{"title":"基于NSGA-II的弧形星蜂窝和双向可重入蜂窝多目标优化","authors":"Chen-Yu Zhao, Hai-Tao Liu","doi":"10.1007/s10999-022-09628-3","DOIUrl":null,"url":null,"abstract":"<div><p>In this paper, the multi-objective optimization design of arc star honeycomb (ASH) and bi-directional reentrant honeycomb (BRH) is carried out by Python script to improve Young's modulus based on the lightweight of the honeycomb. A large number of models of different structural parameters are established by the Python script and analyzed by the finite element method, and then the response surface model (RSM) is established according to the results of finite element analysis. On this basis, the non-dominated sorting genetic algorithm (NSGA-II) and RSM are combined to perform multi-objective optimization of the 2D and 3D configurations of the two types of honeycomb, and the optimal set of parameters is selected by comparing the individual fitness values. The results show that after multi-objective optimization, Young's modulus of the ASH and BRH is enhanced in both 2D and 3D configurations with the smallest possible mass. In addition, the ASH has performance advantages over the BRH in 2D configuration, and BRH is better in 3D configuration. It can also be observed that the ASH and BRH have Poisson ratio adjustable properties. The results also show that this multi-objective optimization method can effectively save the analysis and calculation time. The lightweight, high-strength metamaterial is expected to be used in key fields such as aerospace.</p></div>","PeriodicalId":593,"journal":{"name":"International Journal of Mechanics and Materials in Design","volume":"19 2","pages":"375 - 389"},"PeriodicalIF":2.7000,"publicationDate":"2022-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Multi-objective optimization of arc star honeycomb and bidirectional reentrant honeycomb using NSGA-II\",\"authors\":\"Chen-Yu Zhao, Hai-Tao Liu\",\"doi\":\"10.1007/s10999-022-09628-3\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>In this paper, the multi-objective optimization design of arc star honeycomb (ASH) and bi-directional reentrant honeycomb (BRH) is carried out by Python script to improve Young's modulus based on the lightweight of the honeycomb. A large number of models of different structural parameters are established by the Python script and analyzed by the finite element method, and then the response surface model (RSM) is established according to the results of finite element analysis. On this basis, the non-dominated sorting genetic algorithm (NSGA-II) and RSM are combined to perform multi-objective optimization of the 2D and 3D configurations of the two types of honeycomb, and the optimal set of parameters is selected by comparing the individual fitness values. The results show that after multi-objective optimization, Young's modulus of the ASH and BRH is enhanced in both 2D and 3D configurations with the smallest possible mass. In addition, the ASH has performance advantages over the BRH in 2D configuration, and BRH is better in 3D configuration. It can also be observed that the ASH and BRH have Poisson ratio adjustable properties. The results also show that this multi-objective optimization method can effectively save the analysis and calculation time. The lightweight, high-strength metamaterial is expected to be used in key fields such as aerospace.</p></div>\",\"PeriodicalId\":593,\"journal\":{\"name\":\"International Journal of Mechanics and Materials in Design\",\"volume\":\"19 2\",\"pages\":\"375 - 389\"},\"PeriodicalIF\":2.7000,\"publicationDate\":\"2022-12-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Mechanics and Materials in Design\",\"FirstCategoryId\":\"88\",\"ListUrlMain\":\"https://link.springer.com/article/10.1007/s10999-022-09628-3\",\"RegionNum\":3,\"RegionCategory\":\"材料科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ENGINEERING, MECHANICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Mechanics and Materials in Design","FirstCategoryId":"88","ListUrlMain":"https://link.springer.com/article/10.1007/s10999-022-09628-3","RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, MECHANICAL","Score":null,"Total":0}
Multi-objective optimization of arc star honeycomb and bidirectional reentrant honeycomb using NSGA-II
In this paper, the multi-objective optimization design of arc star honeycomb (ASH) and bi-directional reentrant honeycomb (BRH) is carried out by Python script to improve Young's modulus based on the lightweight of the honeycomb. A large number of models of different structural parameters are established by the Python script and analyzed by the finite element method, and then the response surface model (RSM) is established according to the results of finite element analysis. On this basis, the non-dominated sorting genetic algorithm (NSGA-II) and RSM are combined to perform multi-objective optimization of the 2D and 3D configurations of the two types of honeycomb, and the optimal set of parameters is selected by comparing the individual fitness values. The results show that after multi-objective optimization, Young's modulus of the ASH and BRH is enhanced in both 2D and 3D configurations with the smallest possible mass. In addition, the ASH has performance advantages over the BRH in 2D configuration, and BRH is better in 3D configuration. It can also be observed that the ASH and BRH have Poisson ratio adjustable properties. The results also show that this multi-objective optimization method can effectively save the analysis and calculation time. The lightweight, high-strength metamaterial is expected to be used in key fields such as aerospace.
期刊介绍：
It is the objective of this journal to provide an effective medium for the dissemination of recent advances and original works in mechanics and materials'' engineering and their impact on the design process in an integrated, highly focused and coherent format. The goal is to enable mechanical, aeronautical, civil, automotive, biomedical, chemical and nuclear engineers, researchers and scientists to keep abreast of recent developments and exchange ideas on a number of topics relating to the use of mechanics and materials in design.
Analytical synopsis of contents:
The following non-exhaustive list is considered to be within the scope of the International Journal of Mechanics and Materials in Design:
Intelligent Design:
Nano-engineering and Nano-science in Design;
Smart Materials and Adaptive Structures in Design;
Mechanism(s) Design;
Design against Failure;
Design for Manufacturing;
Design of Ultralight Structures;
Design for a Clean Environment;
Impact and Crashworthiness;
Microelectronic Packaging Systems.
Advanced Materials in Design:
Newly Engineered Materials;
Smart Materials and Adaptive Structures;
Micromechanical Modelling of Composites;
Damage Characterisation of Advanced/Traditional Materials;
Alternative Use of Traditional Materials in Design;
Functionally Graded Materials;
Failure Analysis: Fatigue and Fracture;
Multiscale Modelling Concepts and Methodology;
Interfaces, interfacial properties and characterisation.
Design Analysis and Optimisation:
Shape and Topology Optimisation;
Structural Optimisation;
Optimisation Algorithms in Design;
Nonlinear Mechanics in Design;
Novel Numerical Tools in Design;
Geometric Modelling and CAD Tools in Design;
FEM, BEM and Hybrid Methods;
Integrated Computer Aided Design;
Computational Failure Analysis;
Coupled Thermo-Electro-Mechanical Designs.
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