Mechanical and Manufacturing Engineering
This project aims to develop and validate advanced digital tools that integrate experimental data, engineering models and artificial intelligence to support analysis, visualisation and engineering decision-making across dental and mining applications.
Sub-Project 1 will develop a software platform for reconstructing and visualising the three-dimensional spatial and temporal evolution of polymerisation-induced shrinkage in dental resin composites. Building on previous CFBG-based spatial strain measurements, depth-wise and spatially distributed CFBG data will be used with AI-based modelling to reconstruct 3D shrinkage fields and provide interactive visualisation of shrinkage evolution during polymerisation.
Sub-Project 2 will develop and validate a digital decision-support tool for the selection and preliminary design of composite conveyor components for mining applications. The tool will incorporate user requirements and engineering rules covering structural loads, operating conditions, component configurations, material/design options and relevant compliance requirements. It will be demonstrated across at least three representative use cases and validated against benchmark designs. The overall project will deliver functional software prototypes, associated validation and user documentation, supporting translation of research outcomes into practical digital engineering tools.
School
Suitable for recognition of Work Integrated Learning (industrial training)?
Yes
Research Areas
Composite Materials and Structures | Fibre-Optic Sensing | Artificial Intelligence and Machine Learning | Digital Engineering and Decision-Support Systems | Computational Modelling and Visualisation
- Research environment
- Expected outcomes
- Supervisory team
- Reference material/links
You will join a top-notch Composites research laboratory in Australia (Southern Hemisphere). You will be supported by postdocs and a primary supervisor. The AMAC centre can manufacture, post-cure, test, analyse, and monitor the structural health of composites under one roof. This experience prepares the student for real life challenges and exposure to industry scale research and manufacturing. Ideal applicants should have strong programming skills in C/C++, Python or MATLAB, with foundational knowledge in composites and structures.
- A validated AI-assisted 3D visualisation tool for reconstructing spatial and temporal polymerisation-induced shrinkage in dental resin composites using CFBG sensor data.
- Experimental datasets and modelling approaches for depth-wise and three-dimensional characterisation of polymerisation-induced strain.
- A functional digital decision-support tool for selection and preliminary design of composite conveyor components for mining applications.
- Validation of the mining tool against benchmark designs and demonstration through at least three representative use cases.
- Functional software prototypes, validation methodologies and user documentation supporting future research, industry deployment and technology translation.
