Premium
Accelerating Auxetic Metamaterial Design with Deep Learning
Author(s) -
Wilt Jackson K.,
Yang Charles,
Gu Grace X.
Publication year - 2020
Publication title -
advanced engineering materials
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.938
H-Index - 114
eISSN - 1527-2648
pISSN - 1438-1656
DOI - 10.1002/adem.201901266
Subject(s) - auxetics , finite element method , metamaterial , materials science , engineering design process , actuator , computer science , mechanical engineering , computational mechanics , structural engineering , artificial intelligence , engineering , composite material , optoelectronics
Metamaterials can be designed to contain functional gradients with negative Poisson's ratio (NPR) that have counterintuitive behavior compared with monolithic materials. These NPR materials, referred to as auxetics, are relevant to engineering sciences because of their unique mechanical expansion. Previous studies have explored compliant actuators using analytical and numerically derived mechanics of materials principles. However, the control of compliant gradient mechanisms frequently uses complex analytical equations combined with traditional control algorithms, making them difficult to design. To confront the design processes and computational load, herein, machine learning is used to predict errors in compliant auxetic designs based on a mathematically optimal deformation. Finite element analysis and experimental specimens validate the theoretical mechanical behavior of a specific auxetic configuration as well as demonstrate the capabilities of additive manufacturing of graded auxetic materials. Pseudorandomized images and their respective computational deformation results are used to train a regressive model and predict the deviation from optimal behavior. The model predicts the deviation from the desired behavior with a mean average percent error below 5% for the validation set. Subsequently, a scalable workflow design process connecting the unique performance of auxetics to machine learning design predictions is proposed.