Wang Zhongmin, Chen Jiaojie, Xin Yilong, Guo Yongbin, Li Yizhang, Sun Huanyu, Yang Xiuwei
Institute of Automation, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250100, China.
School of Information Science and Engineering (School of Software), Yanshan University, Qinhuangdao 066000, China.
Materials (Basel). 2025 May 23;18(11):2444. doi: 10.3390/ma18112444.
Multilayer composite materials often develop internal defects at varying depths due to manufacturing and environmental factors. Traditional planar scanning methods lack the ability to pinpoint defect locations in depth. This study proposes a terahertz time-domain spectroscopy (THz-TDS)-based defect detection method using continuous wavelet transform (CWT) to convert spectral signals into time-frequency images. These are analyzed by the ResNet18 model combined with a support vector machine (SVM) classifier. Comparative experiments with four classical deep learning models and three classifiers show that the Residual Network with 18 layers (ResNet18-SVM) approach achieves the highest accuracy of 98.56%, effectively identifying three types of defects. The results demonstrate the method's strong feature extraction, depth resolution, and its potential for nondestructive evaluation of multilayer structures.
由于制造和环境因素，多层复合材料常常在不同深度出现内部缺陷。传统的平面扫描方法缺乏在深度上精确确定缺陷位置的能力。本研究提出一种基于太赫兹时域光谱（THz-TDS）的缺陷检测方法，该方法使用连续小波变换（CWT）将光谱信号转换为时频图像。通过结合支持向量机（SVM）分类器的ResNet18模型对这些图像进行分析。与四个经典深度学习模型和三个分类器的对比实验表明，18层残差网络（ResNet18-SVM）方法实现了98.56%的最高准确率，有效识别了三种类型的缺陷。结果证明了该方法强大的特征提取能力、深度分辨率以及其在多层结构无损评估方面的潜力。
