Project Description
3 Years full time 1st April 2021, 1st July 2021 or 1st October 2021
This PhD project will ignite an exciting new research area for the sustainable production, consumption and recycling of metallic materials for use in Metal Additive Manufacturing (AM) processes. Building on a strong track-record of AM research at Cardiff University, dating back to the mid-1990s, the research will initially investigate the synthesis of titanium powders from recycled titanium chips obtained from conventional machining processes, followed by process optimisation to allow this powder to be used for producing components by Metal AM, namely Selective Laser Melting (SLM). The conventional production of metal powders for AM (via gas or water atomisation) requires a series of extraction and cleaning processes which use enormous quantities of energy. The powder compositions must also be tailored when they are synthesised from crude metals/metal alloys.
With the proposed research route, the energy footprint of producing metal powders will be dramatically reduced. Chips will be collected after machining from titanium blocks/bars whose compositions are already tailored during casting/forging. Thus, the processing time to produce tailor-made powders will also be considerably reduced. The resulting AM parts, in particular cutting tool inserts, will be then used to machine engineered materials to evaluate their performance, in terms of cutting forces, temperature and workpiece surface integrity. The impact of the research will underpin cost effective waste management of metallic chips and their recycling for the use in powder form when producing high value AM components.
The final stage of the project will involve a data-driven hierarchical modelling of the AM process parameters to the measured output quantities of interest (qoi) using a stochastic surrogate modelling techniques. This will render the creation of a qoi response surface in a high dimensional parameter space, thus enabling the robust optimal inverse design of the AM and post-machining conditions to obtain designer-specified values/distributions of the qoi. A Bayesian inference framework would be utilised to perform the robust inverse design.
For further information contact Dr Debajyoti Bhaduri ([email protected])
Funding Information
This is a self funded project.
Eligibility Requirements
Candidates should hold a good bachelor’s degree (first or upper second-class honours degree) or a MSc degree in a relevant engineering/science subject.
Applicants whose first language is not English will be required to demonstrate proficiency in the English language (IELTS 6.5 or equivalent)
Application Process
Applicants should submit an application for postgraduate study via the Cardiff University webpages (http://www.cardiff.ac.uk/study/postgraduate/research/programmes/programme/engineering) including;
- An upload of your CV
- A personal statement/covering letter
- Two references (applicants are recommended to have a third academic referee, if the two academic referees are within the same department/school)
- Current academic transcripts
Applicants should select Doctor of Philosophy (Engineering), with a start date of:
PLEASE CHOOSE – 1st April 2021, 1st July 2021 or 1st October 2021.
In the research proposal section of your application, please specify the project title and supervisors of this project and copy the project description in the text box provided. In the funding section, please specify that you are applying for the advertised self funded project reference DB3-SF-2021
Application deadline September 30th 2021 – We may however close this opportunity earlier if a suitable candidate is identified.
