Optimization and Comparative Modeling of Surface Roughness in Drilling of Cast and Heat-Treated C355 Aluminum Alloy Using RSM and ANN Approaches

ALPARSLAN C., Ozhabiboglu C., Vijayananth K., BAYRAKTAR Ş.

SILICON, 2026 (SCI-Expanded, Scopus)

  • Yayın Türü: Makale / Tam Makale
  • Basım Tarihi: 2026
  • Doi Numarası: 10.1007/s12633-026-03822-0
  • Dergi Adı: SILICON
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Chemical Abstracts Core, Compendex, INSPEC, Materials Science & Engineering Collection (ProQuest), Technology Collection (ProQuest)
  • Recep Tayyip Erdoğan Üniversitesi Adresli: Evet

Özet

The machinability of C355 (Al-5Si-1Cu-Mg) alloy under as-cast and T6 heat-treated conditions was systematically investigated in terms of surface roughness (Ra) during drilling. Experimental trials were conducted at different cutting speeds (V) and feed rates (f), and the experimentally measured Ra values were modeled and optimized using Artificial Neural Network (ANN) and Response Surface Methodology (RSM) approaches. T6 heat treatment produced a more homogeneous and spheroidized microstructure and resulted in significant increases in hardness, tensile strength, and yield strength, accompanied by a reduction in ductility. The ANN model demonstrated high predictive accuracy, with an overall R2 value of 0.982. The second-order regression model developed using RSM identified V = 125 m/min and f = 0.05 mm/rev as the optimum cutting conditions for minimizing surface roughness. Scanning Electron Microscopy (SEM) and Energy-Dispersive X-ray Spectroscopy (EDS) analyses showed that built-up edge (BUE) formation was more pronounced at lower cutting speeds and higher feed rates, adversely affecting surface quality. The combined use of ANN and RSM provided an effective framework for predicting surface roughness and optimizing the drilling parameters of C355 alloy. The findings demonstrate the potential of optimized machining conditions for improving the surface quality of C355 alloy in precision engineering applications, particularly in the aerospace and automotive industries.