An RMRI-ARJA algorithm based on the reversible magnetization ratio increment criterion

A near-zero magnetic environment provided by magnetic shielding device (MSD) is essential for Spin-Exchange Relaxation-Free (SERF) measurements and ultra-weak biomagnetic signal detection. Due to the high permeability, low coercivity, and low remanence, permalloy is widely used as the primary material in MSD. In practical applications, a demagnetization process is typically applied to permalloy to eliminate residual magnetization and restore its optimal magnetic performance, particularly before conducting sensitive measurements. However, it also exhibits four distinct magnetization conditions, which are characterized by significantly different hysteresis behaviors. These variations become particularly evident during the demagnetization process, where the magnetization state evolves continuously under a damped oscillating magnetic field. Traditional Jiles–Atherton (J-A) models mainly focus on single magnetization states and do not account for the variation of hysteresis characteristics across the full magnetization process. This paper proposes the RMRI-ARJA algorithm, which integrates the reversible magnetization ratio increment (RMRI) criterion with an amplitude-responsive Jiles–Atherton (ARJA) model. First, the RMRI criterion is proposed, which quantifies magnetization conditions and identifies states based on the reversible component ratio within a magnetization increment. With RMRI criterion, dynamic parameters are introduced into the J-A model to achieve a amplitude-responsive hysteresis model capable of sensing the current magnetization state and dynamically adjusting parameters accordingly. Next, a single-sheet tester (SST) system is developed to acquire full magnetization process data of permalloy, which is used to train and validate the RMRI-ARJA algorithm. Finally, the RMRI-ARJA algorithm is used to compute magnetic characteristics such as hysteresis loops, basic magnetization curves, remanence curves, and permeability curves across various magnetization conditions. Compared to the conventional hysteresis algorithm, the proposed approach significantly enhances calculation accuracy, particularly in modeling the hysteresis behavior of permalloy.