SiO2 Nanoparticle Concentration and Cutting Parameters in Prediction Model of Cutting Force During Hard Milling of SKD 11 Steel


Quoc-Manh Nguyen†, The-Vinh Do‡,*


† Hung Yen University of Technology and Education, Hung Yen, Vietnam
‡ Thai Nguyen University of Technology, Thai Nguyen city, Vietnam

Corresponding Author Email: thevinh8880@tnut.edu.vn

This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Cutting force is a remarkable indicator in machining because of its influence on tool strength, machining system vibration, and machined surface quality. In this study, response surface methodology (RSM) was applied to build a predictive model of cutting force in hard milling of SKD 11 steel. The cutting parameters (cutting velocity, depth of cut and feedrate) and SiO2 nanoparticle concentration (wt%) were experimentally investigated to find out their influence on the output response. Empirical data were analyzed using a statistical analysis of variance (ANOVA). The results show that a cutting mode including higher cutting velocity, smaller feedrate and smaller depth of cut under MQL nanofluid conditions (with 4wt% nanoparticle) will achieve the minimum cutting force. In addition, efficiency in cutting force reduction of nanofluid MQL was demonstrated when compared with conventional MQL.