This study aims to develop and validate a neural network-based modeling approach to predict particle size distribution characteristics and understand the evolution of powder morphology during mechanical milling of Al- B4C nanocomposites. The ANN model was developed to simulate and predict both normal and cumulative PSD curves with high accuracy, achieving a correlation coefficient (R² > 0.98) across various powder mixtures. By adjusting milling parameters, such as B4C content (5 % and 10 %) and nanoparticle sizes (90 nm, 700 nm, 1200 nm), the model accurately predicted key characteristics like D50, which initially increased by 25 % due to cold welding in the first 4 h, followed by a 30 % reduction as fragmentation became the dominant mechanism. The ANN also effectively captured the bimodal particle size distributions, providing valuable insights into the interaction mechanisms between aluminum and B₄C particles during the milling process. In parallel, SEM analysis provided visual confirmation of the milling process, revealing the transformation of aluminum particles from flake-like morphologies after 2 h to refined, equiaxed structures after 16 h of milling. The topography analysis showed a progressive 40 % reduction in surface roughness and a marked decrease in waviness, indi- cating the smoothening of powder surfaces over time. These findings were supported by Full Width at Half Maximum (FWHM) measurements, which indicated a broadening of the PSD in the early stages of milling, followed by a shift toward a more uniform distribution with increased milling time. Together, the ANN modeling and experimental results provide a comprehensive understanding of the mechanical milling process, offering a powerful approach for optimizing the fabrication of high-performance Al-B4C nanocomposites.

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