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. http://bura.brunel.ac.uk/handle/2438/155 2026-06-29T13:36:45Z 2026-06-29T13:36:45Z Ignacio Ahuir-Torres, J Yang, X West, G Kotadia, HR http://bura.brunel.ac.uk/handle/2438/33513 2026-06-26T02:01:32Z 2024-05-13T00:00:00Z

Title: Corrosion behaviour of SiC particulate reinforced AZ31 magnesium matrix composite in 3.5 % NaCl with and without heat treatment Authors: Ignacio Ahuir-Torres, J; Yang, X; West, G; Kotadia, HR Abstract: Magnesium is a lightweight structural material widely utilised in automotive applications. To enhance its mechanical properties, ceramic particulate reinforcement can be incorporated, particularly for wear resistance and high-temperature applications. However, the addition of ceramic particles to magnesium can compromise its corrosion resistance due to microgalvanic cell formation at the interfaces between the Mg matrix and the second phase. This reduces the chemical protection provided by the passive film. In this study, the corrosion properties of AZ31 and AZ31-5SiC samples were investigated, with a focus on the effect of heat treatment. Detailed microstructural and electrochemical analyses revealed that the AZ31 cast sample forms an effective passive film, resulting in improved corrosion resistance. However, the addition of SiC particles to AZ31 increased the corrosion rate, with corrosion mechanisms evolving over time. To mitigate these effects, a heat treatment process was employed to dissolve β-Mg17Al12 eutectic and Al8Mn5 intermetallic phases. The heat-treated AZ31 with SiC exhibited an improvement in corrosion resistance. These findings highlight the potential for heat treatment to enhance their corrosion resistance, thereby broadening their application prospects. Description: Data availability: Data will be made available on request.

2024-05-13T00:00:00Z Fang, C http://bura.brunel.ac.uk/handle/2438/33446 2026-06-18T02:01:34Z 2026-02-28T00:00:00Z

Title: Editorial 'Multi-Scale Simulation of Metallic Materials (2nd Edition)' Authors: Fang, C Abstract: Metallic materials are some of the most important engineering materials. Current developments in our society are leading to an increasing demand for diverse novel metallic materials with desirable properties. ...

2026-02-28T00:00:00Z Fang, C http://bura.brunel.ac.uk/handle/2438/33445 2026-06-18T02:01:33Z 2026-04-24T00:00:00Z

Title: Multi-Scale Simulation of Metallic Materials (2nd Edition) Authors: Fang, C Editors: Fang, C Abstract: Metallic materials include elemental metals and compounds or alloys. They are important engineering materials and are additionally widely utilized in many new fields. Present developments have led to an increasing demand for diverse new metallic materials in addition to sustainable recycling, digital manufacturing, and environment- and climate-friendly production of devices and parts. Therefore, obtaining comprehensive knowledge regarding metallic materials on scales ranging from the atomic, micro-, meso-, and macroscopic levels has gained importance as of late. Correspondingly, multiscale simulations that combine existing and emerging methods are being employed to incorporate the wide range of time and space scales that are inherent to various disciplines. This Reprint aims to improve our understanding of the structural, microstructural, and physical properties of complex metallic materials via multiscale approaches, including thermodynamics, finite element methods, and ab initio molecular dynamics simulations.

2026-04-24T00:00:00Z Alizadeh, Amir http://bura.brunel.ac.uk/handle/2438/33444 2026-06-18T13:44:32Z 2025-01-01T00:00:00Z

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

2025-01-01T00:00:00Z