Ultrasonic Lamb Wave Damage Detection of CFRP Composites Using the Bayesian Neural Network

Luo, Kai; Zhu, Jiayin; Li, Zhenliang; Zhu, Huimin; Li, Ye; Hu, Runjiu; Fan, Tiankuo; Chang, Xiangqian; Zhuang, Long; Yang, Zhibo

doi:10.1007/s10921-024-01054-z

Cited by 3 publications

(1 citation statement)

References 46 publications

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“…This model assimilates inputs from time-domain Lamb wave (LW) and continuous wavelet transform (CWT) energy signals, significantly bolstering its robustness against interference and precision in damage feature extraction. Luo et al [6] developed a sophisticated online Structural Health Monitoring (SHM) system specifically designed for carbon fiber-reinforced polymer (CFRP) composite plates. This system incorporates a Damage Detection Bayesian Neural Network (DD-BNN) that merges probabilistic modeling with deep learning to enhance damage detection using Lamb waves.…”

Section: Small Target Defect Detection Methodsmentioning

confidence: 99%

MAA-YOLOv8: enhanced steel surface defect detection through multi-head attention mechanism and lightweight feature fusion

Han,

Zhang

et al. 2024

Phys. Scr.

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In the process of industrial production, product defects often arise due to improper operations among other reasons, rendering the detection of such flaws an indispensable procedure. However, the vast array of defect types, coupled with their complex characteristics, poses ongoing challenges for contemporary defect detection algorithms within industrial settings. To solve this problem, the present study introduces an enhanced steel surface defect detection model based on the modified YOLOv8 algorithm—termed the MAA-YOLOv8 model—to augment the accuracy and practicality of the algorithm. Initially, a multi-head attention mechanism was incorporated into the C2f to bolster the feature extraction capabilities within the backbone network and diversify the attention maps. Secondly, in the neck structure, we design a multi-channel feature fusion module (McPAN) to solve the problem of balance between computational efficiency and the ability to capture useful features. A series of experiments conducted on the NEU-DET dataset reveal that the MAA-YOLOv8 model achieves a mean Average Precision (mAP) of 94.4\%, representing an enhancement of 11.1\% over the original YOLOv8s model. The MAA-YOLOv8 model proposed in this study substantially elevates the performance of steel surface defect detection while ensuring the speed of detection.

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Section: Small Target Defect Detection Methodsmentioning

confidence: 99%

MAA-YOLOv8: enhanced steel surface defect detection through multi-head attention mechanism and lightweight feature fusion

Han,

Zhang

et al. 2024

Phys. Scr.

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Eddy Current Testing in the Quantitive Assessment of Degradation State in MAR247 Nickel Superalloy with Aluminide Coatings

Tytko,

Adamczyk-Habrajska,

Luo

et al. 2024

J Nondestruct Eval

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In this paper, the effectiveness of the eddy current methodology for crack detection in MAR 247 nickel-based superalloy with aluminide coatings subjected to cyclic loading was investigated. The specimens were subjected to force-controlled fatigue tests under zero mean level, constant stress amplitude from 300 MPa to 600 MPa and a frequency of 20 Hz. During the fatigue, a particular level of damage was introduced into the material leading to the formation of microcracks. Subsequently, a new design of probe with a pot core was developed to limit magnetic flux leakage and directed it towards the surface under examination. The suitability of the new methodology was further confirmed as the specimens containing defects were successfully identified. The changes in probe resistance values registered for damaged specimens ranged approximately from 8 to 14%.

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An Attention-Guided Complex-Valued Transformer for Intra-Pulse Retransmission Interference Suppression

Wang,

Li,

Zhou

et al. 2024

Remote Sensing

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With the maturation of digital radio frequency memory (DRFM) technology, various intra-pulse retransmission interference methods have emerged. These flexible and changeable retransmission interference methods pose significant challenges to radar detection tasks, particularly in modern battlefields. This paper proposes an attention-guided complex-valued transformer (AGCT) as a solution. First, the encoder maps the received signal contaminated by interference and noise into a high-dimensional space. Then, the dilated convolution block (DCB) group and attention block (AB) group in the mask estimator extract the delicate multi-scale features and large-scale features of the interference, respectively, to obtain a multidimensional space mask. Finally, the decoder restores interference to the time domain and outputs the estimated target echo using residual learning. Considering the characteristics of intra-pulse interference, we introduced the energy attention block (EAB) at the end of the DCBs and the ABs within our network. This addition ensures a heightened focus on extracting interference features. Furthermore, we implemented a curriculum learning strategy during the network training. This approach gradually acclimates the network to fit different types of retransmission interference, starting from simpler to more complex scenarios. Our extensive experiments, conducted under various conditions, have provided compelling evidence of the AGCT’s superior performance. Compared to the comparative network, the AGCT’s advantages are particularly pronounced under more harsh conditions, demonstrating its robustness and effectiveness.

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Ultrasonic Lamb Wave Damage Detection of CFRP Composites Using the Bayesian Neural Network

Cited by 3 publications

References 46 publications

MAA-YOLOv8: enhanced steel surface defect detection through multi-head attention mechanism and lightweight feature fusion

MAA-YOLOv8: enhanced steel surface defect detection through multi-head attention mechanism and lightweight feature fusion

Eddy Current Testing in the Quantitive Assessment of Degradation State in MAR247 Nickel Superalloy with Aluminide Coatings

An Attention-Guided Complex-Valued Transformer for Intra-Pulse Retransmission Interference Suppression

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