This paper presents a complete procedure for the non-destructive analysis of composite laminates, taking advantage of the step-heating infrared thermography and the latest developments of deep neural networks. One-dimensional temperature profiles of the target surface are collected in response to long heat pulses and individually feed a compact network made of convolutional filters, self-tuned to represent the signals in an equivalent feature space of improved discrimination. The resulting features are then classified to obtain the complete three-dimensional characterization of the properties of possible subsurface defects. Experimental validation is proposed to investigate a laminate of glass-fiber-reinforced polymer with several flat-bottom holes by changing the duration of the input heat pulses. This test produces surprisingly good results in the characterization of three classes of defects of increasing depth, including the most challenging at a depth of 6.38 mm, i.e. at the limit of applicability of the step-heating thermography. In the case of an excitation length of 180 s, the average balanced accuracy, precision, and recall are equal to 84.03%, 87.62%, and 82.43%, respectively. Moreover, a threshold operation on the classification scores further boosts the recall values of the class of the deepest defects from 53.87% to 82.41%. This enhancement of sensitivity suggests the applicability of the proposed procedure for the automatic inspection of composites structures in all application fields where safety is mandatory.

本文利用步进加热红外热成像技术和深度神经网络的最新发展，为复合材料层压板的无损分析提供了完整的程序。响应于较长的热脉冲，收集目标表面的一维温度曲线，并分别馈入由卷积滤波器构成的紧凑网络，并对其进行自我调整，以在可改善分辨力的等效特征空间中表示信号。然后对得到的特征进行分类，以获得可能的地下缺陷性质的完整三维特征。提出了实验验证，以通过改变输入热脉冲的持续时间来研究具有几个平底孔的玻璃纤维增​​强聚合物层压板。该测试在表征三类深度不断增加的缺陷方面产生了令人惊讶的良好结果，包括在6.38毫米深度处（即在逐步加热热成像法的适用性极限下）最具挑战性的缺陷。在激励长度为180 s的情况下，平均平衡精度，精度和召回率分别等于84.03％，87.62％和82.43％。此外，对分类分数进行阈值运算可将最深缺陷类别的召回值从53.87％提高到82.41％。灵敏度的提高表明，所建议的程序可用于在安全要求严格的所有应用领域中自动检查复合材料结构的方法。38 mm，即在逐步加热热成像技术的适用范围内。在激励长度为180 s的情况下，平均平衡精度，精确度和召回率分别等于84.03％，87.62％和82.43％。此外，对分类分数进行阈值运算可将最深缺陷类别的召回值从53.87％提高到82.41％。灵敏度的提高表明，所建议的程序可用于在安全要求严格的所有应用领域中自动检查复合材料结构的方法。38 mm，即在逐步加热热成像技术的适用范围内。在激励长度为180 s的情况下，平均平衡精度，精确度和召回率分别等于84.03％，87.62％和82.43％。此外，对分类分数进行阈值运算可将最深缺陷类别的召回值从53.87％提高到82.41％。灵敏度的提高表明，所建议的程序可用于在安全要求严格的所有应用领域中自动检查复合材料结构的方法。对分类分数的阈值操作将最深缺陷类别的召回值从53.87％提高到82.41％。灵敏度的提高表明，所建议的程序可用于在安全要求严格的所有应用领域中自动检查复合材料结构的方法。对分类分数的阈值操作将最深缺陷类别的召回值从53.87％提高到82.41％。灵敏度的提高表明，所建议的程序可用于在安全要求严格的所有应用领域中自动检查复合材料结构的方法。