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EngD Studentship: Intelligent Machining (Sponsors: University of Sheffield AMRC and EPSRC)

University of Sheffield - Advanced Manufacturing Research Centre

Qualification Type: Professional Doctorate
Location: Sheffield
Funding for: UK Students, EU Students
Funding amount: £18,000 annual stipend
Hours: Full Time
Placed On: 8th February 2019
Closes: 31st March 2019
 

Duration – 4 years

Funding Stipend: £18,000 per annum

Funding Body - EPSRC and the Advanced Manufacturing Research Centre

Vacancy Information – Applications are invited for a fully funded 4-year EngD studentship covering UK fees and stipend /EU fees only, as part of The EPSRC sponsored AMRC Industrial Doctorate Centre (IDC).

With today’s ever increasing challenges for reduced part variation, quick adaptability to sudden supply-chain changes and disruptions, and optimized use of energy and resources, the integration of machines and products with information systems is necessary.

Despite the developments made in machine sensor technology to improve manufacturing performance, there are difficulties in obtaining reliable information from the manufacturing system comprised of complex processes interacting with each other. In particular, due to the multiple operations involved and large number of sensors distributed across the complete manufacturing system, a massive amount of data from different sources is obtained.

The focus of this EngD project will be to digitize the whole product lifecycle using advanced modelling and virtual reality tools and implement intelligent control techniques to reduce part variation in multistage manufacturing. Transforming the traditional mechanical production lines into digital systems requires a sufficient understanding of factors contributing to the variation of the machined parts and methods that can be used to control it. The proposed project aim is to develop an informatics system coupled with virtual simulation for multistage manufacturing using a novel multi-agent architecture for implementing autonomous machine learning-based control.

The project goals

  • Investigate and apply methods in manufacturing to comply with environmentally benign practices.
  • Evaluate the contribution of heat treatment process errors to the uncertainties associated with machined parts.
  • Integrate virtual manufacturing tools with multibody system models to enhance the virtual simulation by taking into account flexible machine structural components.
  • Design a novel control strategy based on intelligent and stochastic control theory.

Applicants must have, or expect to get, a 1st or good 2:1 degree (or Masters with Merit) in a relevant science or engineering subject such as Mechanical Engineering, Aerospace Engineering, Materials Science, or Physics.

Due to EPSRC residency requirements, this project is open only to UK and EU applicants who have been resident in the UK for at least 3 years immediately preceding the start of the course. Applicants must meet EPSRC eligibility requirements found here

https://www.ms-idc.co.uk/entry-requirements

http://www.epsrc.ac.uk/skills/studentships/help/eligibility/

Candidates must also be able show that their English language proficiency is at a level which allows them to successfully complete the EngD. All applicants require an English language qualification, typically a GCSE or an IELTS test (a score of 7 or above is required, with a minimum of 6 in each component).

https://www.ms-idc.co.uk/how-to-apply

For an informal discussion, please contact the IDC Centre Team, idc-machining-science@sheffield.ac.uk.

   
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