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