The stacking sequence optimisation of a filament wound composite bicycle frame using the data-driven evolutionary algorithm EvoDN2
This work focusses on identifying the optimal stacking sequence for composite tubes in mountain bike frames using a data-driven model combined with evolutionary algorithms. The objective is to find a frame that is sufficiently stiff while meeting the requirements of weight, strength, and minimum tub...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2024-12-01
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| Series: | Philosophical Magazine Letters |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/09500839.2024.2347899 |
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| author | Anna Malá Zdeněk Padovec Tomáš Mareš Nirupam Chakraborti |
| author_facet | Anna Malá Zdeněk Padovec Tomáš Mareš Nirupam Chakraborti |
| author_sort | Anna Malá |
| collection | DOAJ |
| description | This work focusses on identifying the optimal stacking sequence for composite tubes in mountain bike frames using a data-driven model combined with evolutionary algorithms. The objective is to find a frame that is sufficiently stiff while meeting the requirements of weight, strength, and minimum tube wall thickness. The decision variables are the ply winding angles and the ply thicknesses of each tube. The study performs designs for two load cases – Starting and Uphill – and explores two types of winding: the gradual winding of individual layers (1ply) and the winding of layers between predefined inner and outer layers with variable thicknesses (TW). Additionally, the design process is applied to frames made of isotropic materials, such as steel, aluminium, and titanium, using the same methodology to allow for comparison of results. The article demonstrates the successful application of this methodology to common sports equipment, suggesting its potential for beneficial use in other common composite frame structures. |
| format | Article |
| id | doaj-art-9b650f98aa394548b0c695429ff2d917 |
| institution | Kabale University |
| issn | 0950-0839 1362-3036 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Philosophical Magazine Letters |
| spelling | doaj-art-9b650f98aa394548b0c695429ff2d9172024-12-02T04:03:26ZengTaylor & Francis GroupPhilosophical Magazine Letters0950-08391362-30362024-12-01104110.1080/09500839.2024.2347899The stacking sequence optimisation of a filament wound composite bicycle frame using the data-driven evolutionary algorithm EvoDN2Anna Malá0Zdeněk Padovec1Tomáš Mareš2Nirupam Chakraborti3Department of Mechanics, Biomechanics and Mechatronics, Faculty of Mechanical Engineering, Czech Technical University, Prague, Czech RepublicDepartment of Mechanics, Biomechanics and Mechatronics, Faculty of Mechanical Engineering, Czech Technical University, Prague, Czech RepublicDepartment of Mechanics, Biomechanics and Mechatronics, Faculty of Mechanical Engineering, Czech Technical University, Prague, Czech RepublicDepartment of Mechanics, Biomechanics and Mechatronics, Faculty of Mechanical Engineering, Czech Technical University, Prague, Czech RepublicThis work focusses on identifying the optimal stacking sequence for composite tubes in mountain bike frames using a data-driven model combined with evolutionary algorithms. The objective is to find a frame that is sufficiently stiff while meeting the requirements of weight, strength, and minimum tube wall thickness. The decision variables are the ply winding angles and the ply thicknesses of each tube. The study performs designs for two load cases – Starting and Uphill – and explores two types of winding: the gradual winding of individual layers (1ply) and the winding of layers between predefined inner and outer layers with variable thicknesses (TW). Additionally, the design process is applied to frames made of isotropic materials, such as steel, aluminium, and titanium, using the same methodology to allow for comparison of results. The article demonstrates the successful application of this methodology to common sports equipment, suggesting its potential for beneficial use in other common composite frame structures.https://www.tandfonline.com/doi/10.1080/09500839.2024.2347899bicyclecompositedata-drivenoptimisationevolutionary |
| spellingShingle | Anna Malá Zdeněk Padovec Tomáš Mareš Nirupam Chakraborti The stacking sequence optimisation of a filament wound composite bicycle frame using the data-driven evolutionary algorithm EvoDN2 Philosophical Magazine Letters bicycle composite data-driven optimisation evolutionary |
| title | The stacking sequence optimisation of a filament wound composite bicycle frame using the data-driven evolutionary algorithm EvoDN2 |
| title_full | The stacking sequence optimisation of a filament wound composite bicycle frame using the data-driven evolutionary algorithm EvoDN2 |
| title_fullStr | The stacking sequence optimisation of a filament wound composite bicycle frame using the data-driven evolutionary algorithm EvoDN2 |
| title_full_unstemmed | The stacking sequence optimisation of a filament wound composite bicycle frame using the data-driven evolutionary algorithm EvoDN2 |
| title_short | The stacking sequence optimisation of a filament wound composite bicycle frame using the data-driven evolutionary algorithm EvoDN2 |
| title_sort | stacking sequence optimisation of a filament wound composite bicycle frame using the data driven evolutionary algorithm evodn2 |
| topic | bicycle composite data-driven optimisation evolutionary |
| url | https://www.tandfonline.com/doi/10.1080/09500839.2024.2347899 |
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