Artificial neural networks for inverse design of a semi-auxetic metamaterial

Abstract

This study introduces an artificial neural network approach for the inverse design of a novel semi-auxetic mechanical metamaterial to achieve a specified stress-strain curve and/or Poisson's ratio-strain curve. To accomplish this, after presenting the metamaterial and assessing its characteristics, 1500 structures of the same metamaterial with various parameters are generated using a parametric model. The metamaterials are then gone through a compression test simulation using Finite Element (FE) analysis; accordingly, each metamaterial's stress-strain and Poisson's ratio curves are derived. The results of FE simulations are validated using mesh convergence check and experimental compression tests on a 3D printed specimen of the proposed metamaterial. In the next step, 80 % of the data are randomly selected to be used as training data for the artificial neural networks (ANN), while the remaining 20 % is employed to evaluate the performance of the ANNs using different metrics. The capability of the ANNs to predict the design parameters of the proposed metamaterial is assessed by providing different kinds of inputs, including the stress-strain curve, Poisson's ratio curve, and both. The observations reveal that the ANNs achieve more accurate results when both the stress-strain and Poisson's ratio-strain curves are provided as the inputs. The presented ANN in this study serves as a robust tool for precisely designing the parameters of the proposed metamaterial, allowing for the attainment of the desired stress-strain and/or Poisson's ratio-strain behavior. It is shown that the proposed metamaterial owns potential applications in crawling soft robotics, automotive, and construction industries.