Ultrasonic nondestructive testing for composite bonded structures based on convolutional neural network and bidirectional gated recurrent unit (CNN-BiGRU) optimized by attention mechanism.

IF 1.3 4区 工程技术 Q3 INSTRUMENTS & INSTRUMENTATION
Wenhan Qu, Yintang Wen, Ning Yao, Yuyan Zhang, Xiaoyuan Luo
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引用次数: 0

Abstract

New ceramic matrix composites (CMCs) are commonly used as thermal protection materials bonded to the surfaces of aircraft substrates. Defects in composite bonded structures can cause the protective layer to detach from the airframe, seriously endangering aircraft safety. Ultrasonic nondestructive testing is a promising method for detecting defects in multilayer bonded structures. However, the porous nature and strong sound absorption of CMC result in severe attenuation and scattering of ultrasonic detection signals, reducing the signal-to-noise ratio. This makes it challenging to accurately identify real defect signals. Therefore, a novel method combining a convolutional neural network and a bidirectional gated recurrent unit (CNN-BiGRU) optimized by an attention mechanism, along with ultrasonic inspection, is proposed to identify defects in composite bonded structures. The method learns time and frequency domain features of original signals through convolution, applies an attention mechanism to determine the importance of these features, and delivers weighted results to the bidirectional gated recurrent unit network. Then, time and frequency domain features are fused, and a one-dimensional global average pooling layer is employed to reduce model parameters and prevent network overfitting. A nonlinear support vector machine is utilized as the final classifier instead of the traditional softmax classifier. The results indicated that the proposed CNN-BiGRU model surpasses traditional classifiers that require manual feature extraction, achieving an accuracy of 97.70%. The method addresses the limitations of traditional techniques and provides a valuable reference for defect identification in composite bonded structures for practical engineering applications.

基于卷积神经网络和双向门控循环单元(CNN-BiGRU)的复合材料键合结构超声无损检测。
新型陶瓷基复合材料(CMCs)是一种广泛应用于飞机基板表面的热防护材料。复合材料粘结结构的缺陷会导致保护层脱离机体,严重危及飞机安全。超声无损检测是一种很有前途的多层粘结结构缺陷检测方法。然而,CMC的多孔性和强吸声特性导致超声波探测信号的衰减和散射严重,降低了信噪比。这使得准确识别真正的缺陷信号具有挑战性。为此,本文提出了一种结合卷积神经网络和双向门控循环单元(CNN-BiGRU)并结合超声检测的新方法来识别复合材料键合结构中的缺陷。该方法通过卷积学习原始信号的时频域特征,应用注意机制确定这些特征的重要性,并将加权结果传递给双向门控循环单元网络。然后融合时频域特征,利用一维全局平均池化层减少模型参数,防止网络过拟合;采用非线性支持向量机代替传统的softmax分类器作为最终分类器。结果表明,本文提出的CNN-BiGRU模型优于传统的需要人工提取特征的分类器,准确率达到97.70%。该方法解决了传统方法的局限性,为实际工程应用中的复合材料粘结结构缺陷识别提供了有价值的参考。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Review of Scientific Instruments
Review of Scientific Instruments 工程技术-物理:应用
CiteScore
3.00
自引率
12.50%
发文量
758
审稿时长
2.6 months
期刊介绍: Review of Scientific Instruments, is committed to the publication of advances in scientific instruments, apparatuses, and techniques. RSI seeks to meet the needs of engineers and scientists in physics, chemistry, and the life sciences.
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