Work

Novel Artificial Neural Network Aided Structural Topology Optimization

Year: 2024

Type: article

Abstract: In this paper, novel artificial neural networks are adopted for the topology optimization of full structures at both coarse and fine scales. The novelty of the surrogate-based method is to use neural networks to optimize the relationship from boundary and mesh conditions to structure density distribution. The objective of this study is to explore the feasibility and effectiveness of deep learning techniques for structural topology optimization. The newly developed neural networks are used for optimizing various types of structures with different meshes, partition numbers, and parameters. The finite element computation takes more than 90% of the total operation time of the SIMP method, but it decreases to 40%. It is indicated that the computational cost for the whole structural design process is relatively low, while the accuracy is acceptable. The proposed artificial neural network method is used to perform topology optimization for some numerical examples such as the cantilever beam, the MBB beam, the L-shape beam, the column, and the rod-supported bridge. It is demonstrated that computational efficiency is considerably improved while the proposed neural network method is adopted.

Source: Applied Sciences

DOI: 10.3390/app142311416

Authors: Xiangrui Kong, Yuching Wu, Peng Zhu, Peng Zhi, Qianfan Yang

Institution: Tongji University

Language: English

Topic: Topology Optimization in Engineering

Subfield: Civil and Structural Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,728

Funder: National Natural Science Foundation of China