Statistical modeling and generation of inertial ductile fracture surfaces

Spallation in ductile metals involves complex void nucleation and growth mechanisms, but the interactions between voids and the resulting statistical structure of fracture surfaces remain a persistent challenge for both experimental and theoretical modeling. This study develops a generative model to capture the statistical features of spall-induced fracture surfaces in high-purity aluminum. Aluminum samples were subjected to nanosecond laser-induced spallation, and the resulting fracture surfaces were imaged via scanning electron microscopy (SEM) and reconstructed in 3D. Individual dimples were segmented and analyzed to extract void size distributions and the spatial arrangement of nucleation sites. Boolean models and Gaussian random fields were then used to generate synthetic surfaces and compared against the experimental data using one- and two-point statistics. The analysis revealed a Poisson distribution of nucleation centers within the spall plane but significant out-of-plane spatial correlations in nucleation depth. The extended generative model successfully reproduces both the surface height distribution and the spatial covariance observed experimentally. These results emphasize the need to incorporate large-scale spatial correlations in predictive models of dynamic ductile damage. The proposed framework provides a basis for future studies of collective void growth and spall surface formation in dynamic ductile fracture.