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首页> 外文期刊>Infrared physics and technology >Comparing the efficiency of defect depth characterization algorithms in the inspection of CFRP by using one-sided pulsed thermal NDT
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Comparing the efficiency of defect depth characterization algorithms in the inspection of CFRP by using one-sided pulsed thermal NDT

机译：使用单侧脉冲热NDT比较CFRP检查中缺陷深度表征算法的效率

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The efficiency of eight algorithms of defect depth characterization (pulse phase thermography - PPT, thermographic signal reconstruction by analyzing the first and second derivatives- TSR, early observation - EO, apparent thermal inertia - ATI, thermal quadrupoles - TQ, non-linear fitting - NLF and neural networks - NN) has been comparatively analyzed on both theoretical and experimental IR image sequences obtained in the inspection of CFRP composite. Synthetic noise-free image sequences have been calculated by means of the ThermoCalc-3D software, while experimental results have been obtained by applying a one-sided procedure of pulsed thermal NDT to the inspection of artificial defects in CFRP. A relative error in the evaluation of defect depth has been chosen as a figure of merit. It has been demonstrated that a simple and robust processing technique is the use of the Fourier transform resulting in phase-domain data (PPT). The technique of TSR ensures maximal values of signal-to-noise ratio and is less susceptible to uneven heating and lateral heat diffusion. The calculation of ATI has allowed the characterization of defects at depths up to 1.5 mm, but it is sensitive to uneven heating thus requiring to carefully choose a non-defect area. The EO method, as well as the technique of TQ, have revealed inferior results in defect depth identification because of a noisy character of raw signals. Nonlinear fitting is a convenient processing technique allowing simultaneous characterization of some test parameters, such as material thermal properties, defect depth and thickness, etc., but this technique is time-consuming and can hardly be applied to full-format images. In the whole defect depth range, minimal characterization errors have been ensured by the use of the NN that is a promising tool for automated identification of hidden defects.
机译：通过分析第一和第二衍生物，早期观察 - EO，表观热惯性 - ATI，热四边形 - TQ，非线性拟合，脉冲相位热成像 - PPT，热成像 - PPT。在CFRP复合材料检测中获得的理论和实验IR图像序列的均比和神经网络 - NN）已经相对分析。通过热电态-3D软件计算了可自由无噪声图像序列，而通过将脉冲热NDT的单面方法应用于CFRP中的人工缺陷检查，已经获得了实验结果。已选择评估缺陷深度评估中的相对误差作为优点。已经证明，简单且稳健的处理技术是使用傅里叶变换导致相位域数据（PPT）。 TSR技术确保了信噪比的最大值，并且不易于加热和横向热扩散易感。 ATI的计算允许在深度的缺陷表征高达1.5mm，但对不均匀加热敏感，因此需要仔细选择非缺陷区域。 EO方法以及TQ技术，由于原始信号的噪声特性，缺陷深度识别揭示了缺陷深度识别的劣势。非线性拟合是一种方便的加工技术，允许同时表征一些测试参数，例如材料热性质，缺陷深度和厚度等，但这种技术耗时，并且几乎不能应用于全格式图像。在整个缺陷深度范围内，通过使用NN是一种最有希望的用于自动识别隐藏缺陷的有希望的工具来确保最小的表征误差。

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