Performance assessment of vegetable-based additive enriched cutting fluid for eco-friendly machining environment

The investigation focuses on determining the effects of canola oil-based cutting fluid with three different volume percentages of boric acid additives over the machining forces and surface roughness while turning hardened AISI 1018 mild steel. Experiments were carried out under Taguchi's design of the experiment concept. The minimum quantity lubrication (MQL) technique was followed to minimize the cutting fluid consumption. The homogeneity of the additives dispersed in the fluid has been validated through a zeta potential study. Machining forces and surface roughness were considered as chief machining objectives. The hybrid mathematical model, grey relational analysis (GRA)-artificial neural network (ANN), has been implemented to assess the performance of developed cutting fluid. The results explored that the canola oil cutting fluid with 5 wt% of boric acid additive exhibits lesser cutting forces and surface roughness. The optimal machining parameters identified by the hybrid modeling are 665 rpm of cutting speed, 35 mm/min of feed rate, and 0.3 mm of depth of cut, along with 5 wt% of boric acid composition in cutting fluid. The results explore the 2.677 times improvement in machining objective in comparison with a non-optimal set of parameters. The implementation of hybrid modeling is considered to be a novel attempt to minimize the machining objectives. It has been recorded a negligible error percentage of 0.66% between GRA and ANN prediction.