Crashworthiness optimization of composite hexagonal ring system using random forest classification and artificial neural network
<p dir="ltr">This research aims to enhance the safety level and crash resiliency of targeted woven roving glass/epoxy composite material for various industry 4.0 applications. Advanced machine learning algorithms are used in this study to figure out the complicated relationship betwe...
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2024
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| _version_ | 1864513544647606272 |
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| author | Monzure-Khoda Kazi (17191207) |
| author2 | E. Mahdi (17191210) |
| author2_role | author |
| author_facet | Monzure-Khoda Kazi (17191207) E. Mahdi (17191210) |
| author_role | author |
| dc.creator.none.fl_str_mv | Monzure-Khoda Kazi (17191207) E. Mahdi (17191210) |
| dc.date.none.fl_str_mv | 2024-02-07T06:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1016/j.jcomc.2024.100440 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Crashworthiness_optimization_of_composite_hexagonal_ring_system_using_random_forest_classification_and_artificial_neural_network/29590337 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Engineering Chemical engineering Materials engineering Information and computing sciences Artificial intelligence Crashworthiness Hexagonal ring system Composite design Random forest classification Artificial neural network |
| dc.title.none.fl_str_mv | Crashworthiness optimization of composite hexagonal ring system using random forest classification and artificial neural network |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">This research aims to enhance the safety level and crash resiliency of targeted woven roving glass/epoxy composite material for various industry 4.0 applications. Advanced machine learning algorithms are used in this study to figure out the complicated relationship between the crashworthiness parameters of the hexagonal composite ring specimens under lateral compressive, energy absorption, and failure modes. These algorithms include random forest (RF) classification and artificial neural networks (ANN). The ultimate target is to develop a robust multi-modal machine learning method to predict the optimum geometry (i.e., hexagonal ring angle) and suitable in-plane crushing arrangements of the hexagonal ring system for targeted crashworthiness parameters. The results demonstrate that the suggested RF-ANN-based technique can predict the optimal composite design with high accuracy (precision, recall, and f1-score for test and train dataset were 1). Furthermore, the confusion matrix validates the random forest classification model's accuracy. At the same time, the mean square error value serves as the loss function for the ANN model (i.e., the loss function values were 2.84 × 10<sup>−7</sup> and 6.40 × 10<sup>−7</sup>, respectively, for X1 and X2 loading conditions at 45° angle). Furthermore, the developed models can predict crashworthiness parameters for any hexagonal ring angle within the range of the trained dataset, requiring no additional experimental effort.</p><h2>Other Information</h2><p dir="ltr">Published in: Composites Part C: Open Access<br>License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1016/j.jcomc.2024.100440" target="_blank">https://dx.doi.org/10.1016/j.jcomc.2024.100440</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_e2f2974d31edf6f41bfaa81d4982af74 |
| identifier_str_mv | 10.1016/j.jcomc.2024.100440 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/29590337 |
| publishDate | 2024 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Crashworthiness optimization of composite hexagonal ring system using random forest classification and artificial neural networkMonzure-Khoda Kazi (17191207)E. Mahdi (17191210)EngineeringChemical engineeringMaterials engineeringInformation and computing sciencesArtificial intelligenceCrashworthinessHexagonal ring systemComposite designRandom forest classificationArtificial neural network<p dir="ltr">This research aims to enhance the safety level and crash resiliency of targeted woven roving glass/epoxy composite material for various industry 4.0 applications. Advanced machine learning algorithms are used in this study to figure out the complicated relationship between the crashworthiness parameters of the hexagonal composite ring specimens under lateral compressive, energy absorption, and failure modes. These algorithms include random forest (RF) classification and artificial neural networks (ANN). The ultimate target is to develop a robust multi-modal machine learning method to predict the optimum geometry (i.e., hexagonal ring angle) and suitable in-plane crushing arrangements of the hexagonal ring system for targeted crashworthiness parameters. The results demonstrate that the suggested RF-ANN-based technique can predict the optimal composite design with high accuracy (precision, recall, and f1-score for test and train dataset were 1). Furthermore, the confusion matrix validates the random forest classification model's accuracy. At the same time, the mean square error value serves as the loss function for the ANN model (i.e., the loss function values were 2.84 × 10<sup>−7</sup> and 6.40 × 10<sup>−7</sup>, respectively, for X1 and X2 loading conditions at 45° angle). Furthermore, the developed models can predict crashworthiness parameters for any hexagonal ring angle within the range of the trained dataset, requiring no additional experimental effort.</p><h2>Other Information</h2><p dir="ltr">Published in: Composites Part C: Open Access<br>License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1016/j.jcomc.2024.100440" target="_blank">https://dx.doi.org/10.1016/j.jcomc.2024.100440</a></p>2024-02-07T06:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1016/j.jcomc.2024.100440https://figshare.com/articles/journal_contribution/Crashworthiness_optimization_of_composite_hexagonal_ring_system_using_random_forest_classification_and_artificial_neural_network/29590337CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/295903372024-02-07T06:00:00Z |
| spellingShingle | Crashworthiness optimization of composite hexagonal ring system using random forest classification and artificial neural network Monzure-Khoda Kazi (17191207) Engineering Chemical engineering Materials engineering Information and computing sciences Artificial intelligence Crashworthiness Hexagonal ring system Composite design Random forest classification Artificial neural network |
| status_str | publishedVersion |
| title | Crashworthiness optimization of composite hexagonal ring system using random forest classification and artificial neural network |
| title_full | Crashworthiness optimization of composite hexagonal ring system using random forest classification and artificial neural network |
| title_fullStr | Crashworthiness optimization of composite hexagonal ring system using random forest classification and artificial neural network |
| title_full_unstemmed | Crashworthiness optimization of composite hexagonal ring system using random forest classification and artificial neural network |
| title_short | Crashworthiness optimization of composite hexagonal ring system using random forest classification and artificial neural network |
| title_sort | Crashworthiness optimization of composite hexagonal ring system using random forest classification and artificial neural network |
| topic | Engineering Chemical engineering Materials engineering Information and computing sciences Artificial intelligence Crashworthiness Hexagonal ring system Composite design Random forest classification Artificial neural network |