Towards the digital twin of a permanent magnet

Permanent magnets are a critical component of electric motors and generators in many applications, the most important of which are wind turbines and hybrid/electric vehicles. The rapid growth of these sectors has resulted in an increased demand for high performance Nd- Fe-B-based permanent magnets but the long-term sustainability of using global resources of rare earth elements like Nd, Dy and Tb at this high rate is questionable and there is therefore a clear need to develop a rare-earth-free permanent magnet. Such materials could replace certain types of Nd-Fe-B-based magnets in applications where lower performance is required, thus alleviating the pressure on rare earth resources. A digital twin is a set of information which fully describes the structure and properties of a physical object; any information which could be obtained by inspecting the physical object could also be obtained from its digital twin. In addition to the structure of the material, the digital twin of a permanent magnet must therefore also describe its magnetic state. This is highly challenging as the magnetic state of a material depends not only on its physical structure and magnetic properties but also on its magnetic and thermal history. The digital twin of a permanent magnet has the potential to play a vital role in the development of novel permanent magnets, and in real-time monitoring of the performance of magnets in applications. Obtaining the digital twin of a permanent magnet would therefore deliver important contributions to the digitalisation of materials science, environmental sustainability, clean energy and electromobility. The digital twin of a permanent magnet comprises experimental data and simulations on both the atomistic and the microscale. As the first step, the efforts in this project will be focussed at the microscale. The rare-earth-free magnet, MnAl-C, will be taken as a model system and an enhanced micromagnetic model will be developed. Advanced characterisation combined with magnetic domain images and magnetic measurements will form the basis for the simulations. A machine learning model will then be developed and data assimilation will be employed in order to reduce the offset between predicted and measured magnetic properties. The trained model represents the microscale component of the digital twin of a MnAl-C permanent magnet.

MnAl-C is a potential rare-earth-free permanent-magnet, whose properties would be attractive for aerospace, transport and clean-energy applications, if the coercivity could be increased. Currently, only about 10% of the theoretical maximum coercivity has been realised in practice, thus preventing widespread adoption. This limitation is attributed to the complex microstructure of the material, which contains a variety of internal interfaces that govern magnetisation reversal. This project focussed on understanding magnetic effects at single defects and groups of interacting defects. Advanced electron microscopy, micromagnetic simulations and machine learning were combined as part of an international collaboration in order to make rapid progress. Transmission electron microscopy (TEM) analysis showed that twin boundaries in MnAl-C often contain chemical segregation, whose magnitude varies with twin type. Micromagnetic modelling revealed that greater interfacial disorder raises coercivity, suggesting grain-boundary engineering could improve magnetic performance. At grain- or twin-boundary junctions, secondary-phase inclusions were modelled: soft-magnetic particles are far more detrimental than paramagnetic ones, and twin triple-junctions strongly degrade properties, explaining why hot-deformed samples (which lack such junctions) exhibit higher performance. The intersection of a twin boundary (TB) with an antiphase boundary (APB) was examined in detail. Triangular reverse domains nucleate at TB-APB intersections; the APB dominates coercivity, while the combined TB-APB reduces it slightly. Extending to many nanotwins and APBs, dense networks of intersecting defects were visualised and modelled. Finally, a 3D microstructural workflow was established: FIB-serial sectioning followed by high-quality EBSD yields volumetric data. Initial 3D simulations corroborate surface magneto-optical data. The results in this project enable far greater understanding of magnetisation reversal in MnAl-C, make a valuable contribution towards compiling a digital twin of the material and yield valuable strategies for future optimisation of these rare-earth-free magnets.