Tree-Based Machine Learning Approach for Predicting the Impact Behavior of Carbon/Flax Bio-Hybrid Fiber-Reinforced Polymer Composite Laminates
In this research, the effect of change in stacking sequences on the impact performance of bio-hybrid fiber-reinforced polymer (bio-HFRP) composite materials was analyzed and evaluated. The methodology was developed, based on the mechanical testing and utilization of tree-based machine learning regre...
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MDPI AG
2024-09-01
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| author | Manzar Masud Aamir Mubashar Shahid Iqbal Hassan Ejaz Saad Abdul Raheem |
| author_facet | Manzar Masud Aamir Mubashar Shahid Iqbal Hassan Ejaz Saad Abdul Raheem |
| author_sort | Manzar Masud |
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| description | In this research, the effect of change in stacking sequences on the impact performance of bio-hybrid fiber-reinforced polymer (bio-HFRP) composite materials was analyzed and evaluated. The methodology was developed, based on the mechanical testing and utilization of tree-based machine learning regression models. Low-velocity impact (LVI) testing was performed on five distinct stacking sequences of carbon/flax bio-HFRP at energies ranging from 15 J to 90 J. For all tests, peak impact force was recorded and compared. Symmetric configurations with a uniform distribution of flax layers across the composite laminate exhibited better impact performance. Additionally, two tree-based machine learning (ML) algorithms were used: random forest (RF) and decision tree (DT). The performance metrics used to assess and compare the efficiency were the coefficient of determination (R<sup>2</sup>), mean square error (MSE), and mean absolute error (MAE). The most accurate model for the prediction of peak impact force was DT with the R<sup>2</sup> training and test dataset values of 0.9920 and 0.9045, respectively. Furthermore, lower MSE and MAE values were attained using the DT model as compared to the RF model. The developed methodology and the model serve as powerful tools to predict the damage-induced properties of bio-HFRP composite laminates utilizing minimal resources and saving time as well. |
| format | Article |
| id | doaj-art-8cbb2ff12e9840318185d1aac6330d1f |
| institution | DOAJ |
| issn | 2673-4591 |
| language | English |
| publishDate | 2024-09-01 |
| publisher | MDPI AG |
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| spelling | doaj-art-8cbb2ff12e9840318185d1aac6330d1f2025-08-20T02:42:38ZengMDPI AGEngineering Proceedings2673-45912024-09-017512310.3390/engproc2024075023Tree-Based Machine Learning Approach for Predicting the Impact Behavior of Carbon/Flax Bio-Hybrid Fiber-Reinforced Polymer Composite LaminatesManzar Masud0Aamir Mubashar1Shahid Iqbal2Hassan Ejaz3Saad Abdul Raheem4Computational Mechanics Group, Department of Mechanical Engineering, School of Mechanical and Manufacturing Engineering, National University of Sciences and Technology (NUST), H-12, Islamabad 44000, PakistanComputational Mechanics Group, Department of Mechanical Engineering, School of Mechanical and Manufacturing Engineering, National University of Sciences and Technology (NUST), H-12, Islamabad 44000, PakistanMechanical Engineering Department, Wah Engineering College, University of Wah, Wah Cantt 47040, PakistanComputational Mechanics Group, Department of Mechanical Engineering, School of Mechanical and Manufacturing Engineering, National University of Sciences and Technology (NUST), H-12, Islamabad 44000, PakistanDepartment of Mechanical Engineering, Capital University of Science and Technology (CUST), Islamabad 44000, PakistanIn this research, the effect of change in stacking sequences on the impact performance of bio-hybrid fiber-reinforced polymer (bio-HFRP) composite materials was analyzed and evaluated. The methodology was developed, based on the mechanical testing and utilization of tree-based machine learning regression models. Low-velocity impact (LVI) testing was performed on five distinct stacking sequences of carbon/flax bio-HFRP at energies ranging from 15 J to 90 J. For all tests, peak impact force was recorded and compared. Symmetric configurations with a uniform distribution of flax layers across the composite laminate exhibited better impact performance. Additionally, two tree-based machine learning (ML) algorithms were used: random forest (RF) and decision tree (DT). The performance metrics used to assess and compare the efficiency were the coefficient of determination (R<sup>2</sup>), mean square error (MSE), and mean absolute error (MAE). The most accurate model for the prediction of peak impact force was DT with the R<sup>2</sup> training and test dataset values of 0.9920 and 0.9045, respectively. Furthermore, lower MSE and MAE values were attained using the DT model as compared to the RF model. The developed methodology and the model serve as powerful tools to predict the damage-induced properties of bio-HFRP composite laminates utilizing minimal resources and saving time as well.https://www.mdpi.com/2673-4591/75/1/23bio-hybrid compositesimpact behaviormachine learningdecision treerandom forest |
| spellingShingle | Manzar Masud Aamir Mubashar Shahid Iqbal Hassan Ejaz Saad Abdul Raheem Tree-Based Machine Learning Approach for Predicting the Impact Behavior of Carbon/Flax Bio-Hybrid Fiber-Reinforced Polymer Composite Laminates Engineering Proceedings bio-hybrid composites impact behavior machine learning decision tree random forest |
| title | Tree-Based Machine Learning Approach for Predicting the Impact Behavior of Carbon/Flax Bio-Hybrid Fiber-Reinforced Polymer Composite Laminates |
| title_full | Tree-Based Machine Learning Approach for Predicting the Impact Behavior of Carbon/Flax Bio-Hybrid Fiber-Reinforced Polymer Composite Laminates |
| title_fullStr | Tree-Based Machine Learning Approach for Predicting the Impact Behavior of Carbon/Flax Bio-Hybrid Fiber-Reinforced Polymer Composite Laminates |
| title_full_unstemmed | Tree-Based Machine Learning Approach for Predicting the Impact Behavior of Carbon/Flax Bio-Hybrid Fiber-Reinforced Polymer Composite Laminates |
| title_short | Tree-Based Machine Learning Approach for Predicting the Impact Behavior of Carbon/Flax Bio-Hybrid Fiber-Reinforced Polymer Composite Laminates |
| title_sort | tree based machine learning approach for predicting the impact behavior of carbon flax bio hybrid fiber reinforced polymer composite laminates |
| topic | bio-hybrid composites impact behavior machine learning decision tree random forest |
| url | https://www.mdpi.com/2673-4591/75/1/23 |
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