Steelmakers cannot see the nanoscale structures that control a material’s strength and durability, so they rely on slow, destructive tests or coarse magnetic readings that miss critical details. This project uses micromagnetic modelling—techniques borrowed from spintronics and paleomagnetism—to link invisible nanoscale features in steel (pearlite spacing, nanoprecipitates, grain boundaries) to the material’s full magnetic hysteresis behaviour. Current electromagnetic sensors can measure phase balance and grain size at the mesoscale, but they ignore the nano-level architecture that determines whether a steel part will crack, wear out, or hold its shape under stress. By combining micromagnetic simulations with AI-driven multi-scale modelling, the researchers aim to create a non-destructive way to read those nanoscale fingerprints from standard electromagnetic sensor signals. If successful, the work will let manufacturers monitor steel quality in real time during high-temperature production and optimise alloy design without cutting samples or halting the line. The immediate impact is on industrial process control—making stronger, more consistent steel for bridges, pipelines, car bodies, and wind turbines—rather than on any consumer product. This is fundamental materials science: it builds a quantitative bridge between atomic-scale structure and the magnetic signals engineers already measure, closing a gap that has limited both quality control and the design of next-generation steels.
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This project focuses on the magnetic modelling and characterisation of nano-scale features in advanced steels using electromagnetic (EM) non-destructive testing (NDT) sensors. EM sensors are widely used for in-situ high-temperature measurements of phase transformations in steel production and offline microstructural analysis. While mesoscale finite element models can predict magnetic properties influenced by phase balance and grain size, the impact of nano-scale features such as pearlite interlamellar spacing, nanoprecipitation, and grain boundary structures on magnetic behaviour remains underexplored. This research will employ micromagnetic modelling techniques, traditionally used in spintronics and paleomagnetism, to investigate the role of these features on full hysteresis (BH) behaviour. Advancements in micromagnetic tools, computational power, and AI now enable multi-scale modelling, bridging the gap between micro- and meso-scale steel characterisation. The findings will contribute to enhanced steel design, manufacturing process optimisation, and improved real-time monitoring in industrial applications. This project aligns with several EPSRC research areas, primarily within the Engineering and Materials themes. Specifically, it relates to the "Materials Engineering" research area, which encompasses the development and characterization of materials with advanced properties for engineering applications. The project's focus on electromagnetic non-destructive testing (NDT) and micromagnetic modeling techniques also intersects with the "Sensors and Instrumentation" research area, as it involves the development of advanced sensor technologies for real-time monitoring and characterization of materials. By addressing these areas, the project contributes to the EPSRC's strategic goals of advancing engineering and physical sciences research to benefit industry and society.
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