Deep learning driven real time topology optimisation based on initial stress learning

Yan, Jun, Zhang, Qi, Xu, Qi, Fan, Zhirui, Li, Haijiang ORCID: https://orcid.org/0000-0001-6326-8133, Sun, Wei and Wang, Guangyuan 2022. Deep learning driven real time topology optimisation based on initial stress learning. Advanced Engineering Informatics 51 , 101472. 10.1016/j.aei.2021.101472

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Abstract

Topology optimisation can facilitate engineers in proposing efficient and novel conceptual design schemes, but the traditional FEM based optimization demands significant computing power and makes the real time optimization impossible. Based on the convolutional neural network (CNN) method, a new deep learning approximate algorithm for real time topology optimisation is proposed. The algorithm learns from the initial stress (LIS), which is defined as the major principal stress matrix obtained from finite element analysis in the first iteration of classical topology optimisation. The initial major principal stress matrix of the structure is used to replace the load cases and boundary conditions of the structure as independent variables, which can produce topological prediction results with high accuracy based on a relatively small number of samples. Compared with the traditional topology optimisation method, the new method can produce a similar result in real time without repeated iterations. A classic short cantilever problem was used as an example, and the optimized topology of the cantilever structure is predicted successfully by the established approximate algorithm. By comparing the prediction results to the structural optimisation results obtained by the classical topology optimisation method, it is discovered that the two results are highly approximate, which verifies the validity of the established algorithm. Furthermore, a new algorithm evaluation method is proposed to evaluate the effects of using different methods to select samples on the prediction performance of the optimized topology, and the results were promising and concluded in the end.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Engineering
Publisher:	Elsevier
ISSN:	1474-0346
Date of First Compliant Deposit:	25 November 2021
Date of Acceptance:	16 November 2021
Last Modified:	06 Nov 2023 18:33
URI:	https://orca.cardiff.ac.uk/id/eprint/145731

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