Li, J., Pokkalla, D. K., Wang, Z.-P., & Wang, Y. (2023). Deep learning-enhanced design for functionally graded auxetic lattices. Engineering Structures, 292, 116477. https://doi.org/10.1016/j.engstruct.2023.116477
Abstract:
Auxetic materials with counterintuitive negative Poisson’s ratio have been of significant interest due to potential applications across diverse engineering fields. Functionally grading such auxetics further enables customization of the structural response and harnesses the potential for multi-functional applications. However, a critical challenge in designing functionally graded lattices is to efficiently determine the spatial variation of the functional gradient and the corresponding geometric designs to achieve the desired response. In this paper, a highly efficient deep learning-based inverse design framework for functionally graded tetra-petal auxetics with spatially tailored properties is presented. This framework significantly improves the efficiency of tailoring functionally graded auxetics where many unit cells need to be tailor-designed. The graded tetra-petal auxetics obtained from the inverse design framework are additively manufactured and subjected to impact tests. The results show superior impact performance compared with uniform designs, demonstrating the effectiveness of the proposed inverse design framework, which can be inspirable to promote advanced structures/materials with enhanced impact resistance.
License type:
Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)
Funding Info:
This research / project is supported by the Agency for Science, Technology and Research - Career Development Fund – 2020
Grant Reference no. : C210112026
This research / project is supported by the National Natural Science Foundation of China - NA
Grant Reference no. : 52075184
This research / project is supported by the U.S. Department of Energy, Office of Energy Efficiency and Renewable Energy, Industrial Technologies Program - NA
Grant Reference no. : DE-AC05–00OR22725