Real-time topology optimization based on convolutional neural network by using retrain skill

Yan, Jun, Geng, Dongling, Xu, Qi, Li, Haijiang ORCID: https://orcid.org/0000-0001-6326-8133 and Zhao, Kai 2023. Real-time topology optimization based on convolutional neural network by using retrain skill. Presented at: UK Association for Computational Mechanics 2022 Conference, Nottingham, England, 21-22 April 2022. Engineering with Computers. , vol.39 Springer Verlag, pp. 405-4059. 10.1007/s00366-023-01846-3

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Abstract

To realize a real-time structural topology optimization (TO), it is essential to use the information during the TO process. A step-to-step training method is proposed to improve the deep learning model prediction accuracy based on the solid isotropic material with penalization (SIMP) TO method. By increasing the use of optimization history information (such as the structure density matrix), the step-to-step method improves the model utilization efficiency for each sample data. This training method can effectively improve the deep learning model prediction accuracy without increasing the sample set size. The step-to-step training method combines several independent deep learning models (sub-models). The sub-models could have the same model layers and hyperparameters. It can be trained in parallel to speed up the training process. During the deep learning model training process, these features reduce the difficulties in adjusting sub-model parameters and the model training time cost. Meanwhile, this method is achieved by the local end-to-end training process. During the deep learning model predicting process, the increase in total prediction time cost can be ignored. The trained deep learning models can predict the optimized structures in real time. Maximization of first eigenfrequency topology optimization problem with three constraint conditions is used to verify the effectiveness of the proposed training method. The method proposed in this study provides an implementation technology for the real-time TO of structures. The authors also provide the deep learning model code and the dataset in this manuscript (git-hub).

Item Type:	Conference or Workshop Item - published (Paper)
Date Type:	Publication
Status:	Published
Schools:	Schools > Engineering
Publisher:	Springer Verlag
ISSN:	0177-0667
Date of First Compliant Deposit:	5 April 2022
Date of Acceptance:	13 May 2023
Last Modified:	13 May 2026 14:29
URI:	https://orca.cardiff.ac.uk/id/eprint/149007

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