Accelerating Optimization Design of Bio-inspired Interlocking Structures with Machine Learning  

作　　者：Zhongqiu Ding Hong Xiao Yugang Duan Ben Wang 

机构地区：[1]School of Mechanical Engineering,State Key Lab for Manufacturing Systems Engineering,Xi’an Jiaotong University,Xi’an 710049,China

出　　处：《Acta Mechanica Solida Sinica》2023年第6期783-793,共11页固体力学学报（英文版）

基　　金：supported by the National Natural Science Foundation of China,Grant No.51875440.

摘　　要：Structural connections between components are often weak areas in engineering applications.In nature,many biological materials with remarkablemechanical performance possess flexible and creative sutures.In this work,we propose a novel bioinspired interlocking tab considering both the geometry of the tab head and neck,and demonstrate a new approach to optimize the bio-inspired interlocking structures based on machine learning.Artificial neural networks for different optimization objectives are developed and trained using a database of thousands of interlocking structures generated through finite element analysis.Results show that the proposed method is able to achieve accurate prediction of the mechanical response of any given interlocking tab.The optimized designs with different optimization objectives,such as strength,stiffness,and toughness,are obtained efficiently and precisely.The optimum design predicted by machine learning is approximately 7.98 times stronger and 2.98 times tougher than the best design in the training set,which are validated through additive manufacturing and experimental testing.The machine learning-based optimization approach developed here can aid in the exploration of the intricate mechanism behind biological materials and the discovery of new material designs boasting orders of magnitude increase in computational efficacy over conventional methods.

关 键 词：Interlocking structure Performance prediction Additive manufacturing Artificial neural network Finite element analysis 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]