Optimization of Dry Turning of Inconel 601 Alloy Based on Surface Roughness, Tool Wear, and Material Removal Rate

In this work, the dry turning of Inconel 601 alloy in a environment with PVD-coated cutting inserts was studied. Turning performed at various speeds, feeds, insert shapes, corner radii, rake angles, and approach angles. After machining, arithmetic mean surface roughness (Ra) flank wear (VB) were measured, material removal rate also calculated (MRR). An analysis variance (ANOVA) to determine effects input parameters. For measured values, process modeled using an artificial neural network (ANN). Based on obtained model, parameters optimized genetic algorithm (GA). The objective function simultaneously minimize Ra VB maximize MRR. accuracy model optimal values further validated by confirmation experiments. maximum percentage errors, which are less than 2%, indicate possibility practical implementation hybrid for modeling optimization alloy.