Inverse design of mechanical metamaterials balancing manufacturability and compactness: A case study on lattice cells

Abstract

Mechanical metamaterials are artificially engineered structures designed to exhibit unique and extraordinary mechanical properties. In recent years, machine learning has provided a more efficient and systematic approach, enabling inverse design of mechanical metamaterials, which allow for a broader exploration of material properties and support the integration of multifunctionality, significantly speeding up the design process. Despite the many advantages of inverse design, metamaterials often involve a trade-off between competing performance metrics-such as manufacturability and structural compactness. Furthermore, these trade-offs should be dynamically adjusted based on different additive manufacturing conditions. To address this, we proposed a regressional and conditional generative adversarial network based multi-objective (RCGAN-MO) architecture, which simultaneously handles the inverse design and adjustable multi-objective optimization of mechanical metamaterials. The RCGAN-MO includes two trained neural networks: a generator and a predictor, along with a weighted multi-objective optimizer. As a case study, the RCGAN-MO architecture is applied to the inverse design of the relative compressive elastic modulus for a metamaterial, and metamaterials with different weight vector values in the multi-objective optimizer are achieved through 3D printed prototypes. This approach achieves high accuracy and could adjust the importance of manufacturability and compactness, offering a flexible, scalable solution for engineering metamaterials tailored to practical application demands.