Experiment-informed finite-strain inverse design of spinodal metamaterials

2023-12-18

Extreme Mechanics Letters

AI Summary

Predicting and inversely designing spinodal metamaterials under large deformations remains challenging due to data scarcity and high computational cost. Method: This study proposes a data-driven closed-loop optimization paradigm integrating experimental mechanics with finite-strain constitutive modeling. It pioneers embedding digital image correlation (DIC)-measured nonlinear mechanical responses into a finite-strain inverse design framework—overcoming the restrictive small-deformation assumption. The approach synergistically combines multiscale finite element modeling, topology sensitivity analysis, and gradient-enhanced generative algorithms to co-optimize microstructure and macroscopic performance. Contribution/Results: The designed spinodal metamaterials exhibit stable negative Poisson’s ratio and high energy absorption under 50% compressive strain. Experimental validation yields <8% error, while design cycle time is reduced by a factor of three. This work establishes a robust methodology for precise, functionally programmable metamaterial design under large deformations.

Problem

• Spinodal Metamaterials
• Mechanical Behavior
• Large Deformation

Innovation

• Machine Learning
• Spinodal Metamaterials
• Large Deformation Design