Accelerated design and characterization of non-uniform cellular materials via a machine-learning based framework
Chunping Ma, Zhiwei Zhang, Benjamin Luce, Simon Pusateri, Binglin Xie, Mohammad Hossein Rafiei, Nan Hu
Abstract
Abstract Cellular materials, widely found in engineered and nature systems, are highly dependent on their geometric arrangement. A non-uniform arrangement could lead to a significant variation of mechanical properties while bringing challenges in material design. Here, this proof-of-concept study demonstrates a machine-learning based framework with the capability of accelerated characterization and pattern generation. Results showed that the proposed framework is capable of predicting the mechanical response curve of any given geometric pattern within the design domain under appropriate neural network architecture and parameters. Additionally, the framework is capable of generating matching geometric patterns for a targeted response through a databank constructed from our machine learning model. The accuracy of the predictions was verified with finite element simulations and the sources of errors were identified. Overall, our machine-learning based framework can boost the design efficiency of cellular materials at unit level, and open new avenues for the programmability of function at system level.