http://bura.brunel.ac.uk/handle/2438/155 BCAST is striving for international excellence on both fundamental and applied research on solidification of metallic materials. BCAST sees itself as a reliable source of both new knowledge and new solidification technologies for the metallurgical industry. 2026-05-17T19:34:07Z http://bura.brunel.ac.uk/handle/2438/33297 Title: Growth twins within (Al₃(Sc,Ti) + α-Al) eutectic cells enable novel grain refinement in recycled Al alloys Authors: Que, Z; Niu, Z; Mendis, CL; Negrea, RF; Fan, Z Abstract: Designing sustainable aluminium alloys with superior mechanical performance requires effective microstructural control during solidification. In this study, a cross-over recycled wrought aluminium alloy system spanning the 1xxx–7xxx series was designed with maximum impurity tolerance. A grain refinement strategy using hypoeutectic Sc additions was developed to simultaneously refine grains and second-phase particles. A previously unreported grain refinement mechanism was identified in this multicomponent recycled alloy, where the growth of α-Al grains is constrained by a network of (Al + Al3(Sc,Ti)) eutectic cells rather than by classical heterogeneous nucleation. Remarkably, α-Al growth twins were observed within these eutectic cells, a crystallographic feature rarely reported in high stacking-fault-energy aluminium. Atomic-scale characterization using scanning transmission electron microscopy (STEM) reveals the structural characteristics associated with this phenomenon. These findings provide a new pathway for microstructural control in complex recycled aluminium alloys and offer design principles for next-generation sustainable lightweight materials with enhanced mechanical performance. Description: Highlights: • Cross-over recycled wrought Al alloys from 1xxx to 7xxx series were designed. • α-Al twins were first observed in (Al3(Sc,Ti) + α-Al) eutectic cells in the as-cast state. • (Al3(Sc,Ti) + α-Al) eutectic cells restrict α-Al dendrite growth and refine the microstructure. • 0.5 wt% Sc provides 5 times stronger grain refinement than a commercial refiner. • Hypoeutectic addition enables grain refinement and fine SPPs simultaneously.; Data availability: Data will be made available on request. 2026-04-14T00:00:00Z http://bura.brunel.ac.uk/handle/2438/33279 Title: Optimum scandium addition in A356 cast alloys: alloy design, microstructural evolution and mechanical properties Authors: Khandelwal, P; Negrea, RF; Nunn, J; Honan, S; Ji, S Abstract: The effect of scandium (Sc) addition on the microstructural evolution and mechanical properties of A356.2 cast alloys in both as-cast and T6 heat-treated conditions was investigated using a combined thermodynamic and experimental approach to identify an optimum composition for enhanced performance. Thermodynamic calculations and cooling curve analysis showed that Sc alters solidification behaviour through the formation sequence of Sc-containing intermetallic phases. Experimental results demonstrated that Sc additions up to 0.4 wt% significantly refined primary α-Al grains and reduced secondary dendrite arm spacing (SDAS), while modifying eutectic Si from coarse plate-like to fine fibrous morphology. The transformation of β-Al5FeSi into compact Al-Fe-Si-Sc phases and the formation of nanoscale Al-Si-Sc-(Ti) dispersoids further contributed to strengthening. In the as-cast condition, the A356-0.4Sc alloy exhibited the best combination of strength and ductility, whereas under T6 heat treatment, the A356-0.2Sc alloy showed superior performance due to enhanced precipitation strengthening and thermal stability of Sc-containing dispersoids. At higher Sc content (0.6 wt%), primary AlSc2Si2 formation led to microstructural coarsening and reduced properties. This study establishes an optimum Sc addition range of 0.2–0.4 wt% and provides mechanistic insight into the role of Sc in alloy design. Description: Data availability: Data will be made available on request.; Supplementary data are available online at: https://www.sciencedirect.com/science/article/pii/S0264127526007574?via%3Dihub#s0110 . 2025-05-06T00:00:00Z http://bura.brunel.ac.uk/handle/2438/33202 Title: Modelling of precipitation hardening during non-isothermal thermomechanical treatment of 6 series aluminium alloys Authors: Gharavian, Somayeh Abstract: The future of the automotive industry can be viewed as contingent upon the further development of aluminium alloys. This can be primarily achieved when the behaviour of aluminium alloys during the thermomechanical treatment process of hardening is comprehensively understood and predicted. This study focuses on developing a comprehensive mathematical model for predicting the mechanical behaviour of Al-Mg-Si(Cu) systems subjected to non-isothermal heat treatment to ultimately enable the prediction of mechanical behaviour in the form of a software tool. The Kampmann and Wagner numerical model is among the well-studied mathematical models for precipitation hardening; this model was adapted as the base model for this study where it was further incorporated with critical factors such as multi-stage aging, clustering effects, and the influence of plastic deformation. By coupling the framework to a thermomechanical database and refining precipitation kinetics, the model exhibited improved accuracy in simulating the evolution of microstructure and the mechanical properties under industry specific conditions. Validation of the developed model was carried out by comparing with experimental data obtained from laboratory experiments on Al-Mg-Si (Cu) alloys. These experiments included varying heat treatment duration, temperatures, plastic deformation and different cooling/heating rate to replicate the industrial conditions. The results of the model demonstrate the capability to predict multi-stage aging processes under non-isothermal conditions, facilitating the analysis of various quenching and heating rates. A key advantage is its integration of precipitation and clustering predictions within a unified framework, enabling accurate assessments across a broad range of aging temperatures from natural aging to elevated temperatures like 200°C. Furthermore, the model incorporates the effects of plastic deformation in the form of 4–8% cold stretching, enabling the exploration of not only work hardening but also the influence of deformation on the thermodynamics and kinetics of the process. These findings highlight the significant potential of mathematical modelling to optimize heat treatment process design, substantially reducing the workload in the automotive industry. Moreover, the model has shown significant potential to be used as a helpful tool towards alloy design purposes with further development and validation with experimental data. Description: This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University London 2024-01-01T00:00:00Z http://bura.brunel.ac.uk/handle/2438/33188 Title: Rationalisation of aluminium alloys using machine learning and Artificial Intelligence Authors: Tiwari, Tanu Abstract: Aluminium alloys are widely used across various sectors of engineering due to their lower density combined with higher strength compared to many existing alloys of other metals. These unique characteristics have led to an increased demand for and discovery of new aluminium alloys with targeted properties and compositions. Traditional methods of designing new mate-rials with desired properties, such as trial-and-error and reliance on domain experts' experience, are time-consuming and expensive. These techniques also expand the search area for suitable alloys. In this research, we propose a machine learning-based design system to reduce the number of grades across all series of age-hardenable and non-age-hardenable aluminium alloys. The sys-tem collects features based on chemical composition, mechanical properties, corrosion re-sistance, weldability, and thermal and electrical properties under different tempering and hard-ening conditions for machine learning modelling. A combination of PCA (Principal Compo-nent Analysis) and K-means clustering is applied for clustering and sub-clustering similar al-loys based on their compositional and property profiles into clusters and sub-clusters. Next, an optimisation algorithm, namely a multi-property decision-making method, i.e., TOPSIS (Tech-nique for Order Preference by Similarity to Ideal Solution), identifies the optimum alloys within each sub-cluster. These selected alloys exhibit a balanced set of properties that effec-tively represent the range of characteristics found among other alloys in the same sub-cluster. Subsequently, a recycling algorithm is applied to predict the mixing ratio based on closeness scores generated by the optimisation algorithm. This process mixes the optimum alloy in each sub-cluster with the remaining alloys in the sub-cluster, resulting in a single optimised alloy as determined by the optimisation algorithm. This method significantly reduces the number of alloy grades while maintaining key material properties and enhancing recyclability, which has a metallurgical basis. This design system is enhanced and developed into a dedicated recycling software application, offering a practical tool for the aluminium industry. It supports sustainable development and improves recycling efficiency, aligning alloy manufacturing with the principles of the circular economy. Description: This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University London 2025-01-01T00:00:00Z