Department of Mechanical & Industrial Engineering, Louisiana State University, Baton Rouge, LA, 70803, USA.
Sci Rep. 2020 Nov 26;10(1):20716. doi: 10.1038/s41598-020-77935-w.
Our mother nature has been providing human beings with numerous resources to inspire from, in building a finer life. Particularly in structural design, plenteous notions are being drawn from nature in enhancing the structural capacity as well as the appearance of the structures. Here plant stems, roots and various other structures available in nature that exhibit better buckling resistance are mimicked and modeled by finite element analysis to create a training database. The finite element analysis is validated by uniaxial compression to buckling of 3D printed biomimetic rods using a polymeric ink. After feature identification, forward design and data filtering are conducted by machine learning to optimize the biomimetic rods. The results show that the machine learning designed rods have 150% better buckling resistance than all the rods in the training database, i.e., better than the nature's counterparts. It is expected that this study opens up a new opportunity to design engineering rods or columns with superior buckling resistance such as in bridges, buildings, and truss structures.
我们的大自然为人类提供了无数的资源，这些资源可以激发我们创造更美好的生活。特别是在结构设计中，人们从大自然中汲取了丰富的灵感，以提高结构的承载能力和外观。这里模仿和模拟了植物茎、根和其他在自然界中表现出更好的抗屈曲性的结构，通过有限元分析创建一个培训数据库。通过使用聚合物墨水对 3D 打印仿生杆进行单轴压缩至屈曲的实验，验证了有限元分析的有效性。在特征识别之后，通过机器学习进行正向设计和数据过滤，以优化仿生杆。结果表明，机器学习设计的杆的抗屈曲能力比培训数据库中的所有杆都要好 150%，也就是说，比自然界的同类产品要好。预计这项研究为设计具有优异抗屈曲能力的工程杆或柱，如桥梁、建筑和桁架结构，开辟了新的机会。
