PhD Research Studentships
Computational Additive Manufacturing enabled Materials Discovery
| Supervisor: | Chinnapat PANWISAWAS |
| Apply by: | 29 January 2025 |
| Start in: | September (Semester 1) |
Description
Background:
Additive manufacturing (AM) is a promising, emerging materials processing method used for fabricating complex 3D geometries in various engineering applications. Melting-based AM which fuses materials layer-by-layer using prescribed scanning path can offer precise dimensional control and designable microstructure and property. The challenge lies in process control; melt flow dynamics, materials mixing, and composition change during AM. The composition-process design is vital for predicting laser power-time-temperature processing window and final quality.
The aim of this project is to develop holistic computational AM framework and to enable multi-material AM for engineering, especially, biomedical application. A high-fidelity multi-physics multi-scale modelling approach for accurate tracking of surface shape, thermo-capillary dynamics and vaporisation will be developed and used to search for new materials with processable conditions to have desired micro/nanostructure and controllable property.
PhD candidate specification:
The successful candidate will develop integrate computational materials engineering tools using in-process monitoring data and materials characterisation available from literature to design materials composition for specific process conditions to obtain microstructure-informed property and designable performance. Useful skills include, but not limited to:
- Computational fluid dynamics using volume-of-fluid (VOF) method;
- Phase-field and/or cellular automata finite element calculation;
- Crystal plasticity finite element calculation;
- Reduced-order or data-driven modelling; and
- Proficiency in programming languages, e.g. Python, C/C++, and/or MATLAB.
Utilising both computational and experimental data, the PhD student will be trained to have skills in computational additive manufacturing, data analytics and materials design.
The successful PhD candidate will have full access to the SEMS’ advanced microscopy centre as well as mechanical testing facilities. The developed computational AM model will be validated experimentally and then used to predict processing, structure, and property relationship to discover new AM composition for specific applications.
The candidate should have relevant experience in the following subject areas: computational fluid/solid mechanics, materials process simulations, computational physics, microstructure modelling, data-driven modelling, data analytics. This project will collaborate closely with world-leading academic institutions as well as UK and international industrial partners.
Research group:
Dr Chinnapat Panwisawas's research is concentrated, over the last 16 years, on advanced process science and engineering of investment casting laser fusion welding and powder-bed fusion additive manufacturing particularly for establishing a multi-scale multi-physics approach to understand the process-structure-property-performance relationship of new materials for engineering applications. The research group is committed to facilitating an inclusive and collaborative research environment focused on the personal development of PhD students. Should you want to discuss potential applications informally, please contact me directly (see contact details below).
Funding
Funded by: EPSRCThis EPSRC DTP studentship is fully funded and includes a 3.5 years stipend (currently set at the 2024/25 stipend rate of £21,237 pa) and 3 years fees at the home level.
Overseas applicants are encouraged to apply, but note that UKRI limit the proportion of international students appointed each year to 30% of the total.
Eligibility
- The minimum requirement for this studentship opportunity is a good honours degree (minimum 2(i) honours or equivalent) or MSc/MRes in a relevant discipline.
- If English is not your first language, you will require a valid English certificate equivalent to IELTS 6.5+ overall with a minimum score of minimum score of 6.0 in each of Writing, Listening, Reading and Speaking).
- Candidates are expected to start in September (Semester 1).
Contact
For informal enquiries about this opportunity, please contact Chinnapat PANWISAWAS.
Apply
Start an application for this studentship and for entry onto the PhD Materials Science full-time programme (Semester 1 / September start):
Please be sure to quote the reference "SEMS-PHD-613" to associate your application with this studentship opportunity.
| Related website: | https://www.sems.qmul.ac.uk/staff/c.panwisawas | |
| SEMS Research Centre: | ||
| Keywords: | Artificial Intelligence, Data Science, Machine Learning, Manufacturing, Mechanics, Materials Science - Other, Metallurgy, Data Analysis, Mathematical Modelling, Computational Physics |