Minimizing vibrations and power consumption in milling of AZ31 alloy through parameter optimization

Abstract

This study presents an integrated optimization framework for minimizing vibration and power consumption during the milling of AZ31 magnesium alloy. A total of 25 experiments were performed using a Taguchi L₂₅ orthogonal array to investigate the influence of spindle speed, feed rate, and depth of cut. Real-time data acquisition captured vibrations along the X, Y, and Z axes, as well as power consumption. Stepwise regression models were developed using Pearson’s correlation analysis, revealing spindle speed as the most influential parameter. These models were input into the NSGA-II algorithm, generating a Pareto front of optimal solutions. The best theoretical solution predicted vibration amplitudes of −8.2 mm (X), 7.7 mm (Y), 4.2 mm (Z), and a power consumption of 68.13 W. Experimental validation yielded errors of 4 % (X), 4 % (Y), 10 % (Z), and −6 % (power), confirming the model's accuracy. Correlation analysis indicated that spindle speed had the greatest influence on power consumption and Z-axis vibrations (r = 0.45 and r = 0.16), depth of cut significantly affected X-axis and Y-axis vibrations (r = 0.27 and r = 0.15) The framework effectively improves machining sustainability by optimizing process parameters.