http://bura.brunel.ac.uk/handle/2438/29517 Theses for the Special Research Institutes, BCAST Sun, 21 Jun 2026 08:38:06 GMT 2026-06-21T08:38:06Z http://bura.brunel.ac.uk/handle/2438/33444 Title: Physics-informed data-driven modelling of aluminium processing Authors: Alizadeh, Amir Abstract: In this thesis, the integration of physics-based insights and experimental data in the modelling of precipitation hardening (PH) is investigated. To set up the context, a fitting parameter is calibrated in a partial differential equation (PDE) using Physics-Informed Neural Network (PINN), a machine learning (ML) framework that integrates physical laws in a data-driven neural network (NN). A one-dimensional Burgers equation is employed as a representative test case. PINN merges data-driven and physics-based pathways, where the loss function penalises deviations from experimental data and physics-based PDE residual. Through systematic studies, the effects of NN hyperparameters (depth, width), training dataset size, and parameter initialisation are examined. Results demonstrate PINN’s strengths in robust convergence to the true parameter values even under data-scarce conditions. Gradient-based optimisation ensures physical plausibility, especially in cases of poor or even physically incorrect initial guesses, where gradient-free methods often fail. These findings highlight the advantages of a well-established integration framework, a reliable model structure, and experimental data as important tools for parameter estimation, setting the foundation for the subsequent part of the thesis. PH is the primary mechanism for strengthening 6xxx series aluminium alloys. The characteristics of the precipitates play a crucial role in determining the mechanical properties. Particularly, predicting yield strength (YS) based on microstructure is experimentally complex and costly because its key variables such as precipitate radius, spacing, and volume fraction are difficult to measure. Physics-based modelling (PBM), such as Kampmann–Wagner Numerical (KWN), has emerged to tackle these complications utilising advancements in simulation environments. Nevertheless, these approaches require numerous free parameters and ongoing debates over governing equations. Conversely, purely data-driven models struggle with insufficient datasets and physical interpretability. Moreover, the complex dynamics between internal model variables have led both approaches to adopt heuristic optimisation methods, such as Powell or Nelder-Mead, which fail to exploit valuable gradient information. To overcome these issues, a gradient-based optimisation for KWN is proposed, incorporating CALPHAD (CALculation of PHAse Diagrams) and a strength model. The novelties include facilitating differentiability via smoothed approximations of conditional logic, adding regularisation terms to the loss function to maintain the physical relationship between parameter instances, and using per-parameter learning rates for each instance of the fitting parameters. To reduce the computational complexity, a single size-class and a single precipitate type are assumed. Model calibration is guided by a mean squared error (MSE) loss function that aligns the YS predictions with interpolated experimental data using L2 regularisation for penalising deviations from a purely PBM structure. A comparison shows that gradient-based ADAptive Moment Estimation (Adam) outperforms gradient-free Powell and Nelder-Mead by converging faster, requiring fewer PBM evaluations, and yielding more physically plausible parameters. Finally, the model output is validated with a second set of composition and process parameters, with microstructure data, and a unit test, highlighting the power of gradient-based calibration techniques in the modelling of 6xxx series PH. Description: This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University London Wed, 01 Jan 2025 00:00:00 GMT http://bura.brunel.ac.uk/handle/2438/33444 2025-01-01T00:00:00Z http://bura.brunel.ac.uk/handle/2438/33393 Title: Synergetic effect of surface-active metallic additions on structure modification in aluminium alloys Authors: Asil, Abdul Radim Abstract: Control of Fe-containing intermetallic size and morphology is often achieved using external physical fields, but chemical modification via metallic additions offers a faster, more cost-effective alternative without requiring production-line changes. This work developed a screening method for such additions by adapting Maltsev’s (1964) adsorption-based theory, using a generalised moment formula to rank elements and guide targeted phase modification. However, this theory did not explain the observed changes. Instead, other mechanisms such as Ca-Sr-containing intermetallics behaving as nucleation sites, the reduction in the concentration of Si in the liquid during solidification, and the build-up of metallic additions in the liquid, are the mechanisms suggested to achieve modification resulting in an improved homogenous microstructure. Model alloy experiments demonstrated the critical role of the Fe:Si ratio in phase formation and showed that Ca/Sr additions help retain α(AlFeSi) in the microstructure. In AA6050, optimisation of Ca–Sr-containing intermetallic morphology highlighted the importance of addition ratio and concentration in controlling their size and shape. A mechanism for Bi removal via Ca/Sr additions was proposed, involving oxide formation. The morphology of Al/Mg oxides, combined with the added weight of Bi-, Ca-, and Sr-containing phases, determines whether oxides sink or float in the melt. Machinability of AA6050 improved with combined Ca and Sr additions, attributed to the lower hardness of Alx(Ca,Sr)Siy intermetallics compared to Fe-containing phases. Their relative softness, favourable morphology, and increased intermetallic area fraction likely enhance machinability. Finally, a novel method for assessing alloy machinability was introduced, offering a faster and more process-relevant alternative to current industry practices. Description: This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University London Thu, 01 Jan 2026 00:00:00 GMT http://bura.brunel.ac.uk/handle/2438/33393 2026-01-01T00:00:00Z 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 Mon, 01 Jan 2024 00:00:00 GMT http://bura.brunel.ac.uk/handle/2438/33202 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 Wed, 01 Jan 2025 00:00:00 GMT http://bura.brunel.ac.uk/handle/2438/33188 2025-01-01T00:00:00Z