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

Fourth Author's Department

Mechanical Engineering Department

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https://doi.org/10.1115/1.4068106

All Authors

Zhenling Yang Yilin Guo Zhi Sun Khalil I. Elkhodary Fuyong Feng Zhong Kang

Document Type

Research Article

Publication Title

Journal of Mechanical Design

Publication Date

10-1-2025

doi

10.1115/1.4068106

Abstract

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.

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