Topology Optimization using Deep Learning

2020 ◽  
Vol 140 (12) ◽  
pp. 858-865
Author(s):  
Hidenori Sasaki ◽  
Yuki Hidaka ◽  
Hajime Igarashi
Keyword(s):  
Deep Learning ◽  
Topology Optimization

Algorithmically-consistent deep learning frameworks for structural topology optimization

2021 ◽  
Vol 106 ◽  
pp. 104483
Author(s):  
Jaydeep Rade ◽  
Aditya Balu ◽  
Ethan Herron ◽  
Jay Pathak ◽  
Rishikesh Ranade ◽  
...  
Keyword(s):  
Deep Learning ◽  
Topology Optimization ◽  
Structural Topology Optimization ◽  
Structural Topology ◽  
Learning Frameworks

scholarly journals Topology Optimization Accelerated by Deep Learning

2019 ◽  
Vol 55 (6) ◽  
pp. 1-5 ◽  
Author(s):  
Hidenori Sasaki ◽  
Hajime Igarashi
Keyword(s):  
Deep Learning ◽  
Topology Optimization

scholarly journals Intelligent Generation Method of Innovative Structures Based on Topology Optimization and Deep Learning

Materials ◽  
2021 ◽  
Vol 14 (24) ◽  
pp. 7680
Author(s):  
Yingqi Wang ◽  
Wenfeng Du ◽  
Hui Wang ◽  
Yannan Zhao
Keyword(s):  
Deep Learning ◽  
Topology Optimization ◽  
Computer Aided Design ◽  
Mechanical Performance ◽  
Learning Algorithm ◽  
Structural Models ◽  
Generative Adversarial Networks ◽  
Application Process ◽  
Training Set ◽  
Deep Learning Algorithm

Computer-aided design has been widely used in structural calculation and analysis, but there are still challenges in generating innovative structures intelligently. Aiming at this issue, a new method was proposed to realize the intelligent generation of innovative structures based on topology optimization and deep learning. Firstly, a large number of structural models obtained from topology optimization under different optimization parameters were extracted to produce the training set images, and the training set labels were defined as the corresponding load cases. Then, the boundary equilibrium generative adversarial networks (BEGAN) deep learning algorithm was applied to generate numerous innovative structures. Finally, the generated structures were evaluated by a series of evaluation indexes, including innovation, aesthetics, machinability, and mechanical performance. Combined with two engineering cases, the application process of the above method is described here in detail. Furthermore, the 3D reconstruction and additive manufacturing techniques were applied to manufacture the structural models. The research results showed that the proposed approach of structural generation based on topology optimization and deep learning is feasible, and can not only generate innovative structures but also optimize the material consumption and mechanical performance further.

scholarly journals Stress Field Prediction in Cantilevered Structures Using Convolutional Neural Networks

2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Zhenguo Nie ◽  
Haoliang Jiang ◽  
Levent Burak Kara
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Topology Optimization ◽  
Structural Analysis ◽  
Stress Field ◽  
Convolutional Neural Networks ◽  
Von Mises Stress ◽  
Channel Network ◽  
Promising Alternative ◽  
And Topology

Abstract The demand for fast and accurate structural analysis is becoming increasingly more prevalent with the advance of generative design and topology optimization technologies. As one step toward accelerating structural analysis, this work explores a deep learning-based approach for predicting the stress fields in 2D linear elastic cantilevered structures subjected to external static loads at its free end using convolutional neural networks (CNNs). Two different architectures are implemented that take as input the structure geometry, external loads, and displacement boundary conditions, and output the predicted von Mises stress field. The first is a single input channel network called SCSNet as the baseline architecture, and the second is the multichannel input network called StressNet. Accuracy analysis shows that StressNet results in significantly lower prediction errors than SCSNet on three loss functions, with a mean relative error of 2.04% for testing. These results suggest that deep learning models may offer a promising alternative to classical methods in structural design and topology optimization. Code and dataset are available.2

scholarly journals Neural networks for topology optimization

2019 ◽  
Vol 34 (4) ◽  
pp. 215-223 ◽  
Author(s):  
Ivan Sosnovik ◽  
Ivan Oseledets
Keyword(s):  
Deep Learning ◽  
Topology Optimization ◽  
High Performance ◽  
Current Method ◽  
Optimization Methods ◽  
Proposed Model ◽  
Significant Acceleration ◽  
Decoder Architecture ◽  
Segmentation Task ◽  
Convolutional Encoder

Abstract In this research, we propose a deep learning based approach for speeding up the topology optimization methods. The problem we seek to solve is the layout problem. The main novelty of this work is to state the problem as an image segmentation task. We leverage the power of deep learning methods as the efficient pixel-wise image labeling technique to perform the topology optimization. We introduce convolutional encoder-decoder architecture and the overall approach of solving the above-described problem with high performance. The conducted experiments demonstrate the significant acceleration of the optimization process. The proposed approach has excellent generalization properties. We demonstrate the ability of the application of the proposed model to other problems. The successful results, as well as the drawbacks of the current method, are discussed.

scholarly journals Topology optimization of 2D structures with nonlinearities using deep learning

2020 ◽  
Vol 237 ◽  
pp. 106283 ◽  
Author(s):  
Diab W. Abueidda ◽  
Seid Koric ◽  
Nahil A. Sobh
Keyword(s):  
Deep Learning ◽  
Topology Optimization

scholarly journals Deep Generative Design: Integration of Topology Optimization and Generative Models

2019 ◽  
Vol 141 (11) ◽  
Author(s):  
Sangeun Oh ◽  
Yongsu Jung ◽  
Seongsin Kim ◽  
Ikjin Lee ◽  
Namwoo Kang
Keyword(s):  
Deep Learning ◽  
Topology Optimization ◽  
Research Area ◽  
Generative Models ◽  
Generative Adversarial Networks ◽  
Generative Design ◽  
Design Data ◽  
Research Areas ◽  
Design Integration ◽  
Design Options

Abstract Deep learning has recently been applied to various research areas of design optimization. This study presents the need and effectiveness of adopting deep learning for generative design (or design exploration) research area. This work proposes an artificial intelligent (AI)-based deep generative design framework that is capable of generating numerous design options which are not only aesthetic but also optimized for engineering performance. The proposed framework integrates topology optimization and generative models (e.g., generative adversarial networks (GANs)) in an iterative manner to explore new design options, thus generating a large number of designs starting from limited previous design data. In addition, anomaly detection can evaluate the novelty of generated designs, thus helping designers choose among design options. The 2D wheel design problem is applied as a case study for validation of the proposed framework. The framework manifests better aesthetics, diversity, and robustness of generated designs than previous generative design methods.

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