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AME NDT (Anisotropic Media Evaluation for Non-Destructive Testing)

Reference Number
EP/S001174/1
Title
AME NDT (Anisotropic Media Evaluation for Non-Destructive Testing)
Status
Completed
Energy Categories
Nuclear Fission and Fusion(Nuclear Fission, Other nuclear fission)
Not Energy Related
Research Types
Basic and strategic applied research
Science and Technology Fields
ENGINEERING AND TECHNOLOGY (Mechanical, Aeronautical and Manufacturing Engineering)
UKERC Cross Cutting Characterisation
Not Cross-cutting
Principal Investigator
Miss K Tant
Mathematics
University of Strathclyde
Award Type
Standard
Funding Source
EPSRC
Start Date
29 June 2018
End Date
28 December 2021
Duration
42 months
Total Grant Value
£283,726
Industrial Sectors
Info. & commun. Technol.
Region
Scotland
Programme
ISCF - Skills
Investigators
Principal Investigator
Miss K Tant, Mathematics, University of Strathclyde
Industrial Collaborator
Project Contact, Rolls-Royce PLC
Project Contact, National Physical Laboratory (NPL)
Project Contact, National Nuclear Laboratory
Project Contact, PZFlex Limited (UK)
Project Contact, EDF Energy
Web Site
Objectives
Abstract
Manufacturing is a key activity of the UK economy and accounts for more than half of all UK exports. The ability to reliablytest components at every stage, from manufacture to end of service, is crucial for maximising economic growth, minimisingenvironmental impact and ensuring public safety. End of life inspection is particularly important as much of the UK'sinfrastructure 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 andenvironmentally desirable solution for detecting damage in such components. Similar to medical ultrasound, ultrasonicwaves can be passed through industrial components and subsequently collected, without damaging their internalcomposition. 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 informationencoded within these recorded signals and construct images of the component's interior. Such algorithms are fundamentalenablers of the fourth industrial revolution facilitated by robotics and automated systems, which are largely dependent onaccurate sensing, measurement and imaging systems. In many cases, the component under inspection exhibits ananisotropic, heterogeneous microstructure (that is, the material properties are directionally dependent and vary spatially ina random fashion). This is detrimental to standard imaging methodologies as the ultrasonic wave is bent and scattered bymicrostructural features and the responses from defects are obscured. Examples of such difficult to inspect materialsinclude coarse grained steel welds and carbon-fibre reinforced polymer (CFRP) composites. In fact, materials with complexand highly scattering microstructures are becoming increasingly common as industries continue to invest in thedevelopment of lighter, stronger composite materials. To combat the difficulties in imaging within these materials, thecurrent, cutting-edge imaging research within the NDT community endeavours to map the spatially varying materialproperties using time of flight tomography. However, time-of-flight tomography uses only one data point from each recordedtime series and thus does not fully exploit the wealth of information made available by the inspection. The first objective ofthe proposed research is to develop a material mapping methodology which exploits the full recorded signal, addressingthe non-uniqueness issues faced by time-of-flight tomography. This will be achieved via the development of newmathematical models that capture the varying properties of heterogeneous media using probability theory and stochasticmodels. The resulting material maps will then be incorporated into an advanced imaging system whereby the deviation of theultrasonic wave path in the heterogeneous media can be corrected for so that reliable defect detection can be ensured. The secondobjective of the proposed research is to create an algorithm which can reconstruct complete datasets from incompleteobservations using novel matrix and tensor completion techniques (an emerging area within data-science), facilitating faster inspection times and real-time imaging
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Added to Database
14/09/18