Ndt & E International最新文献

In this study, we propose a class-conditioned Generative Adversarial Autoencoder (cGAAE) to improve the realism of simulated ultrasonic imaging techniques, in particular the Multi-modal Total Focusing Method (M-TFM), based on the availability of both simulated and experimental TFM images. In particular, this work studied the case of the inspection of a complex geometry block representative of weld-inspection problem based on ultrasonic multi-elements probe. The cGAAE is represented by a tailored learning schema, trained in a semi-supervised fashion on a labeled mixture of synthetic (class 0) and experimental (class 1) M-TFM images, obtained under different meaningful inspection set-ups parameters (i.e., the celerity of the transverse ultrasonic wave, the specimen back-wall slope and height, the flaw tilt and heigh). That is, the cGAAE schema consists in a combination of learning stages involving class-conditioned spatial-transformers and arbitrary style transfer endows the cGAAE of powerful generative features, such as quasi real-time generation of M-TFM images by sweep of the inspection parameters. We exploited the cGAAE model to improve the realism of simulated M-TFM images and enhance the accuracy of the inverse problem, aiming at estimating the inspection parameters based on experimental acquisitions.

Various types of noise, which accompany active TNDT procedures using optical heating, have been analyzed, both numerically and experimentally. An emphasis has been made on the suppression of surface clutter, which represents local areas of varying absorptivity/emissivity. The concept of signal-to-noise that is typically used in defect detection has been applied to fixed pattern noise in order to compare capabilities of data processing algorithms in reducing surface clutter. The experimental investigation has been fulfilled on a special sample containing both subsurface air-filled defects and areas with varying emissivity/absorptivity. The best suppression of the fixed pattern noise was provided by the complex wavelet transform and principle component analysis. Because of 3D heat diffusion, clutter spot boundaries are often underlined by particular data processing algorithms thus producing specific contours. The test situations where subsurface defects are located under localized clutter spots have been analyzed to demonstrate an overshadowing effect of such spots when detecting hidden defects.

Early detection of delamination in composite materials is crucial to maintaining operational safety and reducing excessive maintenance costs. Second harmonic Lamb waves have demonstrated exceptional sensitivity to micro defects in materials including breathing delamination. However, differentiating the second harmonic Lamb waves generated by delamination from other inevitable background nonlinearities, exemplified by inherent material nonlinearity in composites, poses a significant challenge for the practical implementation of the second harmonic Lamb wave-based detection methods. To address this bottle-necking issue, this study examines the characteristics of second harmonic Lamb waves generated by delamination and material nonlinearity, respectively, aiming at their differentiation based on their respective amplitude-dependent features. Results are verified through finite element analysis and experimental validations alongside the verification of the effectiveness of the proposed discrimination strategy. It is shown that the amplitude of the second harmonic waves induced by the delamination is linearly proportional to the fundamental wave amplitude, while the one by the material nonlinearity exhibits a quadratic relationship with the fundamental wave amplitude. Based on this understanding, damage indices are proposed, which prove to be effective for characterizing these two sources of nonlinearity, thereby paving the way for practical delamination detection in composite structures.

The extension of metamaterial concepts to the ultrasonic domain is challenging because of the shorter wavelength, which necessitates the use of spatially narrow band receiving techniques to capture wavefields past fine features of the metamaterial. Currently, the Laser Doppler Vibrometer is the only option with several drawbacks hampering its widespread practical implementation, including cost and sensitivity to external disturbances. This paper proposes a novel waveguide based reception technique to capture the amplified evanescent fields transmitted through the subwavelength features of the metamaterials. Numerical simulations and experiments are carried out on a structured channel metamaterial and a thin stainless steel waveguide attached to a commercial transducer. A practical super resolution ultrasonic imaging down to a third of the operating wavelength is successfully demonstrated in comparison with a commercial laser receiver. The physics of the imaging and dispersion characteristics of the waveguide enabling the process are discussed. The promising results showcase broadband, low-cost, portable alternatives with important implications for high-resolution ultrasonic imaging in industrial and biomedical applications.

The increasing length of subsurface pipe causes overlapping, accumulation, and occasionally the old pipe layout is not also available. Consequently, accidents, damages, time delays, and financial losses occur during construction of new structures or installation of new pipes. Therefore, depth, radius, material of the existing pipe, and map of pipe are indispensable for knowing proper construction planning. In this article, an algorithm is proposed to estimate the properties of subsurface pipes and show 3D maps. Using this algorithm, the radius of the field pipes was estimated with 83, 67, and 89 % accuracy and depth with 95, 95, and 98 % accuracy. The effect of pipe radius should be considered to assess the pipe depth with higher accuracy. The material of the field pipe was successfully determined using the evaluated relative permittivity. A 3D map of the field pipe was developed by applying the tracing algorithm and linear regression on estimated depth.