| About this Abstract |
| Meeting |
2022 TMS Annual Meeting & Exhibition
|
| Symposium
|
Biological Materials Science
|
| Presentation Title |
Lightweight, Strong and Tough Structural Materials Inspired from Nature and Optimized by A.I. |
| Author(s) |
Po-Yu Chen, Cheng-Che Tung, Ashish Ghimire, Yen-Shuo Chen, Yu-Yi Lai, Chi-Hua Yu |
| On-Site Speaker (Planned) |
Po-Yu Chen |
| Abstract Scope |
Natural materials are often composites of organic and inorganic constituents self-assembled into complex hierarchical structures which possess remarkable mechanical properties, combining lightweight, high strength and high toughness owing to strengthening and toughening mechanisms from nano-, micro-, meso-, and macro-scales. Learning from Nature can lead to revolutionary breakthrough and innovation in Materials Science and Engineering. In this talk, representative biological structural materials, including nacre (layered), arthropod exoskeletons (helical), dragonfly wings (Voronoi), bamboos (gradient), and cuttlebone (cellular) are selected and investigated. Inspired from the structural designs of these natural materials, we further apply multi-scale simulation/modeling, genetic algorithm, machine learning, and A.I.-related approaches to optimize the bio-inspired designs and validated by 3D printing and mechanical testing. The structure-property relationships and toughening mechanisms are elucidated and key design principles and strategies are proposed. Novel cellular materials and composites inspired from Nature and optimized by A.I. could lead to broad potential applications in industrial fields. |
| Proceedings Inclusion? |
Planned: |
| Keywords |
Biomaterials, Mechanical Properties, Machine Learning |