Machine learning assisted design of new lattice core for sandwich structures with superior load carrying capacity

• Published in: Scientific reports Vol. 11; no. 1; p. 18552
• Main Authors: Challapalli, Adithya, Li, Guoqiang
• Format: Journal Article
• Language: English
• Published: England Nature Publishing Group 17.09.2021; Nature Publishing Group UK; Nature Portfolio
• Subjects: Biomimetics; Carrying capacity; Compression; Learning algorithms; Machine learning; Prediction models; Rods
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-021-98015-7

Herein new lattice unit cells with buckling load 261-308% higher than the classical octet unit cell were reported. Lattice structures have been widely used in sandwich structures as lightweight core. While stretching dominated and bending dominated cells such as octahedron, tetrahedron and octet have been designed for lightweight structures, it is plausible that other cells exist which might perform better than the existing counterparts. Machine learning technique was used to discover new optimal unit cells. An 8-node cube containing a maximum of 27 elements, which extended into an eightfold unit cell, was taken as representative volume element (RVE). Numerous possible unit cells within the RVE were generated using permutations and combinations through MATLAB coding. Uniaxial compression tests using ANSYS were performed to form a dataset, which was used to train machine learning algorithms and form predictive model. The model was then used to further optimize the unit cells. A total of 20 optimal symmetric unit cells were predicted which showed 51-57% higher capacity than octet cell. Particularly, if the solid rods were replaced by porous biomimetic rods, an additional 130-160% increase in buckling resistance was achieved. Sandwich structures made of these 3D printed optimal symmetric unit cells showed 13-35% higher flexural strength than octet cell cored counterpart. This study opens up new opportunities to design high-performance sandwich structures.