No abstract

This paper used graphite nanoparticles with the diameter of 35 and 80 nm and LB2000 vegetable based oil to prepare graphite oil-based nanofluids with different volume fractions by two-step method. The tribological properties of graphite nanoparticles as LB2000 vegetable based oil additive were investigated with a pin-on-disk friction and wear tester. Field emission scanning electron microscope (FE-SEM) and energy dispersive spectroscopy (EDS) were used to examine the morphology and the content of some typical elements of wear scar, respectively. Further, the lubrication mechanism of graphite nanoparticles was explored. It was found that graphite nanoparticles as vegetable based oil additive could remarkably improve friction-reducing and antiwear properties of pure oil. With the increase of volume fraction of graphite nanoparticles, the friction coefficient and the wear volume of disk decreased. At the same volume fraction, the smaller particles, the lower friction coefficient and wear volume. The main reason for the improvement in friction-reducing and antiwear properties of vegetable based oil using graphite nanoparticles was that graphite nanoparticles could form a physical deposition film on the friction surfaces.

No abstract

As environmentally friendly cutting fluids, vegetable-based oil and ester oil are being more and more widely used in metal cutting industry. However, their cooling and lubricating properties are required to be further improved in order to meet more cooling and lubricating challenges in high-efficiency machining. Nanofluids with enhanced heat carrying and lubricating capabilities seem to give a promising solution. In this article, graphite oil-based nanofluids with LB2000 vegetablebased oil and PriEco6000 unsaturated polyol ester as base fluids were prepared by ultrasonically assisted two-step method, and their dispersion stability and thermophysical properties such as viscosity and thermal conductivity were experimentally and theoretically investigated at different ultrasonication times. The results indicate that graphitePriEco6000 nanofluid showed better dispersion stability, higher viscosity, and thermal conductivity than graphite-LB2000 nanofluid, which made it more suitable for application in high-efficiency machining as coolant and lubricant. The theoretical classical models showed good agreement with the thermal conductivity values of graphite oil-based nanofluids measured experimentally. However, the deviation between the experimental values of viscosity and the theoretical models was relatively big. New empirical correlations were proposed for predicting the viscosity of graphite oil-based nanofluids at various ultrasonication times.

Energy conservation and emission reduction is an essential consideration in sustainable manufacturing. However, the traditional optimization of cutting parameters mostly focuses on machining cost, surface quality, and cutting force, ignoring the influence of cutting parameters on energy consumption in cutting process. This paper presents a multi-objective optimization method of cutting parameters based on grey relational analysis and response surface methodology (RSM), which is applied to turn AISI 304 austenitic stainless steel in order to improve cutting quality and production rate while reducing energy consumption. Firstly, Taguchi method was used to design the turning experiments. Secondly, the multi-objective optimization problem was converted into a simple objective optimization problem through grey relational analysis. Finally, the regression model based on RSM for grey relational grade was developed and the optimal combination of turning parameters (ap = 2.2 mm, f = 0.15 mm/rev, and v = 90 m/s) was determined. Compared with the initial turning parameters, surface roughness (Ra) decreases 66.90%, material removal rate (MRR) increases 8.82%, and specific energy consumption (SEC) simultaneously decreases 81.46%. As such, the proposed optimization method realizes the trade-offs between cutting quality, production rate and energy consumption, and may provide useful guides on turning parameters formulation.

The specific energy consumption of machine tools and surface roughness are important indicators for evaluating energy consumption and surface quality in processing. Accurate prediction of them is the basis for realizing processing optimization. Although tool wear is inevitable, the effect of tool wear was seldom considered in the previous prediction models for specific energy consumption of machine tools and surface roughness. In this paper, the prediction models for specific energy consumption of machine tools and surface roughness considering tool wear evolution were developed. The cutting depth, feed rate, spindle speed, and tool flank wear were featured as input variables, and the orthogonal experimental results were used as training points to establish the prediction models based on support vector regression (SVR) algorithm. The proposed models were verified with wet turning AISI 1045 steel experiments. The experimental results indicated that the improved models based on cutting parameters and tool wear have higher prediction accuracy than the prediction models only considering cutting parameters. As such, the proposed models can be significant supplements to the existing specific energy consumption of machine tools and surface roughness modeling, and may provide useful guides on the formulation of cutting parameters.

No abstract

No abstract