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.

The thickness and interface roughness of coatings both affect the interface bonded quality. Existed ultrasonic testing methods based on traditional phase screen approximation or spring model assumption are difficult to simultaneously identify the interface roughness and stiffness of coating. This paper, a new method for integrated identifying coating thickness, interface roughness, and interface stiffness using developed ultrasonic reflection phase derivative spectrum (URPDS) is proposed. A phase-screen-approximated spring-model (PSASM) for ultrasound vertically propagating into rough and weak bonded interface is constructed. On basis of PSASM, a URPDS of coating/substrate structure is developed for identifying the interface stiffness and other parameters of coated parts. Cross-correlation analysis is used to eliminate the phase deviation of URPDS introduced by reference signal and initial phase of tested signal. Sensitivity analysis is used to determine the high-sensitivity regions of URPDS to interface roughness and interface stiffness. Genetic algorithm optimization is used to achieve integrated identification of coating thickness, interface roughness, and interface stiffness. The rationality of PSASM is verified through numerical simulation using a series of coating/substrate models with rough and weak bonded interface, and the relationship between the high-sensitivity regions and the high-precision measurement ranges of interface roughness Rq and interface stiffness K_n is clarified. Ultrasonic experiments are implemented on Nickel-coating samples and coated parts using plane wave probe. The coating thickness, interface roughness, and interface stiffness could be identified accurately, which shows that the proposed URPDS method can identify the interface stiffness of rough contacted dissimilar media or coated parts with rough interface.

We proposed a deep-learning attention-based methodology to predict acoustic sources obtained from pendulum impact experiments using the Cluster-Self Adaptive Network (CSAN) and showed that the experimental data required for training can be reduced by 50% without losing significant localization accuracy. Acoustic signals due to pendulum impacts on a homogeneous steel plate were recorded by an asymmetric microphone array. Important wavelet features were extracted by transforming the acoustic signals using continuous wavelet functions and reduced the data dimensionality by principal component analysis. Two data sampling strategies (random and Latin hypercube) were investigated to study the effect of the density of training domains on the model performance. The attention-based modulation strategy was employed on microphone positions for data augmentation and prediction of acoustic sources. A comprehensive analysis of the CSAN-based localization results including error estimation was performed. The outcome was contrasted against delay-and-sum beamforming localization results.

Online monitoring of weld crack leakage in pressure pipelines of nuclear power ship based on acoustic emission (AE) technology is of great significance for maintaining the safe and stable operation of the system. However, most of the current leakage studies are conducted through artificially designed pipeline hole types, which deviate from the actual crack morphology and are weakly online, with low identification accuracy and slow monitoring speed. Therefore, a convolutional network of FGI and multi-scale channel information cross fusion based on AE technology is proposed in this paper. First, the FBank feature of the AE signal of pipeline weld leakage are extracted. On this basis, the Gini Index (GI) preference feature is used to filter the useless information in the FBank feature. Then, a multi-scale channel information cross fusion module is designed to improve the feature learning ability of the network through the interaction and fusion of different channel information. Finally, the superiority of the proposed FGI feature extraction method and the effectiveness of the proposed multi-scale channel information cross fusion CondenseNet (MCCF-CondenseNet) convolutional neural network are verified by the pipeline leakage AE monitoring experiments under three crack morphologies. The results show that the identification accuracy of the proposed method is as high as 96.42 %, and the identification speed is significantly faster than other state-of-the-art approaches under the premise of ensuring the identification accuracy. This work provides a new method for the online leakage monitoring of nuclear power pressure pipelines, and has important supporting significance for the online leakage monitoring of other large and complex equipment.

Currently, in terms of resolution and excitation efficiency for pipeline inspection, the high-frequency Rayleigh-like wave excited by an EMAT with a traditional Rayleigh wave EMAT structure is not optimal when using the same magnet volume. This paper introduces an EMAT performance evaluation method focused on 'bandwidth' in the high-frequency-thickness region of circumferential guided waves. A wavenumber spectrum analysis method utilizing combined equivalent surface stresses is proposed to quantify this optimize design. Comparative studies, including theoretical analysis and experimental validation, demonstrate that incorporating bandwidth significantly improves the design of Rayleigh-like waves at high frequencies. The proposed EMAT achieves a performance improvement of 2.4 times for inside pipe excitation and 2.6 times for outside pipe excitation over the conventional structure. The occurrence of multiple wave packets outside the optimal excitation frequency range is acknowledged. Therefore, this method offers a new approach for optimizing EMATs for Rayleigh-like waves.