Project title: Characterisation of cast austenitic stainless steels using ultrasonic backscatter and artificial intelligence
Supervisory Team: M.K.Kalkowski and T.Blumensath
Project description
Do you feel at home with finite element modelling? Are you interested in the application of AI to engineering problems? If positive, this may be the right project for you. Its main aim is to merge the power of finite element modelling with artificial intelligence to develop ultrasonic characterisation of castings in-situ. While helping improve the way the industry manages safety-critical assets, you will develop a transferrable skillset comprising modelling, large data analysis and AI, which would prove useful on many potential future career paths.
Stainless steel castings possess outstanding corrosion resistance and mechanical properties. For these reasons, they underpin numerous safety-critical installations and are ubiquitous in, e.g. nuclear power plants. However, their excellent performance comes at a cost – they are challenging to inspect for damage in situ. Ultrasound – the preferred inspection method – suffers from the coarse-grained microstructure, which scatters and attenuates injected acoustic energy making reflections from defects illegible. Having some knowledge about the microstructure before testing offers game-changing possibilities. First, one could tailor the inspection protocol to minimise the detrimental effect of noise. Second, it would help select the correct procedure for a specific defect which is a crucial maintenance challenge. Unfortunately, such information is currently only available from destructive tests.
Your PhD will pave the way towards in-situ microstructure characterisation using ultrasound and help transform common inspection practice. You will employ artificial intelligence to support developing the physical understanding of how microstructure reveals itself in ultrasonic backscatter. Using GPU-powered ultrasound simulations, you will run a large set of virtual experiments for numerous synthetically created microstructures with carefully controlled parameters. Learning from data, including choosing a suitable AI methodology, and proposing in-situ characterisation methods is the promise of this project. You will use Southampton’s High-Performance Computing facility, as well as advanced material characterisation techniques in the School of Engineering. The true potential of the methods you propose will be showcased using ultrasonic measurements on samples of industrial relevance towards the end of the project.
As a PhD student, you will be a part of the Dynamics Group within the University’s Institute of Sound and Vibration Research. You will contribute to developing the exciting capability in AI-powered NDT, working alongside researchers using acoustics to interrogate and characterise structures and materials of different scales and complexities.
Feel free to contact Michal Kalkowski (m.kalkowski@soton.ac.uk) for an informal chat about the project and pursuing a PhD in Southampton. We look forward to hearing from you!
Entry Requirements
A very good undergraduate degree (at least a UK 2:1 honours degree, or its international equivalent). Background in finite element modelling and Python is highly desirable.
Closing date: 31 April 2022.
Funding: For UK students, Tuition Fees and a stipend of £16,062 tax-free per annum for up to 3.5 years.
How To Apply
Apply online: https://www.southampton.ac.uk/courses/how-to-apply/postgraduate-applications.page
Select programme type (Research), 2022/23, Faculty of Physical Sciences and Engineering, next page select “PhD Engineering & Environment (Full time)”. In Section 2 of the application form you should insert the name of the supervisor Michal Kalkowski
Applications should include:
Curriculum Vitae
Two reference letters
Degree Transcripts to date
Apply online: https://www.southampton.ac.uk/courses/how-to-apply/postgraduate-applications.page
For further information please contact: feps-pgr-apply@soton.ac.uk