Mechanical neural networks: An overview of self-learning metamaterials

This work provides an overview of the progress made in the emerging field of mechanical neural networks (MNNs). Inspired by the mathematical layout of artificial neural networks (ANNs), MNNs consist of a physical network of tunable beams that learn the desired behaviors and properties of their interconnected lattice. As such they constitute artificial-intelligent (AI) mechanical metamaterials, that is, architected materials, that can autonomously acquire new abilities with increased exposure to ambient loading conditions. These lattices have been demonstrated with a variety of tunable beam designs on the macro scale and are currently being fabricated to demonstrate their learning capabilities on the micro scale. This paper reviews the specific advances that have been published in this area to date.