Enhanced Ultrasonic Flaw Detection Using an Ultrahigh Gain and Time-Dependent Threshold

Yongfeng Song, Joseph A. Turner, Zuoxiang Peng, Chao Chen, Xiongbing Li

Research output: Contribution to journalArticle

7 Scopus citations

Abstract

In an attempt to improve the ultrasonic testing capability of a conventional C-scan system, a flaw detection method using an ultrahigh gain is developed in this paper. A time-dependent threshold for image segmentation is applied to identify automatically material anomalies present in the sample. A singly scattered response model is used with extreme value statistics to calculate the confidence bounds of grain noise. The result is a time-dependent threshold associated with the grain noise that can be used for segmentation. Ultrasonic imaging experiments show that the presented method has advantages over a traditional fixed threshold approach with respect to false positives and missed flaws. The results also show that a low gain is adverse to the detection of microflaws with subwavelength dimensions. The forward model is expected to serve as an effective tool for the probability of detection of flaws and the inspection of coarse-grained materials in the future.

Original languageEnglish (US)
Pages (from-to)1214-1225
Number of pages12
JournalIEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control
Volume65
Issue number7
DOIs
StatePublished - Jul 2018

Keywords

  • Extreme value theory
  • grain noise statistics
  • image segmentation
  • time-dependent threshold
  • ultrahigh gain
  • ultrasonic C-scan

ASJC Scopus subject areas

  • Instrumentation
  • Acoustics and Ultrasonics
  • Electrical and Electronic Engineering

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