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PhD Studentship: Data-Driven Approach to Predict the Extent of Surface Oxidation during Hot-Rolling and Annealing of New Steels

University of Warwick

Qualification Type: PhD
Location: Coventry
Funding for: UK Students
Funding amount: £15,009 to £18,009
Hours: Full Time
Placed On: 31st July 2019
Expires: 30th October 2019

Start date: ASAP

Project Overview

The development of new steels to meet modern marked demands cause significant challenges with respect to reduce corrosion during production. Whilst the rolling and coating of flat products,
especially for the automotive industry is inevitable, the heating cycles will lead to the formation of
oxides at the surface, which in turn lead to a poor surface-finish of the coated product. Hence costly and time consuming techniques are required to prevent oxidation to happen and/or remove the oxides once formed during the process.

The focus on this work particularly deals with the analysis of such corrosion data to identify both the extent of oxidation, caused by a particular alloy composition, as well as the uncertainty of predictions that can be made from such datasets. In view of the vast amount of measurements that can be found within steel industries and the public domain, the analytics will be gradually improved and the correlations between independently reported datasets will be used to identify additional sources of influence, maybe even not yet recognised as such.

Since automotive lightweighting is a promising route to reduce emissions and meet the targets for a green nation, the focus of this study will be on automotive high strength steel grades such as carbon, manganese and aluminium containing alloys and low density steels. 

Project Aims

The aim of this project will deliver a profound model for the prediction of both the nature and extent of surface oxides that have been formed during the heat treatments of high strength flat steel products. The predictions will be applicable to a wide variety of industrial conditions and consider effects between the partnered industries within the mentioned EPSRC project. In addition to a quantification of the result’s predictive uncertainty, the theoretical model will be used to propose an optimum manufacturing route for automotive steels in view of an optimum surface quality.

Funding Source: EPSRC ICASE award with Tata Steel

Funding: Funded by EPSRC and Tata Steel for 4 years, the 19/20 stipend level will be £15,009 plus an additional top-up of £3,000 so £18,009 in total for 19/20.

To be eligible for this project the successful applicant should have indefinite leave to remain in the UK and have been ordinarily resident here for 3 years prior to the project start-date, apart from occasional or temporary absences. Additional information about this is available on the EPSRC website

*Please note that the offer of the position is subject to confirmation of funding.

Sponsor Company: Tata Steel

Desired Student background: Bachelors/ master’s degree in any of the physical sciences (chemistry, mathematics/statistics, engineering, materials and physics) are welcome to apply. Preferably, the ideal candidate should have some background knowledge on either data analysis, corrosion, programming and/or steel.

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