Analysis of Surface Roughness in Hard Turing of AISI P20 Steel: An Application of Taguchi Method
Author(s):
Anh-Toan Hoang, The-Vinh Do, Thanh-Dat Phan*
Affiliation(s):
Thai Nguyen University of Technology, Thai Nguyen city, Vietnam
Corresponding Author Email: [email protected]
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.
The selection of cutting parameters and cooling conditions play an important role in improving surface roughness. Such purpose, a series of meticulous tests was carried out to improve the machined surface roughness in turning P20 steel. Input factors including cooling condition and cutting parameters were investigated to determine their influence on the output response using the Taguchi method. The cooling conditions have three different levels including dry cutting, conventional MQL and MQL nanofluid with Al2O3 nanoparticles. The cutting parameters selected are cutting velocity, feed rate, and depth of cut. The signal-to-noise (S/N) response and ANOVA were used to find the optimal cutting condition for minimum surface roughness. The result shows that cooling condition and feed rate are the most influential factors on roughness. Furthermore, nanofluid has demonstrated outstanding performance over conventional MQL machining and dry cutting.