Performance assessment of air-water and TiO2 nanofluid mist spray cooling during turning hardened AISI D2 steel

Abstract

Spray cooling has greater potential to dissipate heat from the heating source. Application of spray cooling in hard turning (hardness ≥ 55 HRC) is a newer concept and rarely investigated. In present work, the hard turning of AISI D2 steel under air-water mist spray impingement cooling (SIC) environment has been carried using multilayer (TiN (bottom) / TiCN (middle) / Al₂O₃ (top)) coated carbide tool. Taguchi L₁₆ (4⁵ i.e. 5 factor such as cutting speed, feed, depth of cut, air pressure and water pressure and their 4 levels) orthogonal array-design of experiments have been chosen for the experimentations. The detailed investigation on machining responses like flank wear (VBc), surface roughness (Ra), chip-tool-interface temperature (T), chip morphology, chip reduction coefficient (CRC) and restricted chip-tool contact length (RCL) have been carried out. Tool-wear phenomena like micro-chipping, chipping, abrasion and severe abrasion have been majorly observed on tool-tip and highly affected by cutting speed. The higher magnitude of Ra (1.304 µm, 1.332 µm, 1.344 µm, and 1.420 µm) has been noticed with highest feed rate (0.16 mm/rev) machining condition. The chip-tool interface temperature under SIC surrounding ranges from 49.7ᵒC to 156.2ᵒC, which have been found very low for hard turning concern. Widely popular multi-response technique namely grey relational analysis (GRA) has been implemented to get the optimal combination of input variables. Further, stepwise preparation methodology of nano TiO₂ powder and de-ionized water based TiO₂ nanofluid (0.01% weight concentration) has been discussed briefly. The average size of TiO₂ particle has been found as 10-20 nm. Further, tool life under two different (air-water and air-TiO₂ nanofluid) spraying environments using optimal cutting condition has been evaluated and compared. The tool life under air-TiO₂ nanofluid is found to be 119 minutes which is about 70 % more than the tool life (70 minutes) obtained under air-water spray cooling. From ANN modeling, mean absolute error (MAE) for response Ra, T and VBc have been found to be 2.1 %, 3.1 % and 1.9 %, respectively, which indicated the well fit of models.