Abstract Quantitative non destructive subsurface analysis with increased reliability for defect detection makes it useful for a variety of industrial applications to assess the integrity and subsequent strength of materials either during or post manufacturing. Traditional non stationary thermal wave based subsurface analysis approaches are skill intensive and time consuming for analysis with human intervention. This paper proposes an automated classification and regression tree based quantitative post processing modality along with thermal wave model to characterize the subsurface anomalies using quadratic frequency modulated thermal wave imaging. It also validates the proposed mathematical modeling using experimentation carried over carbon fiber reinforced and glass fiber reinforced plastic specimens used in aerospace industry. Subsurface details have been visualized in terms of their depths using the proposed modality being evaluated from the proposed mathematical model. In addition, its detection capability and reliability over other contemporary approaches have been assessed using signal to noise ratio and probability of detection respectively.

中文翻译：

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纤维增强聚合物的自动定量表面评估

摘要 具有更高可靠性的缺陷检测的定量无损地下分析使其可用于各种工业应用，以在制造期间或制造后评估材料的完整性和随后的强度。传统的基于非稳态热波的地下分析方法在人工干预下进行分析需要大量的技能和时间。本文提出了一种基于自动分类和回归树的定量后处理模态以及热波模型，以使用二次调频热波成像来表征地下异常。它还使用在航空航天工业中使用的碳纤维增强和玻璃纤维增​​强塑料样本进行的实验验证了所提出的数学模型。使用根据提议的数学模型评估的提议的模态，已根据其深度可视化地下细节。此外，已分别使用信噪比和检测概率来评估其检测能力和可靠性超过其他当代方法。