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Predictive modelling for the 3D-Printing process

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  • Full or part time
    Prof C Bailey
    Dr Stoyan Stoyanov
  • Application Deadline
    No more applications being accepted
  • Funded PhD Project (Students Worldwide)
    Funded PhD Project (Students Worldwide)

Project Description

The computational mechanics and reliability group (CMRG) is engaged in a large EU-funded project (NextFactory) which is developing 3D-printing and micro-assembly processes that will provide the ability to fabricate smart electronic products within a single machine. 3D-printing has seen extensive growth over the last 5-years and is now being taken seriously by a number of manufacturing sectors. By 2018, the 3D-printing market is expected to be worth £10Bn (£2.4Bn in 2014). The use of 3D-printing technologies is now set to make a significant impact on the electronics market, but one of the major challenges is to ensure that the quality and reliability of the parts manufactured using this additive manufacturing technique meet customer expectations.

During the 3D-printing process both functional and structural materials are deposited layer by layer to form the 3D manufactured part. Electrically conductive inks are printed and then used to build electronic circuits within this part. Major challenges for 3D-printing include improving the structural integrity (and hence performance) and prediction of the reliability of the final product. Developments in predictive tools that can be used to identify optimal process conditions and determine time to failure are required to meet these challenges. This will be the focus for this PhD award.

The proposed research to be undertaken through this PhD scholarship will require the candidate to:
- Collaborate with colleagues within the EU- funded NextFactory project to share both modelling and test data
- Develop mathematical and numerical models that can be used to simulate residual stresses throughout the materials printed as a function of printing process conditions.
- Develop models (for example based on physics-of-failure methods) to predict reliability of the fabricated part.
- Identify a methodology to embed these models into software within a condition-based monitoring system that can be used to optimise and control the 3D printing

For additional information about the scholarship and links to the application form please go to
http://www2.gre.ac.uk/research/study/studentships (Scholarship Ref: VCS-ACH-06-15-2)

Please read this information before making an application. Applications need to be made online via
No other form of application will be considered.

All applications must include the following information. Applications not containing these documents will not be considered.

- Scholarship Reference Number (Ref VCS-ACH-06-15-2) – included in the personal statement section together with your personal statement as to why you are applying
- a CV including 2 referees
- academic qualification certificates/transcripts and IELTs/English Language certificate if you are an international applicant

In addition to making the online application, please send email the supervisors (see below) to inform you have made an application.

The scholarship must commence before 1st April 2016

For further information please contact the supervisors:
Professor Chris Bailey via [email protected]
Dr. Stoyan Stoyanov via [email protected]

Funding Notes

Bursary (subject to satisfactory performance):

Year 1: £14,057 , Year 2: In line with RCUK rate, Year 3: In line with RCUK rate

In addition, the successful candidate will receive a contribution to tuition fees equivalent to the university’s Home/EU rate (£4,052) for the duration of their scholarship. International applicants will need to pay the remainder tuition fee of £7,648 for the 2015/16 session and is subject to an annual increase. Scholarships are available for three years from the date scholars first register as an MPhil/PhD student with the university. Scholarships are available for full-time study only.

How good is research at University of Greenwich in Aeronautical, Mechanical, Chemical and Manufacturing Engineering?

FTE Category A staff submitted: 9.00

Research output data provided by the Research Excellence Framework (REF)

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