Prediction of mechanical solutions for a laminated LCEs system fusing an analytical model and neural networks

Prediction of mechanical solutions for a laminated LCEs system fusing an analytical model and neural networks

Wang, Jue (Hohai University)
Yuan, Weiyi (Hohai University)
Li, Z. (TU Delft Electronic Components, Technology and Materials)
Zhu, Yingcan (University of Southern Queensland)
Santos, Thebano (Ministry of Science, Technology, Innovation and Communication)
Fan, J. (TU Delft Electronic Components, Technology and Materials; Fudan University)

2022

This paper presents a convenient and efficient method to predict the mechanical solutions of a laminated Liquid Crystal Elastomers (LCEs) system subjected to combined thermo-mechanical load, based on a back propagation (BP) neural network which is trained by machine learning from a database established by analytical solutions. Firstly, the general solutions of temperature, displacement, and stress of any single layer in the LCEs system are obtained by solving the two-dimensional (2D) governing equations of both heat conduction and thermoelasticity. Then, the unknown coefficients in above general solutions are determined by a transfer-matrix method based on the continuity condition at the interface of adjacent layers and the combined thermo-mechanical loads condition at the surface of the LCEs system. The formula derivation and calculator program are verified through convergence studies and comparisons with FEM results. Finally, a database with displacements of LCEs system in a temperature field subjected to 561 sets of mechanical loads is established based on the presented analytical model. The BP neural network based on above database is further applied to establish the relationship between deformation and mechanical load to predict the elastic deformation of the LCEs system in a temperature field subjected to a mechanical load. Moreover, the BP network can also inverse the coefficients of mechanical load which induces the specific deformation in a temperature field. The numerical examples show that: (1) The deformation of a laminated LCEs system due to thermal load is limited within the range of human temperature changes from 36 °C to 40 °C. (2) The thickness of the LCE is a sensitive parameter on the deformation at the bottom surface of the system. (3) The accuracy of predicted displacements induced by the thermo-mechanical load and the inversed mechanical load based on deformation of the LCEs system in a temperature field using BP neural network reaches 99.6% and 98.5% respectively.

Analytical solution
BP neural Network
Laminated liquid crystal elastomers system
Thermo-mechanical load

http://resolver.tudelft.nl/uuid:b96015d2-2592-4cc6-8af3-5fd0a8336e58

https://doi.org/10.1016/j.jmbbm.2021.104918

2023-07-01

1751-6161

Journal of the Mechanical Behavior of Biomedical Materials, 125, 1-11

Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Institutional Repository

journal article

© 2022 Jue Wang, Weiyi Yuan, Z. Li, Yingcan Zhu, Thebano Santos, J. Fan