Data-driven discovery of the design rules for considering the curing deformation and the application on double-double composites
The process-induced deformation (PID) of composite laminates has been one of the critical problems for engineering structures. While lots of design rules has been proposed for standardize the laminate design, there is a lack of specific rule to follow when controlling PID is a necessity due to the n...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-07-01
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| Series: | Composites Part C: Open Access |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666682025000556 |
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| author | Yizhuo Gui Hongwei Song Jinglei Yang Cheng Qiu |
| author_facet | Yizhuo Gui Hongwei Song Jinglei Yang Cheng Qiu |
| author_sort | Yizhuo Gui |
| collection | DOAJ |
| description | The process-induced deformation (PID) of composite laminates has been one of the critical problems for engineering structures. While lots of design rules has been proposed for standardize the laminate design, there is a lack of specific rule to follow when controlling PID is a necessity due to the numerous affecting parameters. In this regard, a data-driven framework was proposed in this paper to determine the layup rules to follow for minimizing PID. Two specific machine learning (ML) models were built. One is combined model of convolutional neural networks (CNN) and principle component analysis (PCA) technique for connecting the layup sequences and their corresponding PID. Another one is the symbolic regression model, as an explainable ML technique, to quantitatively evaluate this connection. With the training data generated from the robust numerical simulation, it is found that a proper asymmetry is the key intrinsic factor that makes a smaller PID as it will counteract with the contributions of other extrinsic mechanisms. More importantly, a formula for easy evaluation of the asymmetry is provided to assist in guiding the layup design considering PID constraints. The formula is applied on the design problem of double-double (DD) composites. With the proper asymmetry added onto the original DD layup, the DD composites show a clear improvement on controlling the PID. |
| format | Article |
| id | doaj-art-a077a3783a554178935a3b4db4523dea |
| institution | DOAJ |
| issn | 2666-6820 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Composites Part C: Open Access |
| spelling | doaj-art-a077a3783a554178935a3b4db4523dea2025-08-20T03:12:15ZengElsevierComposites Part C: Open Access2666-68202025-07-011710061210.1016/j.jcomc.2025.100612Data-driven discovery of the design rules for considering the curing deformation and the application on double-double compositesYizhuo Gui0Hongwei Song1Jinglei Yang2Cheng Qiu3Institute of Mechanics, Chinese Academy of Sciences, Beijing, PR China; University of Chinese Academy of Sciences, Beijing, PR ChinaInstitute of Mechanics, Chinese Academy of Sciences, Beijing, PR ChinaDepartment of Mechanical and Aerospace Engineering, Hong Kong University of Science and Technology, Hong Kong, PR China; HKUST Shenzhen-Hong Kong Collaborative Innovation Research Institute, Shenzhen, PR ChinaInstitute of Mechanics, Chinese Academy of Sciences, Beijing, PR China; Corresponding author.The process-induced deformation (PID) of composite laminates has been one of the critical problems for engineering structures. While lots of design rules has been proposed for standardize the laminate design, there is a lack of specific rule to follow when controlling PID is a necessity due to the numerous affecting parameters. In this regard, a data-driven framework was proposed in this paper to determine the layup rules to follow for minimizing PID. Two specific machine learning (ML) models were built. One is combined model of convolutional neural networks (CNN) and principle component analysis (PCA) technique for connecting the layup sequences and their corresponding PID. Another one is the symbolic regression model, as an explainable ML technique, to quantitatively evaluate this connection. With the training data generated from the robust numerical simulation, it is found that a proper asymmetry is the key intrinsic factor that makes a smaller PID as it will counteract with the contributions of other extrinsic mechanisms. More importantly, a formula for easy evaluation of the asymmetry is provided to assist in guiding the layup design considering PID constraints. The formula is applied on the design problem of double-double (DD) composites. With the proper asymmetry added onto the original DD layup, the DD composites show a clear improvement on controlling the PID.http://www.sciencedirect.com/science/article/pii/S2666682025000556Process-Induced Deformation (PID)Finite Element ModelingMachine LearningLayup Sequences |
| spellingShingle | Yizhuo Gui Hongwei Song Jinglei Yang Cheng Qiu Data-driven discovery of the design rules for considering the curing deformation and the application on double-double composites Composites Part C: Open Access Process-Induced Deformation (PID) Finite Element Modeling Machine Learning Layup Sequences |
| title | Data-driven discovery of the design rules for considering the curing deformation and the application on double-double composites |
| title_full | Data-driven discovery of the design rules for considering the curing deformation and the application on double-double composites |
| title_fullStr | Data-driven discovery of the design rules for considering the curing deformation and the application on double-double composites |
| title_full_unstemmed | Data-driven discovery of the design rules for considering the curing deformation and the application on double-double composites |
| title_short | Data-driven discovery of the design rules for considering the curing deformation and the application on double-double composites |
| title_sort | data driven discovery of the design rules for considering the curing deformation and the application on double double composites |
| topic | Process-Induced Deformation (PID) Finite Element Modeling Machine Learning Layup Sequences |
| url | http://www.sciencedirect.com/science/article/pii/S2666682025000556 |
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