A receiver-optimized total focusing method for detectability enhancement of small defects in coarse grained materials

Tao Ye, Jie Zhang, Yu Du, Jianfeng Xu, Long Bai*

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)peer-review

Abstract

The identification and localization of small defects are among the main objectives of ultrasonic non-destructive testing (NDT). In particular, the inspection of polycrystalline materials remains a challenge due to ultrasonic attenuation and backscatter noise caused by grain scattering. An adaptive ultrasonic array imaging algorithm is proposed in this paper, which is capable of enhancing the signal-to-noise ratio (SNR) of small subwavelength defects. The proposed approach adopts an iterative optimization procedure based on sequential backward selection, and is termed receiver optimized total focusing method (ROTFM). ROTFM is formulated in a baseline subtraction (BS) setting, and it is shown to achieve higher defect SNRs compared to TFM results obtained by directly applying the BS method to measured array data. More importantly, the performance of the proposed ROTFM is robust to time and spatial misalignments, and hence, it alleviates the need for complex compensation schemes when performing imaging computations. Compared to conventional TFM, ROTFM achieved an average SNR improvement of 3.41 dB and 6.05 dB in experiments for four test defects (cracks and holes of sizes 0.5 mm and 1 mm) at the frequencies of 5 MHz and 7 MHz, respectively.

Original languageEnglish
Article number102943
JournalNDT and E International
Volume140
Early online date1 Sept 2023
DOIs
Publication statusPublished - Dec 2023

Bibliographical note

Funding Information:
We would like to thank Dr. Peter Huthwaite from Imperial College London for his support with Pogo. The stainless steel specimens were provided by Dr. Jingwei Cheng from Hefei General Machinery Research Institute Co. Ltd, and this is much appreciated. This work was supported by National Natural Science Foundation of China under grant numbers 52005205 and 52188102, and the National Science Fund for Distinguished Young Scholars, China under grant number 52225506.

Funding Information:
This work was supported by National Natural Science Foundation of China under grant numbers 52005205 and 52188102 , and the National Science Fund for Distinguished Young Scholars, China under grant number 52225506 .

Publisher Copyright:
© 2023 Elsevier Ltd

Keywords

  • Defect detection
  • Imaging algorithms
  • Polycrystalline materials
  • TFM

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