Projects per year
Imaging defects in austenitic welds presents a significant challenge for the ultrasonic non-destructive testing community. Due to the heating process during their manufacture, a dendritic structure develops, exhibiting large grains with locally anisotropic properties which cause the ultrasonic waves to scatter and refract. When basic imaging algorithms, which make straight ray and constant wave speed assumptions, are applied to datasets arising from the inspection of these welds, the resulting defect reconstructions are often distorted and difficult to interpret correctly. However, knowledge of the underlying spatially varying material properties would allow correction of the expected wave travel times and thus result in more reliable defect reconstructions. In this paper, an approximation to the underlying, locally anisotropic structure of the weld is constructed from ultrasonic phased array inspection data. A new forward model of wave front propagation in locally anisotropic media is presented and used within the reversible-jump Markov chain Monte Carlo method to invert for the map of effective grain orientations across different regions of the weld. This forward model and estimated map are then used as the basis for an advanced imaging algorithm and the resulting reconstructions of defects embedded within these polycrystalline materials exhibit a significant improvement across multiple flaw characterisation metrics.
- imaging defects
- imaging algorithms
- Monte Carlo
- grain orientation
Data for: "Effective Grain Orientation Mapping of Complex and Locally Anisotropic Media for Improved Imaging in Ultrasonic Non-Destructive Testing"
Tant, K. M. M. (Creator), University of Strathclyde, 27 Apr 2020
Tant, K. M. M., Galetti, E., Mulholland, A. J., Curtis, A., & Gachagan, A. (2020). Effective grain orientation mapping of complex and locally anisotropic media for improved imaging in ultrasonic non-destructive testing. Inverse Problems in Science and Engineering. https://doi.org/10.1080/17415977.2020.1762596