Quantitative correlative SEM–EBSD analysis of precipitate–particle and precipitate–boundary interactions in Al-Mg-Si alloys

Precipitation of β-Mg₂Si in recycled aluminum alloys is strongly influenced by impurity-driven second-phase particles such as Fe- and Mn-bearing compounds, and by grain and subgrain boundaries. Quantitatively understanding such precipitate–particle and precipitate–boundary interactions is mandatory to improve the recyclability of aluminum alloys. Here, we present CEMIA (Coupled Electron Microscopy and Image Analysis), an open-source Python program that combines high-resolution Backscattered Electron (BSE) imaging with Electron BackScatter Diffraction (EBSD) orientation mapping using projective and Thin Plate Spline (TPS) registration. CEMIA achieves sub-pixel registration accuracy and enables quantitative analysis of the size, distribution, and spatial correlations of the precipitates to identify their homogeneous or heterogeneous nucleation sites. Applied to the precipitation of β-Mg₂Si precipitates in an Al–Mg–Si alloy containing Al–(Fe,Mn)–Si second-phase particles (intermetallics and dispersoids), CEMIA reveals that isothermal hot compression increased β-Mg₂Si number density, reduced mean particle size, and promoted heterogeneous nucleation along grain and subgrain boundaries. Intermetallics exhibit high nucleation potency but low overall impact due to their scarcity, while dispersoids—with lower potency but higher abundance—dominate the heterogeneous nucleation fraction. Fine β-Mg₂Si precipitates exert the strongest Zener pinning boundary-stabilizing effect, particularly along subgrain boundaries after isothermal deformation. CEMIA provides a generalizable and extensible program for microstructure quantification, alloy design, and process optimization.