A holistic research based on RSM and ANN for improving drilling outcomes in Al-Si-Cu-Mg (C355) alloy
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Tarih
2025
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Elsevier
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
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 alpha-Al, eutectic Si, beta-Fe (beta-Al5FeSi), and pi-Fe (pi-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.
Açıklama
Anahtar Kelimeler
Al-Si alloy, Drilling, Machining, Built up-edge, Optimization, RSM, ANN
Kaynak
Journal of Materials Research and Technology-Jmr&T
WoS Q Değeri
Q1
Scopus Q Değeri
Q1
Cilt
35
