GraphDGM: A Generative Data-Driven Design Approach for Frame and Lattice Structures

被引:0
作者
Yang, Zhenling [1 ]
Guo, Yilin [1 ]
Sun, Zhi [1 ,2 ]
Elkhodary, Khalil I. [3 ]
Feng, Fuyong [4 ,5 ]
Kang, Zhong [6 ]
机构
[1] State Key Laboratory of Structural Analysis, Optimization and CAE Software for Industrial Equipment, Department of Engineering Mechanics, Dalian University of Technology, Dalian
[2] International Research Center for Computational Mechanics, Dalian University of Technology, Dalian
[3] The Department of Mechanical Engineering, The American University in Cairo, New Cairo
[4] China North Artificial Intelligence & Innovation Research Institute, Beijing
[5] Collective Intelligence & Collaboration Laboratory, Beijing
[6] China North Vehicle Research Institute, Beijing
关键词
data-driven; generative artificial intelligence; inverse design; multiobjective;
D O I
10.1115/1.4068106
中图分类号
学科分类号
摘要
Generative artificial intelligence offers a more efficient solution for the design of structures. However, an inverse generation of structures, which meet multiple design objectives, remains an open problem. This article thus focuses on the inverse design of frame structures and proposes Graph-based Diffusion-Generative Multiobjective design (GraphDGM), a graph-based generative data-driven surrogate model constrained by multiple targets. By integrating the finite element method (FEM), we construct datasets of frame structures subjected to various conditions. We then developed a conditional graph generation model based on the denoising diffusion probabilistic models (DDPM) and the attention mechanism. We show that our method can efficiently accomplish the inverse design of various frame structures, including a vehicle’s skeleton subjected to five simultaneous constraints. Furthermore, we present comparative experiments against baseline methods to demonstrate the effectiveness and superiority of the GraphDGM. Copyright © 2025 by ASME.
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共 55 条
[1]  
Laksono F. B., Majid I. A., Structural Assessment of Ladder Frame Chassis Using Fe Analysis: A Designed Construction Referring to Ford ac Cobra, Procedia Struct. Integrity, 33, pp. 35-42, (2021)
[2]  
Idrees U., Ahmad S., Shah I. A., Talha M., Shehzad R., Amjad M., Koloor S. S. R., Finite Element Analysis of Car Frame Frontal Crash Using Lightweight Materials, J. Eng. Res, 11, 1, (2023)
[3]  
Saplinova V., Novikov I., Glagolev S., Design and Specifications of Racing Car Chassis as Passive Safety Feature, Transp. Res. Procedia, 50, pp. 591-607, (2020)
[4]  
Danhaive R., Mueller C. T., Design Subspace Learning: Structural Design Space Exploration Using Performance-Conditioned Generative Modeling, Automat. Constr, 127, (2021)
[5]  
Afzal M., Liu Y., Cheng J. C., Gan V. J., Reinforced Concrete Structural Design Optimization: A Critical Review, J. Cleaner Prod, 260, (2020)
[6]  
Pizarro P. N., Hitschfeld N., Sipiran I., Saavedra J. M., Automatic Floor Plan Analysis and Recognition, Automat. Constr, 140, (2022)
[7]  
Bendsoe M. P., Optimal Shape Design as a Material Distribution Problem, Struct. Optim, 1, pp. 193-202, (1989)
[8]  
Bendsoe M. P., Sigmund O., Material Interpolation Schemes in Topology Optimization, Arch. Appl. Mech, 69, pp. 635-654, (1999)
[9]  
Guo X., Zhang W., Zhong W., Doing Topology Optimization Explicitly and Geometrically—A New Moving Morphable Components Based Framework, ASME J. Appl. Mech, 81, 8, (2014)
[10]  
Alberdi R., Murren P., Khandelwal K., Connection Topology Optimization of Steel Moment Frames Using Metaheuristic Algorithms, Eng. Struct, 100, pp. 276-292, (2015)