Improvement of defect size and morphological estimation in shearography inspection by wavelet transform

Abstract

Speckle shearography is often used in the aerospace industry to detect defects in composite materials because it allows noncontact, full field, and fast measurements. However, despite this advantage, this nondestructive technique is still not widely established, due to its limitations in performing defect quantitative estimations. The main issue lies in the extrapolation of phase profile boundaries, which represent defect edges. In this paper, this was achieved by studying the multimodal distribution of a characteristic parameter, called structural intensity, which is the probability of finding a denser population of local maxima of wavelet coefficients at a given position and adding an adaptive threshold selection in the structural intensity distribution. Once the damage boundaries were detected, the defect dimensions were computed by subtracting the local shearing amount, which was defined by a circle detection code, from each coordinate in both Cartesian directions. The paper shows an improvement to the algorithms able to detect defect area and perform morphological estimation previously developed by the authors and an in-depth study of the parameters involved in these algorithms. This resulted in both a high accuracy (0.04%) revealed by the calibration process on a PVC test panel and a close morphological agreement with the ultrasound reconstruction of unknown defects in composite materials. Thanks to this improvement, shearography becomes a quantitative inspection technique that takes few minutes compared to ultrasound inspection of the same area, which may require up to an entire day.