Crashworthiness Prediction of Perforated Foam-Filled CFRP Rectangular Tubes Crash Box Using Machine Learning.
Saved in:
| Title: | Crashworthiness Prediction of Perforated Foam-Filled CFRP Rectangular Tubes Crash Box Using Machine Learning. |
|---|---|
| Authors: | Junaedi, Harri1 (AUTHOR) hlukman@psu.edu.sa, Akkad, Khaled2 (AUTHOR), Khan, Tabrej1,3 (AUTHOR), Abd El-baky, Marwa A.3,4 (AUTHOR), Awd Allah, Mahmoud M.5,6 (AUTHOR), Sebaey, Tamer A.1,5,6 (AUTHOR) |
| Source: | Polymers (20734360). Nov2025, Vol. 17 Issue 21, p2887. 23p. |
| Subjects: | Machine learning, Urethane foam, Technical specifications, Failure analysis, Absorption, Dead loads (Mechanics), Safety, Fiber-reinforced plastics |
| Abstract: | The use of carbon fiber-reinforced polymer (CFRP) tubes as crash boxes has become a subject of interest due to their high specific strength and energy absorption capabilities. This study investigates the crashworthiness performance of rectangular tubes made of CFRP, with and without holes and polyurethane foam (PUF)-filled inner structures. The designed tubes were subjected to quasi-static axial compression loading. In addition to carefully documenting failure histories, data on crash load and displacement responses were methodically recorded during testing. To evaluate crashworthiness performance, three design parameters were considered: hole diameter, the number of holes in both the x and y directions, and whether the tube was filled with foam or left unfilled. Machine learning (ML) was also used to reduce the time and cost by predicting the crashworthiness indicators of the tubes from fewer experiments. A collection of ML algorithms such as decision tree regressor (DTR), linear regressor (LR), ridge regressor (RR), lasso regressor (LAR), elastic nets (ENs), and multi-layer perceptron (MLP) have been utilized to predict crashworthiness indicators such as initial peak force (Pip), mean crushing force (Pm) and energy absorption (EA) of the design tubes from the experimental data. The experimental results showed that PUF-filling significantly enhanced crashworthiness properties, with Pm and EA increasing by nearly threefold compared to unfilled tubes. Furthermore, in unfilled tubes, the introduction of holes led to varying effects depending on the hole diameter and placement. Meanwhile, in PUF-filled tubes, the presence of holes reduced the crashworthiness performance. For ML prediction, the DTR achieved the best accuracy with the lowest value of root mean squared error (RMSE) and mean absolute percentage error (MAPE) of 1251 and 11.37%, respectively. These findings demonstrate both the importance of PUF-filled, perforation configurations and the feasibility of ML models in optimizing CFRP crash box designs. [ABSTRACT FROM AUTHOR] |
| Copyright of Polymers (20734360) is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
|
Full text is not displayed to guests.
Login for full access.
|
|
| FullText | Links: – Type: pdflink Text: Availability: 1 |
|---|---|
| Header | DbId: egs DbLabel: Engineering Source An: 189608523 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
| IllustrationInfo | |
| Items | – Name: Title Label: Title Group: Ti Data: Crashworthiness Prediction of Perforated Foam-Filled CFRP Rectangular Tubes Crash Box Using Machine Learning. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Junaedi%2C+Harri%22">Junaedi, Harri</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> hlukman@psu.edu.sa</i><br /><searchLink fieldCode="AR" term="%22Akkad%2C+Khaled%22">Akkad, Khaled</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Khan%2C+Tabrej%22">Khan, Tabrej</searchLink><relatesTo>1,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Abd+El-baky%2C+Marwa+A%2E%22">Abd El-baky, Marwa A.</searchLink><relatesTo>3,4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Awd+Allah%2C+Mahmoud+M%2E%22">Awd Allah, Mahmoud M.</searchLink><relatesTo>5,6</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Sebaey%2C+Tamer+A%2E%22">Sebaey, Tamer A.</searchLink><relatesTo>1,5,6</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Polymers+%2820734360%29%22">Polymers (20734360)</searchLink>. Nov2025, Vol. 17 Issue 21, p2887. 23p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Urethane+foam%22">Urethane foam</searchLink><br /><searchLink fieldCode="DE" term="%22Technical+specifications%22">Technical specifications</searchLink><br /><searchLink fieldCode="DE" term="%22Failure+analysis%22">Failure analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Absorption%22">Absorption</searchLink><br /><searchLink fieldCode="DE" term="%22Dead+loads+%28Mechanics%29%22">Dead loads (Mechanics)</searchLink><br /><searchLink fieldCode="DE" term="%22Safety%22">Safety</searchLink><br /><searchLink fieldCode="DE" term="%22Fiber-reinforced+plastics%22">Fiber-reinforced plastics</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: The use of carbon fiber-reinforced polymer (CFRP) tubes as crash boxes has become a subject of interest due to their high specific strength and energy absorption capabilities. This study investigates the crashworthiness performance of rectangular tubes made of CFRP, with and without holes and polyurethane foam (PUF)-filled inner structures. The designed tubes were subjected to quasi-static axial compression loading. In addition to carefully documenting failure histories, data on crash load and displacement responses were methodically recorded during testing. To evaluate crashworthiness performance, three design parameters were considered: hole diameter, the number of holes in both the x and y directions, and whether the tube was filled with foam or left unfilled. Machine learning (ML) was also used to reduce the time and cost by predicting the crashworthiness indicators of the tubes from fewer experiments. A collection of ML algorithms such as decision tree regressor (DTR), linear regressor (LR), ridge regressor (RR), lasso regressor (LAR), elastic nets (ENs), and multi-layer perceptron (MLP) have been utilized to predict crashworthiness indicators such as initial peak force (Pip), mean crushing force (Pm) and energy absorption (EA) of the design tubes from the experimental data. The experimental results showed that PUF-filling significantly enhanced crashworthiness properties, with Pm and EA increasing by nearly threefold compared to unfilled tubes. Furthermore, in unfilled tubes, the introduction of holes led to varying effects depending on the hole diameter and placement. Meanwhile, in PUF-filled tubes, the presence of holes reduced the crashworthiness performance. For ML prediction, the DTR achieved the best accuracy with the lowest value of root mean squared error (RMSE) and mean absolute percentage error (MAPE) of 1251 and 11.37%, respectively. These findings demonstrate both the importance of PUF-filled, perforation configurations and the feasibility of ML models in optimizing CFRP crash box designs. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Polymers (20734360) is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=189608523 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3390/polym17212887 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 23 StartPage: 2887 Subjects: – SubjectFull: Machine learning Type: general – SubjectFull: Urethane foam Type: general – SubjectFull: Technical specifications Type: general – SubjectFull: Failure analysis Type: general – SubjectFull: Absorption Type: general – SubjectFull: Dead loads (Mechanics) Type: general – SubjectFull: Safety Type: general – SubjectFull: Fiber-reinforced plastics Type: general Titles: – TitleFull: Crashworthiness Prediction of Perforated Foam-Filled CFRP Rectangular Tubes Crash Box Using Machine Learning. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Junaedi, Harri – PersonEntity: Name: NameFull: Akkad, Khaled – PersonEntity: Name: NameFull: Khan, Tabrej – PersonEntity: Name: NameFull: Abd El-baky, Marwa A. – PersonEntity: Name: NameFull: Awd Allah, Mahmoud M. – PersonEntity: Name: NameFull: Sebaey, Tamer A. IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 11 Text: Nov2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 20734360 Numbering: – Type: volume Value: 17 – Type: issue Value: 21 Titles: – TitleFull: Polymers (20734360) Type: main |
| ResultId | 1 |
