Lightweight Design of Variable-Stiffness Cylinders with Reduced Imperfection Sensitivity Enabled by Continuous Tow Shearing and Machine Learning
The present study investigates how to apply continuous tow shearing (CTS) in a manufacturable design parameterization to obtain reduced imperfection sensitivity in lightweight, cylindrical shell designs. The asymptotic nonlinear method developed by Koiter is applied to predict the post-buckled stiff...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-06-01
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| Series: | Materials |
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| Online Access: | https://www.mdpi.com/1996-1944/15/12/4117 |
| _version_ | 1797484925466705920 |
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| author | Rogério R. dos Santos Saullo G. P. Castro |
| author_facet | Rogério R. dos Santos Saullo G. P. Castro |
| author_sort | Rogério R. dos Santos |
| collection | DOAJ |
| description | The present study investigates how to apply continuous tow shearing (CTS) in a manufacturable design parameterization to obtain reduced imperfection sensitivity in lightweight, cylindrical shell designs. The asymptotic nonlinear method developed by Koiter is applied to predict the post-buckled stiffness, whose index is constrained to be positive in the optimal design, together with a minimum design load. The performance of three machine learning methods, namely, Support Vector Machine, Kriging, and Random Forest, are compared as drivers to the optimization towards lightweight designs. The new methodology consists of contributions in the areas of problem modeling, the selection of machine learning strategies, and an optimization formulation that results in optimal designs around the compromise frontier between mass and stiffness. The proposed ML-based framework proved to be able to solve the inverse problem for which a target design load is given as input, returning as output lightweight designs with reduced imperfection sensitivity. The results obtained are compatible with the existing literature where hoop-oriented reinforcements were added to obtain reduced imperfection sensitivity in composite cylinders. |
| first_indexed | 2024-03-09T23:11:26Z |
| format | Article |
| id | doaj.art-207cd86e358848d5a8e91c55ed9ccbd0 |
| institution | Directory Open Access Journal |
| issn | 1996-1944 |
| language | English |
| last_indexed | 2024-03-09T23:11:26Z |
| publishDate | 2022-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Materials |
| spelling | doaj.art-207cd86e358848d5a8e91c55ed9ccbd02023-11-23T17:42:52ZengMDPI AGMaterials1996-19442022-06-011512411710.3390/ma15124117Lightweight Design of Variable-Stiffness Cylinders with Reduced Imperfection Sensitivity Enabled by Continuous Tow Shearing and Machine LearningRogério R. dos Santos0Saullo G. P. Castro1Division of Mechanical Engineering, Aeronautics Institute of Technology, São José dos Campos 12228-900, BrazilFaculty of Aerospace Engineering, Delft University of Technology, 2629 HS Delft, The NetherlandsThe present study investigates how to apply continuous tow shearing (CTS) in a manufacturable design parameterization to obtain reduced imperfection sensitivity in lightweight, cylindrical shell designs. The asymptotic nonlinear method developed by Koiter is applied to predict the post-buckled stiffness, whose index is constrained to be positive in the optimal design, together with a minimum design load. The performance of three machine learning methods, namely, Support Vector Machine, Kriging, and Random Forest, are compared as drivers to the optimization towards lightweight designs. The new methodology consists of contributions in the areas of problem modeling, the selection of machine learning strategies, and an optimization formulation that results in optimal designs around the compromise frontier between mass and stiffness. The proposed ML-based framework proved to be able to solve the inverse problem for which a target design load is given as input, returning as output lightweight designs with reduced imperfection sensitivity. The results obtained are compatible with the existing literature where hoop-oriented reinforcements were added to obtain reduced imperfection sensitivity in composite cylinders.https://www.mdpi.com/1996-1944/15/12/4117bucklingpost-bucklingimperfectionimperfection sensitivityfilament windingsupport vector machine |
| spellingShingle | Rogério R. dos Santos Saullo G. P. Castro Lightweight Design of Variable-Stiffness Cylinders with Reduced Imperfection Sensitivity Enabled by Continuous Tow Shearing and Machine Learning Materials buckling post-buckling imperfection imperfection sensitivity filament winding support vector machine |
| title | Lightweight Design of Variable-Stiffness Cylinders with Reduced Imperfection Sensitivity Enabled by Continuous Tow Shearing and Machine Learning |
| title_full | Lightweight Design of Variable-Stiffness Cylinders with Reduced Imperfection Sensitivity Enabled by Continuous Tow Shearing and Machine Learning |
| title_fullStr | Lightweight Design of Variable-Stiffness Cylinders with Reduced Imperfection Sensitivity Enabled by Continuous Tow Shearing and Machine Learning |
| title_full_unstemmed | Lightweight Design of Variable-Stiffness Cylinders with Reduced Imperfection Sensitivity Enabled by Continuous Tow Shearing and Machine Learning |
| title_short | Lightweight Design of Variable-Stiffness Cylinders with Reduced Imperfection Sensitivity Enabled by Continuous Tow Shearing and Machine Learning |
| title_sort | lightweight design of variable stiffness cylinders with reduced imperfection sensitivity enabled by continuous tow shearing and machine learning |
| topic | buckling post-buckling imperfection imperfection sensitivity filament winding support vector machine |
| url | https://www.mdpi.com/1996-1944/15/12/4117 |
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