Multi-objective optimization of mechanical metamaterials for enhanced mechanical strength, energy absorption, and negative Poisson’s ratio

Rotating-reentrant metamaterials (RRMs) exhibit negative Poisson’s ratios, which have the potential to enhance structural resilience. However, optimizing the design of RRMs for desired performance while minimizing material consumption introduces challenges. This paper presents a sequential surrogate model-based approach for automating the design of RRMs, focusing on enhancing compressive strength and reducing material usage. The approach integrates sequential surrogate modeling, Latin hypercube sampling, Non-dominated Sorting Genetic Algorithm II, and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) to optimize RRM properties. The framework was implemented to the design of RRMs, and the designed RRMs were tested until failure for performance evaluation. Test results indicate that the approach provides adequate prediction accuracy, and the designed RRM achieves high compressive strength, negative Poisson’s ratio, and low material consumption. This paper advances data-driven techniques for the design of mechanical metamaterials.