A holistic research based on RSM and ANN for improving drilling outcomes in Al–Si–Cu–Mg (C355) alloy
The unique properties of Al–Si-based alloys make them suitable for components that demand structural integrity and wear resistance. This study was conducted to investigate the microstructure, mechanical, and drilling properties of a commercial alloy belonging to the Al–Si casting alloy group and con...
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| Language: | English |
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Elsevier
2025-03-01
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| Series: | Journal of Materials Research and Technology |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2238785425001152 |
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| author | Şenol Bayraktar Cem Alparslan Nurten Salihoğlu Murat Sarıkaya |
| author_facet | Şenol Bayraktar Cem Alparslan Nurten Salihoğlu Murat Sarıkaya |
| author_sort | Şenol Bayraktar |
| collection | DOAJ |
| description | The unique properties of Al–Si-based alloys make them suitable for components that demand structural integrity and wear resistance. This study was conducted to investigate the microstructure, mechanical, and drilling properties of a commercial alloy belonging to the Al–Si casting alloy group and containing approximately 4.5–5.5% Si (Al–5Si–1Cu–Mg). Drilling experiments were conducted with an 8 mm uncoated HSS (High-Speed Steel) drill across a range of cutting speeds (V) and feed rates (f) while maintaining a consistent depth of cut (DoC) parameters. Microstructural analysis using optical microscopy and SEM identified key phases within the alloy, including α-Al, eutectic Si, β-Fe (β-Al5FeSi), and π-Fe (π-Al8Mg3FeSi6) inter-metallics. Statistical analyses of the effects of V and f on thrust force (Fz), surface roughness (Ra), and torque (Mz) were performed using Response Surface Methodology (RSM), Artificial Neural Networks (ANN), and Analysis of Variance (ANOVA). The ANOVA results highlighted the significance of both V and f on the measured outputs, with optimal performance observed at a V of 125 m/min and f of 0.05 mm/rev (confidence level: 95%, P < 0.05). Additionally, predictive models based on RSM and ANN were developed for Fz, Ra, and Mz. |
| format | Article |
| id | doaj-art-7ab034be47124a5cb00bc4ab80f50683 |
| institution | Kabale University |
| issn | 2238-7854 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of Materials Research and Technology |
| spelling | doaj-art-7ab034be47124a5cb00bc4ab80f506832025-01-24T04:45:17ZengElsevierJournal of Materials Research and Technology2238-78542025-03-013515961607A holistic research based on RSM and ANN for improving drilling outcomes in Al–Si–Cu–Mg (C355) alloyŞenol Bayraktar0Cem Alparslan1Nurten Salihoğlu2Murat Sarıkaya3Mechanical Engineering, Recep Tayyip Erdoğan University, Rize, Türkiye; Corresponding author.Mechanical Engineering, Recep Tayyip Erdoğan University, Rize, TürkiyeMechanical Engineering, Recep Tayyip Erdoğan University, Rize, TürkiyeDepartment of Mechanical Engineering, Sinop University, Türkiye; Faculty of Mechanical Engineering, Opole University of Technology, 45-758, Opole, PolandThe unique properties of Al–Si-based alloys make them suitable for components that demand structural integrity and wear resistance. This study was conducted to investigate the microstructure, mechanical, and drilling properties of a commercial alloy belonging to the Al–Si casting alloy group and containing approximately 4.5–5.5% Si (Al–5Si–1Cu–Mg). Drilling experiments were conducted with an 8 mm uncoated HSS (High-Speed Steel) drill across a range of cutting speeds (V) and feed rates (f) while maintaining a consistent depth of cut (DoC) parameters. Microstructural analysis using optical microscopy and SEM identified key phases within the alloy, including α-Al, eutectic Si, β-Fe (β-Al5FeSi), and π-Fe (π-Al8Mg3FeSi6) inter-metallics. Statistical analyses of the effects of V and f on thrust force (Fz), surface roughness (Ra), and torque (Mz) were performed using Response Surface Methodology (RSM), Artificial Neural Networks (ANN), and Analysis of Variance (ANOVA). The ANOVA results highlighted the significance of both V and f on the measured outputs, with optimal performance observed at a V of 125 m/min and f of 0.05 mm/rev (confidence level: 95%, P < 0.05). Additionally, predictive models based on RSM and ANN were developed for Fz, Ra, and Mz.http://www.sciencedirect.com/science/article/pii/S2238785425001152Al–Si alloyDrillingMachiningBuilt up-edgeOptimizationRSM |
| spellingShingle | Şenol Bayraktar Cem Alparslan Nurten Salihoğlu Murat Sarıkaya A holistic research based on RSM and ANN for improving drilling outcomes in Al–Si–Cu–Mg (C355) alloy Journal of Materials Research and Technology Al–Si alloy Drilling Machining Built up-edge Optimization RSM |
| title | A holistic research based on RSM and ANN for improving drilling outcomes in Al–Si–Cu–Mg (C355) alloy |
| title_full | A holistic research based on RSM and ANN for improving drilling outcomes in Al–Si–Cu–Mg (C355) alloy |
| title_fullStr | A holistic research based on RSM and ANN for improving drilling outcomes in Al–Si–Cu–Mg (C355) alloy |
| title_full_unstemmed | A holistic research based on RSM and ANN for improving drilling outcomes in Al–Si–Cu–Mg (C355) alloy |
| title_short | A holistic research based on RSM and ANN for improving drilling outcomes in Al–Si–Cu–Mg (C355) alloy |
| title_sort | holistic research based on rsm and ann for improving drilling outcomes in al si cu mg c355 alloy |
| topic | Al–Si alloy Drilling Machining Built up-edge Optimization RSM |
| url | http://www.sciencedirect.com/science/article/pii/S2238785425001152 |
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