Empirical Model for Surface Roughness in Hard Milling of AISI H13 Steel Under Nanofluid-MQL Condition Based on Analysis of Cutting Parameters

Abstract

In this work, the integration of the Taguchi approach and response surface methodology (RSM)
was used to assess the impact of machining parameters on surface roughness during the hard milling of AISI
H13 steel using a carbide-coated (TiAlN) cutting tool under nanofluid MQL conditions. SiO2 nanoparticles
were chosen to be added to CT232 cutting oil. The experiment was conducted utilizing G. Taguchi's L27
orthogonal array of DOE approach. To determine how the cutting parameters—including cutting velocity, feed
rate, depth of cut, and workpiece hardness at three different levels—affect surface roughness, an investigation
was conducted. In order to forecast surface roughness under nanofluid MQL conditions, an empirical model
was provided. Furthermore, a multi-objective optimization was carried out to determine the maximum and
minimum values of surface roughness.