Investigation and optimization of parameters in face milling of S50C steel under MQL system

Abstract

This paper aims to investigate cutting and lubrication parameters on surface roughness, cutting force, and material removal rate in face milling of JIS S50C carbon steel under a peanut oil-assisted Minimum Quantity Lubricant system. The five 3-level cutting process parameters were considered variants, including cutting speed, feed rate, depth of cut, air pressure, and lubrication flow. The experimental design was based on Taguchi's orthogonal array L27. The Analysis of variance is used to analyze the effect of cutting parameters and lubrication conditions on the surface roughness and cutting force. In addition, both regression optimizer procedures based on regression models and the Multi-Criteria Decision Making method were successfully applied to find the optimum conditions of the cutting parameter. The results showed the advantage and disadvantages of each technique. The Multi-Objective Optimization by Ratio Analysis was used in finding the best alternative. However, these values may not be an optimum condition. Mathematically, a regression optimizer may better determine the optimal value.