Optimization of cutting parameters with Taguchi and grey relational analysis methods in MQL-assisted face milling of AISI O2 steel

Abstract

This study aims to examine the usability of environmentally harmless vegetable oil in the minimum quantity of lubrication (MQL) system in face milling of AISI O2 steel and to optimize the cutting parameters by different statistical methods. Vegetable oil was preferred as cutting fluid, and Taguchi method was used in the preparation of the test pattern. After testing with the prepared test pattern, cutting performance in all parameters has been improved according to dry conditions thanks to the MQL system. The highest tool life was obtained by using cutting parameters of 7.5 m cutting length, 100 m/min cutting speed, 100 mL/h MQL flow rate and 0.1 mm/tooth feed rate. Optimum cutting parameters were determined according to the Taguchi analysis, and the obtained parameters were confirmed with the verification tests. In addition, the optimum test parameter was determined by applying the gray relational analysis method. After using ANOVA analysis according to the measured surface roughness and cutting force values, the most effective cutting parameter was observed to be the feed rate. In addition, the models for surface roughness and cutting force values were obtained with precisions of 99.63% and 99.68%, respectively. Effective wear mechanisms were found to be abrasion and adhesion.