Ultrasonic guided wave technology has played a significant role in the field of non-destructive testing as it employs acoustic waves that have advantages of high propagation efficiency and low energy consumption during the inspect process. However, theoretical solutions to guided wave scattering problems using assumptions such as Born approximation, have led to the poor quality of the reconstructed results. To address this issue, a novel approach to quantitative reconstruction of defects using the integration of data-driven method with the guided wave scattering analysis has been proposed in this paper. Based on the geometrical information of defects and initial results by the theoretical analysis of defect reconstructions, a deep learning neural network model is built to reveal the physical relationship between defects and the received signals. This data-driven model is then applied to quantitatively assess and characterize defect profiles in structures, reduce the inaccuracy of the theoretical modelling and eliminate the impact of noise pollution in the process of inspection. To demonstrate advantages of the developed approach to reconstructions of defects with complex profiles, numerical examples including basic defect profiles and a defect with the noisy fringe have been examined. Results show that this approach has greater accuracy for reconstruction of defects in structures as compared with the analytical method and provides a valuable insight into the development of artificial intelligence-assisted inspection systems with high accuracy and efficiency in the field of non-destructive testing.

中文翻译：

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一种新的理论分析与数据驱动方法结合的结构缺陷重建方法

超声波导波技术在无损检测领域发挥了重要作用，它利用声波在检测过程中具有传播效率高、能耗低的优点。然而，使用波恩近似等假设对导波散射问题的理论解决方案导致重建结果的质量较差。为了解决这个问题，本文提出了一种将数据驱动方法与导波散射分析相结合的缺陷定量重建新方法。基于缺陷几何信息和初始结果，通过缺陷重建的理论分析，建立深度学习神经网络模型，揭示缺陷与接收信号之间的物理关系。然后应用该数据驱动模型对结构中的缺陷轮廓进行定量评估和表征，减少理论建模的不准确性并消除检查过程中噪声污染的影响。为了展示所开发的复杂轮廓缺陷重建方法的优势，已经检查了包括基本缺陷轮廓和带有噪声条纹的缺陷在内的数值示例。结果表明，与分析方法相比，该方法在结构缺陷重建方面具有更高的准确性，并为在无损检测领域开发具有高精度和高效率的人工智能辅助检测系统提供了宝贵的见解。减少理论建模的不准确性，消除检验过程中噪声污染的影响。为了展示所开发的复杂轮廓缺陷重建方法的优势，已经检查了包括基本缺陷轮廓和带有噪声条纹的缺陷在内的数值示例。结果表明，与分析方法相比，该方法在结构缺陷重建方面具有更高的准确性，并为在无损检测领域开发具有高精度和高效率的人工智能辅助检测系统提供了宝贵的见解。减少理论建模的不准确性，消除检验过程中噪声污染的影响。为了展示所开发的复杂轮廓缺陷重建方法的优势，已经检查了包括基本缺陷轮廓和带有噪声条纹的缺陷在内的数值示例。结果表明，与分析方法相比，该方法在结构缺陷重建方面具有更高的准确性，并为在无损检测领域开发具有高精度和高效率的人工智能辅助检测系统提供了宝贵的见解。已经检查了包括基本缺陷轮廓和带有噪声条纹的缺陷在内的数值示例。结果表明，与分析方法相比，该方法在结构缺陷重建方面具有更高的准确性，并为在无损检测领域开发具有高精度和高效率的人工智能辅助检测系统提供了宝贵的见解。已经检查了包括基本缺陷轮廓和带有噪声条纹的缺陷在内的数值示例。结果表明，与分析方法相比，该方法在结构缺陷重建方面具有更高的准确性，并为在无损检测领域开发具有高精度和高效率的人工智能辅助检测系统提供了宝贵的见解。