Asset Details
Do you wish to reserve the book?
Artificial Intelligence‐Driven Prediction and Optimization of Tensile and Impact Strength in Natural Fiber/Aluminum Oxide Polymer Nanocomposites
Hey, we have placed the reservation for you!
By the way, why not check out events that you can attend while you pick your title.
Oops! Something went wrong.
Looks like we were not able to place the reservation. Kindly try again later.
Are you sure you want to remove the book from the shelf?
Artificial Intelligence‐Driven Prediction and Optimization of Tensile and Impact Strength in Natural Fiber/Aluminum Oxide Polymer Nanocomposites
Do you wish to request the book?
Artificial Intelligence‐Driven Prediction and Optimization of Tensile and Impact Strength in Natural Fiber/Aluminum Oxide Polymer Nanocomposites
Please be aware that the book you have requested cannot be checked out. If you would like to checkout this book, you can reserve another copy
We have requested the book for you!
Your request is successful and it will be processed during the Library working hours. Please check the status of your request in My Requests.
Oops! Something went wrong.
Looks like we were not able to place your request. Kindly try again later.
Journal Article
Artificial Intelligence‐Driven Prediction and Optimization of Tensile and Impact Strength in Natural Fiber/Aluminum Oxide Polymer Nanocomposites
2025
Overview
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. This study examines hybrid composites reinforced with jute, kenaf, and glass fibers, with Aluminum Oxide (Al2O3) as a filler. The effects of fiber orientation, sequence, and Al2O3 content on tensile and impact strength were analyzed. ANN outperformed RSM in predictive accuracy, identifying optimal parameters: 90° fiber orientation, three layers, and 5% Al2O3. Results highlight ANN's potential and the role of fiber and nanoparticle integration in enhancing composite properties.
Publisher
John Wiley & Sons, Inc,Wiley
