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Intelligent machining simulation: Process Modelling and Functional Performance Prediction of Superalloys - EPSRC industrial CASE studentship award

Department of Mechanical Engineering

Sheffield United Kingdom Manufacturing Engineering Mechanical Engineering Solid Mechanics

About the Project

A fully funded 4 years PhD studentship (EPSRC industrial CASE award, co-funded by Rolls-Royce Plc and Seco Tools)within the Department of Mechanical Engineering and Advanced Manufacturing Research Centre (AMRC) is available which aims to develop a digital model considering critical material properties to predict the machining processes and the effects of cutting tool geometries on the final product quality including service life under various loading conditions in order to develop a platform for an intelligent optimisation of the processes.

Machining is one of the fundamental manufacturing operations wherein the material experiences a very complex deformation condition during the interaction with a cutting tool. To meet quality standards and achieve efficient productivity, several time-consuming and costly experimental trials are typically used to optimise cutting parameters and show part conformity, particularly for safety critical aerospace components.

The project aims to develop a digital platform for simulation of machining process, produced surface integrity and functional performance of the superalloys in order to create an intelligent framework for process control and optimisation with respect to the applied cutting parameters and cutting tool conditions. A multi-scale physics based Finite Element model of the cutting process will be realised to simulate the chip formation and predict the machining induced deformation and stress state on the workpiece materials. The results are used to model microstructural morphology and surface integrity at the machined surface that will be fed into a functional performance analysis under various service loads.

In this context, a novel experimental method, to quantitatively determine the effect of machining induced surface quality, together with an iterative simulation tool will be developed to generate required data sets to train a predictive model enabling intelligent optimisation of the machining process and lifeing of the produced parts given the required service conditions. 

Entry requirements

The studentship is available to candidates with the equivalent of a first class or upper second-class degree in Mechanical Engineering and/or Materials Science and Engineering (Metallurgy). Skills in Finite Element analysis (preferably using ABAQUS package and programming with Fortran/C++) are required and knowledge of Machining science, mechanics of materials and metallurgy of metallic materials are desirable.

The student should be willing to actively engage with the experimental design and conducting experiments within the university’s laboratories. He/She is expected to present the research outcomes for the industrial/academic audience on a regular basis within the UK and Europe.

How to apply

For further information and informal discussion please contact Dr H. Ghadbeigi: . To apply, please visit  including your CV and two references and indicate on your form that you are replying to this advert. The short-listed candidates will be interviewed at the final stage.

Funding Notes

A fully funded 4 years PhD studentship (EPSRC industrial CASE award, co-funded by Rolls-Royce Plc and Seco Tools) within the Department of Mechanical Engineering and Advanced Manufacturing Research Centre (AMRC).
The studentship is:
• Open to UK and EU nationals subject to EPSRC eligibility criteria.
• Annual Tax-free salary of £18,500 For 4 years
• Includes 3 months training at company premises within the UK and Sweden

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