| Abstract: | It is difficult to quantitatively detect defects by using the time domain or frequency domain features of Lamb wave signals due to their dispersion and multimodal characteristics.Therefore,it is important to discover an intrinsical parameter of Lamb waves that could be used as a damage sensitive feature.In this paper,quantitative defect detection in aluminium plates is carried out by means of wavenumber analysis approach.The wavenumber of excited Lamb wave mode is a fixed value,given a frequency,a thickness and material properties of the target plate.When Lamb waves propagate to the structural discontinuity,new wavenumber components are created by abrupt wavefield change.The new wavenumber components can be identified in the frequency-wavenumber domain.To estimate spatially dependent wavenumber values,a short-space two-dimensional Fourier transform(FT)method is presented for processing wavefield data of Lamb waves.The results can be used to determine the location,size and depth of rectangular notch.The analysis techniques are demonstrated using simulation examples of an aluminium plate with a rectangular notch.Then,the wavenumber analysis method is applied to simulation data that are obtained through a range of notch depths and widths.The results are analyzed and rules of the technique with regards to estimating notch depth are determined.Based on simulation results,guidelines for using the technique are developed.Finally,experimental wavefield data are obtained in aluminium plates with rectangular notches by a full noncontact transceiving method,i.e.,laser-laser method.Band-pass filtering combined with continuous wavelet transform is used to extract a certain frequency component from the full laser-induced wavefield with wide band.Shortspace two-dimensional FT method is used for further processing full wavefield data at a certain frequency to estimate spatially dependent wavenumber values.The consistency of simulation and experimental results shows the effectiveness of proposed wavenumber method for quantitative rectangular notch detection. |
