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Comparative Study of Diverse Techniques for Flaw Segmentation in TOFD Images of Austenitic Stainless Steel Weld

机译:奥氏体不锈钢焊缝TOFD图像中的多种缺陷分割技术的比较研究

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Background/Objectives: Grain structure of Austenitic Stainless Steel (ASS) weld causes difficulty in defect detection while inspecting using Time of Flight Diffraction (TOFD) method. This study aims at comparing segmentation methods to overcome this difficulty based on defect characterization. Methods/Statistical Analysis: This study makes use of TOFD and Radiographic images of ASS weld pads fabricated with defined linear and volumetric defects. The Region-Based Level Set algorithm and Discontinuity Based Segmentation algorithm were explored for achieving flaw segmentation and quantitative characterization and validation of the result with that of standard radiographic results. Findings: The efficiency of the algorithms was analyzed by comparing and validating the size of defect with that of standard radiographic results in the form of error percentage. The consistency of error percentage in defect sizing (up to 11%) achieved by Region- Based Level Set algorithm for all the test images given in the database indicate that, this algorithm is the best as compared to Discontinuity Based Segmentation algorithm(error percentage up to 47%) for defect segmentation and characterization in TOFD images. Application/Improvements: The segmentation algorithm enables automation of measurement process and enhanced detection and characterization of defects at the initial stage. Further, it reduces human fatigue caused by operator while defect detection as the volume of data increases. The future direction is to fully automate the system in order to save time for interpretation and to modify the algorithm to segment images with multiple defects.
机译:背景/目的:奥氏体不锈钢(ASS)焊缝的晶粒结构在使用飞行时间衍射(TOFD)方法进行检查时会导致难以发现缺陷。这项研究旨在比较分割方法以克服基于缺陷表征的这一困难。方法/统计分析:本研究利用具有确定的线性和体积缺陷的ASS焊垫的TOFD和射线照相图像。探索了基于区域的水平集算法和基于不连续性的分割算法,以实现缺陷分割和定量表征,并对结果进行标准射线照相结果验证。结果:通过以误差百分比的形式比较和验证缺陷的大小与标准射线照相结果的大小,分析了算法的效率。基于区域的级别集算法针对数据库中给出的所有测试图像实现的缺陷大小调整中的错误百分比一致性(最高11%)表明,与基于不连续性的分割算法相比,该算法是最佳的。至47%)用于TOFD图像中的缺陷分割和表征。应用/改进:分割算法可在初始阶段实现测量过程的自动化并增强缺陷的检测和表征。此外,随着数据量的增加,它在减少缺陷检测的同时减少了由操作员引起的人为疲劳。未来的方向是使系统完全自动化,以节省解释时间,并修改算法以分割具有多个缺陷的图像。

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