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Machine learning to locate defects in ultrasonic inspection images


Project Description

It is important that manufactured components are inspected to identify defects which may cause early failure, particularly in safety critical systems. Non-destructive techniques, such as ultra-sound, are used regularly to be able to see below the surface to identify hidden defects. This project aims to develop automatic techniques to help identify the defects. The student will combine image analysis and machine learning methods to build a system that can reliably distinguish between normal parts and regions with abnormalities. The student will be part of the Research Centre for Non-Destructive Evaluate (RCNDE) (www.rcnde.ac.uk) – a collaboration between six universities and many industrial partners. This project will investigate the application of novel analysis techniques to ultrasonic NDE inspection, aiming to support the analysis of phased-array or Time-of-Flight-Diffraction images.
The student will be supervised by Prof Cootes, who has extensive experience of image analysis for both industrial inspection and medical applications (https://personalpages.manchester.ac.uk/staff/timothy.f.cootes/).

The project will be actively supported by BAE Systems Maritime who deploy these techniques in a large scale manufacturing environment. The benefits of a robust automated analysis process would be very significant and could potentially reduce the inspection cost and duration for large scale welded structures across many industrial sectors.

Entry Requirements:
Applicants are expected to hold, or about to obtain, a minimum upper second class undergraduate degree (or equivalent) in a scientific subject such as Physics, Engineering, Mathematics, Computer Science or equivalent. A Masters degree in a relevant subject and/or experience in computer vision or machine learning is desirable.

For information on how to apply for this project, please visit the Faculty of Biology, Medicine and Health Doctoral Academy website (https://www.bmh.manchester.ac.uk/study/research/apply/). Informal enquiries may be made directly to the primary supervisor. On the application form select PhD Imaging Sciences.

Please note: Applications will close when a suitable candidate is found so please apply as soon as possible.

Funding Notes

EPSRC iCASE with BAE. Studentship funding is for a duration of four years to commence in September 2019 and covers UK/EU tuition fees and an annual minimum stipend (£15,009 per annum 2019/20). Due to funding restrictions the studentship is open to UK and EU nationals with 3 years residency in the UK.

As an equal opportunities institution we welcome applicants from all sections of the community regardless of gender, ethnicity, disability, sexual orientation and transgender status. All appointments are made on merit.

References

X.Dong, C.J.Taylor and T.F.Cootes, "Small Defect Detection Using Convolutional Neural Network Features and Random Forests", ECCV Workshops, Vol.11132

X.Dong, C.J.Taylor and T.F.Cootes "Automatic Inspection of Aerospace Welds Using X-Ray Images", ICPR 2018, pp.2002-2007

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