Dongzhou Jia

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

ABSTRACT 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 (Ra). 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.

Microscale bone grinding temperature by dynamic heat flux in nanoparticle jet mist cooling with different particle sizes

ABSTRACT Nanoparticle jet mist cooling (NJMC) is an effective solution to prevent heat injuries in clinical neurosurgery bone grinding. A simulation study on temperature field of microscale bone grinding was performed to discuss the effect of nanoparticle size on heat convection during this cooling method by the dynamic heat flux density model. Such dynamic heat flux density model was established through real-time acquisition of grinding force signals. Results showed that given the real-time dynamic heat flux, workpiece surface temperature changes with time. Nanofluids using 30 nm nanoparticles show the largest heat convection coefficient (1.8723 W/mm2 · K) and the lowest average surface temperature followed by nanofluids of 50, 70, and 90 nm nanoparticles successively. An experimental verification using fresh bovine femur was conducted with 2% (volume fraction) of different sizes of Al2O3 nanoparticles. The simulated temperature under dynamic heat flux comes close to the actual measured temperature. Under testing conditions, temperature under mist cooling is 33.6°C, temperatures under NJMC using nanofluids (30, 50, 70, and 90 nm) are 21.4, 17.6, 16.1, and 8.3% lower, respectively. This result confirmed the positive correlation between the average workpiece surface temperature and nanoparticle size. Experimental results agreed with theoretical analysis, verifying the validity of theoretical modeling.