Research Projects

This project aims to develop a multiscale modelling framework to predict fracture and fatigue behaviour in additively manufactured (AM) structures and components, addressing challenges posed by microstructural heterogeneity, residual stresses, and anisotropic properties. AM technology is increasingly utilised in aerospace, automotive, space, and energy sectors to produce complex, high-performance components and enable efficient field repairs. However, AM materials often exhibit microstructural variations, including grain size differences, porosity, and residual stresses due to rapid thermal cycling. These factors create stress concentrations that drive crack initiation and propagation, particularly under cyclic loading, making it essential to predict and optimise the fracture and fatigue resistance of AM components with advanced modelling. This project proposes a concurrent modelling approach that combines Crystal Plasticity Fast Fourier Transform (CPFFT) with Phase Field Modelling (PFM) to achieve accurate and computationally feasible numerical simulations. CPFFT captures grain-level plasticity, anisotropy, and residual stress effects. High-fidelity image-based data, such as grain orientation and defect distributions from micro-computed tomography (µ-CT) and electron backscatter diffraction (EBSD), will calibrate the CPFFT model, reflecting actual AM microstructure complexity. Phase Field Modelling (PFM) will operate at the macroscopic scale to simulate crack initiation, propagation, branching, and merging. By integrating CPFFT with PFM, the developed model will accurately represent the multiscale interactions driving failure in AM components under varying conditions. This framework will also support virtual testing of AM components in various scenarios, including field repair conditions where issues like incomplete fusion and contamination affect crack behaviour. By predicting crack growth rates, fatigue resistance, and defect tolerances, the model will guide the optimisation of AM processes and improve the reliability of AM components in high-stakes applications. This research aims to advance AM as a safe, reliable manufacturing and repair solution across critical engineering sectors.