Tailoring the material properties, nanostructure and grain alignment of Alnico magnets through micromagnetic simulations

TL;DR

This study uses micromagnetic simulations to analyze how nanoscale structuring, material properties, and grain alignment affect the magnetic performance of Alnico magnets, aiming to optimize their coercivity.

Contribution

It introduces a comprehensive simulation approach combining finite element micromagnetics and machine learning to predict and tailor Alnico magnet properties.

Findings

Coercivity depends on interrod spacing and magnetostatic interactions.

Analytical stray field calculations confirm coercivity scaling with packing fraction.

Machine learning models accurately predict magnetic properties based on structural parameters.

Abstract

Alnico magnets have gained renewed interest in the search for rare-earth free permanent magnets due to their high thermal stability and magnetisation. However, the limited coercivity of these shape-anisotropy-based alloys constrains their performance. Starting from a reference Alnico sample, we realised a finite elements micromagnetic study of exchange-decoupled rods by varying their dimensions and interrod spacing across those observed experimentally. We computed the hysteresis properties by progressing from micromagnetic simulations of a small number of rods within the magnetostatic field of their neighbours to large systems treated statistically based on the distribution of orientations of the grains. We compared the coercivity of an isolated rod with that of the exchange-decoupled system to highlight the effect of magnetostatic interactions. We computed analytically the stray field acting on a single rod as a consequence of its surrounding rods in order to confirm the scaling of the coercivity with the packing fraction p. We explored how intrinsic material properties influence magnetic behaviour by examining materials with different magnetocrystalline anisotropy constants and saturation polarisation values. Results from several hundred simulations were used to train a multi-layer perceptron regressor and predict the magnetic properties as function of the dimensions of the rods, interrod spacing and orientation of the grains. With this approach, we highlight the underlying trends by which nanoscale structuring, intrinsic material properties and grain alignment can be tailored to improve the magnetic properties of Alnico alloys.