The Analysis of Response Surface Methodology-Based Optimization of Surface Roughness in Vibration-Assisted Micro-Milling of Aluminium T6061

Abstract

This study investigates the application of response surface methodology (RSM) to optimize machining parameters for minimizing surface roughness in vibration-assisted micro-milling. Experimental trials were designed and conducted using Taguchi’s experimental design approach. The primary machining parameters considered include spindle speed, feed rate, vibration amplitude, and vibration frequency. The influence of these parameters on surface roughness was systematically analysed, and the optimal cutting conditions for achieving minimum surface roughness were identified. A second-order regression model was developed using RSM to describe the inherent relationship between the machining parameters and surface roughness. Experimental findings indicate that vibration amplitude is the most influential factor affecting surface roughness, followed by feed rate. A close agreement between the predicted and experimental results demonstrates the reliability and accuracy of the proposed model. Validation experiments further confirm that the developed RSM model is effective in predicting surface roughness in vibration-assisted machining of aluminium T6061 workpieces.