A systematic computational method to simulate and detect subsurface flaws, through nondestructive transient thermography, in aluminum (AL) sheets and friction stir (FS) welded sheets is proposed in this article. The proposed method relies on feature extraction methods and a data-driven machine learning modeling structure. Here, we propose the use of a multilayer perceptron feed-forward neural network with feature extraction methods to improve the flaw-probing depth of transient thermography inspection. Furthermore, for the first time, we propose thermographic signal linear modelling (TSLM), a hyper-parameter-free feature extraction technique for transient thermography. The new feature extraction and modeling framework was tested with out-of-sample experimental transient thermography data, and results show effectiveness in subsurface flaw detection of up to 2.3 mm deep in AL sheets [99.8% true positive rate (TPR) and 92.1% true negative rate (TNR)] and up to 2.2 mm deep in FS welds (97.2% TPR and 87.8% TNR).

中文翻译：

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用于搅拌摩擦焊缺陷检测的瞬态热成像：一种机器学习方法


本文提出了一种通过无损瞬态热成像技术模拟和检测铝 (AL) 板和搅拌摩擦 (FS) 焊接板的表面下缺陷的系统计算方法。所提出的方法依赖于特征提取方法和数据驱动的机器学习建模结构。在这里，我们建议使用具有特征提取方法的多层感知器前馈神经网络来提高瞬态热成像检测的缺陷探测深度。此外，我们首次提出热成像信号线性建模（TSLM），这是一种用于瞬态热成像的无超参数特征提取技术。新的特征提取和建模框架使用样本外实验瞬态热成像数据进行了测试，结果表明在 AL 板材中深度达 2.3 毫米的地下缺陷检测中的有效性 [99.8% 的真阳性率 (TPR) 和 92.1% 的真负率 (TNR)] 且 FS 焊缝深度可达 2.2 mm（97.2% TPR 和 87.8% TNR）。