Speaker
Description
Mechanical metamaterials or architected materials are becoming increasingly popular due to rapid developments in additive manufacturing. These architected materials exhibit mechanical properties that significantly differ from those of their base material and offer a high degree of customizability. This is enabled by a rationally designed spatial structure of the base material on the mesoscale, which allows tuning the effective properties on the macroscale. This freedom in design naturally raises the question: How can we systematically find a structure that matches some given requirements? Since working directly on the structure itself is difficult to handle practically, the most important features of the structure are usually captured in low-dimenional descriptors. Consequently, the inverse design approach used here focuses on suggesting appropriate descriptor values for given target properties. This contribution focuses on spinodoid metamaterials [1] with primarily elastic target features. Thus, the goal is to identify the most suitable spinodoid metamaterial for a given nonlinear-elastic behavior. First, a neural network-based surrogate model is created, which predicts the corresponding elastic material behavior for a given descriptor. Creating such a model requires a sufficiently large dataset of descriptor-property pairs. To efficiently generate this computationally expensive dataset, an appropriate sampling method is presented. After calibrating the surrogate model, inverse design for complex elastic target properties can be efficiently performed using various methods [1,2]. The effectiveness of the framework is demonstrated with selected examples and the results are compared with existing work [3].
[1] Kumar, S., Tan, S., Zheng, L. & Kochmann, D. (2020). Inverse-designed spinodoid metamaterials. Computational Materials, 6, 73. https://doi.org/10.1038/s41524-020-0341-6
[2] Asmus, J., Müller, C.L. & Sbalzarini, I.F. (2017) Lp-Adaptation: Simultaneous Design Centering and Robustness Estimation of Electronic and Biological Systems. Scientific Reports, 7, 6660. https://doi.org/10.1038/s41598-017-03556-5
[3] Thakolkaran, P., Espinal, M., Dhulipala, S., Kumar, S., Portela, C.M. (2023). Experiment-informed finite-strain inverse design of spinodal metamaterials. arXiv-preprint.
https://doi.org/10.48550/arXiv.2312.11648
