Dr Katherine Tant

Publications

A probabilistic approach to modelling ultrasonic shear wave propagation in locally anisotropic heterogeneous media

Ferguson Alistair S, Tant Katherine MM, Foodun Mohammud, Mulholland Anthony J

Waves in Random and Complex Media, pp. 1-24 (2024)

https://doi.org/10.1080/17455030.2024.2341283

Towards an in-process ultrasonic phased array inspection method for narrow-gap welds

Nicolson Ewan, Mohseni Ehsan, Lines David, Tant Katherine MM, Pierce Gareth, MacLeod Charles N

NDT and E International Vol 144 (2024)

https://doi.org/10.1016/j.ndteint.2024.103074

Travel times and ray paths for acoustic and elastic waves in generally anisotropic media

Ludlam James, Tant Katherine, Dolean Victorita, Curtis Andrew

Journal of Computational Physics Vol 494 (2023)

https://doi.org/10.1016/j.jcp.2023.112500

Machine learning for real-time inversion of locally anisotropic weld properties using in-process ultrasonic array measurements

Pyle Richard, MacLeod Charles Norman, Tant Katherine Margaret Mary, Sweeney Nina, Ludlam James, Nicolson Ewan

60th Annual British Conference on NDT (2023)

Machine learning for real-time inversion of locally anisotropic weld properties using in-process layer by layer ultrasonic array measurements

Pyle Richard, MacLeod Charles Norman, Tant Katherine Margaret Mary, Sweeney Nina, Ludlam James, Nicolson Ewan, McKnight Shaun, Lines David

IEEE International Ultrasonics Symposium 2023 (2023)

Ultrasonic wave propagation in randomly layered heterogeneous media

Ferguson Alistair S, Mulholland Anthony J, Tant Katherine MM, Foondun Mohammud

Wave Motion Vol 120 (2023)

https://doi.org/10.1016/j.wavemoti.2023.103138

More publications

Professional Activities

Traveltime Tomography of Locally Anisotropic Media Using SVGD

Speaker

23/3/2023

Mathematical theory and applications of multiple wave scattering

Participant

20/3/2023

Traveltime Tomography Using Stein Variational Gradient Descent

Speaker

22/2/2023

NDT and E International (Journal)

Peer reviewer

2023

Proceedings of the Royal Society A : Mathematical, Physical and Engineering Sciences (Journal)

Peer reviewer

2023

Mechanical Systems and Signal Processing (Journal)

Peer reviewer

2023

More professional activities

Projects

Improving Ultrasonic Imaging using Machine Learning - FIND CDT EngD with Rolls Royce

Tant, Katherine Margaret Mary (Principal Investigator) MacLeod, Charles Norman (Co-investigator)

01-Jan-2023 - 30-Jan-2027

Fusing a future from Glasgow’s proud heritage: Schedule Guaranteed High-Integrity Structures for a Secure, Safe and Resilient Transition to Net Zero (Innovation Accelerator)

MacLeod, Charles Norman (Principal Investigator) Dobie, Gordon (Co-investigator) Fitzpatrick, Stephen (Co-investigator) Gachagan, Anthony (Co-investigator) Javadi, Yashar (Co-investigator) Mohseni, Ehsan (Co-investigator) Pierce, Gareth (Co-investigator) Stratoudaki, Theodosia (Co-investigator) Tant, Katherine Margaret Mary (Co-investigator) Wathavana Vithanage, Randika Kosala (Co-investigator)

01-Jan-2023 - 31-Jan-2025

Remote ultrasound tomography using deep neural networks and laser ultrasonics

Tant, Katherine Margaret Mary (Co-investigator) Stratoudaki, Theodosia (Principal Investigator)

31-Jan-2021 - 20-Jan-2022

Ultrasonic Evaluation of Additively Manufactured Titanium Components

Tant, Katherine Margaret Mary (Principal Investigator) Blackburn, Dion (Post Grad Student) Wynne, Bradley (Co-investigator)

PhD studentship funded by the EPSRC CDT in Future UltraSonic Engineering,

Additive Manufacturing (AM) is a key driver of Industry 4.0, and has the potential to automate manufacturing of bespoke products with reduced costs, waste and energy consumption. This is of course highly desirable for companies not only in terms of profit, but in terms of sustainability and compliance with environmental targets and mandates. However, to unlock the full potential of AM in safety critical industries, it is imperative that the structural integrity of built parts is assured. Unfortunately, the lack of AM process stability, robustness and repeatability mean that it is difficult to control and predict the material properties of the built components due to the presence of defects induced by the equipment, process and/or feedstock inconsistencies. If this challenge cannot be overcome, the value of AM in high value industries is severely threatened.

To address this challenge, the development of reliable and robust in-process materials characterisation is vital. This would allow operators to adjust the AM control parameters online to compensate for any variations, or alternatively to discard poorly constructed components before they are completed, saving time, energy and money.

The central aim of this project is to develop models of ultrasonic wave propagation in the evolving microstructures of titanium alloys observed during metal additive manufacturing processes. We will examine ultrasonic propagation through both α-β phase (fig. 1a) and pure β phase (fig. 1b) structures to determine whether the larger scale β phase structures consistently dominate the effects on wave propagation (note that during the AM process, the alloy will cycle between these two states due to the applied thermal fields). These models will enhance our understanding of how ultrasound interacts with both the spatially and temporally changing microstructures, and will in turn facilitate the development of bespoke ultrasonic materials characterisation algorithms which will enable superior in-process imaging of the AM component. The student will study mathematical models of wave propagation through complex materials represented using statistical distributions, which will be informed by models of the microstructure evolution itself. These will be validated using commercial finite element modelling software. An inverse relationship between the transmitted wave forms and the descriptive statistics of the microstructure will be explored and used to monitor changes as the AM process progresses.

01-Jan-2021 - 01-Jan-2024

Maths DTP 2020 University of Strathclyde | Ludlam, James

Tant, Katherine Margaret Mary (Principal Investigator) Dolean Maini, Victorita (Co-investigator) Ludlam, James (Research Co-investigator)

01-Jan-2020 - 01-Jan-2024

Ultrasonic Characterisation of Micro-Texture Regions

Tant, Katherine Margaret Mary (Principal Investigator) Robertson-Harra, David (Post Grad Student)

PhD studentship funded by the EPSRC CDT in Future Innovation in NDE.

To ensure optimal creep and fatigue properties of Titanium alloys, it is desirable that the micro-structure of these components is fine grained and exhibits no local textures. However, due to the sensitivity of Titanium alloys to manufacturing process parameters, the near α phase Titanium alloy, Ti-6Al-4V, can develop micro-texture regions (MTRs), where large regions of contiguous crystals exhibit a single preferential orientation. When these regions have an area greater than 1mm2, they present potential sites for preferential nucleation of cracks and the material becomes more susceptible to fatigue, thus reducing the component’s lifespan. Ultrasonic testing has been used to detect the presence of these MTRs [3] but as yet, no method exists which can localise and characterise these defects from the ultrasonic inspection data. This EngD will build upon the findings of a previous feasibility study conducted by Dr K. Tant and Prof. A Mulholland carried out for IHI, which examined the feasibility of using non-destructive ultrasonic time of flight measurements to assess the texture of Ti-6Al-4V.

Objectives
1. To develop an automated framework for processing high resolution electron backscatter diffraction (EBSD) measurements into a format suitable for finite element simulation studies.
2. To derive a relationship between the distribution of crystal sizes and orientations encountered by a propagating wave, and its transmitted phase and amplitude.
3. To detect, localise and quantitatively characterise the size and properties of any detected MTRs.
4. To use mathematical analysis to comment on the resolution of the approach as a function of the material parameters and comment on the probability of detection.

01-Jan-2020 - 01-Jan-2024

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