2025/06/14 by Ataei, Masoud, Mohammad Jafar Molaei, Molaei, Mohammad Jafar +2
Engineering · Materials Science · #Applications (stat.AP) #Data Analysis #FOS: Computer and information sciences #FOS: Physical sciences #Magnetic Properties and Applications #Materials Science (cond-mat.mtrl-sci) #Microstructure and Mechanical Properties of Steels #Non-Destructive Testing Techniques #Statistics and Probability (physics.data-an)
paper · pdf · doi:10.48550/arxiv.2506.12566
openalex publication_date 2025/06/14 · openalex created_date 2025/10/13 · openalex updated_date 2026/07/28
We develop a stochastic-dynamic framework to infer latent grain size distribution from magnetic hysteresis data in M-type hexaferrite materials, offering an alternative to imaging-based characterization. A stochastic nucleation-growth process yields a Modified Lognormal Power-law grain size distribution. This is combined with Brown's relation to obtain a coercivity probability distribution, which is embedded within a dynamic magnetization model. A key feature is the joint estimation of microstructural parameters, including the critical grain radius, through inverse optimization of full hysteresis loops. Experimental validation on hydrothermally synthesized strontium hexaferrite subjected to nitrogen treatment and recalcination reveals interpretable trajectories of nucleation, growth, and structural memory encoded in the magnetic response.