Normalized evolution of analytical compaction parameters in Al–TiB2 and Al–SiC powders as a function of ceramic fraction

The compaction behavior of aluminum-ceramic powder mixtures is commonly modeled using analytical equations whose parameters are explicitly determined for each composition. However, systematic relationships between ceramic volume fraction and these parameters have not previously been quantified, limiting predictive capability. In this study, Al-TiB 2 and Al-SiC powders with 0-100 vol.% reinforcement were compacted using a floating-die setup to minimize density gradients, and the resulting compaction curves were fitted using the Parilak and Shapiro equations. The initial die-filling density exhibited a classical bimodal packing trend and was compared with a coarse-fine interaction packing model to distinguish geometric packing from deformation-controlled densification. All analytical compaction parameters decreased with increasing ceramic content, reflecting progressive suppression of densification mechanisms. Normalization relative to pure aluminum revealed a previously unreported hierarchical sensitivity to reinforcement addition, expressed as . This hierarchy indicates that high-pressure plastic deformation of the aluminum matrix is most strongly inhibited by ceramic particles, whereas rearrangement and curvature-related pressure sensitivity persist to higher volume fractions. Image-based microstructural analysis shows formation of larger clustered ceramic-rich regions with increasing ceramic content. This evolution enhances geometric constraint on the aluminum matrix and provides direct microstructural support for the observed parameter hierarchy. Analytical functions were fitted to describe parameter evolution with reinforcement content, enabling interpolation of compaction behavior within the studied composition range without additional experiments. Overall, the approach provides a predictive framework for densification modeling in particle-reinforced aluminum powders within the investigated systems, while offering mechanistic insight into the transition from matrix-dominated to constraint-dominated densification.