Mapping the material microstructure of safety critical components using ultrasonic phased arrays

Research output: Chapter in Book/Report/Conference proceedingConference contribution book

4 Citations (Scopus)
131 Downloads (Pure)


Traditional imaging algorithms within the ultrasonic NDE community typically assume that the material being inspected is homogeneous. Obviously, when the medium is of a heterogeneous or anisotropic nature this assumption can contribute to the poor detection, sizing and characterisation of defects. Knowledge of the internal structure and properties of the material would allow corrective measures to be taken. The work presented here endeavours to reconstruct coarsened maps of the locally anisotropic grain structure of industrially representative samples from ultrasonic phased array data. This is achieved via application of the reversible-jump Markov Chain Monte Carlo (rj-MCMC) method: an ensemble approach within a Bayesian framework. The resulting maps are used in conjunction with the total focussing method and the reconstructed flaws are used as a quantitative measure of the success of this methodology. Using full matrix capture data arising from a finite element simulation of a phased array inspection of an austenitic weld, a 71% improvement in flaw location and an 11dB improvement in SNR is achieved using no a priori knowledge of the material's internal structure.
Original languageEnglish
Title of host publication2016 IEEE International Ultrasonics Symposium (IUS)
Place of PublicationPiscataway, NJ.
Number of pages4
ISBN (Electronic)9781467398978
Publication statusPublished - 18 Sept 2016
EventIEEE International Ultrasonics Symposium 2016 - VINCI Convention Center, Tours, France
Duration: 18 Sept 201621 Sept 2016


ConferenceIEEE International Ultrasonics Symposium 2016
Abbreviated title2016 IEEE IUS
Internet address


  • imaging algorithms
  • ultrasonic NDE
  • defect detection
  • anisotropic grain structure
  • reversible-jump Markov Chain Monte Carlo method
  • Bayesian framework
  • Markov chain
  • non-destructive evaluation


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