Mengbao Fan

The defects, including cavities, inclusions, and microcracks, are prone to being formed during the directional solidification process of superalloy turbine blades. Eddy current testing (ECT) is challenged by low sensitivity and liftoff noise due to the low conductivity and geometric changes of curved surfaces. An eddy current array (ECA) system is designed in this work, integrating a self-adaptive elastic probe and signal processing algorithms. First, an in-plane differential probe based on the can-shaped magnetic core is developed, where the eddy current density is directionally enhanced through magnetic field shielding to amplify defect response, while liftoff noise is compensated using a self-adaptive elastic structure. Second, an adaptive wavelet threshold denoising algorithm and multichannel fusion algorithm based on array topology prior knowledge enabled effective separation of defect signals from noise. Finally, a hardware system based on multichannel demodulation is developed to improve detection speed and capability. Experimental results demonstrate that the system achieves reliable detection of 0.1 mm defects at a scanning speed of 80 cm/s and achieves a detection probability of 98%. This performance significantly outperforms existing methodologies and provides an effective solution for identifying microdefects in low-conductivity materials, particularly in critical components such as turbine blades.

The thermal insulation performance of thermal barrier coatings (TBCs) depends significantly on the ceramic topcoat (TC) thickness, necessitating precise measurement. The uneven and complicated porose microstructure of TC brings a big challenge for accurate and reliable thickness measurement due to the resultant change of acoustic velocity. This article proposes an ultrasonic thickness measurement method for the TC based on the acoustic velocity-amplitude mapping and damping attenuation model. First, the longitudinal wave transmission equation is derived from interface boundary conditions and attenuation effects, establishing a correlation between acoustic velocity and echo amplitude to characterize the coating’s acoustic properties. Then, a damping attenuation model describes signal decay in the TC, yielding an equation with acoustic velocity as the variable to estimate the required attenuation coefficient in the mapping relationship online. By solving these equations simultaneously, the acoustic velocity is determined online. Experimental results show the proposed method achieves a maximum relative error below 6.49%, outperforming conventional fixed acoustic velocity approaches.

Polyethylene (PE) pipes are vital for residential gas systems, but hole defects at hot-melt joints are prone to lead to pipe failure. Affected by the complex background noise interference at the hot-melt joint, traditional detection methods are difficult to realize the high precision hole defect detection in PE pipes. Therefore, we propose Mask-CBAM-I-RCNN—a stable attention-guided defect detection framework that integrates a Convolutional Block Attention Module (CBAM) and an improved I-Smooth L₁ loss into the Mask R - CNN architecture. First, the first systematic incorporation of CBAM into Mask R-CNN for THz nondestructive testing, suppressing noise and enhancing defect features from spatial and channel dimensions. Then, a novel I-Smooth L₁ loss that ensures first - order derivative continuity and stable gradient propagation, eliminating gradient jumps and improving algorithm stability. Finally, an improved Mask R-CNN model was developed to achieve high-precision, stable defect detection in THz PE pipe images. The experiment results show that our method achieves a defect detection precision of 95.44% and an mAP50 of 92.54%, significantly outperforming existing approaches and providing a reliable solution for intelligent defect detection of THz PE pipes imaging.

Thermal barrier coatings (TBCs) play a critical role in extending engine service life. Consequently, regular thickness monitoring of TBCs is essential. However, it is difficult to decouple the eddy current (EC) signals of TBCs due to its complex multi-layer structure. In order to accurately measure the coating thickness, a coordinate transformation method based on shape functions is proposed in this paper. Firstly, the simplified mathematical expressions of the characteristics are derived from a transformer model; secondly, the BC and TC signals are successfully decoupled by constructing a characteristic grid and applying a four-point shape function coordinate transformation; subsequently, the optimal combination and frequency are obtained by the evaluation of errors on various characteristic combinations at different frequencies. Finally, an EC detection system is established to evaluate our method. The results demonstrate that the relative errors for BC and TC measurements are less than 6.4% and 4.2%, respectively. Furthermore, this method is effectively immune to the temperature influence on TC thickness detection. Crucially, the approach successfully decoupled the TC and BC layers, with experimental validation confirming high measurement accuracy.

Detecting defects in Polyethylene (PE) pipes is essential for safe gas transportation. Terahertz (THz) time-of-flight tomography method can characterize the sample’s internal structure and defect morphology, making it an ideal technique for PE pipe detection. However, tomography produces images contaminated by noise and artifacts resembling defects, resulting from the pipe’s geometry. Conventional detection algorithms often perform poorly under these conditions, leading to misinterpretations. To solve the challenge of detecting rotationally invariant defects in PE pipes, like circular holes, we propose a complete, physics-informed, and data-efficient framework for THz tomography image quality evaluation. First, the PE pipe THz tomography images are reconstructed using the time-of-flight method. Second, a physical information guided multi-template matching method is adopted to pre-screen defect images. Next, an asynchronous and independent whale optimization algorithm is employed to adaptively determine the optimal multi-template threshold combination. Finally, the optimal defect image is selected based on the composite image evaluation indicator. The experimental results show that the proposed method can select clear, well-defined, and low-noise defect tomography images, enhancing defect detection accuracy.

Accurate detection of weak wire rope (WR) defect signal is a significant challenge in magnetic flux leakage (MFL) testing. To address this, a novel variable symmetry Gaussian (VSG) wavelet basis is developed, and an adaptive feature extraction method is proposed. First, the sources of weak defect signals including small-size defects and attenuation caused by large lift-off are analyzed, and the variable symmetry characteristics of the damage signals on the helical structure of the wire rope at different detection angles are found and explained. Second, a novel VSG wavelet is designed based on the physical helical structure of the wire rope, which can flexibly adjust the direction and degree of variable symmetry to enhance the similarity with the defective signals while reducing the similarity with the noise components. Finally, an interquartile range combined with the upper tail range (IQ-UT) feature is proposed to adaptively select convolutional features for improved weak signal identification. The proposed method utilizes signal variable symmetry characteristics to construct the VSG wavelet basis for adaptive weak defect signal detection. Comparative experiments with various methods demonstrate the superior accuracy of the proposed method.

Eddy current (EC) testing demonstrates engineering value for evaluating quality of thermal barrier coatings (TBCs) in aerospace engines and gas turbines, where top ceramic coating (TC) thickness directly determines thermal protection efficiency and component lifespan. Addressing the dual challenges of impedance coupling in multicoating and low sensitivity caused by low conductivity of bond coating (BC) and substrate, this study proposes a method of TC thickness measurement based on high-frequency excitation and near-resonance inductance. A transformer model of EC axial attenuation in TBCs was established, revealing frequency-dependent EC distribution in both BC and substrate. It was demonstrated that increasing the excitation frequency could effectively reduce the interfacial EC density discrepancy, thereby enabling the isolation of TC thickness from coupled impedance. System sensitivity enhancement was achieved through near-resonance impedance amplification, enabled by an equivalent circuit in a targeted frequency band. A mathematical mapping between resonant impedance and TC thickness was established, validating the efficacy of resonant signal enhancement in EC thickness measurement. The experiment of TC thickness measurement demonstrates a tenfold improvement in sensitivity compared to conventional methods, with measurement errors below 4% . This advancement enables precise quality assessment during TBC manufacturing, addressing critical nondestructive evaluation challenges in multicoating systems.