A holistic research based on RSM and ANN for improving drilling outcomes in Al–Si–Cu–Mg (C355) alloy
Şenol Bayraktar, Cem Alparslan, Nurten Salihoğlu, Murat Sarıkaya
Abstract
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-Al 5 FeSi), and π-Fe (π-Al 8 Mg 3 FeSi 6 ) 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 .