Algorithmically-Consistent Deep Learning Frameworks for Structural Topology Optimization
This work offers a more efficient and accurate method for structural topology optimization, which is crucial for engineers and designers working with complex 3D geometries.
This paper addresses the computational intensity of topology optimization by developing deep learning frameworks that are consistent with traditional algorithms. The approach uses multiple networks, each learning a different step of the optimization process, leading to a 5.76x reduction in total compliance MSE in 2D and a 2.03x reduction in 3D compared to existing ML-based methods.
Topology optimization has emerged as a popular approach to refine a component's design and increase its performance. However, current state-of-the-art topology optimization frameworks are compute-intensive, mainly due to multiple finite element analysis iterations required to evaluate the component's performance during the optimization process. Recently, machine learning (ML)-based topology optimization methods have been explored by researchers to alleviate this issue. However, previous ML approaches have mainly been demonstrated on simple two-dimensional applications with low-resolution geometry. Further, current methods are based on a single ML model for end-to-end prediction, which requires a large dataset for training. These challenges make it non-trivial to extend current approaches to higher resolutions. In this paper, we develop deep learning-based frameworks consistent with traditional topology optimization algorithms for 3D topology optimization with a reasonably fine (high) resolution. We achieve this by training multiple networks, each learning a different step of the overall topology optimization methodology, making the framework more consistent with the topology optimization algorithm. We demonstrate the application of our framework on both 2D and 3D geometries. The results show that our approach predicts the final optimized design better (5.76x reduction in total compliance MSE in 2D; 2.03x reduction in total compliance MSE in 3D) than current ML-based topology optimization methods.