Tant, Katherine M. M. and Galetti, Erica and Mulholland, Anthony J. and Curtis, Andrew and Gachagan, Anthony (2016) Mapping the material microstructure of safety critical components using ultrasonic phased arrays. In: 2016 IEEE International Ultrasonics Symposium (IUS). IEEE.
Tant_etal_IUS2016_Mapping_the_material_miocrostructure_of_safety_critical_components.pdf - Accepted Author Manuscript
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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.
|Item type:||Book Section|
|Notes:||© 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.|
|Keywords:||imaging algorithms, ultrasonic NDE, defect detection, anisotropic grain structure, reversible-jump Markov Chain Monte Carlo method, Bayesian framework, Markov chain, non-destructive evaluation, Electrical engineering. Electronics Nuclear engineering, Electrical and Electronic Engineering|
|Subjects:||Technology > Electrical engineering. Electronics Nuclear engineering|
|Department:||Faculty of Science > Mathematics and Statistics
Faculty of Engineering > Electronic and Electrical Engineering
|Depositing user:||Pure Administrator|
|Date Deposited:||03 Oct 2016 10:09|
|Last modified:||01 May 2017 01:11|