Start date: 3 September 2018
Stipend: Tax free stipend of £18,000 per year, plus all tuition fees paid at UK/EU rates.
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
About the AMRC with Boeing
The University of Sheffield Advanced Manufacturing Research Centre (AMRC) with Boeing is a world-class centre for advanced machining and materials research for aerospace and other high-value manufacturing sectors. The AMRC with Boeing is a multi-million pound industry/university partnership which builds on the shared scientific excellence and technological innovation. It has internationally acknowledged research in developing innovative and advanced technology solutions for materials-forming and metal working and is housed in the Factory of the Future, a BREEAM-rated building with a zero carbon footprint. The AMRC with Boeing now employs around 500 highly qualified researchers and engineers from around the globe, in two purpose-built centres on the Advanced Manufacturing Park in South Yorkshire.
The University of Sheffield AMRC cluster also includes the Nuclear AMRC; AMRC Training Centre; and the AMRC Knowledge Transfer Centre. The AMRC with Boeing has over 70 partner companies and is part of the High Value Manufacturing Catapult network. The AMRC with Boeing machining group is the largest group, with over 85 R&D engineers across its flagship Factory of the Future and Factory 2050 facilities.
How to Apply for the Studentship
All applications should be submitted online. Please follow the instructions in the How to Apply section carefully, and ensure that you specify which project you are applying for and include in your statement why you are interested in the project and how you will contribute.
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.
Candidates must also be able to 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).
For an informal discussion, in the first instance please contact the IDC Centre Team [email protected]