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Abstract This study investigates the potential of copper oxide (CuO) nanoparticles as additives to enhance the viscosity and vibration-damping characteristics of shock absorber oil. Shock absorbers play a critical role in vehicle safety and handling by mitigating vibrations from road irregularities. However, their effectiveness deteriorates over time. To address this, CuO nanoparticles were explored for their ability to improve lubricant performance. Nano-lubricants were prepared by dispersing CuO nanoparticles at varying concentrations of 0.25 wt%, 0.5 wt%, 1 wt%, and 1.5 wt% in a base oil using ultrasonication. The novelty of this research lies in the innovative use of CuO nanoparticles to significantly enhance the viscosity and vibration-damping properties of shock absorber oil. The viscosity of these nano-lubricants increased significantly, with the 1 wt% CuO nano-lubricant achieving a 20% increase at 25 °C compared to the base oil, indicating improved load-carrying capacity and potential friction reduction. Vibration damping performance was evaluated using a dedicated shock absorber test rig. The nano-lubricants exhibited reduced overall vibration acceleration compared to plain oil, with a 15% improvement in damping effectiveness at the optimal CuO concentration. However, the transmissibility ratio, a key damping metric, did not show significant variation, suggesting that traditional shock absorber designs might require modifications to fully leverage the benefits of CuO nanoparticles. These findings demonstrate the potential of CuO nanoparticles to enhance the viscosity and damping characteristics of shock absorber oil, leading to improved performance at lower temperatures.

Improving the performance of drop-in hydrocarbon refrigerants for the increasing in-flow of conventional refrigerators to Nigeria is a necessary sustainable strategy. This experimental study investigates the enhancement limits of a liquid-line magnetic field, CNT nano-lubricant, and a combination of both on an R134a domestic refrigerator with 40–70 g each of R600a refrigerants. The study compares the performance of R600a refrigerant without enhancements (pure) and with two pairs of 3000 Gauss liquid-line mounted O-ring permanent magnets (Mag), 0.2 g L−1 concentration of CNT nano-lubricant (Nano), and both liquid-line magnet and CNT nano-lubricant (Mag-Nano). Test parameters include evaporator air temperature, discharge pressure, power consumption, coefficient of performance (COP), and total equivalent warming impact (TEWI). The results show that enhancement methods led to higher COP in the range of 16.64–42.38%. At the same time, evaporator air temperature, discharge pressure, power consumption, and TEWI were lower by 9.76% to 20.96%, 17.75% to 34.26%, 5.22% to 13.42%, and 5.88% to 10.86%, respectively. In conclusion, the proposed enhancement techniques in the refrigeration system provide normal, safe, and efficient operation. [ABSTRACT FROM AUTHOR]

Background: Nanofluids are the most widely used materials in various engineering fields. They have different properties under different conditions, and predicting their properties requires several experiments. Artificial intelligence can predict the properties of nanofluids in the shortest time and cost. Methodology: This study aims to predict the viscosity and share rate of MWCNT-TiO2 (40–60)-oil SAE50 nano-lubricant (NL). Machine learning algorithms and neural networks can respond best to this important matter. For this purpose, the Group Method of Data Handling (GMDH) neural network is combined with the meta-heuristic algorithm Multi-Objective Gray Wolf Optimizer (MOGWO). This way, the experimental data is first given to the artificial neural network (ANN). Then, the meta-heuristic algorithm optimizes the hyperparameters of the ANN to bring the predicted results closer to the experimental data and minimize the error. The MOGWO algorithm's regulators are the number of iterations and the number of wolves investigated in this study to better select this algorithm. Then, these modes are measured using two criteria, correlation coefficient (R) and rote mean squared error (RMSE), to choose the best mode. Finally, by using the extracted equations by the GMDH neural network, the best models or the Pareto front can be obtained using the MOGWO meta-heuristic algorithm. Results: The error histogram diagram shows the excellent performance of the combination of the GMDH neural network and the MOGWO meta-heuristic algorithm. The values of R and RMSE for viscosity and shear rate are equal to 0.99217, 15.8749, and 0.99031, 68.7723, respectively. The optimization results showed that the best conditions to meet viscosity and cutting rate are when φ, T, and γ equal 1.21∗e−5, 46.71, and 50.11.

In this study, the dynamic viscosity ( μ nf) of oil-based hybrid nanofluid (NF) containing MWCNTs nanoparticles (NPs) was investigated using the artificial neural network (ANN) method at temperatures from 5 to 55 ∘ C , volume fractions from φ = 0.0625 to 1%, and different shear rates (SRs). One of the advantages of this research is obtaining the μ nf through ANN and choosing the appropriate method in ANN from the set of hypothetical tools in terms of economic efficiency and time dependencies. In this study, parameters of temperature, SR, and φ are the input variables to ANN, and μ nf is the output variable of ANN. To evaluate the accuracy of ANN results, various graphs and R and MSE indices were used to determine the quality of the model. The value of R and MSE for the proposed model was 0.999987 and 0.01801948, which indicates the high accuracy of this model in predicting μ nf . The margin of deviation diagram shows that the dispersion of the data obtained from the model predicted by ANN is between ± 3%. Based on obtained results from the correlation charts and deviation margin as well as the R and MSE indices, it was found that the results obtained from the artificial neural network are highly accurate. In the review and analysis of three data groups including laboratory data, correlation outputs, and ANN, it can be seen that ANN is suitable for estimating laboratory data. [ABSTRACT FROM AUTHOR]

Abstract This study delves into optimizing nanoparticle attributes to enhance the anti-wear performance of nano-lubricants, specifically exploring the influence of nanoparticle material hardness and concentration. Investigating the impact of contamination-induced abrasive wear in lubricants and the subsequent enhancement of anti-wear properties through nanoparticle integration into base oil, the research focuses on, CaCO3, TiO2, and Al2O3 materials representing varied hardness levels. Using ASTM D4172 standards, the study examines the wear resistance of base oil infused with these nanoparticles. Employing a response surface methodology model based on experimental data, the criticality of the interaction between nanoparticle material hardness and concentration in determining wear effects is revealed. Analysis through atomic force microscopy and energy dispersive spectrometry aids in comprehending alterations in wear mechanisms. The research highlights the nuanced relationship between nanoparticle material hardness and concentration in shaping wear behavior within lubricants. Softer materials, like CaCO3, demand higher concentrations for comparable wear reduction as observed with lower concentrations of harder materials, such as Al2O3. Conversely, higher concentrations of harder materials can exacerbate wear, as confirmed by EDS analysis and surface topography studies. This study underscores the importance of nanoparticle material hardness and concentration interaction in determining the efficacy of nanoparticles as anti-wear agents in lubricants. It emphasizes the need to optimize both factors for enhanced anti-wear properties in nanoparticle-based nano-lubricants, offering insights crucial for their application in practical scenarios.

This study delves into optimizing nanoparticle attributes to enhance the anti-wear performance of nano-lubricants, specifically exploring the influence of nanoparticle material hardness and concentration. Investigating the impact of contamination-induced abrasive wear in lubricants and the subsequent enhancement of anti-wear properties through nanoparticle integration into base oil, the research focuses on, CaCO3, TiO2, and Al2O3 materials representing varied hardness levels. Using ASTM D4172 standards, the study examines the wear resistance of base oil infused with these nanoparticles. Employing a response surface methodology model based on experimental data, the criticality of the interaction between nanoparticle material hardness and concentration in determining wear effects is revealed. Analysis through atomic force microscopy and energy dispersive spectrometry aids in comprehending alterations in wear mechanisms. The research highlights the nuanced relationship between nanoparticle material hardness and concentration in shaping wear behavior within lubricants. Softer materials, like CaCO3, demand higher concentrations for comparable wear reduction as observed with lower concentrations of harder materials, such as Al2O3. Conversely, higher concentrations of harder materials can exacerbate wear, as confirmed by EDS analysis and surface topography studies. This study underscores the importance of nanoparticle material hardness and concentration interaction in determining the efficacy of nanoparticles as anti-wear agents in lubricants. It emphasizes the need to optimize both factors for enhanced anti-wear properties in nanoparticle-based nano-lubricants, offering insights crucial for their application in practical scenarios. [ABSTRACT FROM AUTHOR]

Vegetable oil-based lubricants can substitute mineral oil-based lubricants for environmental cleanliness. In this paper, nano-lubricant containing blend of coconut and mustard oil added with CeO2 nano-particles was developed. Tribological characteristics, viz., coefficient of friction and specific wear rate of nano-lubricants by varying concentration of nano-particles, blend ratio, load and speed, were measured using a pin-on-disc tribometer. Furthermore, three different MLMs, such as random forest (RF), support vector machine (SVM), and feed forward neural network (FFNN) were used for predicting tribological characteristics. The results of model performance statistics showed that RF, SVM and FFNN have greater ability and stability to regress tribological properties of nano-lubricants effectively. The most influential parameter in predicting wear has been found to be concentration of nano-particles, which occupies 60.78% of coefficient of friction and 88.78% of specific wear rate. The study could help to optimize the cost and time required for modelling tribological properties of lubricants. [ABSTRACT FROM AUTHOR]

Artificial neural network (ANN) is one of the best models with good performance for predicting laboratory data, Due to its high accuracy, this design can be a suitable alternative to frequent and costly testing. In this study, the viscosity (μnf) of MWCNT-CuO (10-90)/Oil 10W40 nano-lubricant is modeled by ANNs by experimental data. μnf is measured in φ=SVF=(0.05-1% and temperature range T=5 to 55°C to train the ANNs. To check the precision of predicted data by ANN, mean square error (MSE), regression coefficient, and also margin of deviation (MOD) are used. The optimal structure was selected from among 400 ANN samples for MWCNT-CuO (10:90)/Oil 10W40 nano-lubricant, which has two hidden layers and the number of 4 and 8 neurons, as well as tansig and logsig transfer functions. The inputs of the ANN model are solid volume fraction (SVF or φ), temperature (T), and shear rate (SR), and the output of the ANN is the μnf. A comparison shows that the ANN calculates the laboratory data more accurately.

The evolution of materials of cutting tools and their geometry, is currently leading to a technological development of many other sectors linked to machining. It is, therefore, a progress in the construction of machine tools, the machining of new materials, improvement of cutting fluids (lower toxicity and costs related to the maintenance and treatment of used cutting fluids, chemical formulation). Regarding the cutting fluids, international regulations are moving towards eco-elimination or limiting the use of certain molecules for ecological reasons. In addition, the formulation must be adapted as best as possible to the industrial demand, both in terms of performance and costs. The characteristics of nanofluids meet all these requirements and seem to be a promising solution for major problems encountered when using conventional cutting fluids. The present paper reports a literature review emphasizing essentially the investigations performed during the last 2 years. This literature focuses on the use of nano-lubricant in machining processes such as turning, milling and grinding processes. Analyzing the current review, it has been found that the use of nanofluids especially hybrid ones reduces the tool wear, the surface roughness, cutting forces, and heat generation. However, the optimal choice of nanoparticles and their concentrations that suites more the most severe cutting conditions, needs more attention in future works. [ABSTRACT FROM AUTHOR]

Iraqi petroleum refineries produce large quantities of base lubricating oils (lube oils). Managing the influence of nano-additives on the lube oil nanofluids is required deep understanding to explain the resulting new specifications of produced nano-lubricants. The present study investigated the effect of Al2O3 NPs addition on the thermal properties of lube oil stock-60. Different mass additions of 0.25, 0.65, 1.05, 1.45, and 1.85 wt.% of Al2O3 NPs at operating temperatures of 20-50°C were evaluated. Also, the thermal conductivity coefficient of the prepared nanofluid was studied at the full range of the experimental temperatures (20-50°C). It was noted that the addition of Al2O3 NPs improved the thermal properties of the prepared nano-lubricant due to the high thermal conductivity of the added Al2O3 NPs. Moreover, the greatest improvement in the thermal conductivity of modified nano-lubricating oil was 13.02% at added Al2O3 mass fraction of 1.85%. The results indicated that the viscosity index of the prepared nano-lubricant was improved dramatically with Al2O3 NPs addition increase at measured standard temperatures of 40 and 100°C. The viscosity index of lubricant nanofluid is increased up to 2.46% at a weight fraction of 1.85%. The flashpoint increased by 1.33, 3.54, 5.75, 7.52, and 9.73% for mass fraction of 0.25, 0.65, 1.05, 1.45, and 1.85 wt.%, respectively. Furthermore, the highest flashpoint value was 248oC of prepared nanofluid lube oil with 1.85 wt.% of Al2O3 NPs. Finally, the produced nano-lubricating oil has high operating quality with economic feasibility. Furthermore, an accurate correlation for predicting the viscosity of both types of nano-lubricants was provided.

In the present work, a 3-D CFD analysis has been implemented successfully to analyze the thermo-elastohydrodynamic (TEHD) performance of Nano-lubricated journal bearing with considering the cavitation effect. The performance of a bearing lubricated with pure oil as well as with the base oil dispersed with different volume fractions of TiO2, Al2O3, and CuO nanoparticles with and without cavitation effect has been implemented and compared. A Two-way fluid-structure interaction and Zwart–Gerber–Balamri cavitation models are used to perform the elastic deformation of the bearing material and the cavitation effects on its performance by using ANSYS-FLUENT 2019 R2. The effects of journal speed, eccentricity ratios, and different types of nano-lubricants with different volume fractions of nanoparticles on the TEHD performance of such bearing have been considered. The mathematical model is verified by comparing the results with that obtained researchers and found a good agreement. The simulation results show that the oil film pressure and hence the load carried by the bearing increased when the bearing is lubricated with nano-lubricant that has a higher volume fraction of the nanoparticles while decreased when considering the elastic deformation of the bearing material and the cavitation effect. [ABSTRACT FROM AUTHOR]

Artificial neural network (ANN) is one of the best models with good performance for predicting laboratory data, Due to its high accuracy, this design can be a suitable alternative to frequent and costly testing. In this study, the viscosity (μ nf ) of MWCNT-CuO (10-90)/Oil 10W40 nano-lubricant is modeled by ANNs by experimental data. μ nf is measured in φ = S V F =(0.05-1% and temperature range T=5 to 55°C to train the ANNs. To check the precision of predicted data by ANN, mean square error (MSE), regression coefficient, and also margin of deviation (MOD) are used. The optimal structure was selected from among 400 ANN samples for MWCNT-CuO (10:90)/Oil 10W40 nano-lubricant, which has two hidden layers and the number of 4 and 8 neurons, as well as tansig and logsig transfer functions. The inputs of the ANN model are solid volume fraction (SVF or φ), temperature (T), and shear rate (SR), and the output of the ANN is the μ nf . A comparison shows that the ANN calculates the laboratory data more accurately. [ABSTRACT FROM AUTHOR]

This study investigates double-diffusive mixed convection and entropy generation under magnetic influence within a novel cam-shaped enclosure, with applications in automotive engineering. The unique geometry creates distinct flow patterns that enhance heat and mass transport. Using the Galerkin finite element method, we analyzed the effects of key parameters, including buoyancy ratio, Reynolds number, Hartmann number, magnetic field inclination angle on average Nusselt and Sherwood numbers, and total entropy generation. Results show that increasing the buoyancy ratio and Reynolds number enhances heat transfer, while the Hartmann number significantly impacts flow characteristics and entropy generation. The magnetic field inclination angle exhibits a non-linear effect on heat transfer and entropy production. These findings provide valuable insights for optimizing heat transfer systems in complex automotive geometries. [ABSTRACT FROM AUTHOR]

The current work focuses on the experimental research of a vapor compression cycle using Polyol Oil Ester (POE) with nano copper oxide (CuO) and a fluidized bed for condenser cooling to enhance its performance. The efficiencies of the modified and normal systems using only POE oil have been compared to present the actions of using nano CuO and a fluidized bed. Three volume fractions of CuO 0.1%, 0.3%, and 0.5% have been used. The fluidized bed contained a uniform particle size (0.5 mm) to sink heat from the condenser, where the bed height was 2.5 mm to get good mixing of particles. The experiment outcomes indicated that adding nanoparticles to the lubricant and using a fluidized bed for condenser cooling improves the refrigeration system's performance. The results demonstrated that employing nano-lubricant (POE oil+0.5% CuO) rather than just POE oil boosted the coefficient of performance of the system by approximately 15.96% while reducing power consumption by 50%. Also, the refrigeration impact was raised, and the compressor's performance was reduced with the volume fractions of CuO rising.

The main Objective of the present work is to study the behavior of nano lubricated journal bearing considering elasticity and variable viscosity effects. A mathematical model for a journal bearing was employed using three-dimensional computational fluid dynamics. The study was implemented for a journal bearing with laminar flow and smooth surfaces lubricated with pure oil as well as lubricants containing different concentrations of Al2O3 Nano-particles. The dependence of the oil viscosity on the temperature was considered by using modified Krieger Dougherty model. Pressure, temperature and elastic deformation in addition to the bearing load carrying capacity of the bearing working under different eccentricity ratios (0.1-0.6) have been studied. The mathematical model was confirmed by comparing the results of the pressure and temperature distributions obtained in the current work with those obtained by Ferron et al.(1983) for a bearing lubricated with pure oil. Also, the pressure obtained for nanolubricated bearing of the present work was validated with that obtained by Solighar (2015). The results are found in a good agreement with maximum deviation not exceed 5%. The obtained results show that the oil film pressure increases by about 17.9% with slight decrease in oil film temperature and friction coefficient.

This paper represents a new lubricant method which is able to one-stroke drill deep holes with a length-to-diameter of 8, on the AISI SUS 304 stainless steel. By adding graphene nanosheet into typical soluble emulsion and then mixing with water, a nano fluid can be made. The results revealed that using nanofluid can provide a reduction of 4.4-fold of the drilling torque, and thus expand the tool life as many as 20 times, compared with using typical emulsion lubricant. The proper set of cutting parameters was found by using Taguchi L9 experiments as 550 rpm spindle speed and 0.05 mm/rev. The results can be expanded to apply in other deep drilling of hard-to-cut material, using inexpensive devices and avoiding peck-drilling. The proposed lubricant can also be promissing for other machining operations. [ABSTRACT FROM AUTHOR]

Considering the issues of energy saving and environment protection, the performance of refrigeration systems requires to be improved. In recent years, nano-fluids have attracted greatly attention from the researchers due to their outstanding thermal characteristics. In this work, the published investigations on the preparation and characterization of nano-fluids have been discussed at first. Furthermore, the key thermo-physical properties of nano-fluids, such as thermal conductivity, viscosity, specific heat and density have been summarized. Finally, the performance enhancements in different types of refrigeration systems by using nano-fluids have been reviewed. It is concluded that nano-fluids as refrigerant, lubricant or secondary fluid have wide potential application in refrigeration systems. [ABSTRACT FROM AUTHOR]

The special mechanical properties of cemented carbide with high strength and hardness will cause complex stress due to excessive force and heat in the process of precision manufacturing, which will affect precision retention and endurance limit. Given the health and environmental threat of conventional flood cooling and the harsh processing environment of dry grinding, minimum quantity lubrication (MQL) has become an irreplaceable method to machining cemented carbide. However, the addition of nanoparticles changes the force and heat during grinding, which makes the influence on the residual stress of cemented carbide complicated. Therefore, based on the single abrasive grinding force model, the effective abrasive particle number was obtained by simulating the distribution of abrasive particles on the grinding wheel surface, and the mechanical stress model was established, which was loaded onto the workpiece in iterative attenuation mode. The thermal stress model was established based on the temperature field model. The final residual stress prediction model was obtained by determining whether the grinding process yields results and carrying out stress loading and stress relaxation. Experimental verification of the model was carried out under four different grinding conditions of YG8. The minimum friction coefficient of 0.385 was obtained under nanofluid minimum quantity lubrication (NMQL). In the precision analysis of the model, the minimum error value was 5.9% in the direction perpendicular to the feed direction of the workpiece in the dry grinding condition, which proved that the residual stress model had certain reliability. [ABSTRACT FROM AUTHOR]

Machining is the art of developing sustainable mechanical components by transforming solid raw materials into the finished product. Because for any Nation to achieve sustainable development, the Nation must have quality manufacturing industries. This paper summarizes existing articles on the effects of high-pressure lubrication conditions on chip formation and temperature distribution. Also, surface roughness, tool wear, and vibrations in machining operations when considering the current trend. Furthermore, the study of nano-lubricant and their application in reducing friction and temperature were also reviewed. The study also examined other lubrication conditions, cutting parameters with the high-pressure machining operations to draw a definite conclusion. The review confirms that applying a high-pressure lubrication system is very efficient. However, it has some challenges. Cooling technology is not built into the system, discovered during this review. Therefore, the study will recommend a developed machine that can function in multiple faces. Industrial 4.0 additive manufacturing techniques can build the cryogenic system— making the lubricant delivery machine a sustainable technology in machining operation. A high-pressure-cryogenic-MQL lubrication process is needed for sustainable machining operations of various alloys and metal composite materials for automobile, aerospace, and structural applications. The sustainable lubrication system will also help eradicate high temperature occurrence in the machining region with a sustainable way of removing the chips without experiencing chip breakage at the cutting region. [ABSTRACT FROM AUTHOR]

Nano-lubricant machining of Aluminum 8112 alloy is the art of sustainable manufacturing of mechanical components used for defense technology and aerospace application. However, machining aluminum alloys generates excess heat, which tends to increase the cutting force (F.C.), due to the material adhesion of the workpiece on the cutting tool. The challenge has drawn researchers' attention to introducing nano-lubrication processes. This study focused on the comparative assessment of eco-friendly vegetable oil-based-TiO2 and MWCNTs nano-lubricant on cutting force during the machining of the Aluminum 8112 alloy. Nanoparticles were implemented on the base oil using an ultrasonic vibrator and magnetic stirrer before the application in the machining, via the minimum quantity lubrication process. Quadratic central composite designs were employed to carry out the experiment, using five factors at five levels, having experimental runs of 50. The input parameters are helix angle (H.A.), spindle speed (S.S.), axial depth of cut (ADOC), feed rate (F.R.), and length of cut (LOC). The results show that the application of the nanoparticle increases the performance of the vegetable oil on the cutting force. TiO2 nano-lubricant reduces the cutting force by 0.26%, compared with the MWCNTs, and 6% compared with the vegetable oil. Furthermore, the MWCNT nano-lubricant reduces the cutting force by 5% compared with the vegetable oil lubrication environment. [ABSTRACT FROM AUTHOR]

In this study, the rheological behavior and tribological properties of a nano-lubricant containing SiO2 nanoparticles in SAE40 engine oil are experimentally investigated. Nano-lubricant has been prepared with two-step method using an ultrasonic homogenizer. The rheological behavior of nano-lubricant checked out in all studied temperatures (ranging from 15˚C to 65˚C) and different solid volume fractions (ranging from 0 to 1%) and it shows non-Newtonian behavior (pseudoplastic). Also, an accurate correlation is presented for the prediction of nano-lubricant's viscosity based on experimental data. A pin-on-disk tribometer was performed to investigate the tribological behavior of nano-lubricant. Results revealed that in φ = 0.1%, the wear rate and friction coefficient have been decreased by 50% and 18.46%, respectively, compared with the base oil. In addition, the nano-lubricant with optimum concentration and base oil was tested in the operational condition of diesel engines at the same condition and the abrasive elements of these engine oils were analyzed. Other important factors including pour point and flash point were also determined which showed that the addition of SiO2 nanoparticles to the base oil in φ = 0.1% will cause a 3.8% improvement in flash point compared with the base oil. [ABSTRACT FROM AUTHOR]

The main objective of the present work is to study the behavior of Nano-lubricated journal bearing considering elasticity and variable viscosity effects. A mathematical model for a journal bearing is employed using three-dimensional computational fluid dynamics. The study is implemented for a journal bearing with laminar flow and smooth surfaces lubricated with pure oil as well as lubricants containing different concentrations of Al2O3 Nano-particles. The dependence of the oil viscosity on the temperature is considered by using the modified Krieger Dougherty model. Pressure, temperature and elastic deformation in addition to the bearing load-carrying capacity of the bearing working under different eccentricity ratios (0.1-0.6) have been studied. The mathematical model is confirmed by comparing the results of the pressure and temperature distributions obtained in the current work with those obtained by Ferron et al.(1983) for a bearing lubricated with pure oil. Also, the pressure obtained for the Nano-lubricated bearing of the present work is validated with that obtained by Solighar (2015). The results are found in good agreement with a maximum deviation not exceeding 5%. The obtained results show that the oil film pressure increases by about 17.9% with a slight decrease in oil film temperature and friction coefficient. [ABSTRACT FROM AUTHOR]

Difficult-to-machine materials, e.g., Titanium alloys, are highly applicable in diverse industries that yield strength and wear resistance. However, they prove difficult to machine due to high vibration, leading to high cutting forces during the machining process. This vibration occurs from chip discontinuity and thereby leads to high friction between the cutting tool and workpiece. In order to minimize these challenges, lubricants are employed in machining operations to reduce frictional and other unnecessary cutting forces and improve surface finish. This research focuses on studying the nano-lubricant effects in reducing cutting forces in the machining of TI-6AL-4V-ELI alloy. Also, carry out a comparative study of dry, mineral oil, and TiO2 nano-lubricant during face-milling machining for optimal performance. Additionally, the study develops a predictive mathematical model for cutting force using a Taguchi L9 orthogonal array. A two-step approach was employed to develop the nano-lubricant before the machining process. The dynamometer is used to collect the cutting force data at the end of each sample. The Results show that the lubrication conditions play a significant role in the reduction of cutting forces. The mineral oil-based-TiO2 nano-lubricant reduces the cutting force by 19 % compared with the mineral oil during the machining of TI-6AL-4V-ELI alloy. Furthermore, the optimal parameters to reduce cutting forces during face milling of TI-6AL-4V-ELI alloy are cutting speed at 3000 rpm, 200 mm/min feed rate, 0.3 mm depth of cut to obtain the minimum cutting force 30 (N). This study concludes that the application of TiO2 nanoparticles in mineral oil significantly improves the thermal and mechanical properties, which leads to a reduction of cutting force. [ABSTRACT FROM AUTHOR]

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One of the most important challenges industry has always been facing is the wear phenomenon. Wear is the cause of huge deteriorations in parts and results in a drop in performance and lifetime of different machines. Therefore, finding solutions to reduce friction coefficient and wear is of special importance. The present research aims at numerical and experimental investigation of friction coefficient and wear in the presence of nano-lubricants. In the numerical section, to tackle different scales of contact components, two sub-models are developed. In the first one, contact of asperities is modeled and the properties of contact surfaces are taken into account. Second sub-model simulates nano-particles in the contact region. Furthermore, a series of experiments are conducted under different loads, speeds, and different values for Zinc Oxide nano-particle weight percent using a pin-on-disk test rig. Results show that predicted friction coefficient and wear volume in theory are reasonably in agreement with experimental results. It was found that adding nanoparticle to the lubricant can be beneficial in terms of friction reduction.

This paper represents a new lubricant method which is able to one-stroke drill deep holes with a length-to-diameter of 8, on the AISI SUS 304 stainless steel. By adding graphene nanosheet into typical soluble emulsion and then mixing with water, a nano fluid can be made. The results revealed that using nanofluid can provide a reduction of 4.4-fold of the drilling torque, and thus expand the tool life as many as 20 times, compared with using typical emulsion lubricant. The proper set of cutting parameters was found by using Taguchi L9 experiments as 550 rpm spindle speed and 0.05 mm/rev. The results can be expanded to apply in other deep drilling of hard-to-cut material, using inexpensive devices and avoiding peck-drilling. The proposed lubricant can also be promissing for other machining operations.

The main goal of this study is to present a model to investigate the effect of nano-particles' weight fraction on the friction coefficient of rough contact in the mixed-lubrication regime. Experimental testing involves pin-on-disk measurements of the friction coefficient with CuO nano-particles added to engine oil. Theoretical analyses involve developing a method for treating an EHL line contact with provision for surface roughness that takes into account the load-carrying capacity of surface asperities, lubricant, and nano-particles. Results show that theoretical and experimental results for friction coefficients are in good agreement. A parametric study is conducted to investigate effect of load, the geometry of the nano-particles, and their mechanical properties as well as their weight fraction on the friction coefficient. [ABSTRACT FROM AUTHOR]

Nano-lubricant machining of Aluminum 8112 alloy is the art of sustainable manufacturing of mechanical components used for defense technology and aerospace application. However, machining aluminum alloys generates excess heat, which tends to increase the cutting force (F.C.), due to the material adhesion of the workpiece on the cutting tool. The challenge has drawn researchers’ attention to introducing nano-lubrication processes. This study focused on the comparative assessment of eco-friendly vegetable oil-based-TiO2 and MWCNTs nano-lubricant on cutting force during the machining of the Aluminum 8112 alloy. Nanoparticles were implemented on the base oil using an ultrasonic vibrator and magnetic stirrer before the application in the machining, via the minimum quantity lubrication process. Quadratic central composite designs were employed to carry out the experiment, using five factors at five levels, having experimental runs of 50. The input parameters are helix angle (H.A.), spindle speed (S.S.), axial depth of cut (ADOC), feed rate (F.R.), and length of cut (LOC). The results show that the application of the nanoparticle increases the performance of the vegetable oil on the cutting force. TiO2 nano-lubricant reduces the cutting force by 0.26%, compared with the MWCNTs, and 6% compared with the vegetable oil. Furthermore, the MWCNT nano-lubricant reduces the cutting force by 5% compared with the vegetable oil lubrication environment.

The present work displays an extensive numerical analysis for the thermo-hydrodynamic (THD) behavior in finite length journal bearings lubricated with different types of nano-lubricants considering cavitation effect. The effects of nanoparticle concentrations, cavitation and temperature rise on the performance parameters of such bearings have been explored. The bearing is simulated using Computational Fluid Dynamic (CFD) approach. The effect of using different types of nano-lubricants with different volume fractions of TiO2 and Al2O3 nanoparticles dispersed in Veedol Avalon ISO Viscosity grade 46 oil has been demonstrated. Modified Krieger- Dougherty equation has been implemented with the thermal viscosity model to to evaluate the oil effective viscosity. The obtained results show that concerning the TiO2 nanoparticles results in a higher oil film pressure and load carrying capacity in comparison with Al2O3. The bearing equilibrium position was obtained by using Response Surface analysis (RSA) with optimal spacefilling design technique. The numerical model was validated by comparing the results obtained in the present work with that obtained by Feron et al. The results were found to be in a good confirmation. The attained results show that the maximum pressure grows by 21% when the bearing is lubricated with nano-lubricant. [ABSTRACT FROM AUTHOR]

Aluminium 8112 alloy has become a leading material in the aluminium family and currently used in the aerospace and automobile industries due to its excellent chemical and mechanical properties. However, during machining of aluminium alloy, one of the challenges faced by the manufacturing industry is the material adhesion, which increases the rate of chips discontinuity at the machining region and leads to a high surface roughness of the workpiece. The focus of this research is to proffer solutions to this material adhesion by implementing vegetable oil that is copra oil-lubricant, titanium dioxide (TiO2) and multi-walled carbon nanotube (MWCNTs) nano-lubricants during end-milling of AA8112 alloy. Also, using quadratic rotatable central composite design (QRCCD) to study the effects of the machining parameters under minimum quantity lubrication condition, this research used the two-step method to synthesise the nano-lubricants and carried out the homogenisation using the magnetic stirrer and ultrasonic cleaner machine. The study considered five machining factors, including spindle speed, feed rate, length of cut, depth of cut and helix angle. The result from the surface roughness shows that the TiO2 nano-lubricant reduces the surface roughness with 10% and 17% when compared with the MWCNTs nano-lubricant and copra oil. The minimum surface roughness of 1.15 μm, 1.16 μm and 1.35 μm, for the three machining environments, was achieved, respectively. Spindle speed is the most influential machining parameter, followed by the feed rate. The result of this study will aid the manufacturing industry to produce an excellent quality product for a cleaner manufacturing system. [ABSTRACT FROM AUTHOR]

One of the most important challenges industry has always been facing is the wear phenomenon. Wear is the cause of huge deteriorations in parts and results in a drop in performance and lifetime of different machines. Therefore, finding solutions to reduce friction coefficient and wear is of special importance. The present research aims at numerical and experimental investigation of friction coefficient and wear in the presence of nano-lubricants. In the numerical section, to tackle different scales of contact components, two sub-models are developed. In the first one, contact of asperities is modeled and the properties of contact surfaces are taken into account. Second sub-model simulates nano-particles in the contact region. Furthermore, a series of experiments are conducted under different loads, speeds, and different values for Zinc Oxide nano-particle weight percent using a pin-on-disk test rig. Results show that predicted friction coefficient and wear volume in theory are reasonably in agreement with experimental results. It was found that adding nanoparticle to the lubricant can be beneficial in terms of friction reduction. [ABSTRACT FROM AUTHOR]

Al-Li alloy has excellent overall performance and great development potential, which has been considered as a very ideal structural material for aerospace applications. However, the high ductility causes material adhesion to the tool surface, giving rise to severe tool wear, tool breakage, and other machining problems, which restrict the machining quality and practical application of Al-Li alloy structural parts. The multi-filed coupling of cold plasma and nano-lubricant minimum quantity lubrication (CPNMQL) may provide new enlightenment to break this bottleneck. Effects of the multi-field on the micro-milling process were characterized by tool wear, micro-milling force, three-dimensional surface roughness (Sa), and micromorphology. The research results demonstrated that cold plasma (CP) could not only increase the permeability of nano-lubricant on the Al-Li alloy surface but also enhance the material removal efficiency. When the micro-milling distance was 2 m, the average V B value (3.7 µm) of the three tool teeth under CPNMQL condition was reduced by 80.7 %, 54.9 %, and 51.3 %, while micro-milling edge radius (4.4 µm) decreased by 73.2 %, 24.1 %, and 44.3 % compared to dry, nano-lubricant minimum quantity lubrication (NMQL), and CP conditions, respectively. [Display omitted] • The cold plasma and bio-oil-based nano-lubricant coupling-assisted micro-milling Al-Li alloy. • The mechanism of CPNMQL on material machinability was systematically analyzed. • It is proven the CP could promote permeation of NMQL and fracture of Al-Li alloy. • It is proven the CPNMQL could restrain tool wear and improve surface quality. • The optimum Sa of 17 nm was obtained under CPNMQL condition with 6 mL/h. [ABSTRACT FROM AUTHOR]

This study examines the MWCNT-CuO (20%–80%)/SAE50 nanofluid (NF) by considering the temperature changes, solid volume fraction (SVF), and shear rate (SR) parameters. The temperature range is 30–50 °C and SVF of 0.0625, 0.125, 0.25, 0.5, 0.75 and 1%, and different SRs are investigated. Viscosity is obtained using a Brookfield CAP 2000+ viscometer. The results show that the NF has a non-Newtonian behavior and the values of the power-law index are variable. Increasing the temperature of the NF decreases the viscosity because the intermolecular distance increases and the intermolecular forces decrease. Increasing the SVF in all SVFs except SVF = 0.0625 and 0.125% increases the viscosity, and therefore the SVF = 0.0625 and 0.125% have special importance. Modeling the viscosity of NF with the response surface methodology (RSM) has good accuracy so that the values of R2 and R2-adj are equal to 0.9993 and 0.9992, respectively. The sensitivity of NFs increases by adding 10% of the SVF in higher SVFs. [Display omitted] • Viscosity of MWCNT-CuO /oil SAE50 nanofluid was experimentally investigated. • Comparison shows that this NF behavior is non-Newtonian. • A new correlation was proposed for NF and R2 = 0.9993 proved its high accuracy. • In SVF = 0.0625% and 0.125%, the viscosity reduces by about 15% and 10% in comparison to pure fluid. [ABSTRACT FROM AUTHOR]

This experimental study was carried out based on the nanotechnology approach to enhance the efficacy of engine oil. Atomic and surface structures of graphene oxide (GO) nanoparticles were investigated by using a field emission scanning electron microscope and X-ray diffraction. The nano lubricant was produced by using a two-step method. The stability of nano lubricant was analyzed through dynamic light scattering. Various properties such as thermal conductivity, dynamic viscosity, flash point, cloud point and freezing point were investigated and the results were compared with the base oil (Oil- SAE-50). The results show that the thermal conductivity of nano lubricant was improved compared to the base fluid. This increase was correlated with progressing temperature. The dynamic viscosity was increased by variations in the volume fraction and reached its highest value of 36% compared to the base oil. The cloud point and freezing point are critical factors for oils, especially in cold seasons, so the efficacy of nano lubricant was improved maximally by 13.3% and 12.9%, respectively, compared to the base oil. The flash point was enhanced by 8%, which remarkably enhances the usability of the oil. It is ultimately assumed that this nano lubricant to be applied as an efficient alternative in industrial systems.

The present study aims at enhancing the thermal performance of SAE 50 engine oil by adding zinc oxide nanoparticles. The investigations are performed in the thermal range of 25 to 55 °C and at the volume fractions of 0.125 to 1.5%. In this regard, first, the characterization of nanoparticles was performed to examine their surface and atomic structure. Moreover, following the manufacture of the nano-lubricant, its stability was investigated by the DLS test. After ensuring the results of nano-lubricant stability, different samples were prepared according to the variations in volume fraction, followed by the experimental measurement of the thermal conductivity of nano-lubricant. The obtained results revealed an ascending trend in thermal conductivity by increasing temperature and concentration. The maximum thermal conductivity enhancement was 8.74%. Based on the experimental results, a sharply precise relation was proposed to predict the ratio of nano-lubricant thermal conductivity to that of the tested base fluid. Unlabelled Image • Enhancing the thermal performance of SAE 50 engine oil by adding zinc oxide nano-powder. • Performing the characterization of nanoparticles to examine their surface and atomic structure. • Investigating the stability by the DLS test. • An ascending trend in thermal conductivity by increasing temperature and concentration. • Proposing a novel correlation to predict the nano-lubricant thermal conductivity. [ABSTRACT FROM AUTHOR]

Reducing crude oil reserves and also environmental pollution caused by its excessive use has led to numerous researches to find alternatives to petroleum-based oils. Thus, owing to lower pollution and higher lubrication efficiency, the use of vegetable base lubricants has been widely considered. Due to the unique properties of different nanoparticles such as sphericality and high surface area besides low environmental risk, the subjected nanoparticles can be applied as additives to the base lubricants and create optimal tribological properties. In this study, in order to improve the lubricating efficiency of vegetable base lubricants, SiO2 nanoparticles with different weight concentrations were used in the cold forging process of aluminum alloy. Then, the lubrication proficiency of both nano-lubricants and conventional solid powder lubricants in the forging industry was evaluated. Friction coefficient was determined by standard compression test and friction calibration curves. In order to evaluate the lubricants’ efficiency, two key parameters, namely shear friction coefficient and surface roughness have been considered. Experimental results showed that the presence of SiO2 nanoparticles in the base lubricants significantly increased the lubrication efficiency of the base lubricants and notably reduced both the friction coefficient and surface roughness. [ABSTRACT FROM AUTHOR]

Abstract To enhance the tribological performance of piston ring-cylinder liner pair in engines, Ni-P-TiN coated piston rings were prepared by electroless plating technology, and novel Fe 3 O 4 @MoS 2 nanocomposites were used as lubricating additives in oil. The tribological behavior of the coated friction pairs was evaluated on a multifunctional piston ring-cylinder liner tribometer. The results show that the concentration of TiN nanoparticles in the coatings has an important effect on the tribological performances. The coated piston ring-cylinder liner contact presented a mild abrasive wear with the concentration of TiN nanoparticles at 1.5 g/L. The coated piston rings did not increase the wear loss the cylinder liner due to the lubricating roles of TiN nanoparticles and the formation of transfer layer. The friction coefficient and wear loss of the Ni-P-1.5TiN coatings reduced by 23.8 ± 3.1 wt% and 64.3 ± 1.8 wt%, respectively, comparing to that of the Ni-P coating. Compared with nano-MoS 2 and nano-Fe 3 O 4 , the Fe 3 O 4 @MoS 2 nanocomposites as oil additives have a better effect on reducing the friction and wear of the Ni-P-TiN composite coating, mainly due to the synergistic effects of the coatings and the nanocomposite additives. Highlights • Ni-P-TiN coated piston rings were prepared by electroless plating. • The tribological performances of the coated piston ring-cylinder liner pair were investigated. • Synergistic effects between the coatings and the nano-additives were found. • The corresponding antiwear mechanisms were illuminated. [ABSTRACT FROM AUTHOR]

• Eight machine learning algorithms were used to estimate viscosity of nano-lubricants. • The results reveal that the trained MLP and CNN models are the most accurate methods. • The effects of amount of MWCNT, nanoparticle, type of lubricant, temperature, shear rate, and solid volume fraction on viscosity are also ascertained utilizing sensitivity analysis. The importance of improving and optimizing the energy transfer systems has been highlighted recently due to ever-increasing demand for energy in the world, and therefore the lubrication of systems has received significant attentions. The improvement of thermophysical properties by nano-particles can be considered as an efficient approach to this end. A comprehensive study dealing with constructing myriad machine learning-based models for predicting the viscosity of nano-lubricants is lacking in the literature. This research, therefore, aims at development of eight different artificial intelligence models including, Adaptive Boosting, Random Forest (RF), Ensemble Learning, Decision Tree (DT), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Convolutional Neural Network (CNN), and Multi-Layer Perceptron Artificial Neural Network (MLP-ANN) to estimate viscosity of different nano-lubricants as a function of amount of MWCNT, nanoparticle, type of lubricant, temperature, shear rate, and solid volume fraction. These models are prepared and tested based on a databank including 1086 viscosity points. The results show that the MLP and CNN models are the most accurate methods with R-squared of 0.950131 and 0.95035, respectively, while the estimation of computational time expresses that MLP and CNN models require the most computational time for training. Moreover, sensitivity analysis reveals that temperature has the most effect on viscosity of nano-lubricants. [ABSTRACT FROM AUTHOR]

In recent years, due to the increase in the speed of rotary machineries, demands for enhanced lubrication and bearing design to overcome this challenge has increased. To satisfy these need, researchers have proposed additive contained lubricants such as Nano-lubricants and bearings with different designs such as noncircular lobed bearings. In this article, effects of preload and aspect ratio on static performance of noncircular lobed journal bearings of finite length lubricated with lubricant containing TiO2 Nano-particles for particle volume fraction of 0.01 are studied. Using finite element method, the steady-state film pressure is obtained by solving the modified Reynolds equation based on the Nano-lubricants and Couple Stress model theories. With the help of film pressure, attitude angle, friction coefficient, friction force, and side leakage of noncircular lobed journal bearings are obtained. The results show that using lubricants containing TiO2 Nano-particles can enhance the performance of static characteristics of two, three, and four lobed journal bearings. According to results, increase in preload and bearing length will increase load carrying capacity noncircular lobed bearings. Based on results, choosing proper design parameters can have great impact on static performance of noncircular lobed journal bearings. [ABSTRACT FROM AUTHOR]

In the current research, dynamic viscosity of MWCNT(%40)-SiO 2 (%60)/5W50 nano-lubricant were investigated experimentally. Dynamic viscosity of Nano-lubricant was measured at temperature range of 5 °C–55 °C, solid volume fraction between 0% and 1%, and fluid shear rate from 50 to 800 rpm. Study on rheological behavior of nanofluid against shear stress showed that the nanofluid has non-Newtonian behavior. For presenting a relation between relative dynamic viscosity and independent parameters two methods were employed that are: artificial neural network and mathematical correlation. Results showed that, proposed correlation can estimate the value of relative dynamic viscosity with an acceptable accuracy. As an example the coefficient of determination (R-squared) was 0.9914, which represents a desirable value. An artificial neural network (ANN) for relative viscosity based on obtained data using the multi-layer perceptron (MLP) algorithm was designed. The results showed that the neural network with the appropriate instruction can estimate accurate value for dynamic viscosity. [ABSTRACT FROM AUTHOR]