A deep learning approach for efficient topology optimization based on the element removal strategy

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. (c) 2021 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).