TFM imaging of aeroengine casing ring forgings with curved surfaces using acoustic field threshold segmentation and vector coherence factor

• Published in: Chinese journal of aeronautics Vol. 35; no. 11; pp. 401 - 415
• Main Authors: GUAN, Shanyue, WANG, Xiaokai, HUA, Lin, LI, Yixuan
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.11.2022; Hubei Key Laboratory of Advanced Technology for Autornotive Components,Wuhan University of Technology,Wuhan 430070,China%Hubei Collaborative Innovation Center for Automotive Components Technology,Wuhan 430070,China%School of Automotive Engineering,Wuhan University of Technology,Wuhan 430070,China
• Subjects: Acoustic Field Threshold Segmentation (AFTS); Aeroengine casing; Curved surfaces; Total Focusing Method (TFM); Vector Coherence Factor (VCF); Curved surfaces; Vector Coherence Factor (VCF); Acoustic Field Threshold Segmentation (AFTS); Aeroengine casing; Total Focusing Method (TFM); Acoustic Field Threshold Segmentation(AFTS); Total Focusing Method(TFM); Vector Coherence Factor(VCF)
• DOI: 10.1016/j.cja.2022.07.002
• ISSN: 1000-9361

The aeroengine casing ring forgings have complex cross-section shapes, when the conventional ultrasonic or phased array is applied to detect such curved surfaces, the inspection images always have low resolution and even artifacts due to the distortion of the wave beam. In this article, taking a type of aeroengine casing ring forging as an example, the Total Focusing Method (TFM) algorithms for curved surfaces are investigated. First, the Acoustic Field Threshold Segmentation (AFTS) algorithm is proposed to reduce background noise and data calculation. Furthermore, the Vector Coherence Factor (VCF) is adopted to improve the lateral resolution of the TFM imaging. Finally, a series of 0.8 mm diameter Side-Drilled Holes (SDHs) are machined below convex and concave surfaces of the specimen. The quantitative comparison of the detection images using the conventional TFM, AFTS-TFM, VCF-TFM, and AFTS-VCF-TFM is implemented in terms of data volume, imaging Signal-to-Noise Ratio (SNR), and defect echo width. The results show that compared with conventional TFM, the data volume of AFTS-VCF-TFM algorithm for convex and concave is decreased by 32.39% and 73.40%, respectively. Moreover, the average SNR of the AFTS-VCF-TFM is gained up to 40.0 dB, while the average 6 dB-drop echo width of defects is reduced to 0.74 mm.