About this Abstract |
| Meeting |
2026 TMS Annual Meeting & Exhibition
|
| Symposium
|
Thin Films and Coatings: Properties, Processing and Applications
|
| Presentation Title |
Self-Aligning Shape Memory Metasurfaces for Scalable Modular Micro-Assembly |
| Author(s) |
Hesam Askari |
| On-Site Speaker (Planned) |
Hesam Askari |
| Abstract Scope |
Modular products contribute to a circular economy because they are easier to manufacture, assemble, maintain, re-purpose, re-use, and recycle. We aim to develop 3D, self-aligning / keyed metasurfaces with optimized structure, stiffness, and traction for precise assembly and triggerable adhesion. These properties are driven by the geometry of the features and the material properties. The connections between these parameters and the performance of the metasurfaces is not well understood, especially at small micron-sized length scales where surface properties are of extreme importance. We use the Random Forest approach to understand the contribution of design parameters and material properties to the performance of the metasurfaces. We then use an active learning approach by defining a mechanics-based objective function to identify optimized metasurface features that are the most effective at different length-scales. The outcomes of this effort will set forth scaling laws for the performance metrics of metasurfaces across length scales . |
| Proceedings Inclusion? |
Planned: |
| Keywords |
Machine Learning, Computational Materials Science & Engineering, Thin Films and Interfaces |