Schematic representation of solids in nanoindentation model
Example of workflow for surrogate in FEA
tually test this material. Then, an objective function is built to compare the loading and unloading performan- ce by the indenter. The agreement between curves is computed, e.g., using the -quality parameter. Finally, the surrogate algorithm builds the metamodel to find the best elastic constants that make -value as close to uni- ty as possible.
The recent increase in computational power among industrial organizations as well as academic instituti- ons opens the use of surrogate algorithms in novel Digi- tal Twinning of structures. The use of such Artificial Intelligence systems helps engineers in design with new high-performance materials.
Literature references Angelini, Davide; Cestino, Enrico; Piana, Paolo; Mallamo, Fabio. 2025. “Review of micromechanical homogenization models and comparison with experimental data.” International Journal of Advanced Manufacturing and Technology.
Cestino, Enrico; Catapano, Juri; Galvano, Francesco; et al. 2024. “Effectiveness of Nanotechnology Treatments in Composite Aircraft Applications.” Applied Sciences.
Layek, Rama K.; Vijay Singh Parihar; Skrifvars, Mikael; et al. 2021. “Tailoring of the physical and mechanical properties of biocompatible graphene oxide/gelatin composite nanolaminates via altering the crystal structure and morphology.” Materials Advances.
6/2025 MUOVIPLAST 17
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