DepartmentSchool of Mechanical and Design Engineering
October, February and April
Applications accepted all year round
The work on this project will involve:
- Simulation of the temperature history and molten pool development in selective laser melting (SLM) environment using computational fluid dynamics (CFD)
- Simulation of the microstructure evolution using phase field model coupled with CFD results
- Experimental investigation of the effect of process parameters on microstructure evolution and defects formation
Selective laser melting (SLM) is a powder-based additive manufacturing process designed to use a high power-density laser to melt and fuse powders layer-by-layer to form a three-dimensional part. SLM has many advantages over other conventional manufacturing methods in the aspect of design flexibility, material usage and manufacturing cycle time.
Components of a wide variety of materials including metal alloys and metal-based composites can be successfully produced using SLM. Despite this, defects like porosity and micro-cracking during the rapid solidification process are still difficult to predict and yet to eliminate.
The ultimate aim of this project is to develop a multi-scale modelling framework to predict the microstructure development in SLM process and gain a better knowledge of the porosity and micro-cracking formation during the solidification process. This evolves understanding and improvement of the existing modelling approach in macro scale using computational fluid dynamic (CFD) model coupled with discrete element method (DEM).
Temperature history and molten pool development of the process will be analysed, and the related experimental work will be conducted to validate this CFD model. Subsequently, phase field modelling in micro scale will be developed to couple the microstructure development with the temperature gradient result obtained from the CFD model to predict the grain growth inside the molten pool with different material compositions and under various operation conditions. A deeper understanding of the defects including cracking and porosity will be achieved using this model and relevant process optimization will be employed to eliminate these defects.
The proposal aligns with the Future and Emerging Technologies research theme - which is one of the five interconnected research themes of the University of Portsmouth - to transform the theoretical and experimental knowledge in ALM into practical solutions. The project also utilises our capabilities in the Future Technology Centre (FTC) to developing creative answers to global challenges through hands-on experience of ALM specialist technology.
The project might further lead to collaborative work with industrial partners and could lead to applying to larger funding applications in the future.
You'll need an upper second class honours degree from an internationally recognised university or a Master’s degree in an appropriate subject. In exceptional cases, we may consider equivalent professional experience and/or qualifications. English language proficiency at a minimum of IELTS band 6.5 with no component score below 6.0.
You’ll need to have a good basis in computer programming and ideally a background in mechanical engineering and metallurgy. Some experience with code development (e.g. Matlab) and CFD modelling is desirable. You should have excellent oral and written communication skills with ability to prepare presentations, reports and journal papers to the highest levels of quality.
How to apply
We’d encourage you to contact Dr Khaled Giasin at firstname.lastname@example.org to discuss your interest before you apply, quoting the project code.
When you are ready to apply, you can use our online application form. Make sure you submit a personal statement, proof of your degrees and grades, details of two referees, proof of your English language proficiency and an up-to-date CV. An extended statement as to how you might address the proposal would be welcomed.
Our ‘How to Apply’ page offers further guidance on the PhD application process.
If you want to be considered for this self-funded PhD opportunity you must quote project code SMDE4600220 when applying.