Torbali Muhammet E, Zolotas Argyrios, Avdelidis Nicolas P, Alhammad Muflih, Ibarra-Castanedo Clemente, Maldague Xavier P
School of Aerospace, Transportation and Manufacturing, Cranfield University, Cranfield MK43 0AL, UK.
Department of Electrical and Computer Engineering, Universite Laval, Quebec, QC G1V 0A6, Canada.
Materials (Basel). 2024 Jul 11;17(14):3435. doi: 10.3390/ma17143435.
Combinative methodologies have the potential to address the drawbacks of unimodal non-destructive testing and evaluation (NDT & E) when inspecting multilayer structures. The aim of this study is to investigate the integration of information gathered via phased-array ultrasonic testing (PAUT) and pulsed thermography (PT), addressing the challenges posed by surface-level anomalies in PAUT and the limited deep penetration in PT. A center-of-mass-based registration method was proposed to align shapeless inspection results in consecutive insertions. Subsequently, the aligned inspection images were merged using complementary techniques, including maximum, weighted-averaging, depth-driven combination (DDC), and wavelet decomposition. The results indicated that although individual inspections may have lower mean absolute error (MAE) ratings than fused images, the use of complementary fusion improved defect identification in the total number of detections across numerous layers of the structure. Detection errors are analyzed, and a tendency to overestimate defect sizes is revealed with individual inspection methods. This study concludes that complementary fusion provides a more comprehensive understanding of overall defect detection throughout the thickness, highlighting the importance of leveraging multiple modalities for improved inspection outcomes in structural analysis.
在检测多层结构时,组合方法有潜力解决单峰无损检测与评估(NDT & E)的缺点。本研究的目的是研究通过相控阵超声检测(PAUT)和脉冲热成像(PT)收集的信息的整合,解决PAUT中表面级异常以及PT中有限的深度穿透所带来的挑战。提出了一种基于质心的配准方法,以对齐连续插入中的无形状检测结果。随后,使用互补技术(包括最大值、加权平均、深度驱动组合(DDC)和小波分解)对对齐后的检测图像进行合并。结果表明,尽管单个检测的平均绝对误差(MAE)评级可能低于融合图像,但使用互补融合可在结构的多个层的总检测次数中提高缺陷识别率。分析了检测误差,并揭示了单个检测方法高估缺陷尺寸的趋势。本研究得出结论,互补融合能更全面地了解整个厚度范围内的整体缺陷检测情况,突出了利用多种模态以改善结构分析中检测结果的重要性。