The object of this research is to investigate the tribological properties of glycerol lubricant with aluminum nanoparticles as an additive and sodium dodecyl sulfate (SDS) as the dispersive medium for iron to iron friction using a thrust collar tribotester. Meanwhile, the effects of different concentrations of aluminum nanoparticles, SDS, and deionized water in glycerol on tribology properties of iron to iron friction were studied. The experimental parameters were set up according to the Taguchi technique, their influence on the coefficient of friction (COF) and wear rate were examined by response surface methodology (RSM) and analysis of variance (ANOVA) methods. The analysis results were employed to optimize the parameters to obtain the best lubricant effects. The optimal combination of the parameters for both minimum COF and wear rate was found to be 0.6667 weight percent (wt %) of aluminum nanoparticles, 2 wt % of SDS, and 10 wt % of deionized water content of glycerol. The wear surface topography and the average roughness of the surface were also examined using a scanning electron microscope (SEM) and a Mitutoyo Surftest SJ-400 instrument. The results show that aluminum nanoparticles used as an additive in lubricant reduce the surface roughness of a collar remarkably. The energy dispersive spectrometer (EDS) was utilized to confirm the deposition of aluminum nanoparticles on the collar surface leading to decreased friction and wear.

This study investigates the influence of aluminum nanoparticles, oleic acid as dispersants, and rotational speed on the tribological behavior of a lubricant. The experiments are performed on a pin-on-disc tribotester at a normal force of 90 N and a rotational speed ranging from 150 rpm to 600 rpm. Both the aluminum nanoparticles and oleic acid are in concentrations from 0 to 1 wt% and are added to the SN150 base oil. The results revealed that the addition of aluminum nanoparticles and oleic acid to the base oil will lead to significant friction reduction and anti-wear properties. The coefficient of friction (COF) and wear rate decreased after an increase in the concentration of nanoparticles and oleic acid, and an optimum concentration level was exhibited in which both COF and wear-rate were lowest. The viscosity and temperature of the lubricant are also evaluated. Further, the topography of discs after performance of sliding test have been analyzed through the use of an optical microscope, a scanning electron microscope (SEM), and an energy dispersive spectrometer (EDS) in order to interpret the mechanisms of nanoparticle action used to prevent friction and subsequent wear.

The adhesion strength of the coated SiO₂ thin film to SUS304 stainless steel substrates with various surface treatment conditions is studied in this research. The surface of the SUS304 stainless steel substrate is first treated with 1000-W plasma and then a SiO₂ thin film is deposited onto the surface via radio-frequency magnetron sputtering. Scanning electron microscopy is employed to observe the surface and cross section of the coating and X-ray diffraction is used to analyze the crystallographic structure. Moreover, a nanoscratch test instrument was employed to examine the indentation, scratches, coating hardness, modulus of elasticity, coefficient of friction, and critical adhesion of the SiO₂ film and to obtain surface profiles. A comparison of the coating adhesion of the substrate surfaces with and without plasma treatment indicates that critical adhesion increases significantly after Ar/N₂/O₂ plasma treatment.

This study focuses on the effects of the hard turning of SKD11 steel with nanofluid-based Minimum Quantity Lubrication (MQL) on machining efficiency, optimizing the surface roughness (Ra) and Material Removal Rate (MRR). For this purpose, a hybrid Response Surface Methodology (RSM) and Particle Swarm Optimization (PSO) approach are utilized for the SKD11 hard turning under Al₂O₃ nanofluid-MQL conditions. Initially, 27 experiments were conducted using a Box-Behnken design with Al₂O₃ concentration (0–3% wt), cutting speed (v) of 60–100 m/min, depth of cut (ap) ranging from 0.2 to 0.6 mm, and feed rate (f) from 0.1 to 0.2 mm/rev, followed by four additional runs, totaling 31 experiments. The resulting RSM models for Ra and MRR achieved high accuracy with an R² value of 97.69%. The PSO optimization identified extreme solutions: A minimum Ra of 0.43 µm at 3.0% Al₂O₃, v of 95 m/min, ap of 0.4 mm, f of 0.12 mm/rev, and a maximum MRR of 9000 mm³/min at 1.5% Al₂O₃, v of 100 m/min, ap of 0.6 mm, and f of 0.15 mm/rev. Additionally, a balanced multi-objective solution was obtained at 2.0% Al₂O₃: 98 m/min, 0.5 mm, and 0.14 mm/rev, yielding Ra ≈ 0.55 µm and MRR ≈ 8400 mm³/min. The proposed RSM-PSO hybrid approach effectively balances surface quality and productivity, outperforming traditional methods. The findings highlight the benefits of iterative refinement and provide practical parameter optimization for the sustainable machining of hardened steels.