Data and Code underlying publication: Experiment-informed finite-strain inverse design of spinodal metamaterials
doi: 10.4121/bdc2b8d2-d000-4f56-ab0e-e64a147220fd
,
Michael Espinal
,
Somayajulu Dhulipala
,
Siddhant Kumar
,
Carlos M. Portela
Spinodal metamaterials, with architectures inspired by natural phase-separation processes, have presented a significant alternative to periodic and symmetric morphologies when designing mechanical metamaterials with extreme performance. While their elastic mechanical properties have been systematically determined, their large-deformation, nonlinear responses have been challenging to predict and design, in part due to limited data sets and the need for complex nonlinear simulations. This work presents a novel physics-enhanced machine learning (ML) and optimization framework tailored to address the challenges of designing intricate spinodal metamaterials with customized mechanical properties in large-deformation scenarios where computational modeling is restrictive and experimental data is sparse. By utilizing large-deformation experimental data directly, this approach facilitates the inverse design of spinodal structures with precise finite-strain mechanical responses. The framework sheds light on instability-induced pattern formation in spinodal metamaterials—observed experimentally and in selected nonlinear simulations—leveraging physics-based inductive biases in the form of nonconvex energetic potentials. Altogether, this combined ML, experimental, and computational effort provides a route for efficient and accurate design of complex spinodal metamaterials for large-deformation scenarios where energy absorption and prediction of nonlinear failure mechanisms is essential.
- 2024-12-30 first online, published, posted
Massachusetts Institute of Technology, Department of Mechanical Engineering
DATA
To access the source code, use the following command:
git clone https://data.4tu.nl/v3/datasets/335d3b61-a83a-4c28-8a2f-0e622ac3ac0e.git "finite-strain-inverse-designed-spinodoids"
