results show that the echo signal amplitude of the micro-crack decreases significantly with an increase in roughness,whereas the noise ampli-tude slightly increases.Moreover,the effect of the rough front surface on the echo signal of the micro-crack is greater than that of the rough back surface.When the root-mean-square(RMS)height of the surface microtopography is less than 15μm,the two rough surfaces have less influence on the echo signals detected by a focused transducer with a frequency of 5 MHz and diameter of 6 mm.A method for predicting and evaluating the detection accuracy of micro-cracks under different rough surfaces is proposed by combining the theoretical model and a finite element simulation.Then,a series of rough surface samples containing different micro-cracks are fabricated to experimentally validate the evaluation method.
基金
Supported by the Key Research and Development Plan of Anhui Province(Grant No.202004a05020003)
Anhui Provincial Natural Science Foundation(Grant Nos.2008085QE233,2008085J24)
the Science and Technology Major Project of Anhui Province(Grant No.201903a05020010)
the Doctoral Science and Technology Foundation of Hefei General Machinery Research Institute(Grant No.2019010383).
