Prediction of lap shear strength of GNP and TiO2/epoxy nanocomposite adhesives
In this study, graphene nanoplatelets (GNPs) and titanium dioxide nanofillers were added to epoxy resin P-5005 at five different weight percentages (wt%), viz., 1, 5, 10, 15, and 20 wt%. The tensile properties of the nanocomposites were experimentally tested following ASTM D638-14. Then, the above-m...
| Main Authors: | , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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De Gruyter
2023-11-01
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| Series: | Nanotechnology Reviews |
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| Online Access: | https://doi.org/10.1515/ntrev-2023-0134 |
| _version_ | 1797561983174705152 |
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| author | Ozankaya Görkem Asmael Mohammed Alhijazi Mohamad Safaei Babak Alibar Mohamed Yasin Arman Samaneh Kotrasova Kamila Kvocak Vincent Weissova Michala Zeeshan Qasim Hui David |
| author_facet | Ozankaya Görkem Asmael Mohammed Alhijazi Mohamad Safaei Babak Alibar Mohamed Yasin Arman Samaneh Kotrasova Kamila Kvocak Vincent Weissova Michala Zeeshan Qasim Hui David |
| author_sort | Ozankaya Görkem |
| collection | DOAJ |
| description | In this study, graphene nanoplatelets (GNPs) and titanium dioxide nanofillers were added to epoxy resin P-5005 at five different weight percentages (wt%), viz., 1, 5, 10, 15, and 20 wt%. The tensile properties of the nanocomposites were experimentally tested following ASTM D638-14. Then, the above-mentioned nanocomposites were applied as adhesives for an overlap joint of two A5055 aluminum sheets. The apparent shear strength behavior of joints was tested following ASTM D1002-01. Moreover, experimentally obtained results were applied to train and test machine learning and deep learning models, i.e., adaptive neuro-fuzzy inference system, support vector machine, multiple linear regression, and artificial neural network (ANN). The peak tensile strength (TS) and joint failure load (FL) values were observed in epoxy/GNP samples. The ANN model exhibited the least error in predicting the TS and FL of the considered nanocomposites. The epoxy/GNP nanocomposites exhibited the highest TS of 28.49 MPa at 1 wt%, and the peak overlap joints exhibited an FL of 3.69 kN at 15 wt%. |
| first_indexed | 2024-03-10T18:22:15Z |
| format | Article |
| id | doaj.art-e548adba00cd439db8c71a9a6585bd6d |
| institution | Directory Open Access Journal |
| issn | 2191-9097 |
| language | English |
| last_indexed | 2024-03-10T18:22:15Z |
| publishDate | 2023-11-01 |
| publisher | De Gruyter |
| record_format | Article |
| series | Nanotechnology Reviews |
| spelling | doaj.art-e548adba00cd439db8c71a9a6585bd6d2023-11-20T07:16:34ZengDe GruyterNanotechnology Reviews2191-90972023-11-0112119920510.1515/ntrev-2023-0134Prediction of lap shear strength of GNP and TiO2/epoxy nanocomposite adhesivesOzankaya Görkem0Asmael Mohammed1Alhijazi Mohamad2Safaei Babak3Alibar Mohamed Yasin4Arman Samaneh5Kotrasova Kamila6Kvocak Vincent7Weissova Michala8Zeeshan Qasim9Hui David10Department of Mechanical Engineering, Eastern Mediterranean University, Famagusta, North Cyprus via Mersin 10, TurkeyDepartment of Mechanical Engineering, Eastern Mediterranean University, Famagusta, North Cyprus via Mersin 10, TurkeySchool of Engineering, Lebanese International University LIU, Bekaa, LebanonDepartment of Mechanical Engineering, Eastern Mediterranean University, Famagusta, North Cyprus via Mersin 10, TurkeyDepartment of Mechanical Engineering, Eastern Mediterranean University, Famagusta, North Cyprus via Mersin 10, TurkeySchool of Science and Technology, The University of Georgia, Tbilisi 0171, GeorgiaInstitute of Structural Engineering and Transportation Structures, Faculty of Civil Engineering, Technical University of Kosice, Vysokoskolska 4, 042 00 Kosice, SlovakiaInstitute of Structural Engineering and Transportation Structures, Faculty of Civil Engineering, Technical University of Kosice, Vysokoskolska 4, 042 00 Kosice, SlovakiaInstitute of Structural Engineering and Transportation Structures, Faculty of Civil Engineering, Technical University of Kosice, Vysokoskolska 4, 042 00 Kosice, SlovakiaDepartment of Mechanical Engineering, Eastern Mediterranean University, Famagusta, North Cyprus via Mersin 10, TurkeyDepartment of Mechanical Engineering, University of New Orleans, New Orleans, LA 70148, United States of AmericaIn this study, graphene nanoplatelets (GNPs) and titanium dioxide nanofillers were added to epoxy resin P-5005 at five different weight percentages (wt%), viz., 1, 5, 10, 15, and 20 wt%. The tensile properties of the nanocomposites were experimentally tested following ASTM D638-14. Then, the above-mentioned nanocomposites were applied as adhesives for an overlap joint of two A5055 aluminum sheets. The apparent shear strength behavior of joints was tested following ASTM D1002-01. Moreover, experimentally obtained results were applied to train and test machine learning and deep learning models, i.e., adaptive neuro-fuzzy inference system, support vector machine, multiple linear regression, and artificial neural network (ANN). The peak tensile strength (TS) and joint failure load (FL) values were observed in epoxy/GNP samples. The ANN model exhibited the least error in predicting the TS and FL of the considered nanocomposites. The epoxy/GNP nanocomposites exhibited the highest TS of 28.49 MPa at 1 wt%, and the peak overlap joints exhibited an FL of 3.69 kN at 15 wt%.https://doi.org/10.1515/ntrev-2023-0134graphene nanoplateletstitanium dioxidemechanical characteristicsmachine learning |
| spellingShingle | Ozankaya Görkem Asmael Mohammed Alhijazi Mohamad Safaei Babak Alibar Mohamed Yasin Arman Samaneh Kotrasova Kamila Kvocak Vincent Weissova Michala Zeeshan Qasim Hui David Prediction of lap shear strength of GNP and TiO2/epoxy nanocomposite adhesives Nanotechnology Reviews graphene nanoplatelets titanium dioxide mechanical characteristics machine learning |
| title | Prediction of lap shear strength of GNP and TiO2/epoxy nanocomposite adhesives |
| title_full | Prediction of lap shear strength of GNP and TiO2/epoxy nanocomposite adhesives |
| title_fullStr | Prediction of lap shear strength of GNP and TiO2/epoxy nanocomposite adhesives |
| title_full_unstemmed | Prediction of lap shear strength of GNP and TiO2/epoxy nanocomposite adhesives |
| title_short | Prediction of lap shear strength of GNP and TiO2/epoxy nanocomposite adhesives |
| title_sort | prediction of lap shear strength of gnp and tio2 epoxy nanocomposite adhesives |
| topic | graphene nanoplatelets titanium dioxide mechanical characteristics machine learning |
| url | https://doi.org/10.1515/ntrev-2023-0134 |
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