Real-time topology optimization based on multi-scale convolutional attention mechanism

Abstract

This article proposes a deep learning model based on a multi-scale convolutional attention mechanism network SegNext for improving Topology Optimization (TO-Next), aimed at addressing the computational challenges associated with finite element iterations in traditional density-based methods. TO-Next is trained on diverse topology structures with varying loads, constraints and enriched physical information after initial iterations to enhance its generalization. Following training on three distinct decoder architectures, the optimal encoder–decoder network structure is determined. This study also investigates the algorithm's generalization capability under both single and multiple load constraints. The results indicate the superior performance and efficiency of the proposed method compared with the Solid Isotropic Material with Penalization (SIMP) method, enabling real-time generation of near-optimal topology structures within a short timeframe.

Keywords:

• Topology optimization
• attention mechanisms
• convolutional neural network
• multi-scale networks
• real-time optimization

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Additional information

Funding

This work was supported by the Major Program of the National Natural Science Foundation of China [grant numbers 72192830, 72192831]; the National Natural Science Foundation of China [grant number 72072029]; the 111 Project [grant number B16009].