Prediction of mechanical behavior of epoxy polymer using Artificial Neural Networks (ANN) and Response Surface Methodology (RSM)
The aim of this study is to analyze the effect of different geometries and sections on the mechanical properties of epoxy specimens. Five tensile tests were carried out on three types of series. The experimental results obtained were 1812.21 MPa, 3.90% and 41.91 MPa for intact specimens, 1450.41 MPa...
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| Language: | English |
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Gruppo Italiano Frattura
2023-10-01
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| Series: | Fracture and Structural Integrity |
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| Online Access: | https://www.fracturae.com/index.php/fis/article/view/4339/3873 |
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| author | Khalissa Saada Salah Amroune Moussa Zaoui |
| author_facet | Khalissa Saada Salah Amroune Moussa Zaoui |
| author_sort | Khalissa Saada |
| collection | DOAJ |
| description | The aim of this study is to analyze the effect of different geometries and sections on the mechanical properties of epoxy specimens. Five tensile tests were carried out on three types of series. The experimental results obtained were 1812.21 MPa, 3.90% and 41.91 MPa for intact specimens, 1450.41 MPa, 2.16% and 21.28 MPa for specimens with hole and 750.77 MPa, 2.77% and 11.89 MPa for specimens with elliptical -notched for Young's Modulus , strain and stress respectively. In addition, the experimental results indicated that the mechanical properties of both (Young's Modulus value and stress value) were higher in an intact specimen. Afterwards, the nonlinear functional relationship of input parameters between epoxy sample geometries and sections was established using the response surface model (RSM) and the artificial neural network (ANN) to predict the output parameters of mechanical properties (Young's Modulus and stress). In addition, the design of experiment was developed by the Analysis of the Application of Variance (ANOVA). The results showed the superiority of the ANN model over the RSM model, where the correlation coefficient values for the model datasets exceed ANN (R2 = 0.984 for Young's Modulus and R2 = 0.981 for the stress) |
| format | Article |
| id | doaj-art-7aa6481d5411451ba77ebd665b80ea0b |
| institution | Kabale University |
| issn | 1971-8993 |
| language | English |
| publishDate | 2023-10-01 |
| publisher | Gruppo Italiano Frattura |
| record_format | Article |
| series | Fracture and Structural Integrity |
| spelling | doaj-art-7aa6481d5411451ba77ebd665b80ea0b2025-02-03T10:44:05ZengGruppo Italiano FratturaFracture and Structural Integrity1971-89932023-10-01176619120610.3221/IGF-ESIS.66.1210.3221/IGF-ESIS.66.12Prediction of mechanical behavior of epoxy polymer using Artificial Neural Networks (ANN) and Response Surface Methodology (RSM)Khalissa SaadaSalah AmrouneMoussa ZaouiThe aim of this study is to analyze the effect of different geometries and sections on the mechanical properties of epoxy specimens. Five tensile tests were carried out on three types of series. The experimental results obtained were 1812.21 MPa, 3.90% and 41.91 MPa for intact specimens, 1450.41 MPa, 2.16% and 21.28 MPa for specimens with hole and 750.77 MPa, 2.77% and 11.89 MPa for specimens with elliptical -notched for Young's Modulus , strain and stress respectively. In addition, the experimental results indicated that the mechanical properties of both (Young's Modulus value and stress value) were higher in an intact specimen. Afterwards, the nonlinear functional relationship of input parameters between epoxy sample geometries and sections was established using the response surface model (RSM) and the artificial neural network (ANN) to predict the output parameters of mechanical properties (Young's Modulus and stress). In addition, the design of experiment was developed by the Analysis of the Application of Variance (ANOVA). The results showed the superiority of the ANN model over the RSM model, where the correlation coefficient values for the model datasets exceed ANN (R2 = 0.984 for Young's Modulus and R2 = 0.981 for the stress)https://www.fracturae.com/index.php/fis/article/view/4339/3873annmechanical propertiesanovarsmepoxygeometry |
| spellingShingle | Khalissa Saada Salah Amroune Moussa Zaoui Prediction of mechanical behavior of epoxy polymer using Artificial Neural Networks (ANN) and Response Surface Methodology (RSM) Fracture and Structural Integrity ann mechanical properties anova rsm epoxy geometry |
| title | Prediction of mechanical behavior of epoxy polymer using Artificial Neural Networks (ANN) and Response Surface Methodology (RSM) |
| title_full | Prediction of mechanical behavior of epoxy polymer using Artificial Neural Networks (ANN) and Response Surface Methodology (RSM) |
| title_fullStr | Prediction of mechanical behavior of epoxy polymer using Artificial Neural Networks (ANN) and Response Surface Methodology (RSM) |
| title_full_unstemmed | Prediction of mechanical behavior of epoxy polymer using Artificial Neural Networks (ANN) and Response Surface Methodology (RSM) |
| title_short | Prediction of mechanical behavior of epoxy polymer using Artificial Neural Networks (ANN) and Response Surface Methodology (RSM) |
| title_sort | prediction of mechanical behavior of epoxy polymer using artificial neural networks ann and response surface methodology rsm |
| topic | ann mechanical properties anova rsm epoxy geometry |
| url | https://www.fracturae.com/index.php/fis/article/view/4339/3873 |
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