Effect of chemical composition and microstructure on the behavior of ferrous alloys for magnetic applications

This PhD thesis explores the correlation between chemical composition, microstructure, and magnetic behavior of Fe–Si alloys for applications in energy conversion and electrical machines. The study integrates theoretical modeling, experimental analysis, and computational methods to optimize magnetic properties—namely permeability, coercivity, and energy losses—in the framework of high-efficiency and low-environmental-impact materials. Experimentally, a rapid-heating annealing procedure was developed to accelerate recrystallization kinetics in non-oriented Fe–Si steels. Specimens were inserted into a furnace prestabilized at the treatment temperature, inducing strong thermal gradients and promoting diffusion processes. Electron Backscatter Diffraction (EBSD) analyses enabled the quantitative evaluation of grain size, texture, and residual deformation. Results demonstrated that complete recrystallization can occur in less than one minute at suitable temperatures, showing promising potential for industrial energy saving. On the theoretical side, a unified mathematical framework was developed for describing crystalline microstructure, elasticity, and magnetization. Using tensor analysis and dislocation theory, correlations were established among lattice curvature, dislocation density, and stored elastic energy. A generalized magnetization model was proposed by extending the Langevin formulation to include the orientation distribution function f(g), thus accounting for crystallographic anisotropy and texture effects. The outcomes provide a quantitative link between microstructure and macroscopic magnetic performance, demonstrating how the control of texture, defect density, and heat treatment can minimize hysteresis and eddy-current losses. The developed methodology offers a predictive and physically grounded approach for designing next-generation Fe–Si magnetic materials for sustainable energy applications.