Multi-response optimization of cutting parameters in MQL assisted turning of Haynes 25 alloy with Taguchi based grey relational analysis
Haynes 25 is cobalt based superalloy gaining its importance in aerospace, heat treatment applications, chemical handling equipment, commercial gas turbine engines, bearing material, etc. This alloy is featured with low thermal conductivity, wear and corrosion resistance, strength with good resistance to oxidation at high temperatures. In the present study, optimization of process parameters in turning Haynes 25 alloy with uncoated and coated carbide tools under the minimum quantity lubrication (MQL) using Taguchi based grey relational analysis (GRA) method is attempted. The influence of cutting parameters and nano-particle concentration on surface roughness, tool wear, cutting and thrust forces is analyzed to improve the alloy machinability. The work also compares the responses obtained with uncoated and coated tool inserts and analyses the effect of nano-particle concentration. Further, the cutting and thrust forces are computed and validated using FE based DEFORM 3D machining software. The results obtained through simulation are in good accordance with experimental data within an average relative error of about 12%.