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Sensors and IoT for electrical machine manufacture: Towards digital twin and intelligent automation

  • Full or part time
  • Application Deadline
    Wednesday, March 11, 2020
  • Funded PhD Project (European/UK Students Only)
    Funded PhD Project (European/UK Students Only)

Project Description

In support of the research activities of the recently launched EPRSC funded Future Electrical Machines Manufacturing Hub, the University of Sheffield is offering a funded PhD scholarship to undertake research in the area of sensors and IoT for defect free manufacture of electrical machines.

This project will focus on new digital approaches to improve the manufacturing process of electrical machines. This project includes: (1) Utilising variety of sensors (for example machine vision) to track manufacturing processes (2) Sensor fusion to develop an IoT based sensorised workbench for manufacturing

Currently, the lack of in-process tracking of manufacturing processes means that defects in components are only commonly discovered during end-of-line test. The research will involve collaboration with the Universities of Newcastle and Strathclyde as well as with a number of industrial partners. Further details are available at: https://electricalmachineshub.ac.uk/electrical-machines-phd-scholarships/

This project will require candidates to have excellent engineering and computational skills. It will be an advantage if the candidates have familiarity with machine vision and/or electrical machine.

Funding Notes

The Scholarship provides funding for 3 and a half years. The Scholarship covers UK/EU tuition fees and pays a maintenance stipend at the UK rate (£15,009 per annum).

Nationality and residency eligibility are according to EPSRC requirements
(View Website).

References

It is anticipated that this PhD will commence before September 2020, but there may be flexibility in some cases.

For further information please contact Professor Ash Tiwari on [email protected]

How good is research at University of Sheffield in General Engineering?

FTE Category A staff submitted: 21.80

Research output data provided by the Research Excellence Framework (REF)

Click here to see the results for all UK universities

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