Topology optimization based on improved DoubleU-Net using four boundary condition datasets

Abstract

Topology optimization is extensively employed in engineering design. However, applying deep learning to solve topology optimization problems faces challenges owing to limited training data and model adaptability to various boundary conditions. To address these challenges, this research has employed a solid isotropic material with a penalization method to produce a dataset of 400,000 2D samples covering four boundary conditions. Each boundary condition includes two resolution datasets. Moreover, an improved DoubleU-Net model is proposed for real-time topology optimization, ensuring high-accuracy predictions. On the test dataset, the average Intersection over Union (IoU) accuracies of the proposed model for the four boundary conditions above are 93.26%, 96.71%, 96.35% and 97.38%. This research also investigated the impact of different structural datasets on the model's generalization capability. The experimental results indicate that the model trained on random structures exhibits excellent testing performance and demonstrates robust generalization capability.