Taguchi-based GRA for parametric optimization in turning of AISI L6 tool steel under cryogenic cooling

Cutting fluids have frequent use in industrial sector to improve the machinability. Due to the negative impact on our ecology, recent focus has shifted to explore some environment-friendly cooling techniques such as cryogenic cooling. Cryogenic cooling involving liquid nitrogen is one of the alternative techniques which improves the efficiency of the machining process and is environmentally friendly as well. In current work, cutting parameters in turning such as cutting speed and feed rate were optimized under cryogenic cooling for machining of AISI L6 tool steel which is difficult to cut material. The output parameters under consideration are surface roughness, cutting energy, tool wear and Material Removal Rate (MRR). The optimization for multi-responses was carried out through Taguchi based Grey Relational Analysis (GRA). For experimental design, tests were based on L9 orthogonal array. According to the GRA optimization results, optimum cutting speed level was 160 m/min and the feed rate was 0.16 mm/rev. The percentage improvement in Grey Relational Grade (GRG) was calculated as 19.07%, thus showing the advantage of using the GRA.