Investigation on the Mechanical Behavior of Date Palm Fibers Reinforced Composites: Predictive Modelling Using Artificial Neural Networks (ANNs)
This paper aims to strengthen composites by treated and untreated date palm fibers (PDF), with sodium hydroxide (NaOH), for light applications. With 75% cellulose content and a density of 1.2 g/cm3, the palm fibers were exposed to a preparatory treatment with 1.5% NaOH for 24 h prior to integration...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2024-12-01
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| Series: | Journal of Natural Fibers |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/15440478.2024.2396899 |
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| author | Hocine Makri Salah Amroune Moussa Zaoui Khalissa Saada Mohammad Jawaid Yasemin Seki Bilal Aichouche Hassan Fouad Imran Uddin |
| author_facet | Hocine Makri Salah Amroune Moussa Zaoui Khalissa Saada Mohammad Jawaid Yasemin Seki Bilal Aichouche Hassan Fouad Imran Uddin |
| author_sort | Hocine Makri |
| collection | DOAJ |
| description | This paper aims to strengthen composites by treated and untreated date palm fibers (PDF), with sodium hydroxide (NaOH), for light applications. With 75% cellulose content and a density of 1.2 g/cm3, the palm fibers were exposed to a preparatory treatment with 1.5% NaOH for 24 h prior to integration into a polyester. Four polyester samples comprising 30% of palm fiber were manufactured. Additionally, the palm fiber interface was evaluated using scanning electron microscopy (SEM) and optical microscopy. The specimens underwent mechanical testing and it shows that tensile (18% increase in stress and 1.2% increase in Young’s modulus) and flexural properties (20% increase in strength and 10% increase in Young’s modulus) of treated composites as compared with untreated fibers. A MATLAB-based Artificial Neural Network (ANN) model was applied to estimate stress and strain at break as well as the Young’s modulus, based on three input characteristics: section, sample length, and chemical treatment. It was obtained that the polyester reinforced by NaOH-treated palm fibers increased the mechanical characteristics relative to the untreated fibers. The coefficient of determination R2 in the ANN models is 0.87. These results suggest that the ANN model is a useful tool for predicting mechanical properties. |
| format | Article |
| id | doaj-art-f8ef7b9ca1f14bc88aa23f9f32b889c4 |
| institution | OA Journals |
| issn | 1544-0478 1544-046X |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Journal of Natural Fibers |
| spelling | doaj-art-f8ef7b9ca1f14bc88aa23f9f32b889c42025-08-20T02:22:02ZengTaylor & Francis GroupJournal of Natural Fibers1544-04781544-046X2024-12-0121110.1080/15440478.2024.2396899Investigation on the Mechanical Behavior of Date Palm Fibers Reinforced Composites: Predictive Modelling Using Artificial Neural Networks (ANNs)Hocine Makri0Salah Amroune1Moussa Zaoui2Khalissa Saada3Mohammad Jawaid4Yasemin Seki5Bilal Aichouche6Hassan Fouad7Imran Uddin8Department of Mechanical Engineering, Faculty of Technology, University of M’sila, M’sila, AlgeriaDepartment of Mechanical Engineering, Faculty of Technology, University of M’sila, M’sila, AlgeriaDepartment of Mechanical Engineering, Faculty of Technology, University of M’sila, M’sila, AlgeriaDepartment of Mechanical Engineering, Faculty of Technology, University of M’sila, M’sila, AlgeriaChemical and Petroleum Engineering Department, College of Engineering, United Arab Emirates University (UAEU), Al Ain, United Arab EmiratesDepartment of Textile Engineering, Dokuz Eylul University, Izmir, TurkeyDepartment of Mechanical Engineering, Faculty of Technology, University of M’sila, M’sila, AlgeriaDepartment of Applied Medical Science, Community College, King Saud University, Riyadh, Saudi ArabiaNanolab, Environmental Mineralogy Research Group, Research Institute of Biomolecular and Chemical Engineering, University of Pannonia, Veszprem, HungaryThis paper aims to strengthen composites by treated and untreated date palm fibers (PDF), with sodium hydroxide (NaOH), for light applications. With 75% cellulose content and a density of 1.2 g/cm3, the palm fibers were exposed to a preparatory treatment with 1.5% NaOH for 24 h prior to integration into a polyester. Four polyester samples comprising 30% of palm fiber were manufactured. Additionally, the palm fiber interface was evaluated using scanning electron microscopy (SEM) and optical microscopy. The specimens underwent mechanical testing and it shows that tensile (18% increase in stress and 1.2% increase in Young’s modulus) and flexural properties (20% increase in strength and 10% increase in Young’s modulus) of treated composites as compared with untreated fibers. A MATLAB-based Artificial Neural Network (ANN) model was applied to estimate stress and strain at break as well as the Young’s modulus, based on three input characteristics: section, sample length, and chemical treatment. It was obtained that the polyester reinforced by NaOH-treated palm fibers increased the mechanical characteristics relative to the untreated fibers. The coefficient of determination R2 in the ANN models is 0.87. These results suggest that the ANN model is a useful tool for predicting mechanical properties.https://www.tandfonline.com/doi/10.1080/15440478.2024.2396899Compositesbiodegradablestress and strainreinforcementANN复合材料 |
| spellingShingle | Hocine Makri Salah Amroune Moussa Zaoui Khalissa Saada Mohammad Jawaid Yasemin Seki Bilal Aichouche Hassan Fouad Imran Uddin Investigation on the Mechanical Behavior of Date Palm Fibers Reinforced Composites: Predictive Modelling Using Artificial Neural Networks (ANNs) Journal of Natural Fibers Composites biodegradable stress and strain reinforcement ANN 复合材料 |
| title | Investigation on the Mechanical Behavior of Date Palm Fibers Reinforced Composites: Predictive Modelling Using Artificial Neural Networks (ANNs) |
| title_full | Investigation on the Mechanical Behavior of Date Palm Fibers Reinforced Composites: Predictive Modelling Using Artificial Neural Networks (ANNs) |
| title_fullStr | Investigation on the Mechanical Behavior of Date Palm Fibers Reinforced Composites: Predictive Modelling Using Artificial Neural Networks (ANNs) |
| title_full_unstemmed | Investigation on the Mechanical Behavior of Date Palm Fibers Reinforced Composites: Predictive Modelling Using Artificial Neural Networks (ANNs) |
| title_short | Investigation on the Mechanical Behavior of Date Palm Fibers Reinforced Composites: Predictive Modelling Using Artificial Neural Networks (ANNs) |
| title_sort | investigation on the mechanical behavior of date palm fibers reinforced composites predictive modelling using artificial neural networks anns |
| topic | Composites biodegradable stress and strain reinforcement ANN 复合材料 |
| url | https://www.tandfonline.com/doi/10.1080/15440478.2024.2396899 |
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