A physical adaptive material motor unit neural network: a hygromorph composite material machine
Organizations: Bristol Composites Institute, School of Civil, Aerospace and Mechanical Engineering, University of Bristol, University Walk, Bristol BS8 1TR, United Kingdom · School of Architecture, University of Waterloo, 7 Melville Street South, Cambridge, Ontario, N1S 2H4, Canada · Research School of Chemistry and John Curtin School of Medical Research, Australian National University, Canberra ACT2601, Australia · School of Cellular and Molecular Medicine, University of Bristol, University Walk, Bristol BS8 1TD, United Kingdom · School of Engineering Mathematics and Technology, University of Bristol, Bristol, United Kingdom
Abstract
Advances in novel materials science enable structures to function as intelligent machines by embedding memory and learning capabilities directly into materials. Our work introduces a physical adaptive material motor unit neural network,leveraging a new generation of controllable actuators composed of wood- and carbon black-based composites, sensitive to temperature and relative humidity. These material actuators are assembled into a motor unit-like structure inspired by muscle contraction trigger, forming an intelligent machine capable of dynamic shading control that can be used, for example, in buildings. The machine is governed by a neural network trained on over 350 experimental data points collected under diverse environmental conditions. By establishing a new data-aware backpropagation training, we show that the machine predicts shading responses and learns to predict appropriate behaviour incrementally as the database expands. We also demonstrate the ability of the machine to optimise configurations to achieve similar shading outputs under two distinct conditions.