The paper describes the concept of an automated defect characterization procedure by using infrared nondestructive testing of glass fiber reinforced composite. The proposed algorithms have allowed determination of defect depth, lateral dimensions and area, as well as coordinates of defect centers. The algorithms are based on the use of the neural network trained on both experimental and theoretical temperature profiles. An acceptable for practice accuracy of defect characterization has been obtained on the experimental data (0-15% by defect depth and 26-139% by defect area).

中文翻译：

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使用热非破坏性测试自动检测和表征GFRP复合材料中缺陷的程序

本文描述了通过使用玻璃纤维增​​强复合材料的红外无损检测来自动进行缺陷表征的概念。提出的算法允许确定缺陷深度，横向尺寸和面积以及缺陷中心的坐标。该算法基于使用在实验和理论温度曲线上训练的神经网络。在实验数据上已经获得了可接受的缺陷表征实践精度（缺陷深度为0-15％，缺陷面积为26-139％）。