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Data Analytics to Inform Manufacturing Process Optimisation in the High Pressure Die-Casting Industry


School of Mechanical and Aerospace Engineering

, Applications accepted all year round Funded PhD Project (Students Worldwide)

About the Project

The School of Mechanical & Aerospace Engineering invites applications for a 3-year research PhD studentship commencing no later than September 2020. The proposed project addresses an industrial need to increase manufacturing efficiency and decrease production downtime through the use of state of the art data analytics and optimization.

Manufacturing downtime is defined as any time a process is stopped. While this most often occurs because of tool or machine breakage, downtime can also be caused by planned maintenance, equipment adjustments, or even operator comfort breaks. Downtime is considered one of the biggest and most challenging problems in manufacturing, costing British manufacturers in excess of £180bn per year (OneServe, 2017). Reducing downtime makes UK industry significantly more competitive, leading to increased economic growth, reduced energy use, reduced environmental impact, and enhanced UK industrial sustainability.

Understanding what disrupts machine uptime is critical to preventing machine downtime and improving lean manufacturing processes. The core innovation of the research will be to leverage advanced data analytics techniques in order to operate on underutilized real-time manufacturing data. Further, this analysis will be coupled with advanced physics-based simulations and high risk/ high value experimentation in an actual mass production environment. The result will be a comprehensive analytic framework that operates on real-time data, and will be used to identify trends, predict the likelihood of downtime in real-, near-, and far-time, as well as quantify the resulting impact, including quantification of risk and reliability. Further, the research will be used to minimize production disruption, optimize resource allocation and cash flow, and identify and implement clear strategies to minimize and/or prevent downtime.

This research offers the unique opportunity to access large quantities of actual mass production data produced in a working industrial environment. The successful candidate will work side by side with a full time research professional, and will be fully integrated into the research team. There will be further opportunity to undertake experimental research in a mass production environment. The research will involve international travel as well as industry-related company visits and interactions.

As part of the project, the successful candidate will be expected to travel to Europe, Japan and possibly other parts of Asia and the USA. These trips will be used to visit the parent company as well as disseminate results at appropriate international conferences.

Key skills required for the post:


A minimum of 2:1 (or equivalent) in Mechanical Engineering, Aerospace Engineering, Mathematics, Operations Research, or similarly appropriate degree. Applicants should be able to demonstrate an existing capability or interest in developing advanced mathematical and statistical analysis skills. Experience with programming in Excel, Matlab, and use of SAS JMP would also be advantageous. Applicant must have excellent oral and written communication capabilities in English.

Key transferable skills that will be developed during the PhD:


- Simulation and modelling techniques
- Advanced data analytics techniques
- Industrial engagement and technology transfer
- Interpersonal skills within a multidisciplinary team including academics and industrialists
- Project and time management training to ensure milestones of the project are delivered
- Effective dissemination of research findings through presentation at international conferences and publication in high quality technical journals.

Lead supervisor:
Dr. Danielle Soban
+44 (0)2890 974181;

Other supervisor(s):
Dr. Dave Thornhill
+44 (0) 2890 975533;

Queens University Belfast is a diverse and international institution which is strongly committed to equality and diversity, and to selection on merit. Currently women are under-represented in research positions in the School and accordingly applications from women are particularly welcome.

Funding Notes

UK/EU applicants: Full stipend award of £15,009 per annum (index linked and payable over 3 years) plus full University registration fees for 3 years.

International applicants are welcome to apply at the above stipend, however the project will not cover the additional cost of international registration fees.

Conditional top-up available:
A top-up of up to £10k total over the three year period may be available to exceptional candidates.

PhD students in the School have the opportunity to apply to be demonstrators on undergraduate modules. Compensation for this can amount to in excess of £2,400 per year.

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