Parameter optimization of bidirectional re-entrant auxetic honeycomb metamaterial based on genetic algorithm - Wang, Liang; Liu, Hai-Tao (Composite structures, 2021)
In this work, we design and test a parameter optimization method by Python script to meet the urgent demand for lightweight honeycomb metamaterial. The method mainly focuses on the selection of parameters according to the mass and Poisson’s ratio of the honeycomb metamaterial. The bidirectional re-entrant honeycomb is proposed as an objective to be optimized and the formula of Poisson's ratio is deduced theoretically to establish the internal relation. Besides, the accuracy of the Python script results is verified by static compression experimental results and theoretical results. Combined the Python script programming model with the genetic algorithm optimization method, the optimal honeycomb metamaterial solutions are obtained. Results show that the parameter optimization method using multi-island genetic algorithm (GA) can avoid a local solution’s appearance, and both the shell model and the solid model can obtain the ideal optimal solution. Furthermore, the 3D honeycomb has an admirable auxetic effect according to the optimized parameters, which provides a piece of strong evidence for the continuous application of optimization algorithms to improve the mechanical properties of honeycomb metamaterial.