Study of Cutting Forces in Drilling of Aluminum Alloy 2024-T351
Duralumin 2024-T351 is an alloy characterized by a good mechanical strength, relatively high hardness and corrosion resistance frequently used in the aeronautical, automotive, defense etc. industries. In this paper, the variation of axial forces and torques when drilling aluminum alloy 2024-T351 was...
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MDPI AG
2024-12-01
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| Online Access: | https://www.mdpi.com/2075-1702/12/12/937 |
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| author | Răzvan Sebastian Crăciun Virgil Gabriel Teodor Nicușor Baroiu Viorel Păunoiu Georgiana-Alexandra Moroșanu |
| author_facet | Răzvan Sebastian Crăciun Virgil Gabriel Teodor Nicușor Baroiu Viorel Păunoiu Georgiana-Alexandra Moroșanu |
| author_sort | Răzvan Sebastian Crăciun |
| collection | DOAJ |
| description | Duralumin 2024-T351 is an alloy characterized by a good mechanical strength, relatively high hardness and corrosion resistance frequently used in the aeronautical, automotive, defense etc. industries. In this paper, the variation of axial forces and torques when drilling aluminum alloy 2024-T351 was investigated, analyzing the measured values for different cutting regimes. Experimental data on the forces and moments generated during the drilling process were collected using specialized equipment, and these data were preprocessed and analyzed using MatLab R218a. The experimental plan included 27 combinations of the parameters of the cutting regime (cutting depth, cutting speed, and feed), for which energetic cutting parameters were measured, the axial force and the torsion moment, respectively Based on these data, a neural network was trained, using the Bayesian regularization algorithm, in order to predict the optimal values of the cutting energy parameters. The neural model proved to be efficient, providing predictions with a relative error below 10%, indicating a good agreement between measured and simulated values. In conclusion, neural networks offer an accurate alternative to classical analytical models, being more suitable for materials with complex behavior, such as aluminum alloys. |
| format | Article |
| id | doaj-art-f0190eb6695b44cc872a739744a52c79 |
| institution | Kabale University |
| issn | 2075-1702 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Machines |
| spelling | doaj-art-f0190eb6695b44cc872a739744a52c792024-12-27T14:37:14ZengMDPI AGMachines2075-17022024-12-01121293710.3390/machines12120937Study of Cutting Forces in Drilling of Aluminum Alloy 2024-T351Răzvan Sebastian Crăciun0Virgil Gabriel Teodor1Nicușor Baroiu2Viorel Păunoiu3Georgiana-Alexandra Moroșanu4Department of Manufacturing Engineering, “Dunarea de Jos” University of Galati, 800201 Galati, RomaniaDepartment of Manufacturing Engineering, “Dunarea de Jos” University of Galati, 800201 Galati, RomaniaDepartment of Manufacturing Engineering, “Dunarea de Jos” University of Galati, 800201 Galati, RomaniaDepartment of Manufacturing Engineering, “Dunarea de Jos” University of Galati, 800201 Galati, RomaniaResearch Center in Manufacturing Engineering Technology (ITCM), “Dunarea de Jos” University of Galati, 800201 Galati, RomaniaDuralumin 2024-T351 is an alloy characterized by a good mechanical strength, relatively high hardness and corrosion resistance frequently used in the aeronautical, automotive, defense etc. industries. In this paper, the variation of axial forces and torques when drilling aluminum alloy 2024-T351 was investigated, analyzing the measured values for different cutting regimes. Experimental data on the forces and moments generated during the drilling process were collected using specialized equipment, and these data were preprocessed and analyzed using MatLab R218a. The experimental plan included 27 combinations of the parameters of the cutting regime (cutting depth, cutting speed, and feed), for which energetic cutting parameters were measured, the axial force and the torsion moment, respectively Based on these data, a neural network was trained, using the Bayesian regularization algorithm, in order to predict the optimal values of the cutting energy parameters. The neural model proved to be efficient, providing predictions with a relative error below 10%, indicating a good agreement between measured and simulated values. In conclusion, neural networks offer an accurate alternative to classical analytical models, being more suitable for materials with complex behavior, such as aluminum alloys.https://www.mdpi.com/2075-1702/12/12/937cutting forcesaluminum 2024-T351neural networksdrilling |
| spellingShingle | Răzvan Sebastian Crăciun Virgil Gabriel Teodor Nicușor Baroiu Viorel Păunoiu Georgiana-Alexandra Moroșanu Study of Cutting Forces in Drilling of Aluminum Alloy 2024-T351 Machines cutting forces aluminum 2024-T351 neural networks drilling |
| title | Study of Cutting Forces in Drilling of Aluminum Alloy 2024-T351 |
| title_full | Study of Cutting Forces in Drilling of Aluminum Alloy 2024-T351 |
| title_fullStr | Study of Cutting Forces in Drilling of Aluminum Alloy 2024-T351 |
| title_full_unstemmed | Study of Cutting Forces in Drilling of Aluminum Alloy 2024-T351 |
| title_short | Study of Cutting Forces in Drilling of Aluminum Alloy 2024-T351 |
| title_sort | study of cutting forces in drilling of aluminum alloy 2024 t351 |
| topic | cutting forces aluminum 2024-T351 neural networks drilling |
| url | https://www.mdpi.com/2075-1702/12/12/937 |
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