1. NIMS has succeeded in simulating the magnetization reversal of Nd-Fe-B magnets using large-scale finite element models constructed based on tomographic data obtained by electron microscopy. Such simulations have shed light on microstructural features that hinder the coercivity, which quantifies a magnet's resistance to demagnetization in opposing magnetic fields. New tomography-based models are expected to guide toward the development of sustainable permanent magnets with ultimate performance.
2. Green power generation, electric transportation, and other high-tech industries rely heavily on high-performance permanent magnets, among which the Nd-Fe-B magnets are the strongest and most in demand. The coercivity of industrial Nd-Fe-B magnets is far below its physical limit up to now. To resolve this issue, micromagnetic simulations on realistic models of the magnets can be employed.
3. A new approach to reconstruct the real microstructure of ultrafine-grained Nd-Fe-B magnets in large-scale models is proposed in this research. Specifically, the tomographic data from a series of 2D images obtained by scanning electron microscopy (SEM) in combination with consistent focused ion beam (FIB) polishing can be converted into a high-quality 3D finite element model (Figure). This tomography-based approach is universal and can be applied to other polycrystalline materials addressing a wide range of materials science problems.
4. Micromagnetic simulations on the tomography-based models reproduced the coercivity of ultrafine-grained Nd-Fe-B magnets and explained its mechanism. The microstructural features relevant to the coercivity and nucleation of magnetization reversal were revealed. Thus, the developed model can be considered as a digital twin of Nd-Fe-B magnets – a virtual representation of an object designed to reflect its physics accurately.
5. The proposed digital twins of the Nd-Fe-B magnets are precise enough in reproducing both the microstructure and magnetic properties that can be implemented for the inverse problem in designing on-demand high-performance permanent magnets. For instance, when researchers input the magnetic properties required for a specific application (e.g., traction or variable magnetic force motor), a data-driven research pipeline with integrated digital twins will be able to propose the optimal composition, processing conditions, and microstructure of the magnet for that application, significantly reducing development time.
