Algorithmically-consistent deep learning frameworks for structural topology optimization. (November 2021)

• Record Type: Journal Article
• Authors: Rade, Jaydeep; Balu, Aditya; Herron, Ethan; Pathak, Jay; Ranade, Rishikesh; Sarkar, Soumik; Krishnamurthy, Adarsh
• Is Part Of: Engineering applications of artificial intelligence. Volume 106(2021)
• Publication Date: 2021-11
• Subjects: Topology optimization -- Deep learning -- Sequence models -- Algorithmically-consistent learning; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2021.104483
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: 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 (5.76 × reduction in total compliance MSE in 2D; 2.03 × reduction in total compliance MSE in 3D) than current ML-based topology optimization methods. Highlights: A deep learning framework consistent with SIMP algorithm for topology optimization. Framework scalable to high resolution in 3D structural topology optimization. Uses intermediate density and compliance to improve the final topology prediction. Framework results validated using ground truth SIMP-based topology optimization. More than 5x reduction in error compared to a single density-based ML model.