Document Type
Article
Publication Date
2-2024
Keywords
TOPOLOGY OPTIMIZATION; VARIATIONAL APPROACH; DESIGN; ULTRALIGHT; ALGORITHM
Abstract
The conflict between stiffness and toughness is a fundamental problem in engineering materials design. However, the systematic discovery of microstructured composites with optimal stiffness-toughness trade-offs has never been demonstrated, hindered by the discrepancies between simulation and reality and the lack of data-efficient exploration of the entire Pareto front. We introduce a generalizable pipeline that integrates physical experiments, numerical simulations, and artificial neural networks to address both challenges. Without any prescribed expert knowledge of material design, our approach implements a nested-loop proposal-validation workflow to bridge the simulation-to-reality gap and find microstructured composites that are stiff and tough with high sample efficiency. Further analysis of Pareto-optimal designs allows us to automatically identify existing toughness enhancement mechanisms, which were previously found through trial and error or biomimicry. On a broader scale, our method provides a blueprint for computational design in various research areas beyond solid mechanics, such as polymer chemistry, fluid dynamics, meteorology, and robotics.
Citation
Li, B., Deng, B., Shou, W., Oh, T., Hu, Y., Luo, Y., Shi, L., & Matusik, W. (2024). Computational Discovery of Microstructured Composites with Optimal Stiffness-Toughness Trade-Offs. Science Advances, 10 (5), eadk4284. https://doi.org/10.1126/sciadv.adk4284
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
