In this study, methodology of acoustic emission source localization in composite materials based on artificial intelligence was presented. Carbon fiber reinforced plastic was selected for specimen, and acoustic emission signal were measured using piezoelectric devices. The measured signal was wavelet-transformed to obtain scalograms, which were used as training data for the artificial intelligence model. AESLNet(acoustic emission source localization network), proposed in this study, was constructed convolutional layers in parallel due to anisotropy of the composited materials. It is regression model to detect the coordinates of acoustic emission source location. Hyper-parameter of network has been optimized by Bayesian optimization. It has been confirmed that network can detect location of acoustic emission source with an average error of 3.02mm and a resolution of 20mm.

翻译：本研究提出了一种基于人工智能的复合材料声发射源定位方法。选用碳纤维增强塑料作为试样，并利用压电装置测量声发射信号。对测量信号进行小波变换以获得尺度图，将其作为人工智能模型的训练数据。本研究提出的AESLNet（声发射源定位网络）针对复合材料的各向异性特性，构建了并行卷积层结构。该网络为回归模型，用于检测声发射源位置的坐标。通过贝叶斯优化对网络超参数进行了优化。实验证实，该网络能以平均误差3.02毫米、分辨率20毫米的精度实现声发射源定位。