Multi Objective Optimization of Process Parameters in Machining Hardened Steels under Sustainable Hybrid Nanofluid-MQL Strategy

Machining hardened steel components has garnered significant interest because of its wide application in the automotive, press-tooling, mold-die, gear, bearing, and aerospace sectors, but optimizing efficiency is difficult due to the high levels of heat, friction, cutting forces, and tool wear that can compromise the quality of the products. Dispensing conventional cutting fluids while machining is an effective technique but it has significant effects on both environment and human health. Therefore, it is essential to explore new environmentally friendly cooling and lubrication techniques. One of these alternatives is machining with minimum quantity lubrication. In this study the effects of rice bran oil-based molybdenum disulphide and carbon black hybrid nano cutting fluid in minimum quantity lubrication (nMQL) on cutting performances in respect of chip formation, cutting temperature, and surface roughness have been studied using coated carbide insert (SNMG) for medium carbon hardened steels (30 HRC, 35 HRC, 40 HRC). The result indicated that the machining with nMQL performed much better than dry machining mainly due to substantial reduction in cutting temperature enabling favorable chip-tool interaction and substantial reduction in surface roughness. The process parameters were predicted using an ANN model. Afterward, the ANN was utilized as the fitness function for the multi-objective optimization of the parameters using NSGA-II.