Artificial Intelligence‐Driven Prediction and Optimization of Tensile and Impact Strength in Natural Fiber/Aluminum Oxide Polymer Nanocomposites
ABSTRACT This study investigates the mechanical properties of hybrid composites reinforced with jute, kenaf, and glass fibers, incorporating Aluminum Oxide (Al2O3) as a nanoparticle filler. The effects of three key parameters—fiber orientation, fiber sequence, and weight percentage of Al2O3 on—the t...
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Wiley
2025-04-01
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| Series: | Engineering Reports |
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| Online Access: | https://doi.org/10.1002/eng2.70093 |
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| author | Solairaju Jothi Arunachalam Rathinasamy Saravanan Nashwan Adnan Othman Sathish Thanikodi Jayant Giri Muzhda Azizi Taoufik Saidani |
| author_facet | Solairaju Jothi Arunachalam Rathinasamy Saravanan Nashwan Adnan Othman Sathish Thanikodi Jayant Giri Muzhda Azizi Taoufik Saidani |
| author_sort | Solairaju Jothi Arunachalam |
| collection | DOAJ |
| description | ABSTRACT This study investigates the mechanical properties of hybrid composites reinforced with jute, kenaf, and glass fibers, incorporating Aluminum Oxide (Al2O3) as a nanoparticle filler. The effects of three key parameters—fiber orientation, fiber sequence, and weight percentage of Al2O3 on—the tensile and impact strength of the composites were examined. Three levels for each factor were considered: fiber orientation (0°, 45°, and 90°), fiber sequence (1, 2, and 3 layers), and varying Al2O3 content (3%, 4%, and 5%). The response surface methodology (RSM) was employed to optimize the parameters, providing insights into the interactions between these factors and their influence on the composite's mechanical performance. Additionally, artificial neural networks (ANN) were used for prediction modeling. The outcome presented that the ANN model outpaced RSM in terms of accuracy, with a higher correlation between predicted and experimental values. The optimal parameters for achieving the highest tensile and impact strength were determined, with fiber orientation at 90°, fiber sequence at 3, and Al2O3 content at 5%. This study demonstrates the effectiveness of ANN in predicting the mechanical properties of the laminated composite and highlights the significant role of fiber orientation, sequence, and nanoparticle reinforcement in enhancing composite performance. |
| format | Article |
| id | doaj-art-57edf9bc232e466497bd2f06153ff9f4 |
| institution | DOAJ |
| issn | 2577-8196 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Wiley |
| record_format | Article |
| series | Engineering Reports |
| spelling | doaj-art-57edf9bc232e466497bd2f06153ff9f42025-08-20T03:12:02ZengWileyEngineering Reports2577-81962025-04-0174n/an/a10.1002/eng2.70093Artificial Intelligence‐Driven Prediction and Optimization of Tensile and Impact Strength in Natural Fiber/Aluminum Oxide Polymer NanocompositesSolairaju Jothi Arunachalam0Rathinasamy Saravanan1Nashwan Adnan Othman2Sathish Thanikodi3Jayant Giri4Muzhda Azizi5Taoufik Saidani6Department of Mechanical Engineering Saveetha School of Engineering, SIMATS Chennai Tamil Nadu IndiaDepartment of Mechanical Engineering Saveetha School of Engineering, SIMATS Chennai Tamil Nadu IndiaDepartment of Computer Engineering, College of Engineering Knowledge University Erbil IraqDepartment of Mechanical Engineering Saveetha School of Engineering, SIMATS Chennai Tamil Nadu IndiaDepartment of Mechanical Engineering Yeshwantrao Chavan College of Engineering Nagpur IndiaDepartment of Petrochemical & Gas Industrial Engineering, Faculty of Chemical Engineering Jawzjan University AfghanistanCenter for Scientific Research and Entrepreneurship Northern Border University Arar Saudi ArabiaABSTRACT This study investigates the mechanical properties of hybrid composites reinforced with jute, kenaf, and glass fibers, incorporating Aluminum Oxide (Al2O3) as a nanoparticle filler. The effects of three key parameters—fiber orientation, fiber sequence, and weight percentage of Al2O3 on—the tensile and impact strength of the composites were examined. Three levels for each factor were considered: fiber orientation (0°, 45°, and 90°), fiber sequence (1, 2, and 3 layers), and varying Al2O3 content (3%, 4%, and 5%). The response surface methodology (RSM) was employed to optimize the parameters, providing insights into the interactions between these factors and their influence on the composite's mechanical performance. Additionally, artificial neural networks (ANN) were used for prediction modeling. The outcome presented that the ANN model outpaced RSM in terms of accuracy, with a higher correlation between predicted and experimental values. The optimal parameters for achieving the highest tensile and impact strength were determined, with fiber orientation at 90°, fiber sequence at 3, and Al2O3 content at 5%. This study demonstrates the effectiveness of ANN in predicting the mechanical properties of the laminated composite and highlights the significant role of fiber orientation, sequence, and nanoparticle reinforcement in enhancing composite performance.https://doi.org/10.1002/eng2.70093artificial neural networksmechanical characterization and fiber orientationnano‐particleresponse surface methodology |
| spellingShingle | Solairaju Jothi Arunachalam Rathinasamy Saravanan Nashwan Adnan Othman Sathish Thanikodi Jayant Giri Muzhda Azizi Taoufik Saidani Artificial Intelligence‐Driven Prediction and Optimization of Tensile and Impact Strength in Natural Fiber/Aluminum Oxide Polymer Nanocomposites Engineering Reports artificial neural networks mechanical characterization and fiber orientation nano‐particle response surface methodology |
| title | Artificial Intelligence‐Driven Prediction and Optimization of Tensile and Impact Strength in Natural Fiber/Aluminum Oxide Polymer Nanocomposites |
| title_full | Artificial Intelligence‐Driven Prediction and Optimization of Tensile and Impact Strength in Natural Fiber/Aluminum Oxide Polymer Nanocomposites |
| title_fullStr | Artificial Intelligence‐Driven Prediction and Optimization of Tensile and Impact Strength in Natural Fiber/Aluminum Oxide Polymer Nanocomposites |
| title_full_unstemmed | Artificial Intelligence‐Driven Prediction and Optimization of Tensile and Impact Strength in Natural Fiber/Aluminum Oxide Polymer Nanocomposites |
| title_short | Artificial Intelligence‐Driven Prediction and Optimization of Tensile and Impact Strength in Natural Fiber/Aluminum Oxide Polymer Nanocomposites |
| title_sort | artificial intelligence driven prediction and optimization of tensile and impact strength in natural fiber aluminum oxide polymer nanocomposites |
| topic | artificial neural networks mechanical characterization and fiber orientation nano‐particle response surface methodology |
| url | https://doi.org/10.1002/eng2.70093 |
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