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

• Published in: Journal of Central South University Vol. 28; no. 1; pp. 112 - 125
• Main Authors: Kursuncu, Bilal, Biyik, Yasin Ensar
• Format: Journal Article
• Language: English
• Published: Changsha Central South University 2021; Springer Nature B.V
• Subjects: Abrasion; Cutting fluids; Cutting force; Cutting parameters; Cutting speed; Engineering; Face milling; Feed rate; Flow velocity; Metallic Materials; Optimization; Statistical methods; Surface roughness; Taguchi methods; Tool life; Tool steels; Vegetable oils; Wear mechanisms; 灰色关联分析; grey relational analysis; 最小润滑量; AISI O2 钢; 硬铣; tool wear; 刀具磨损; AISI O2 steel; minimum quantity of lubrication; Taguchi 法; hardmilling; Taguchi method
• ISSN: 2095-2899; 2227-5223
• DOI: 10.1007/s11771-021-4590-4

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.