A deep learning approach for efficient topology optimization based on the element removal strategy. (15th December 2021)

• Record Type: Journal Article
• Authors: Qiu, Cheng; Du, Shanyi; Yang, Jinglei
• Is Part Of: Materials & design. Volume 212(2021)
• Publication Date: 2021-12-15
• Subjects: Topology optimization -- Deep learning -- Structural design -- Data-driven; Materials -- Periodicals; Engineering design -- Periodicals; Matériaux -- Périodiques; Conception technique -- Périodiques; Electronic journals; 620.11
• DOI: 10.1016/j.matdes.2021.110179
• Languages: English
• ISSNs: 0264-1275

Abstract

Graphical abstract: Highlights: The boundary conditions are encoded as inputs, making the model can provide solutions with arbitrary loading on the perimeters. The DL model learns the structural evolutionary process, making it able to provide solutions with arbitrary target volume fractions. The model is extended into 3D cases by using image data with multiple channels representing 3D structures. The well-trained DL model has huge time efficiency (0.9 s) than conventional FEM (12.5 min) on one 3D case. Abstract: In this paper, a deep learning-based model is proposed which is capable of automatically generating the structural topology configurations with the minimum structural compliance and deformation under various load conditions and volume fraction limitations. The deep-learning model combines the advanced algorithms of Convolutional Neural Network (CNN) with U-net architecture and Recurrent Neural Network (RNN) with Long-Short Term Memory (LSTM) architecture. The established data-driven framework learns the structural evolution process from training data samples, which are randomly generated in finite element simulations employing an element removal strategy. The well-trained model is successfully utilized for two types of cases: two-dimensional and three-dimensional cantilever-beam structural topology designs. The deep-learning model outperforms the traditional methods in terms of lower time cost and broader applicability, demonstrating the potential of such a data-driven approach to accelerate the process of preliminary structural design.