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Infrared Thermography Approach for Pipelines and Cylindrical Based Geometries

机译:管道管道和圆柱形几何形状的红外线热成像方法

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Infrared thermography (IRT) is a competitive method for nondestructive testing; yet it is susceptible to errors when testing objects with complex geometries. This work investigates the effects of regulating different thermographic testing parameters to optimize the IRT outcomes when testing complex shaped geometries, particularly cylindrical coupons. These parameters include the scanning routine, feed-rate, and heat intensity. Fine-tuning these parameters will be performed with respect to three different variables consisting of workpiece density, defect size, and defect depth. The experimental work is designed around 3D-printed cylindrical coupons, then the obtained thermal images are stitched via image processing tool to expose defects from different scans. The analysis employs a Signal-to-Noise Ratio (SNR) metric in an orthogonal tabulation following a Taguchi Design of Experiment. Moreover, test sensitivity and the best combination of factor levels are determined using Analysis of Means (ANOM) and Analysis of Variance (ANOVA). The outcomes show that the heating intensity factor is the most dominant in exposing flaws with close to 40% mean shift and up to 47% variance fluctuation. The paper introduces the tools employed in the study, and then explains the methodology followed to test one sample quadrant. The results for running the testing on all the scenarios are presented, interpreted, and their implications are recommended.
机译:红外热成像(IRT)是无损检测的竞争方法;然而,当测试具有复杂几何形状的物体时,它易于错误。这项工作调查了调节不同热敏测试参数的影响,当测试复杂形状的几何形状时,特别是圆柱形优惠券时,可以在优化IRT结果。这些参数包括扫描程序,进料速率和热强度。微调这些参数将相对于由工件密度,缺陷尺寸和缺陷深度组成的三种不同变量来执行。实验工作围绕3D印刷圆柱形优惠券设计,然后通过图像处理工具缝合所获得的热图像,以暴露不同扫描的缺陷。在实验的Taguchi设计之后,该分析采用正交标记中的信噪比(SNR)度量。此外,使用装置(ANOM)的分析和方差分析(ANOVA)确定测试敏感性和因子水平的最佳组合。结果表明,加热强度因子是暴露缺陷的最主突出性,接近40%的平均偏移和高达47%的方差波动。本文介绍了研究中使用的工具,然后解释了测试一个样品象限的方法。呈现,解释,展示了在所有方案上运行测试的结果,并建议使用它们的影响。

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