SAE 15W-40 Research Articles

Predicting water content in engine oil using SPA-ISSA-BP methods  

Contamination of engine oil with water can accelerate oil oxidation and decomposition, resulting in oil degradation. Therefore, the detection and prediction of water content in engine oil is crucial to ensure the long-term safe operation of engines. In this study, near-infrared spectroscopy was used to analyse engine oils with varying water contents. Spectral data were obtained, and various methods, including Savitzky-Golay (SG) smoothing, orthogonal signal correction (OSC), and successive projection algorithm (SPA) were employed to compare the performance differences of principal component regression models. An Improved Sparrow Searches Algorithm (ISSA) was then applied to optimise the Back-Propagation Neural Network (BPNN) for predicting water content in SAE 10W-30 engine oil. The results showed that the performance of the principal component regression model constructed from spectral data after SG smoothing, OSC, and the SPA feature wavelength selection had improved. The BPNN optimised by the improved Sparrow Search Algorithm accelerated the convergence speed of the model and effectively improved the prediction accuracy of the BPNN. The obtained coefficient of determination (R2) was 0.99103, the root mean square error (RMSE) was 2. 2136 10-4, and the mean absolute error (MAE) was 1. 8889×10-4. These results provide an effective method for detecting and predicting the water content in engine oil.

Influence of additive nano calcium carbonate (CaCO3) on SAE 10W-30 engine oil: A study on thermophysical, rheological and performance

Researchers have used nanomaterials as additives in base oil to improve its specifications, especially to minimize wear and friction during its applications. In this study, calcium carbonate (CaCO3) nanoparticles were selected as an additive to serve as a protective layer between components and anti-wear properties. In this study, calcium carbonate (CaCO3) nanoparticles were selected as an additive to serve as a protective layer between components and anti-wear properties. Nano lubricant samples were prepared using mass variations of CaCO3 and SAE 10W-30 base oil with concentrations of 0.05, 0.1, 0.15, and 0.2%, then homogenized. The nanolubricant samples obtained were analyzed for thermophysical, rheological properties and lubricant performance with the addition of nano CaCO3 in improving the wear resistance of FC25 cast iron. The results of thermophysical and rheological properties analysis suggest that the nanolubricant has better tribological properties compared to base lubricants. The highest values of thermal conductivity, density, and viscosity (40 oC) are 0.139 W/m.K, 812.203 kg/m3, and 106 mPa.s (40 oC). Meanwhile, the highest CoF, disc mass loss, and surface roughness of nanolubricant are 0.0706, 0.0037 grams, and 0.50 µm, respectively. These results indicate that the greatest wear-reducing agent is from the nanolubricant with the addition of CaCO3 nanopowder additives at 0.1 wt% concentration. These results are expected to give significant insights into the advancement of nano technology-based lubricants in the future.

Physicochemical characterization of 10W-40 engine oil irradiated with gamma-rays

Today, the increasing use of oil and grease in industry with the aim of improving the performance of industrial machinery, equipment and protecting against damage to mechanical parts during work is not hidden from anyone. In this experimental study, the effects of gamma-rays on some physicochemical properties including the pour point, flash point, viscosity index, and kinematic viscosity of an engine oil at 40 °C, and 100 °C were investigated at various radiation doses up to 100 kGy. For this purpose, a commercial engine oil of type SAE 10W-40 was selected. To explore the structural changes of the samples at different doses, FTIR, and UV–Vis analyses were performed. These analyses confirmed the deformation of CH2 and CH3 groups in aldehydes at 2976-2838 cm−1, 1468 cm−1, and 1454 cm−1, and also predominance of the chain-scission or degradation up to 50 kGy. The findings indicated that within 0–50 kGy, the viscosity decreased significantly as the dose increased, while at 100 kGy, the hydrocarbons underwent crosslinking, leading to a negligible increase in the viscosity. Therefore, the stability of certain properties of the oil plays a crucial role in ensuring radiation tolerance designed for radiation applications.

Austenitizing Temperature and Quenching Media Effects on Hardness and Microstructure in Hardening of AISI 1040 Steel

Hardening process is an important to make materials have harder properties related to wear resistance. This research determined the effects of austenitizing temperature and quenching media on the hardness and microstructure of AISI 1040. The austenitizing temperatures varies at 850°C, 900°C, and 950°C with PVA 10%, SAE 20W-50 oil, and salt water as quenching media. The method to measure hardness was the Brinell hardness test. The results showed changes in the microstructure from untreated steel with the structures of ferrite and pearlite to bainite and martensite. The highest hardness value of 416.22 kgf/mm2 was obtained in specimens with salt water media at an austenitizing temperature of 850°C.

The Environmental and Economic Importance of Mixed and Boundary Friction

One route to reducing global CO2 emissions is to improve the energy efficiency of machines. Even small improvements in efficiency can be valuable, especially in cases where an efficiency improvement can be realized over many millions of newly produced machines. For example, conventional passenger car combustion engines are being downsized (and also downspeeded). Increasingly, they are running on lower-viscosity engine lubricants (such as SAE 0W-20 or lower viscosity grades) and often also have stop–start systems fitted (to prevent engine idling when the vehicle is stopped). Some of these changes result in higher levels of mixed and boundary friction, and so accurate estimation of mixed/boundary friction losses is becoming of increased importance, for both estimating friction losses and wear volumes. Traditional approaches to estimating mixed/boundary friction, which employ real area of contact modelling, and assumptions such as the elastic deformation of asperities, are widely used, but recent experimental data suggest that some of these approaches underestimate mixed/boundary friction losses. In this paper, a discussion of the issues involved in reliably estimating mixed/boundary friction losses in machine elements is undertaken, highlighting where the key uncertainties lie. Mixed/boundary lubrication losses in passenger car and heavy-duty internal combustion engines are then estimated and compared with published data, and a detailed description of how friction is related to fuel consumption in these vehicles, on standard fuel economy driving cycles, is given. Knowing the amount of fuel needed to overcome mixed/boundary friction in these vehicles enables reliable estimates to be made of both the financial costs of mixed/boundary lubrication for today’s vehicles and their associated CO2 emissions, and annual estimates are reported to be approximately USD 290 billion with CO2 emissions of 480 million tonnes.

Design of experiments coupled with Bayesian optimisation for nanolubricant formulation

Nanoparticle-enhanced lubricants (nanolubricants) achieve tribological properties that can signficantly outperform the unmodified base oil. However, this performance enhancement is known to be extremely senstive to the concentration of nanoparticles used. Here, we introduce a new approach to optimizing nanolubricants combining design of experiments (DOE) with machine learning, addressing the challenge of determining optimal nanoparticle concentration in base oils and demonstrating significant performance improvements. Combining Box-Behnken design with Bayesian optimization, we identify an optimal concentration of 0.04 wt% copper oxide (CuO) nanoparticles in a standard engine oil (SAE 20W50), resulting in a 42% reduction in coefficient of friction (COF) relative to the base oil. Surface analysis (AFM, SEM) confirmed improvement of anti-wear and anti-friction characteristics. Signficant enhacements of thermophysical properties were observed (demonstrated by increased flash and pour points, by 10% and 7% respectively), signifying greater thermal stability. The dynamic viscosity remained consistent, with a beneficial reduction observed under conditions of high temperature and shear, which aligns with the demands of engine lubricant standards for PC-12B oils, with a reduction from 3.49 to 3.18 cP (8%). This research introduces a novel hybrid optimization technique that provides a versatile framework for optimizing nanofluids, addressing the critical challenge of determining optimal nanoparticle concentration.

Prediction of thrust bearing’s performance in Mixed Lubrication regime

A hydrodynamic thrust bearing could be forced to operate in mixed lubrication regime under various circumstances. At this state, the tribological characteristics of the bearing could be affected significantly and the developed phenomena would have a severe impact on the performance of the mechanism. Until recently, researchers were modeling the hydrodynamic lubrication problem of the thrust bearings either with analytical or with numerical solutions. The analytical solutions are very simple and do not provide enough accuracy in describing the actual problem. To add to that, following only computational methodologies, can lead to time consuming and complex algorithms that need to be repeated every time the operating conditions change, in order to draw safe conclusions. Recent technological advances, especially on the field of computer science, have provided tools that enhance and accelerate the modeling of thrust bearings’ operation. The aim of this study is to examine the application of Artificial Neural Networks as Machine Learning models, that are trained to predict the coefficient of friction for lubricated pad thrust bearings in mixed lubrication regime. The hydrodynamic analysis of the thrust bearing is performed by solving the Average 2-D Reynolds equation numerically. In order to describe the roughness of the profiles, both the flow factors suggested by N. Patir and H.S. Cheng (1978) and the model of J.A. Greenwood and J. H. Tripp (1970) are taken into consideration. Three lubricants, the SAE 0W30, the SAE 10W40 and the SAE 10W60, are tested and compared for a variety of operating velocities and applied coatings. The numerical analysis results are used as training datasets for the machine learning algorithms. Four different ML methods are applied in this investigation: Artificial Neural Networks (ANNs), Multi- Variable Quadratic Polynomial Regression, Quadratic SVM and Regression Trees. The coefficient of determination, R2 is calculated and used to determine the most accurate ML method for the current study. The results showed that ANNs provide very good accuracy in the prediction of friction coefficient compared to the rest of the ML models discussed.

The Corrosive Effects of Aftermarket Oil Additives on High-Leaded Tin Bronze Alloy.

Aftermarket additives are used to enhance the performance of internal combustion engines in specific aspects such as reducing wear, increasing power, and improving fuel economy. Despite their advantages, they can sometimes cause corrosion-related problems. This research evaluated the corrosiveness of four aftermarket additives on the corrosion of a high-leaded tin bronze alloy over 28 days at 80 °C in immersion tests. Among the evaluated products, three showed corrosive effects ranging from intermediate to severe. Notably, the visual appearance of the surfaces often did not indicate the underlying corrosive damage. Therefore, the assessment of corrosiveness was based on chemical characterizations conducted on both the drained oils and the bronze surfaces. The study found minimal oil degradation under the testing conditions, indicating that the primary cause of corrosion was the interaction between the specific additives and the metal elements of the alloy, rather than oil degradation itself. A direct correlation was observed between the dissolution of lead and copper and the adsorption of S and Cl-containing additives on the surfaces, respectively. The corrosive impact of Cl-containing additives in aftermarket formulations was significantly reduced when mixed with engine oil SAE 10W-30 (at a 25:1 ratio), suggesting a mitigated effect in combined formulations, which is the recommended usage for engines.

Tribological Properties of a Sliding Joint with an a-C:H:W Coating under Lubrication Conditions with PAO8 Oil and the Addition of 2% MoS2 Nanoparticles.

One of the promising methods for improving the durability and reliability of friction joints in combustion engines is the use of thin and hard coatings, including coatings based on amorphous DLC. The a-C:H:W coating was produced using the commercial PVD method. The tested tribological joints were made of AISI 4337 steel and SAE-48 bearing alloy (conformal contact) and AISI 4337 steel and valve shims (non-conformal contact). The contact area was lubricated with SAE 5W40 engine oil and PAO8 oil + 2 wt.% MoS2 nanoparticles. The objective of this work is to explore the influence of PAO8 + MoS2 on the tribological properties of a sliding joint with an a-C:H:W coating and the change in the properties of the oils. In the conformal contact, the lubrication of the a-C:H:W coating with PAO8 + MoS2 caused a significant increase in the friction resistance (than in) as compared to the joints with a quenching and tempering surface layer and lubricated SAE 5W40, while in the non-conformal contact, the lubrication of the a-C:H:W coating with PAO8 + MoS2 caused a decrease in the friction resistance and temperature of the contact area. The joints with the a-C:H:W coating were characterized by higher wear of the SAE-48 bearing alloy, as compared to the joints with the surface layer without coating (lubricated with SAE 5W40 oil-11-fold increase, PAO8 + MoS2-46-fold increase). The wear of valve shims with the a-C:H:W coating was significantly lower as compared to the wear of the commercial version of the valve shims (the difference between joints lubricated with SAE 5W40 oil and PAO8 + MoS2 was 12%, 36% and 29% for unit pressures of 10, 15 and 20 MPa). Lubrication of the a-C:H:W coating with PAO8 oil + MoS2 protected the sliding joints against seizing in non-conformal contact.

Hardness and Microstructural Characterization of Pack Carburizing AISI 1020 Low-Carbon Steel by Temperature and Holding Time Variations

Recently, low-carbon steel is often used as a basic material for automotive spare parts on the market. This study aims to improve the quality of carbon steel which is not inferior to that made by manufacturers where the price is relatively affordable by carrying out pack carburizing. This study used the pack carburizing method, AISI 1020 low-carbon steel as a starting material and used fine coconut shell charcoal powder as a carbon source mixed with Na2CO3 as an energizer. Pack carburizing uses temperature variations of 850oC, 875oC and 900oC, aims to find out how the results of hardness and microstructure values for variations of temperature and holding time. And also use SAE 20w-40 oil as a quenching medium. The highest hardness results were obtained on specimens with a heating temperature of 900oC with a holding time of 60 minutes, with an average hardness value of 54.21 HRC. Accompanied by the phase formed, namely 61% pearlite 39% ferrite.

Friction and Wear of the Piston Ring – Cylinder Liner System with Artificially Aged Ultra-low Viscosity Engine Oils

This study aims to investigate the performance of artificially aged prototype engine oils through friction and wear experiments. Experiments were performed on a piston ring – cylinder liner model system with boundary conditions derived from real-life operating conditions. The experimental design implemented two prototype oils (SAE 0W-12 and 0W-16) in unaltered and artificially aged form. An additional fully formulated off-the-shelf engine oil (SAE 0W-20) was also aged and analyzed as reference. Oil samples were artificially aged in a custom rig, to simulate long-term in-engine use through thermal cycling at 180 °C. Fourier Transformed Infrared Spectroscopy of the lubricant samples highlighted a depletion of zinc dialkyl-ditiophosphate antiwear additives in all cases, which is comparable to a selected in-service oil. Oxidation was also measurable, albeit lower compared to the in-service sample. Averaged friction coefficients showed a ranking of aged 0W-12 < aged 0W-16 < unaltered 0W-12 < unaltered 0W-16. A decrease in surface roughness was experienced with aged oil samples, whereas unaltered 0W-16 oil produced an unexpected transition in the wear phenomenon and resulted in severe wear.

Tribology Characteristic of Ball Bearing SKF RB-12.7/G20W using SAE 5W-30 Lubricant with Carbon-Based Nanomaterial Addition

The thermophysical, rheological, and tribological properties in lubricant are essential for preserving the engines, power steering systems, and engine components from damage caused by friction and wear. The incorporation of nano-sized particles into lubricating oils has been reported to enhance these properties. Therefore, this study explores the impacts resulting from the incorporation of graphene, multiwalled carbon nanotubes (MWCNT), double-walled carbon nanotubes (DWCNT), and single-walled carbon nanotubes (SWCNT) at a quantity of 0.04 grams into the SAE 5W-30 base oil. These nanoparticles were synthesized using a two-step method, resulting in nanolubricants. The addition of carbon-based nanomaterials increases the density and decreases the viscosity of the base lubricant. The flow of nanolubricants exhibits non-Newtonian shear thinning behavior during the test at elevated temperatures. The results of the four-ball tribo-tester test reveal the increase of Coefficient of Friction (CoF) values and the extent of wear scar area. Further, the Scanning Electron Microscopy (SEM) analysis was employed to observe the wear morphology of the nanolubricants on the SKF RB-12.7/G20W ball bearing. Its results indicate a prevalence of groove-type wear as compared to conventional lubricants.

An RBF-based artificial neural network for prediction of dynamic viscosity of MgO/SAE 5W30 oil hybrid nano-lubricant to obtain the best performance of energy systems

Technological progress and complications in microfluidics usage have led researchers to use nanomaterials in different scientific fields. The properties and characteristics of hybrid Nanofluids are more enhanced compared to nanofluids based on single nanoparticles and conventional liquid. Recently, modeling methods have replaced most common statistical methods. Due to the high accuracy of the response and generalizability in various conditions, artificial neural networks (ANNs) to estimate nanofluids’ viscosity and thermal conductivity have become common. Dynamic viscosity (μ) (estimation analyzes one of the key factors in determining the hydrodynamic behavior of nanofluids. In this manuscript, an RBF-ANN is used to simulate the input-output relation of dynamic viscosity of the MgO- SAE 5W30 Oil hybrid nanofluid versus three important parameters, including volume fraction of nanoparticles, temperature, and shear rate. The results show that for this nanofluid, by increasing temperature and shear rate, the dynamic viscosity is decreased. In contrast, the volume fraction of nanoparticles directly affects the output, although this consequence can be neglected. By increasing the temperature from 5° to 55°C, the dynamic viscosity would decrease. Also, changing the shear rate from 50 to 1000 rpm decreases the dynamic viscosity from 400 cP to 25 cP. It is worth mentioning that the obtained trends and deviation of dynamic viscosity for MgO-SAE 5W30 Oil hybrid nanofluid versus temperature, the volume fraction of nanoparticles, and shear rate can be used by the academic community as well as an industrial section to obtain the best performance of energy systems based on this nanofluid.

Determining the best structure for an artificial neural network to model the dynamic viscosity of MWCNT-ZnO (25:75)/SAE 10W40 oil nano-lubricant

In this paper, an artificial neural network (ANN) was utilized to examine the dynamic viscosity of MWCNT-ZnO (25:75)/SAE 10W40 oil nano-lubricant. The effect of temperature, shear rate (SR) and solid volume fraction (SVF) on dynamic viscosity is studied at a temperature ranging from T = 5–55 °C, SR varying SR= 50–900 rpm, and SVF= 0.05–1%. A set of 172 experimental data is determined and applied as a training dataset of ANNs with various structures. A two-layer ANN with 17 neurons in the hidden layer is selected with R2 = 0.9999 and MSE= 7.77e-5 to predict the dynamic viscosity. Results show that SR is the most influential parameter having an inverse effect on the dynamic viscosity, i.e. by increasing this parameter from 50 to 900 rpm, the viscosity reduces from 600 cP to 40 cP.

Potential use of graphene oxide as an engine oil additive for energy savings in a diesel engine

This study investigates the effects of graphene oxide (GO) nanosheets as an engine oil additive in fully synthetic SAE 10W-40 engine oil. GO concentrations in 0.5, 1.0, 1.5, and 2.0 mg/mL were added. The viscosity index (VI) increased by up to 7 % and thermal conductivity by 4–15 %. Tribological tests with a ball-on-disc mechanism showed that 1.5 mg/mL GO nanosheets additive gave the best results in reducing the coefficient of friction (COF) by 17 % and the total wear mass loss by 44 % on average. Based on the engine friction tests on the dynamometer, GO nanosheets reduced motored torque by up to 6 %, which represents the engine frictional losses, and improved mechanical efficiency by up to 2.8 % at certain engine speeds. An average improvement of 2.6 % in brake-specific fuel consumption (bsfc) and 2.7 % in brake thermal efficiency (bte) was observed across all engine speeds. The results of detailed engine tests suggest that the GO nanosheets additive has the potential to enhance the mechanical efficiency of internal combustion engines, leading to energy and fuel savings and indirectly contributing to reducing CO2 emissions.

Evaluation of tribological properties and sustainability of bio-lubricant developed from neem seed oil for real-life application

The Tribological property of Neem seed oil is evaluated with various proportions of Zinc Dialkyl Dithiophosphate (ZDDP) ranging from 0.5% to 3% by weight and compared with SAE 20W40 oil. The best weight percentage concentration of the additive with the base oil was selected for further property analysis. The formulated bio lubricant is tested on real-life application as engine lubricant on a working motorbike engine to arrive at the feasibility of these oils as a practical engine oil. The biodegradability and toxicity of the best additive concentration were evaluated. The study showed that the addition of 2% ZDDP increased the anti-wear properties of the base oil and it was found comparable to industrial standard oil after the engine run test.

Effect of a Metal Conditioner on the Physicochemical Properties and Tribological Performance of the Engine Oil SAE 5W-30 API SN

Metal conditioners (MC) are added to lubricants to enhance their friction and wear in friction pairs, mainly in engines, gearboxes, and rolling bearings. Its growth in the Brazilian market is primarily focused on internal combustion engines. The effect of mixing MC with commercial engine oil (SAE 5W-30 API SN) was studied regarding the rheological and thermal properties. Also, the tribological performance of steel–steel contact was investigated. The rheological and thermal properties were determined by flow curves (at 20, 40, and 100 °C) and differential scanning calorimetry (DSC), respectively. Reciprocating fully-lubricated tests were performed at 40 °C and 80 °C (Po = 1.7 GPa, 5 Hz). Differences in the chemical composition between SAE 5W-30 and its mixture with MC were identified by infrared spectroscopy and related to their tribological performance. The coefficient of friction remained within the range of 0.09–0.1 for all conditions, typical of lubricated steel–steel contacts under boundary and mixed lubrication regimes. However, the mixture improved the wear resistance by around 33% when lubricated at 80 °C compared to the wear resistance offered by 5W-30. The formation of tribofilms with different chemical compositions was confirmed by SEM-EDS for all conditions. At both temperatures, the tribological performance reveals beneficial synergy between the metal conditioner and fully formulated oil additives. The tests lubricated with the mixture at 40 °C showed a less severe wear mechanism when compared to the tests lubricated with neat 5W-30. The study demonstrated that the mixture maintained the physicochemical properties of the commercial oil with a substantial anti-wear action at 80 °C.

An overview of experimental methods for cam-tappet interactions

In an engine, the valve train is responsible for approximately 6% to 35% of the total friction losses, with the value varying with the design and operating conditions. Even a 1% improvement in the fuel economy of a vehicle model is of great economic and environmental relevance. One of the engine systems subject to considerable friction is the control system and actuation of gas admission and discharge valves, the valve control system. Friction efforts and wear between the cam and the tappet depend on the relative speeds of the parts, the state of the surfaces in contact, the lubrication and temperature conditions, and the materials and geometries of the cam and tappet. A carefully project of the cam-tappet system is needed to preserve parts integrity and system functionality. This work presents some important experiments carried out with various cam-tappet systems, measured on engines or constructed using parts of an engine. Several important quantities are measured and analyzed by the reported experiments. The main objective of this paper is to promote a better understanding of these quantities that can be useful to engine designers to minimize energy losses or for optimize desirable mechanical characteristics of the system. Also, this paper allows researchers to plan new experiments and/or rapidly identify typical experimental results. It was possible to conclude that typical friction coefficients between cam and tappet varies from 0.05 to 0.15, that the force between valve stem-valve guide represents only about 1.5 -2% of the force acting on the valve stem, that the cam-follower friction was low for viscous oil SAE 50, compared with other lubricants, whereas camshaft bearing friction was low for lubricant SAE 5W-40 in the same engine, that the friction at the cam-follower interface decreases with increasing engine speed, that viscous oil gave greater oil film thickness as compared to the low-viscosity lubricant at the cam-follower interface, among others.

ИССЛЕДОВАНИЕ ОПТИЧЕСКИХ СВОЙСТВ МОТОРНЫХ МАСЕЛ

One of the main physico-chemical indicators of motor oils is the refractive index. In this work, the oils M-5z/12G SAE 10w-30 and ZEPRO IDEMITSU 5w-30 SN (commercial and used) were studied

Formulation and tribological performance of engine oil blended with various non‐edible vegetable oils

AbstractThis present study formulated eco‐friendly lubricants by combining non‐edible vegetable oils with a mineral oil and investigated their efficacy on the friction and wear characteristics of four AISI 52100 chrome steel balls. Multiple formulations containing varying combinations of 0 to 100 vol% of a mineral oil SAE 15W40 (S100), castor oil (C100), and jatropha oil (J100) were prepared using the sonification technique in an ultrasonic homogeniser. A fourier transform spectrometer (FTIR) was then used to investigate the molecular vibrations of each formulation. A tribological test was also performed with a four‐ball tribometer according to ASTM D4172‐94 engineering standards. Finally, a scanning electron microscope (SEM) equipped with an energy dispersive X‐ray spectroscope (EDX) was used to examine surface morphologies. The 80% S100, 10% C100, and 10% J100 (S80C10J10) formulation provided excellent tribological performance as it contained fatty acids composed of carbohydrates and carbonyl groups, particularly polysaccharides and glycerols.