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Details of Grant 

EPSRC Reference: EP/S001174/1
Title: AME NDT (Anisotropic Media Evaluation for Non-Destructive Testing)
Principal Investigator: Tant, Dr K
Other Investigators:
Researcher Co-Investigators:
Project Partners:
EDF Energy National Nuclear Laboratory National Physical Laboratory
PZFlex Limited (UK) Rolls-Royce Plc (UK)
Department: Mathematics and Statistics
Organisation: University of Strathclyde
Scheme: EPSRC Fellowship - NHFP
Starts: 29 June 2018 Ends: 28 December 2021 Value (£): 283,726
EPSRC Research Topic Classifications:
Image & Vision Computing Manufacturing Machine & Plant
EPSRC Industrial Sector Classifications:
Related Grants:
Panel History:
Panel DatePanel NameOutcome
10 May 2018 EPSRC UKRI CL Innovation Fellowship Interview Panel 10 - 10 and 11 May 2018 Announced
Summary on Grant Application Form
Manufacturing is a key activity of the UK economy and accounts for more than half of all UK exports. The ability to reliably

test components at every stage, from manufacture to end of service, is crucial for maximising economic growth, minimising

environmental impact and ensuring public safety. End of life inspection is particularly important as much of the UK's

infrastructure is ageing and, due to global financial pressures, cannot be replaced. Thus, the lifetimes of key UK assets,

such as nuclear plants, must be extended. Ultrasonic non-destructive testing presents an economically and

environmentally desirable solution for detecting damage in such components. Similar to medical ultrasound, ultrasonic

waves can be passed through industrial components and subsequently collected, without damaging their internal

composition. Large networks of sensors, typically arranged in linear arrays, are deployed to carry out these inspections,

resulting in large volume, noisy, time-series data. Mathematical algorithms are then required to decipher the information

encoded within these recorded signals and construct images of the component's interior. Such algorithms are fundamental

enablers of the fourth industrial revolution facilitated by robotics and automated systems, which are largely dependent on

accurate sensing, measurement and imaging systems. In many cases, the component under inspection exhibits an

anisotropic, heterogeneous microstructure (that is, the material properties are directionally dependent and vary spatially in

a random fashion). This is detrimental to standard imaging methodologies as the ultrasonic wave is bent and scattered by

microstructural features and the responses from defects are obscured. Examples of such difficult to inspect materials

include coarse grained steel welds and carbon-fibre reinforced polymer (CFRP) composites. In fact, materials with complex

and highly scattering microstructures are becoming increasingly common as industries continue to invest in the

development of lighter, stronger composite materials. To combat the difficulties in imaging within these materials, the

current, cutting-edge imaging research within the NDT community endeavours to map the spatially varying material

properties using time of flight tomography. However, time-of-flight tomography uses only one data point from each recorded

time series and thus does not fully exploit the wealth of information made available by the inspection. The first objective of

the proposed research is to develop a material mapping methodology which exploits the full recorded signal, addressing

the non-uniqueness issues faced by time-of-flight tomography. This will be achieved via the development of new

mathematical models that capture the varying properties of heterogeneous media using probability theory and stochastic

models. The resulting material maps will then be incorporated into an advanced imaging system whereby the deviation of the

ultrasonic wave path in the heterogeneous media can be corrected for so that reliable defect detection can be ensured. The second

objective of the proposed research is to create an algorithm which can reconstruct complete datasets from incomplete

observations using novel matrix and tensor completion techniques (an emerging area within data-science), facilitating

faster inspection times and real-time imaging.
Key Findings
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Potential use in non-academic contexts
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Date Materialised
Sectors submitted by the Researcher
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Further Information:  
Organisation Website: http://www.strath.ac.uk