Fully funded Four Year EngD Studentship sponsored by AMRC with Boeing and EPSRC: Intelligent Machining

University of Sheffield

Partners: University of Sheffield AMRC with Boeing & EPSRC
Start date: 3 September 2018
Duration: 4 years
Stipend: Tax free stipend of £18,000 per year, plus all tuition fees paid at UK/EU rates.

Please check eligibility requirements before applying.

Project Description

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 production lines 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 are:

  • 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.

  • Predict product quality propagation in multistage manufacturing using modern mathematical and statistical models.

  • Integrate virtual manufacturing tools with multibody system models to enhance the virtual simulation by taking into account flexible machine structural components, feed drive dynamics, guideways, axis controllers and the motion trajectory generation of the NC control.

  • Design a novel control strategy based on intelligent and stochastic control theory.

  • Implement stochastic model-based control to optimize multistage manufacturing performance metrics such as quality, energy, time, and cost.

Entry requirements and eligibility

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.

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).

If in doubt about any aspect of Eligibility, please email idc-machining-science@sheffield.ac.uk for clarification.

How to apply

To apply please submit a Doctoral application using our online application system via the Apply link.

Within the application, please state the title of the project you are applying for.

For an informal conversation about the project please contact: Dr Pete Crawforth (p.crawforth@sheffield.ac.uk)

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Type / Role:

PhD

Location(s):

Northern England