| Accelerating the Layup Sequences Design of Composite Laminates via Theory-Guided Machine Learning Models | |
| Liao, Zhenhao1; Qiu C(邱诚)2,3; Yang, Jun4; Yang, Jinglei3,5,6; Yang, Lei1 | |
| Corresponding Author | Yang, Lei([email protected]) |
| Source Publication | POLYMERS
 |
| 2022-08-01 | |
| Volume | 14Issue:15Pages:14 |
| Abstract | Experimental and numerical investigations are presented for a theory-guided machine learning (ML) model that combines the Hashin failure theory (HFT) and the classical lamination theory (CLT) to optimize and accelerate the design of composite laminates. A finite element simulation with the incorporation of the HFT and CLT were used to generate the training dataset. Instead of directly mapping the relationship between the ply angles of the laminate and its strength and stiffness, a multi-layer interconnected neural network (NN) system was built following the logical sequence of composite theories. With the forward prediction by the NN system and the inverse optimization by genetic algorithm (GA), a benchmark case of designing a composite tube subjected to the combined loads of bending and torsion was studied. The ML models successfully provided the optimal layup sequences and the required fiber modulus according to the preset design targets. Additionally, it shows that the machine learning models, with the guidance of composite theories, realize a faster optimization process and requires less training data than models with direct simple NNs. Such results imply the importance of domain knowledge in helping improve the ML applications in engineering problems. |
| Keyword | composite laminate mechanical property layup design finite element simulation neural network |
| DOI | 10.3390/polym14153229 |
| Indexed By | SCI ; EI |
| Language | 英语 |
| WOS ID | WOS:000838958200001 |
| WOS Keyword | OPTIMIZATION |
| WOS Research Area | Polymer Science |
| WOS Subject | Polymer Science |
| Funding Project | University Stability Support Program Project of the Shenzhen Natural Science Foundation[20200814105851001] ; National Key R&D Program of China[2018YFB2100901] ; Induction of Entrepreneurship Talents Program - Foshan-HKUST Projects[FSUST20-ETP06] ; Project of Hetao Shenzhen-Hong Kong Science and Technology Innovation Cooperation Zone[HZQB-KCZYB-2020083] |
| Funding Organization | University Stability Support Program Project of the Shenzhen Natural Science Foundation ; National Key R&D Program of China ; Induction of Entrepreneurship Talents Program - Foshan-HKUST Projects ; Project of Hetao Shenzhen-Hong Kong Science and Technology Innovation Cooperation Zone |
| Classification | 二类 |
| Ranking | 2 |
| Contributor | Yang, Lei |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://dspace.imech.ac.cn/handle/311007/89947 |
| Collection | 流固耦合系统力学重点实验室 |
| Affiliation | 1.Shenzhen Univ, Coll Civil & Transportat Engn, Dept Civil Engn, Shenzhen 518060, Peoples R China; 2.Chinese Acad Sci, Inst Mech, Beijing 100190, Peoples R China; 3.Hong Kong Univ Sci & Technol, Dept Mech & Aerosp Engn, Hong Kong 999077, Peoples R China; 4.China Railway 5th Bur Construct Engn Co Ltd, Guiyang 550081, Guizhou, Peoples R China; 5.HKUST Shenzhen Hong Kong Collaborat Innovat Res I, Shenzhen 518031, Peoples R China; 6.Foshan SMN Mat Tech Co Ltd, Foshan 528200, Peoples R China |
| Recommended Citation GB/T 7714 | Liao, Zhenhao,Qiu C,Yang, Jun,et al. Accelerating the Layup Sequences Design of Composite Laminates via Theory-Guided Machine Learning Models[J]. POLYMERS,2022,14,15,:14.Rp_Au:Yang, Lei |
| APA | Liao, Zhenhao,邱诚,Yang, Jun,Yang, Jinglei,&Yang, Lei.(2022).Accelerating the Layup Sequences Design of Composite Laminates via Theory-Guided Machine Learning Models.POLYMERS,14(15),14. |
| MLA | Liao, Zhenhao,et al."Accelerating the Layup Sequences Design of Composite Laminates via Theory-Guided Machine Learning Models".POLYMERS 14.15(2022):14. |
| Files in This Item: | Download All | |||||
| File Name/Size | DocType | Version | Access | License | ||
| Jp2022FA243_2022_Acc（2225KB） | 期刊论文 | 出版稿 | 开放获取 | CC BY-NC-SA | View Download | |
Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.
Edit Comment