Mechanical Neural-Network Architecture Materials that Learn
Abstract
The objective of this research is to apply the concept of artificial neural networks to enable the creation of a new kind of architectured material called mechanical neural-network (MNN) architectured materials that can learn desired properties via a complex web of active flexible elements (AFEs) that constitute the materials microstructure. Although significant research has been conducted toward enabling advanced materials that utilize active elements to achieve programmable properties, many scenarios exist where a materials environment may change and it is necessary that the material can autonomously adapt its properties accordingly to successfully fulfill its desired purpose. In such scenarios, designers rarely have the time o rknowledge of each environmental change to program and upload new control instructions to change the properties as required. Thus, it is necessary that the material can learn to change its properties on its own.
Document Details
- Document Type
- Technical Report
- Publication Date
- Sep 10, 2022
- Accession Number
- AD1230348
Entities
People
- Jonathan B. Hopkins
Organizations
- University of California, Los Angeles