![]() ![]() ![]() Such adaptation exhibits 20% higher experimental load-bearing capacity than the uniform design. Furthermore, inspired by the knowledge learned from the neural networks, we develop machine-human synergy, adapting the ML-designed architecture to fix a macroscale, irregularly shaped animal bone defect. Compared to uniform designs, our experience-free method designs microscale heterogeneous architectures with a biocompatible elastic modulus and higher strength. Specifically, we apply our method to orthopedic implant design. Here, we present a data-efficient method for the high-dimensional multi-property optimization of 3D-printed architected materials utilizing a machine learning (ML) cycle consisting of the finite element method (FEM) and 3D neural networks. However, the rational design of these materials generally relies on experts’ prior knowledge and requires painstaking effort. Architected materials that consist of multiple subelements arranged in particular orders can demonstrate a much broader range of properties than their constituent materials.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |