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PhD in Mechanical Engineering: Strategies for Optimizing Manufacturing Systems for Industry 4.0

The University of Manchester

Qualification Type: PhD
Location: Manchester
Funding for: UK Students, EU Students
Funding amount: Covers both Home/EU tuition fees and stipend
Hours: Full Time
Placed On: 10th July 2019
Closes: 13th September 2019
 

Lead Supervisor

Dr April Bryan

Co-Supervisor(s)

Dr John Francis

Description

The School of MACE at the University of Manchester is one of the leading research institutions in Industry 4.0 in the UK. It is the home of the annual Industry 4.0 Summit, and has several researchers and academics who are actively engaged in research in this field. Their research activities cover a range of key Industry 4.0 areas including advanced material processing, 3D printing, robotics, automation and simulation. 

This doctoral research project will specifically focus on the modelling and optimization of manufacturing systems for product personalization in Smart Factories, a strategic Industry 4.0 initiative that is expected to yield significant increases in manufacturing productivity and profitability. While the benefits of product personalization are expected to be significant, the adoption of this approach is also expected to result in several new challenges for the manufacturing sector. These challenges mainly centre on the need to design, balance and sequence novel automated assembly systems that meet the needs of the manufacture of ‘one-of-a-kind’ products in ways that optimize production costs, lead times and quality. In this project, innovative modelling and algorithmic solutions for automated Smart Factory assembly systems, that mitigate the anticipated adverse impacts associated with these challenges would be developed. 

The successful candidate is expected to have a good knowledge of manufacturing systems, modelling, design optimization, and mathematical analysis techniques. This project offers the candidate an opportunity, to not only develop her/his skills and knowledge in this area, but to also work with the leaders in the field in world class facilities while doing so. By the completion of this thesis, the doctoral researcher would be well established as an expert in Industry 4.0, and would be ideally positioned to pursue a successful career in either industry or academia. 

This studentship will be fully funded by the Department of Mechanical, Aerospace and Civil Engineering at The University of Manchester. However, the methods developed in this project are particularly relevant to the following industrial sectors: automotive, electronics, and machine tools and industrial equipment industries. Throughout this project, students would have opportunities to interface with industry at conferences, trade shows, and meetings. 

Applicants would be expected to have knowledge in the following areas: 
- Expert knowledge of a programming language such as Matlab, C, or C++ 
- Sound knowledge of Linear Programming and Product Design and Development Processes 
- Knowledge of Continuous and Discrete Design Optimization would be an asset 

Skills required

Entry requirements can be found by selecting the relevant PhD programme at this link:

https://www.mace.manchester.ac.uk/study/postgraduate-research/research-programmes/

Industrial Links

Funding for this project is offered through The School of Mechanical, Aerospace and Civil Engineering and EPSRC DTP. The duration of the studentship is for 3 years and will cover both Home/EU tuition fees and a stipend to cover living costs at the RCUK-standard rate (£15,009 for the 2019-20 academic session).

Project specific enquiries

We welcome informal enquiries from prospective applicants. Please direct these to Dr April Bryan (april.bryan@manchester.ac.uk)

General enquiries:

For general enquiries about the application process, please contact:

Martin Lockey - Senior PG Recruitment & Admissions Administrator

Tel: +44(0)161 2754345

Further information about how to apply can be found at:

http://www.mace.manchester.ac.uk/study/postgraduate-research/how-to-apply/

   
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