http://bura.brunel.ac.uk/handle/2438/29517 Theses for the Special Research Institutes, BCAST Tue, 07 Apr 2026 21:09:15 GMT 2026-04-07T21:09:15Z https://bura.brunel.ac.uk:443/retrieve/121316/BCAST pic.jpg http://bura.brunel.ac.uk/handle/2438/29517 http://bura.brunel.ac.uk/handle/2438/33079 Title: Rationalisation of steel grades and specifications using machine learning techniques Authors: Sadegh, Jalalian Abstract: There are an excessive number of steel grades currently in use. However, many of them are used in the same application despite differences in chemical composition and processing conditions, and in some cases shows equivalent ranges of properties. These huge number of grades poses challenges for sustainable recycling and increases production complexity and cost. This study introduces a multi-phase, application-driven framework to simplify the steel grade system and reduce the number of grades by proposing a novel approach called K-Means Reduction Process (KMRP). The framework was applied to 148 carbon and 288 stainless steel grades, including chemical composition, processing conditions, and mechanical properties (hardness, UTS, YS, and elongation). Machine learning models were first used to quantify the influence of alloying elements and processing conditions on mechanical performance. K-Means clustering was then applied to group grades based on performance to identify steels that shared equivalent property profiles, with four distinct clusters identified including ferritic/low-carbon steels, medium-carbon and martensitic steels, high-carbon steels, and austenitic steels. These clusters revealed significant redundancy, with multiple grades from existing steel classifications occupying the same mechanical property space. In the reduction phase, KMRP identified the minimal set of grades required to preserve full mechanical property coverage within the generated clusters. Two sustainability-driven strategies were implemented: (1) tramp-element avoidance, favouring grades with low Cu and Sn, and (2) tramp-element tolerance, prioritising grades compatible with scrap-based recycling. While both approaches reduced reliance on critical raw materials (Mo, Ni, V, Ti), this study focused on the tramp-tolerance strategy as the most relevant for advancing circular economy objectives. Under this approach, the number of carbon steel grades were reduced by 38.4% (from 146 to 90) and stainless steel grades by 52.8% (from 288 to 136), while fully preserving the original mechanical property ranges, including UTS ranges of 295–2450 MPa for carbon steels and 120–1970 MPa for stainless steels, and elongation ranges of 7–41% and 2–55%, respectively. These results demonstrated that KMRP can successfully simplify the steel grade system while supporting circularity, reducing dependency on critical elements, and improving the sustainability of future steel production. Moreover, the methodology is generalisable and can be applied to other domains where reducing redundant options is essential, such as pharmaceutical applications. 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/2438/33079 2025-01-01T00:00:00Z http://bura.brunel.ac.uk/handle/2438/32328 Title: Machine learning for alloy design: Interpolation, extrapolation, and dataset integration in the prediction of 6xxx-series aluminium alloy properties Authors: Birchall, Adam Abstract: The optimisation of alloy compositions and processing has always been a goal in aluminium alloy design, and the recent increase in volume of data generated has enabled the use of data driven approaches to attempt to aid in this area. The potential for accurate predictions of mechanical properties without the need for lengthy and costly experiments hails as a means to quickly evaluate large areas of the design space. Machine learning models coupled with optimisation methods pose a method of finding sets of input parameters to achieve tailored properties. These methods however are highly data-dependent and in the domain of alloy design, obtaining data detailing alloy compositions, processing conditions and properties can be a costly affair. This work introduces 3 novel datasets to attempt to complete such work on and contributes to the availability of data in the domain for further research. An opensourced based dataset and synthetic dataset were curated and curated, and can be used by all for further research, a proprietary dataset was also curated with the support of an industry partner but is not publicly available. These three datasets all include compositional and thermomechanical processing variance and property values of corresponding alloy yield tensile strength values. Yield strength was used as a target property for the majority of this work due to its relative simplicity as a tensile property, less impacted by plastic deformation behaviour than other properties, but also its value as a property used to define material use. These datasets include an open-source dataset consisting of low quality, highly varied data, a proprietary dataset consisting of accurate measurements of a small part of the domain, and a synthetic dataset generated from a physics-based model, used in later experiments. Interpolation has been shown to be a relatively straightforward task in literature time and time again, with accurate predictions from decision tree based models dominating this type of problem. This work demonstrates their use can effectively predict yield strength values within the domain of existing data to an error of 19.5MPa on proprietary datasets detailing 6000 series aluminium alloys, with typical yield strength values of 350MPa. Similar models are able to achieve close results. These models however lack insight into the aluminium domain, and are frequently subject to the biases of the data which trained them. Qualitative results go further and show that the responses of these models can be unphysical. This work shows that the use of physical metallurgy variables through averaging methods, commonly used to attempt to aid with domain knowledge inclusion in literature, demonstrates no improvement to model accuracies, contributing to the discussion of this method’s use in similar work. Investigations into the extrapolation capabilities showed that typical models used in the domain demonstrate poor extrapolation performances, a metric not commonly highlighted currently in the domain despite the common purpose of models to guide experiment to outperforming alloys. The best performing typical model for extrapolation being linear regression, achieving errors of 57MPa on 6000 series aluminium alloys. The introduction of Delta modelling techniques taken from other machine learning domains and applied to this property prediction was able to reduce these extrapolation errors to 51MPa however a significant margin, the best performing method for improving extrapolation capability was a transfer learning derivative, domain adaptation. Applying domain adaptation techniques to the datasets compiled and curated in this work coupled with a novel implementation of delta modelling via the introduced ’Composite model’ further reduced these extrapolation errors by 31% from previous best models. Multiple datasets, with varying quality and differing input feature sets, were used in a composite model leveraging domain adaption. This model was able to make extrapolation predictions of the same sets to an error of 35Mpa for the same 6000 series aluminium alloys. 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/2438/32328 2025-01-01T00:00:00Z http://bura.brunel.ac.uk/handle/2438/32239 Title: Developing diagnostic methods for fatigue damage assessment Authors: Izadi Najafabadi, Maryam Abstract: Most of the metal’s failure happens because of the fatigue which is associated with metals that is subjected to cyclic loading over time. Fatigue damage detection is one of important technological issues in both academic and industrial fields. Early fatigue damage detection promotes circular economy and sustainability by prolonging the lifespan and durability of metals. In most metals, in low and high cycle fatigue, the stages of fatigue are pre-crack nucleation, crack nucleation, micro and macro crack growth, and final failure. Several techniques have been proposed and developed for detecting fatigue damage in metals. However, comparatively less attention has been given to early fatigue damage detection, specifically targeting the pre-crack nucleation stage. The pre-crack nucleation stage begins with an increase in dislocation density, followed by the formation of dislocation entanglements and ultimately, the development of slip bands. Subsequently, these slip bands induce intrusion and extrusion, serving as nucleation sites for cracks. The identification of these defects plays an important role as it can facilitate the use of appropriate treatment to either eliminate or mitigate the defects, consequently leading to increase in metals lifespan. The use of non-destructive testing (NDT) methods is particularly crucial in this context, given their wide applicability within industrial environments. Thus, in this thesis appropriate NDT methods for early damage detection fatigue in 316L stainless steel had been used. NDT methods enable the detection of fatigue damage without destruction of specimen. Techniques such as electrical resistivity measurement and nonlinear ultrasonic testing are employed to detect these defects. The electrical resistance method operates on the principles of Ohm's law, whereby a current is applied to the metal and the resulting voltage drop is measured to determine its electrical resistance. The resistivity is then calculated based on the sample’s geometry. Structural defects including dislocations, entanglements, slip bands, and cracks contribute to scattering and elevation in electrical resistivity. However, to make this method works effectively, a responsive technique with the capability of nΩ resolution is needed. The used method in this study is a combination of delta mode and four-probe technique that effectively eliminates thermoelectric voltages resulting from temperature variations in the circuit and minimizes the impact of lead resistance. Another approach that is used in this study is nonlinear ultrasonic. In this technique, a wave is propagated through the metal specimen, and upon interaction with defects, higher frequency waves are generated. By detecting and analysing these signals, the presence of defects can be identified. This unique capability enables the detection of early fatigue defects such as dislocations and slip bands evolution, providing improved sensitivity and precision in defect identification. Findings indicate that both electrical resistivity measurement and nonlinear ultrasonic testing proficiently detect early-stage fatigue defects in 316L stainless steel. These methods reveal significant changes in two distinct regions prior to 10% of the component's fatigue life. Following the identification of two distinct regions of significant signal variation prior to 10% of the fatigue life, advanced microscopy techniques were employed to investigate the underlying mechanisms responsible for these observations. Optical microscopy and Scanning Transmission Electron Microscopy with High-Angle Annular Dark Field (STEM-HAADF) imaging were utilized to observe the microstructural evolution in 316L stainless steel. These methods confirmed that the detected signals are correlated with early microstructural changes, specifically the increase in dislocation density, the formation of dislocation tangles, and the onset of cellular structure formation. 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/2438/32239 2025-01-01T00:00:00Z http://bura.brunel.ac.uk/handle/2438/30248 Title: Applications of the MC-DC casting technology to 6xxx series automotive aluminium alloys Authors: Katikaridou, Kyriaki Abstract: Environmental problems, such as global warming due to the ozone layer depletion related to greenhouse gas (GHG) emissions from fossil fuels have been drawing attention in recent years, and attempts are being made in various fields. Efforts in the field of automobiles are being made to decrease CO2 emissions by improving fuel efficiency through producing vehicle bodies with reduced weight, as well as electric cars, fuel-cell vehicles, etc. The characteristic properties of aluminium, high strength to weight ratio, good formability, good resistance to corrosion and recycling potential make it the perfect candidate to substitute heavier materials (steel) in the car to meet the need for weight reduction in the automotive industry. The 6xxx series alloy has been the most commonly used for extrusion products due to its light weight, good extrudability, strong corrosion resistance, high strength with good machining performance and weldability. Semi-continuous direct-chill (DC) casting is a well-established method and the most commonly used in wrought alloy extrusion billet manufacturing. For aluminium billets produced by direct-chill (DC) casting a fine and uniform microstructure is always desirable. A novel direct chill (DC) casting process, melt conditioned direct chill (MC-DC) casting process, has been developed for production of high- quality aluminium alloy billets. In the MC-DC casting process, a high shear device is submerged in the sump of the DC mould to provide intensive melt shearing, which in turn, disperses potential nucleating particles, creates a macroscopic melt flow to uniformly distribute the dispersed particles, and maintains a uniform temperature and chemical composition throughout the melt in the sump. Experimental results have shown that the MC-DC casting process can produce aluminium alloy billets with a microstructure that is comparably refined to those produced by other casting methods, while also demonstrating a reduction in cast defects. This work focuses on extending current knowledge of MC-DC casting process and address the capabilities and the effect of intensive melt shearing in DC cast billets on thermomechanical processing of 6xxx series wrought aluminium alloys, and mechanical properties to serve the ever-increasing demands in the automotive industry with regards to light-weighting and reducing carbon footprints in general. The study found that the Melt Conditioned Direct Chill (MC-DC) casting process demonstrated an ability to achieve grain sizes comparable to traditional DC casting methods, indicating its potential for controlling microstructural characteristics. Additionally, MC-DC casting showed some improvement in the distribution and morphology of Fe-bearing intermetallics, contributing to a more uniform microstructure than what is typically observed in conventional DC-GR casting. The mechanical properties of MC-DC cast alloys, particularly in the 6xxx series, suggested possible enhancements in tensile strength and fatigue resistance, which could make them suitable for certain safety-critical applications in the automotive industry. However, the MC-DC V3 variant experienced challenges in crash testing scenarios, highlighting the need for further process optimization and a deeper investigation into the relationship between microstructure and mechanical performance under dynamic stress conditions. While MC-DC casting may reduce reliance on chemical grain refiners, suggesting a possible greener production approach, further research is necessary to fully understand its impact on supply chains and production costs. Overall, the study suggests that MC-DC casting has potential as a promising innovation in aluminium alloy production, with opportunities to enhance efficiency and sustainability in specific applications. Description: This thesis was submitted for the award of Master of Philosophy and was awarded by Brunel University London Mon, 01 Jan 2024 00:00:00 GMT http://bura.brunel.ac.uk/handle/2438/30248 2024-01-01T00:00:00Z