Surface integrity and material removal rate optimization in CNC turning of a cast Al–Zn–Mg–Cu based lightweight alloy

Cast Al–Zn–Mg–Cu-based alloys are attractive lightweight materials for engineering components, but their machining performance is strongly affected by casting-related heterogeneity, surface defects, and the selection of turning parameters. This study introduces a sequential multi-stage TA–RSM–GA–SA optimization framework for model-guided optimization of CNC turning responses of cast lightweight alloys. The alloy was fabricated through a casting route and characterized using scanning electron microscopy (SEM) and energy dispersive spectroscopy (EDS) to examine elemental composition, surface morphology, and casting-related defects. The main objective of this research was to establish a multi-response optimization framework to minimize surface roughness (R a ) and maximize material removal rate (MRR), which are often categorized as conflicting machining responses. A sequential TA-RSM-GA-SA optimization framework was employed to determine the optimum machining conditions. Feed rate was identified as the dominant machining parameter affecting both R a and MRR. The final TA–RSM–GA–SA1 framework identified a candidate machining condition at which the fitted models estimated μm and mm 3 /min. A confirmation experiment at this condition produced μm and mm 3 /min. Because the R a model exhibited moderate fit and limited point-prediction capability, the estimated R a is interpreted as a model-guided value within the investigated design space rather than a definite minimum. Relative to the initial Taguchi-stage estimate, the hybrid search produced an approximately 86% reduction in model-estimated R a and a 45% increase in model-estimated MRR. These percentages describe changes in the fitted-model outputs and should not be interpreted as exact experimentally established improvements.