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Volume 10
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Article

An Efficient Method for the Inverse Design of Thin-Wall Stiffened Structure Based on the Machine Learning Technique

by
Yongtao Lyu
1,2,
Yibiao Niu
1,
Tao He
3,*,
Limin Shu
4,
Michael Zhuravkov
5 and
Shutao Zhou
6,*
1
Department of Engineering Mechanics, Dalian University of Technology, Dalian 116024, China
2
DUT-BSU Joint Institute, Dalian University of Technology, Dalian 116024, China
3
Wuhan Second Ship Design and Research Institute, Wuhan 430205, China
4
Department of Mechanical Engineering, Dalian University of Technology, Dalian 116024, China
5
Faculty of Mechanics and Mathematics, Belarusian State University, Minsk 220030, Belarus
6
Beijing Institute of Structure and Environment Engineering, Beijing 100076, China
*
Authors to whom correspondence should be addressed.
Aerospace 2023, 10(9), 761; https://doi.org/10.3390/aerospace10090761
Submission received: 27 July 2023 / Revised: 21 August 2023 / Accepted: 23 August 2023 / Published: 28 August 2023
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Abstract

In this paper, a new method using the backpropagation (BP) neural network combined with the improved genetic algorithm (GA) is proposed for the inverse design of thin-walled reinforced structures. The BP neural network model is used to establish the mapping relationship between the input parameters (reinforcement type, rib height, rib width, skin thickness and rib number) and the output parameters (structural buckling load). A genetic algorithm is added to obtain the inversely designed result of a thin-wall stiffened structure according to the actual demand. In the end, according to the geometric parameters of inverse design, the thin-walled stiffened structure is reconstructed geometrically, and the numerical solutions of finite element calculation are compared with the target values of actual demand. The results show that the maximal inversely designed error is within 5.1%, which implies that the inverse design method of structural geometric parameters based on the machine learning and genetic algorithm is efficient and feasible.
Keywords: thin-walled stiffened structure; buckling load; back propagation neural network; inverse design thin-walled stiffened structure; buckling load; back propagation neural network; inverse design

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MDPI and ACS Style

Lyu, Y.; Niu, Y.; He, T.; Shu, L.; Zhuravkov, M.; Zhou, S. An Efficient Method for the Inverse Design of Thin-Wall Stiffened Structure Based on the Machine Learning Technique. Aerospace 2023, 10, 761. https://doi.org/10.3390/aerospace10090761

AMA Style

Lyu Y, Niu Y, He T, Shu L, Zhuravkov M, Zhou S. An Efficient Method for the Inverse Design of Thin-Wall Stiffened Structure Based on the Machine Learning Technique. Aerospace. 2023; 10(9):761. https://doi.org/10.3390/aerospace10090761

Chicago/Turabian Style

Lyu, Yongtao, Yibiao Niu, Tao He, Limin Shu, Michael Zhuravkov, and Shutao Zhou. 2023. "An Efficient Method for the Inverse Design of Thin-Wall Stiffened Structure Based on the Machine Learning Technique" Aerospace 10, no. 9: 761. https://doi.org/10.3390/aerospace10090761

APA Style

Lyu, Y., Niu, Y., He, T., Shu, L., Zhuravkov, M., & Zhou, S. (2023). An Efficient Method for the Inverse Design of Thin-Wall Stiffened Structure Based on the Machine Learning Technique. Aerospace, 10(9), 761. https://doi.org/10.3390/aerospace10090761

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