Process parameter optimization and experimental evaluation for nanofluid MQL in grinding Ti-6Al-4V based on grey relational analysis

Nanofluid minimum quantity lubrication is an environmental-friendly, resource-saving, and sustainable process compared with traditional flood lubrication. Especially, it is widely applied in difficult-to-cutting material, such as Ti-6Al-4V. However, optimized process parameters have not been obtained with considering grinding temperature, tangential grinding force, specific grinding energy, and surface roughness (R-a). And it is important for reaching the best surface quality and highest grinding efficiency. Henceforth, grinding parameters were set reasonably through an orthogonal experiment in this study and they were optimized preliminarily through a signal-to-noise analysis, getting four optimal groups of single grinding parameter. Next, a grey relational analysis was implemented based on the optimal signal-to-noise analysis of signal objective, getting two optimal combinations of multiple objectives. Finally, surface qualities in several groups of optimized experiments were characterized and analyzed by the profile supporting length ratio, surface morphology, and energy spectra. Furthermore, the grinding efficiency experiment was evaluated by material removal rate and specific grinding energy based on satisfying workpiece surface quality, and the optimal parameter combinations of surface quality and processing efficiency were gained. Research results provide theoretical basis for industrial production.