Use of Advanced Analytics to Predict Defects in Surface Inspection Systems

Key Information

Industry 4.0 and Big Data Analysis is receiving more attention in the steel industry to help with improved product quality. Surface inspection systems are used to validate the surface quality, however these then need to be dealt with, possibly resulting in coil rejection. It would be beneficial if processing data could be used to predict the occurrence of certain defects and in time be used to adjust mill processing to reduce the occurrence of defects in real time. Machine learning may also offer some opportunities to improve processes and product consistency.

Project Aims

The project will aim to develop data analytical, machine learning and modelling techniques that can be used on strip steel rolling mills to predict the occurrence of surface defects. This will involve developing data modelling knowledge as well as a sound process knowledge in order to make the connection between data and process. The model would be the basis for other process models for use on other units.

Suitable candidate 

Mathematical or engineering background with strong data science/computer modelling experience. Candidate will need to develop a good process knowledge for steelmaking and rolling


Academic Supervisor:

Dr Xianghua Xie, Computer Science, Swansea University

Sponsoring Company Tata Steel

Candidates should hold an Engineering or Physical Sciences degree with a minimum classification level of 2:1 or equivalent relevant experience. 

 Our funders require applicants to also meet the following eligibility criteria:

  • You must be a UK or EU citizen (i.e. eligible for ‘home’ tuition fees at the University) and have the right to work in Wales at the end of your studies.
  • You must be resident in West Wales and the Valleys at the point of enrolment and throughout the duration of your studies.
  • You must not be financially able to participate without the award of grant funding.

Further information regarding eligibility criteria can be found at: http://www.materials-academy.co.uk/eligibility

The Athena SWAN Charter recognises work undertaken by institutions to advance gender equality. The College of Engineering is an Athena SWAN bronze award holder and is committed to addressing unequal gender representation.



The studentship covers the full cost of UK/EU tuition fees, plus a tax free stipend of £20,000 p.a.

Closing Date 28 February 2018

Start Date October 2018

Apply Now

Informal enquiries about this studentship are welcome and may be directed by email to: M2A@swansea.ac.uk