Multi-Response Optimization of Milling Process of Hardened S50C Steel Using SVM-GA Based Method

This study aims to find the optimized parameters for surveying milling process of S50C steel in a minimum quantity lubrication (MQL) environment using support vector machine-genetic algorithm (SVM-GA). Based on experimental matrix designed by Taguchi method, surface roughness and cutting force data were collected corresponding each experiment with changes input such as speed, tooth feed rate, axial depth cut, along two system: flow rates injection pressure. Through analysis SVR-NSGAII obtained optimal lubricating conditions when prioritizing either or focusing force; however, most comprehensive result is believed be achieved balancing these factors. So, striving neutral value both output parameters, which are (µm) (N), optimum including pressure (MPa), (mL/h), speed (m/min), rate (mm/tooth), cut (mm) proposed.