Ndt & E International最新文献

Pulsed Eddy Current Testing (PECT) stands out as an advanced method in Non-Destructive Testing due to its extensive spectrum characteristics in comparison to traditional ECT techniques, making it exceptionally suitable for identifying corrosion. Nonetheless, the analysis of PECT signals for corrosion detection poses a challenge due to the transient nature of these signals and the impact of sensor lift-off effects. As a result, conventional methods are facing hurdles in dealing with corrosion signals of poor quality. In this study, the challenge is addressed by employing unsupervised learning methods utilizing an autoencoder neural network. This autoencoder integrates Long Short-Term Memory and 1D convolutional layers, acquiring the underlying features of normal PECT signals from non-corrosive regions. Significantly, the model is trained exclusively on this normal data, thereby obviating the necessity for pre-existing corrosion information. Through learning the inherent structure of normal signals, the model can detect anomalies in unseen data, potentially indicating corrosion. The unsupervised framework presents several advantages, such as reducing reliance on prior corrosion knowledge, mitigating inherent noise, and addressing sensor lift-off effects. Experimental results were conducted to compare with traditional methods like the lift-off of intersection and lift-off compensation methods. This approach resulted in a significant improvement in SNR, ranging from 100 % to 200 %, thus facilitating more robust NDT applications employing smart PECT sensors empowered by unsupervised learning techniques.

We demonstrate the effectiveness of total focusing methods (TFM) using Rayleigh waves for surface and sub-surface nondestructive inspection of different metals. The relatively low velocity of Rayleigh waves leads to sub-100 μm resolution imaging, with a penetration depth approximately equal to its wavelength. This allows for imaging and sizing sub-millimetric holes, possibly on coated material, as well as cracks, segregations, and other defects. The waves can propagate over long distances and works with curved surfaces or very close to edges. This shows potential for a new type of real-time surface inspection of large surfaces, with excellent spatial resolution. The process is free of chemical preparation and cleaning, and can be fully automated, from acquisition to decision or for making surface digital twin.

The magneto acoustic emission (MAE) technique is an emerging non-destructive testing (NDT) method that has shown potential for defect detection in the bulk of material, proven vital for the safety and integrity of the material. The MAE technique has shown promising results among modern non-destructive testing techniques for ferromagnetic materials. Although MAE technique is a long-established NDT method, it has the potential for further development. This paper provides an in-depth review of MAE signals' physical origin and characteristics, compares them with their counterpart, Magneto Barkhausen Noise (MBN), and explores data acquisition methods and applications, including specific case studies in material defect detection. The research gaps in the MAE technique are discussed, and the paper explores potential developments, highlighting future perspectives and possible improvements.

A novel unilaterally excited electromagnetic acoustic transducer (EMAT) is proposed as a solution to address the challenge of weak electromagnetic ultrasonic detection signals being susceptible to interference from clutter noise signals. EMAT employs Halbach permanent magnet array (HPMA) structure and a coil designed based on Huygens superposition principle. This design enables the generation and reception of highly directive Rayleigh surface waves in aluminum plates. To enhance the operational efficiency of EMAT while maintaining a high level of directivity for surface waves, a penalty function is implemented in COMSOL Multiphysics for the topological optimization of EMAT. The objective function in this optimization process is based on covariance (COV) of the magnetic induction intensity. The research results suggest that the homogeneity of the magnetic induction intensity within the coil region is enhanced by 50 % following topological optimization compared to the original design. The energy conversion efficiency of EMAT is enhanced by 5 times compared to traditional designs. The surface wave speed was determined to be 2631 m/s when measured at a frequency of 400 kHz. The value indicates a relative error of 6.37 % in comparison to the theoretical speed. The results indicate that EMAT has the capability to generate high-directional and pure Rayleigh waves.

Three-dimensional nondestructive location of defects, such as delaminations, in glass fiber-reinforced polymer (GFRP) laminates remains a challenge. Terahertz techniques have shown promise, but their success relies on advanced signal-processing techniques applied to the raw data. The current work presents an advance in the quantitative three-dimensional nondestructive location of delaminations in GFRP laminates. Namely, terahertz time-of-flight tomography, together with adaptive sparse deconvolution based on a two-step iterative shrinkage-thresholding algorithm, as well as the Canny edge-detection operator, are employed in nondestructive measurement of layer thicknesses and to extract the edges of delaminations in GFRP laminates. Compared with the commonly used frequency wavelet-domain deconvolution method or previous implementations of sparse deconvolution, the adaptive sparse deconvolution approach provides a clearer and rapid stratigraphic reconstruction of GFRP laminates while yielding accurate thickness information for each resin layer and low sensitivity to noise. In addition, the proposed edge-detection algorithm presents better performance in estimating the transverse size of delaminations, compared to the common −6 dB drop approach. Finally, our experiments verify the effectiveness of the proposed signal and image processing approaches for three-dimensional localization of delamination defects in GFRP laminates and the quantitative characterization of layer thickness.

This study presents a novel stress measurement method utilizing the Rayleigh waves virtual superimposed interference spectrum (RW-VSIS). This method achieves stress measurements by exploiting the effect of stress on the superimposed interference spectrum of two beams of Rayleigh waves. Firstly, the effect of stress on Rayleigh wave velocity is theoretically investigated by partial wave theory and matrix solving algorithm. The theoretical results show that the Rayleigh wave propagation direction versus the stress direction will affect the wave velocity and the time of flight (TOF). Then, a theoretical model of RW-VSIS under pre-stress is derived. It's found that the stress will dominate the first characteristic frequency (FCF). The regulation effects of propagation distance and angle on FCF are discussed. Finally, the feasibility of stress measurement based on the FCF is validated through experiments. The impact of stress on TOF and FCF is comparatively analyzed. The results show a significant improvement of stress measurement by FCF in the superimposed interference spectrum, compared to the TOF in time domain waveform. With a calibration and verification test for the unknow coefficient of an aluminum specimen, the experimental examination of the stress shows a maximum error of less than 4 MPa indicating good measurement accuracy.

As additive manufacturing enables the production of intricate, high-value parts with functional integration, inspection is gaining importance to ensure safety for use. Since the surface quality of laser beam powder bed fusion parts has proven to be inherently inhomogeneous, the measured values are dependent on the measurement spot, making surface quality difficult to characterise using conventional methods. Combined with the fact that the complex shape of the parts potentially complicates measurements further, a new surface characterisation method is required to adequately capture the quality of additively manufactured parts on the entire surface. In this work, a novel method is proposed that is both capable of meeting the above requirements and additionally allows the correlation of the results with the process data and the evaluation of the near-surface porosity. At the same time, the local quality deviations can be visualised and roughness hotspots found and correlated with the process.

This paper proposes an effective ultrasonic detection methodology for assessing the damage state of cylindrical structures. The methodology depends on the interaction between a bounded ultrasonic beam and the cylinder. First, the theoretical derivation of the scattered sound field generated by a bounded ultrasonic beam incident obliquely onto a cylinder is presented. Next, by means of FE simulations and experimental verification, we demonstrate that when the bounded ultrasonic beam emitted by the transmitter is obliquely incident upon the cylinder at either the first or second critical angles, as defined within this study, the early initiation of damage results in a significant increase in the received sound pressure amplitude detected by the receiver positioned symmetrically relative to the transmitter. Specifically, the simulation results indicate that a mere 5 % decrease in the elastic modulus of the cylinder correlates with a staggering 447.88 % surge in the received sound pressure amplitude at the first critical angle. Experimental evidence also demonstrates that for varying states of impact-induced damage of the cylinder, the received sound pressure amplitude detected by the symmetric receiver exhibits highly sensitive characteristics when a bounded ultrasonic beam is incident at the first critical angle onto the cylinder. This approach represents a significant advancement over traditional ultrasonic detection techniques, combining the reliability and stability of linear ultrasonic methods with the sensitivity for early damage assessment provided by nonlinear ultrasonic techniques. The proposed assessment method holds great promise in providing fresh insights for inspecting cylindrical structures in practical applications.

The damage detection of the stiffened composite panel, as a typical aircraft structure, is a research hotspot in Structural Health Monitoring (SHM). where guided waves propagate with multi-modal and dispersion characteristics. The traditional damage detection method manually extracts the potential discriminative features of the signal to achieve damage identification, depending on expert experience. In this paper, we propose a two-stage residual networks (ResNets) framework based on guided waves to locate damage in the stiffened composite panel, which automatically mines the high-dimensional features with sensitive discriminant information. The guided wave signal acquisition system collects four types of data: health data, stringer damage data, damage data on the skin of the stringer-side, and damage data on the skin-side. The first-stage utilizes a ResNet to classify the structure condition, while in the second-stage, three separate ResNets are employed to locate the damage according to the classification results of the first-stage. The experimental results show that the accuracy of the first-stage damage classification and the damage localization of the stringer and the skin of the stringer-side in the second-stage has reached 100%, and that of the skin-side is 99.13%, which significantly outperforms single-stage methods. This strategy of inter-class discrimination and intra-class precise localization of damage can not only identify the damaged regions but also determine the specific location of the damage, which greatly increases the performance of SHM. The present two-stage method is a potential solution for future SHM strategies and further investigation is warranted.