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Details of Grant 

EPSRC Reference: EP/S010076/1
Title: EPSRC Strategic Equipment - High Speed CT
Principal Investigator: Williams, Professor MA
Other Investigators:
Davis, Professor C Loveridge, Dr M Montana, Professor G
Shollock, Professor B Dixon, Professor SM Warnett, Dr J
Researcher Co-Investigators:
Project Partners:
Corin Group PLC Inora Technologies Ltd Nikon
Department: WMG
Organisation: University of Warwick
Scheme: Standard Research
Starts: 07 November 2018 Ends: 06 November 2021 Value (£): 1,080,777
EPSRC Research Topic Classifications:
Design & Testing Technology
EPSRC Industrial Sector Classifications:
Related Grants:
Panel History:
Panel DatePanel NameOutcome
10 Jul 2018 EPSRC Strategic Equipment Interview Panel July 2018 Announced
Summary on Grant Application Form
X-ray Computed Tomography (XCT) is a scanning technique that enables full 3D visualisation and interrogation of internal and external geometries. It has become popular within industry (particularly manufacturing) and academic research as it enables us to see more than ever before at a variety of length scales and is completely non-destructive. The time taken to obtain this wealth of data is prohibitive to a number of applications with a single scan taking tens of minutes, up to a few hours. The equipment outlined in this proposal will enable high-resolution scans in tens of seconds, and even faster with some fundamental research. This is a UK first that will generate a wealth of scientific advancement.

There have been a small countable number of "dynamic" experiments using lab based XCT scanners where a sample such as a novel material is sequentially loaded (e.g. compression) and scanned at each loading step. Here one can observe the changes in the material through time, identifying failure mechanisms, highlighting potential manufacturing improvements and aids in determining material properties. The reason for so few studies is that the number of scans required can lead to acquisition time of days. The substantial gain in speed with this equipment will reduce the total scan time to a matter of minutes with a continuously acquired dataset. The sample can then be evaluated at discrete points in time, and concentrate around the critical onset of failure observed. Scientific advancement in the development of new polymers, ceramics and metal alloys will be considerably accelerated with this unique characterisation capability.

Manufacturing applications are often limited to a few high-value components because of the time taken to scan. The significant step change in speed will allow for high-throughput scanning that is desirable within the manufacturing line. This is the first step in a major revolution that will require big data analytics powered by machine learning algorithms to deliver accept/reject decisions in a reasonable time scale. Together this will be a driver for change in achieving 100% inspection of large component batches, at high resolution and at relevant cycle times.

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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Organisation Website: http://www.warwick.ac.uk