Exploring the process—structure–property relationship of nylon aramid 3D printed composites and parameter optimization using supervised machine learning techniques
The main goals of this research are to identify the significant input parameters using supervised machine learning methods and investigate the relationship between the process, structure, and properties of components created using fused deposition modeling utilizing nylon aramid composite filaments....
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| Format: | Article |
| Language: | English |
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SAGE Publishing
2025-02-01
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| Series: | Journal of Engineered Fibers and Fabrics |
| Online Access: | https://doi.org/10.1177/15589250241293883 |
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| author | Mohammed Raffic Noor Mohamed Ganesh Babu Karuppiah Dharani Kumar Selvan Rajasekaran Saminathan Shubham Sharma Shashi Prakash Dwivedi Sandeep Kumar Mohamed Abbas Dražan Kozak Jasmina Lozanovic |
| author_facet | Mohammed Raffic Noor Mohamed Ganesh Babu Karuppiah Dharani Kumar Selvan Rajasekaran Saminathan Shubham Sharma Shashi Prakash Dwivedi Sandeep Kumar Mohamed Abbas Dražan Kozak Jasmina Lozanovic |
| author_sort | Mohammed Raffic Noor Mohamed |
| collection | DOAJ |
| description | The main goals of this research are to identify the significant input parameters using supervised machine learning methods and investigate the relationship between the process, structure, and properties of components created using fused deposition modeling utilizing nylon aramid composite filaments. To develop an experimental layout using Taguchi’s L 18 orthogonal array, six different FDM parameters such as infill pattern, infill density, layer thickness, component orientation, print temperature, and raster angle have been taken into consideration.Using an Ultimaker FDM printer, rectangular samples were created, and the values of face hardness, thickness regions, printing time, and component weight were assessed. ANOVA and the signal to noise ratio method are two techniques that have been used to find the significant influencing parameter and the ideal combination of parameters. When comparing thickly layered samples with a suitable increase in time and weight, thin-layered samples are shown to have greater hardness values at both tested areas. With a 50.09% contribution to face hardness and a 30.11% contribution to hardness at the thickness area, raster angle is found to be significant over hardness.Layer thickness is an important element that contributes 81.95% to printing time and 42.09% to part weight, respectively. In an 80:20 train-test split, the decision tree approach outperformed the k -nearest neighbor algorithm for all four output responses, with classification accuracy ranging from 83.33% to 100%. Infill density is recommended by the decision tree method to be extremely significant over face hardness and component weight, and layer thickness is similarly recommended to be highly significant over printing time and hardness at the thickness region. The presence of surface pores, interior voids, and layer abnormalities is confirmed by FESEM images. |
| format | Article |
| id | doaj-art-830a752d1da2405d948dac1c0485c80e |
| institution | Kabale University |
| issn | 1558-9250 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | SAGE Publishing |
| record_format | Article |
| series | Journal of Engineered Fibers and Fabrics |
| spelling | doaj-art-830a752d1da2405d948dac1c0485c80e2025-02-08T11:03:53ZengSAGE PublishingJournal of Engineered Fibers and Fabrics1558-92502025-02-012010.1177/15589250241293883Exploring the process—structure–property relationship of nylon aramid 3D printed composites and parameter optimization using supervised machine learning techniquesMohammed Raffic Noor Mohamed0Ganesh Babu Karuppiah1Dharani Kumar Selvan2Rajasekaran Saminathan3Shubham Sharma4Shashi Prakash Dwivedi5Sandeep Kumar6Mohamed Abbas7Dražan Kozak8Jasmina Lozanovic9Department of Aeronautical Engineering, Nehru Institute of Technology, Coimbatore, IndiaDepartment of Mechanical Engineering, Chendhuran College of Engineering and Technology, Pudukkottai, IndiaCentre for Machining and Material Testing, Department of Mechanical Engineering, KPR Institute of Engineering and Technology, Coimbatore, IndiaDepartment of Mechanical Engineering, College of Engineering and Computer Science, Jazan University, Kingdom of Saudi ArabiaCentre for Research Impact and Outcome, Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, Punjab, IndiaLloyd Institute of Engineering & Technology, Knowledge Park II, Greater Noida, Uttar Pradesh, IndiaCentre for Research Impact and Outcome, Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, Punjab, IndiaElectrical Engineering Department, College of Engineering, King Khalid University, Abha city, Saudi ArabiaUniversity of Slavonski Brod, Mechanical Engineering Faculty in Slavonski Brod, CroatiaDepartment of Engineering, FH Campus Wien - University of Applied Sciences, Vienna, AustriaThe main goals of this research are to identify the significant input parameters using supervised machine learning methods and investigate the relationship between the process, structure, and properties of components created using fused deposition modeling utilizing nylon aramid composite filaments. To develop an experimental layout using Taguchi’s L 18 orthogonal array, six different FDM parameters such as infill pattern, infill density, layer thickness, component orientation, print temperature, and raster angle have been taken into consideration.Using an Ultimaker FDM printer, rectangular samples were created, and the values of face hardness, thickness regions, printing time, and component weight were assessed. ANOVA and the signal to noise ratio method are two techniques that have been used to find the significant influencing parameter and the ideal combination of parameters. When comparing thickly layered samples with a suitable increase in time and weight, thin-layered samples are shown to have greater hardness values at both tested areas. With a 50.09% contribution to face hardness and a 30.11% contribution to hardness at the thickness area, raster angle is found to be significant over hardness.Layer thickness is an important element that contributes 81.95% to printing time and 42.09% to part weight, respectively. In an 80:20 train-test split, the decision tree approach outperformed the k -nearest neighbor algorithm for all four output responses, with classification accuracy ranging from 83.33% to 100%. Infill density is recommended by the decision tree method to be extremely significant over face hardness and component weight, and layer thickness is similarly recommended to be highly significant over printing time and hardness at the thickness region. The presence of surface pores, interior voids, and layer abnormalities is confirmed by FESEM images.https://doi.org/10.1177/15589250241293883 |
| spellingShingle | Mohammed Raffic Noor Mohamed Ganesh Babu Karuppiah Dharani Kumar Selvan Rajasekaran Saminathan Shubham Sharma Shashi Prakash Dwivedi Sandeep Kumar Mohamed Abbas Dražan Kozak Jasmina Lozanovic Exploring the process—structure–property relationship of nylon aramid 3D printed composites and parameter optimization using supervised machine learning techniques Journal of Engineered Fibers and Fabrics |
| title | Exploring the process—structure–property relationship of nylon aramid 3D printed composites and parameter optimization using supervised machine learning techniques |
| title_full | Exploring the process—structure–property relationship of nylon aramid 3D printed composites and parameter optimization using supervised machine learning techniques |
| title_fullStr | Exploring the process—structure–property relationship of nylon aramid 3D printed composites and parameter optimization using supervised machine learning techniques |
| title_full_unstemmed | Exploring the process—structure–property relationship of nylon aramid 3D printed composites and parameter optimization using supervised machine learning techniques |
| title_short | Exploring the process—structure–property relationship of nylon aramid 3D printed composites and parameter optimization using supervised machine learning techniques |
| title_sort | exploring the process structure property relationship of nylon aramid 3d printed composites and parameter optimization using supervised machine learning techniques |
| url | https://doi.org/10.1177/15589250241293883 |
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