AI-based Tribology Prediction for Lubricants

A method, system, and panel for monitoring the condition of lubricating oil in industrial equipment using a combination of oil sensors, laboratory analyses, and equipment data. The method involves continuously monitoring oil parameters using sensors, analyzing lab oil samples, and integrating equipment data. AI tools identify when equipment is operating within limits versus when issues are starting. The method provides earlier and more accurate failure prediction compared to just lab analyses or sensors alone. The system includes a pressurized oil panel with sensors, a forwarding means to send data, a data integration and AI system, and a web app for visualization.

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Abstract Friction and wear between mating surfaces significantly affect the efficiency and performance of mechanical systems. Traditional tribological research relies on post-observation methods, limiting the understanding of dynamic friction behavior. In contrast, in-situ monitoring provides real-time insights into evolving friction dynamics. This study employs machine learning to monitor polymer wear performance through friction noise. The predictive accuracy of various machine learning methods, including Extremely Randomized Trees, Gradient-Boosting Decision Trees, AdaBoost, LightGBM, Deep Forest, and Deep Neural Networks, is compared for wear type classification. Additionally, the LSBoost regression is selected as the optimal method for predicting polymer wear rates across various temperatures. The results underscore the potential of using friction noise and machine learning for real-time wear monitoring, offering valuable insights for tribological system maintenance and failure prediction.

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Method for determining the aging state of a lubricant using machine learning, comprising: (1) constructing a training database by associating physicochemical analysis data with sensor measurements from multiple lubricants, (2) training a model to predict physicochemical analysis data from sensor measurements, and (3) using the trained model to monitor the aging state of a target lubricant in real-time through sensor measurements.

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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... Read More

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Elastohydrodynamic lubrication (EHL) is crucial for the longevity and performance of machine parts subjected to high loads and speeds. Analyzing EHL thickness settings with accuracy is crucial for improving construction and mitigating premature wear. Current approaches for forecasting such variables might be limited in their precision due to computation difficulties. This study presents a novel remora-optimized Gaussian process regression (RO-GPR) approach in calculating the factors related to EHL film dimension, addressing the requirement for a more effective and precise predictive model. The suggested RO-GPR approach is trained and verified by utilizing a dataset that was acquired through methods that are either simulation-based or practical. To show how well the suggested RO-GPR strategy handles the complex structure of EHL mechanisms, its efficacy is compared with that of other approaches. The study's findings reveal that the suggested RO-GPR model can predict EHL film thickness properties with an excellent level of accuracy, indicating its potential as a useful tool for tribolog... Read More

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Purpose This study aims to propose an optimization framework using deep neural networks (DNN) coupled with nondominated sorting genetic algorithm II and technique for order preference by similarity to an ideal solution method to improve the tribological properties of camshaft bearing pairs of internal combustion engine. Design/methodology/approach A lubrication model based on the theory of elastohydrodynamic lubrication and flexible multibody dynamics was developed for a V6 diesel engine. Setting DNN model as fitness function, the multi-objective optimization genetic algorithm and decision-making method were used to optimize the bearing pair structure with the goal of minimizing the total friction loss and the difference of the average values of minimum oil film thickness. Findings The results show that the lubrication state corresponding to the optimized bearing pair structure is elastohydrodynamic lubrication. Compared with the original structure, the optimized structure significantly reduces the total friction loss. Originality/value The optimized performance and corresponding str... Read More

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Abstract The need to move toward more sustainable lubricant materials has sparked an ever growing interest on the tribological performances of additives based on environmentally friendly molecules, such as carbon-based compounds, and green liquid media as aqueous solutions. The prediction of the solubility of the additives into the liquid and the tribochemistry of decomposition and polymerization of the additive molecules under harsh conditions is essential for understanding the atomistic mechanisms leading to the formation in situ of the carbon-based lubricious tribofilms so effective in reducing friction and wear at solid interfaces. To this extent, the application of tools like ab initio molecular dynamics based on first-principle density functional theory is severely hindered by the size of the systems of interests and the need to simulate their dynamics over relatively long times. To enable tribological simulations with quantum accuracy for a first time, we develop a workflow for smart configuration sampling in active learning, to obtain machine learning interatomic potentials w... Read More

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A prediction system for estimating the density of lubricating oil films formed by base oils. The system uses molecular simulation to predict the density of the oil film based on the three-dimensional structure of the base oil molecules. The prediction is achieved by creating a liquid structure of the base oil molecules, relaxing the structure to obtain an optimized configuration, and calculating molecular features such as interatomic distances and radial distribution functions.

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Electric potential controlled lubrication, also known as triboelectrochemistry or electrotunable tribology, is an emerging field to regulate the friction, wear, and lubrication performance under charge distribution on the solidliquid interfaces through an applied electric potential, allowing to achieve superlubrication. Electric potential controlled lubrication is of great significance for smart tunable lubrication, micro-electro-mechanical systems (MEMS), and key components in high-end mechanical equipment such as gears and bearings, etc. However, there needs to be a more theoretical understanding of the electric potential controlled lubrication between micro- and macro-scale conditions. For example, the synergistic contribution of the adsorption/desorption process and the electrochemical reaction process has not been well understood, and there exists a significant gap between the theoretical research and applications of electric potential controlled lubrication. Here, we provide an overview of this emerging field, from introducing its theoretical background to the advantages and c... Read More

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Abstract In the field of tribology, many studies now use machine learning (ML). However, ML models have not yet been used to evaluate the relationship between the friction coefficient and the elemental distribution of a tribofilm formed from multiple lubricant additives. This study proposed the possibility of using ML to evaluate that relationship. Friction tests revealed that, calcium tribofilms formed on the friction surface, with the friction coefficient increasing as a result of the addition of OBCS. Therefore, we investigated whether the convolutional neural network (CNN) model could recognize the tribofilms formed from OBCS and classify image data of the elemental distributions of these tribofilms into high and low friction-coefficient groups. The CNN model classifies only output values, and its difficult to see how the model has learned. Gradient-weighted class activation mapping (Grad-CAM) was performed using a CNN-based model, and this allowed the visualization of the areas important for classifying elemental distributions into friction coefficient groups. Furthermore, dime... Read More

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The intricate development of liquid-crystal lubricants necessitates the timely and accurate prediction of their tribological performance in different environments and an assessment of the importance of relevant parameters. In this study, a classification model using Gaussian noise extreme gradient boosting (GNBoost) to predict tribological performance is proposed. Three additives, polysorbate-85, polysorbate-80, and graphene oxide, were selected to fabricate liquid-crystal lubricants. The coefficients of friction of these lubricants were tested in the rotational mode using a universal mechanical tester. A model was designed to predict the coefficient of friction through data augmentation of the initial data. The model parameters were optimized using particle swarm optimization techniques. This study provides an effective example for lubricant performance evaluation and formulation optimization.

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Tribology, the study of friction, wear, and lubrication, has been a subject of interest for researchers exploring the complexities of materials and surfaces [...]

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Tribology problems generally, and particularly high-temperature tribology (HTT), is a critical and complex topic based on the interaction between several intrinsic and extrinsic parameters. This involved complex phenomena, resulting in synergistic effects between mechanical, physical, chemical, and thermal solicitations. Introducing artificial intelligence tools, coupled with the design of the experiment, is an original approach to implement a successful transition from traditional "experimental guidance" to "experimental guidance associated with a data-driven" approach. The current study delves into the utilization of machine learning (ML) with simulation to help in the choice of the parameters for experimentation, and the development of predictive models. A detailed framework that takes into account the coupling between such tools is presented. Different scenarios are discussed to data drive the collaborative schema between the design of experiment, numerical development, and ML algorithms. This approach gives several opportunities such as the identification of the well-impacted pa... Read More

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A method and system for monitoring lubricant oil condition using photoacoustic modeling. The system simulates photoacoustic signals from lubricant oil samples using a photoacoustic simulation model, extracts statistical features from the simulated signals, trains a machine learning model with the features and corresponding oil conditions, and classifies the condition of a test oil sample based on its photoacoustic signal.

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A method and device for establishing a quick oil property evaluation model that combines spectral data and viscosity-temperature curve data to improve model accuracy. The method involves obtaining sample data, dividing it into calibration and verification sets, and using dimensionality reduction and multiple correlation algorithms to construct and select physical property analysis models. The device includes acquisition, preprocessing, modeling, and screening modules to implement this method.

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The tribological performance of lubricant is significantly affected by additive concentration. To realize optimization of additive concentrations, an eXtreme Gradient Boosting machine learning method was proposed to predict the tribological performance of a lubricant, reflected by wear volume, with data collected from four-ball friction experiments. The Shapley Additive exPlanation tool was used to explain the predictions of the model. Particle swarm optimization was used to optimize additive concentration proportions. The tribological properties of grease additives were verified through validation experiment. The chemical components of wear scars include MXenes, carbon films, iron oxides, and iron phosphides, which facilitate direct contact between friction surfaces. This study provides a novel data-driven approach for optimization of additive concentrations with effective and predictable tribological performance.

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Tribo-behavior is a complex system-based timedependent process, and it is difficult to accurately model a tribo-system and predict its behavior.Hence, tribology research, in most cases, has relied on extensive experimentation.Driven by the artificial intelligence (AI)-for-science revolution, AI and other emerging computational approaches provide opportunities to explore the complex processes in tribo-systems and the physical mechanisms of tribo-behavior in an efficient way, significantly pushing the boundaries of tribology research.This special issue of Friction aims to gather the latest developments of AI and machine learning (ML), as well as computational approaches and solutions for tribology-related problems and real-world applications.Hence, 15 papers by tribologists and scientists have been compiled to cover the theory, methodologies, tools, and computational aspects of tribology, and guide readers into the emerging fields of AI and computational approaches for tribology.Among these publications, one review article entitled "AI for tribology: Present and future" comprehensively... Read More

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Abstract Conventional liquid lubricants prove inadequate for effective lubrication in conditions characterized by high temperatures and high vacuum environments. In such extreme scenarios, powder lubricants emerge as a more viable solution. The present study is to conduct a series of experiments using a reciprocating wear test setup and evaluate the capability of four different machine learning models in analysing the tribological attributes of metals when lubricated with three distinct powder types: zirconium dioxide, copper oxide, and molybdenum disulfide, specifically under conditions of elevated contact pressures and dry environments. The experiments were systematically carried out encompassing a range of load and temperature combinations. Four different machine learning models (MLP, KNN, extreme gradient boosting and light gradientboosting machine) were used for predicting the coefficient of friction of metals lubricated with different powders. Extreme gradient boosting machine learning model gives better result than the other models with mean absolute error, root mean squared ... Read More

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<title>Abstract</title> Tribological phenomena, encompassing friction, wear, and lubrication, significantly impact the performance and efficiency of mechanical systems across various industries. This research investigates the application of machine learning approaches to minimize wear depth and coefficient of friction in tribometer systems by modeling the effects of applied load, sliding speed, and coating material. Through comprehensive experimentation and analysis, the influence of these critical parameters on the tribological responses is quantified. Several machine learning algorithms, including linear regression, decision trees, random forests, support vector regression, k-nearest neighbors, and neural networks, are employed to capture the complex relationships between the input parameters and the responses. The neural network model achieved the best performance with a low mean squared error (MSE) of 0.0023 and high R-squared of 0.9977 for predicting wear depth, along with an MSE of 567.89 and R-squared of 0.9654 for coefficient of friction predictions. Random forests also exhib... Read More

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Tribology is the study on friction, wear and lubrication for contacting and moving two solid materials with fluid lubricants. Friction can be found easily in our life and should be reduced, and lubricants are a powerful answer for this purpose. The mechanism behind friction and lubrication has been primarily analyzed by the Stribeck curve, which should be 1) interpretable, 2) non-homogeneous in variances and 3) a mixture of Stribeck curves further. We propose a machine learning approach, Estribec, to estimate the Stribeck curves from observed data in tribology. Estribec is, considering all above three characteristics, a finite mixture model, with a component of a piecewise function, where each piece is a comprehensible simple (primarily, linear) function with a unique variance. Entirely our method keeps linear time complexity for all processes, including parameter estimation and prediction. Empirical results with synthetic data showed Estribec achieved favorable predictive performance against Gaussian process regression (GPR) and its tree variant (TGPR). Importantly Estribec ran alwa... Read More

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Abstract With remarkable learning capabilities and swift operational speeds, artificial intelligence (AI) can assist researchers in swiftly extracting valuable patterns, trends, and associations from subjective information. Tribological behaviors are characterized by dependence on systems, evolution with time, and multidisciplinary coupling. The friction process involves a variety of phenomena, including mechanics, thermology, electricity, optics, magnetics, and so on. Hence, tribological information possesses the distinct characteristics of being multidisciplinary, multilevel, and multiscale, so that the application of AI in tribology is highly extensive. To delineate the scope, classification, and recent trends of AI implementation in tribology, this review embarks on exploration of the tribology research domain. It comprehensively outlines the utilization of AI in basic theory of tribology, intelligent tribology, component tribology, extreme tribology, bio-tribology, green tribology, and other fields. Finally, considering the emergence of tribo-informatics as a novel interdiscip... Read More

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This contribution demonstrates the potential of machine learning (ML) algorithms in predicting elastohydrodynamic lubrication (EHL) film thickness in elliptical contact with varying direction of lubricant entrainment, ranging from wide to slender elliptical configurations. The input parameters pertain to worm gear contacts, which are characterized by slender-like elliptical contact between a steel and a soft metal component. The study encompasses generating a database using numerical Finite Element Method (FEM) simulations, training artificial neural network (ANN) models, and evaluating their performance in terms of bias and variance. Key outcomes include the successful training of the ANN models, detailed analysis of the impact of tailored architecture on the ANN models' performance, and the superiority of the ANN compared to other ML regression algorithms. The study further identifies key input parameters that influence prediction accuracy and introduces a strategic dataset augmentation procedure to increase local and overall prediction accuracy. This strategic dataset augmentation... Read More

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Developing stable and high precision solution methods has always been the most important research topic in the field of elastohydrodynamic lubrication (EHL). The emergence of artificial neural network(ANN) provide new ideas for the research of EHL solving methods. In this article, a model based on back propagation neural network (BPNN) is proposed to obtain the film thickness and pressure distribution of EHL. The model is established to predict the EHL characteristics with the data obtained from numerical calculation, and more numerical data are used to validate the results predicted by the ANN model. The orthogonal experimental method is utilized to tune model's parameters. In addition, the method of increasing hidden layers is used to enhance the predictive ability of model. Finally, a double layer BPNN is completed that could predict oil pressure and film thickness accurately. And the exploratory research presented in this paper will provide a simple method for predicting lubrication behavior in industrial applications.

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"In numerous technical applications, gaining insights into the behavior of tribological systems is crucial for optimizing efficiency and prolonging operational lifespans. Experimental investigations of such systems require considerable costs and time investments, particularly in the field of sealing, notably reciprocating seals for fluid power systems. A more feasible method is the application of elastohydrodynamic lubrication (EHL) simulation models, such as the dynamic description of sealings (DDS) model, which compute friction of seals by the hydrodynamics within the sealing contact according to the Reynolds equation, the seals deformation, and the contact mechanics. The main drawback of these distributed parameter simulations is the necessity of a time-intensive resolution process. Given these experimental and computational constraints, machine learning algorithms offer a promising solution. Physics-informed machine learning (PIML) represents a noteworthy advancement in machine learning in tribology, extending traditional models with physics-based rules and enhancing accuracy in... Read More

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In tribology, maintaining good lubrication of friction pairs is an important technical means to reduce wear and failure, which is very important but difficult to monitor the lubrication state of friction pairs. In this paper, when measuring the thickness and shape of the lubricating oil film by the ball-disk elastohydrodynamic oil film measuring instrument, a current amplifier is integrated to measure the electrical signal generated during the friction process. By monitoring the change of lubricating oil film, the change of ball-disk friction force, and the change of triboelectric signal during the friction process in real time, the relationship between the tribological behavior and the triboelectric behavior of the friction pair is explored. The experimental results show that under the conditions of limited oil supply, different rotational speeds and different lubricating oil viscosities, the time of the discharge phenomenon of the steel ball corresponds one-to-one with the time when the friction pair starts to wear under oil-scarce lubrication. The discharge occurs due to the elect... Read More

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Lubrication technology plays a key role in solving tribological problems in engineered structural systems, and this technology has become a focus of research in order to enhance the efficiency and lifetime of the system. This review integrates the mechanism and the effect of many factors, such as surface charge, surface morphology, temperature, humidity, acidity, and fluid properties, on the lubrication performance enhancement by triboelectricity-controlled fluid attached to the coupled surface. Additionally, this review discusses methods for improving the lubrication performance of solid-liquid surfaces. Finally puts forward some future prospects on the lubrication performance enhancement by triboelectricity-controlled fluid attached to the coupled surface.

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The material steadily wears away from touching surfaces when two solid entities are constantly moving against one other. When more parameters and extreme materials are involved in tribological testing, then it is very difficult to analyze and observe the working phenomena. With this aim, this study uses the gaussian process regression (GPR) approach to estimate friction forces when testing SS 316 L against 100 Cr6 alloy under cryogenic and cryo + minimum amount lubrication conditions. The friction forces from ball-on test experiments were used to develop the prediction models. Then, the wear surfaces and surface morphology are analyzed under cryo and cryo+MQL conditions. The results demonstrated that the combination of MQL and CRYO cooling reduced the friction forces more than 10 times for sliding distances above ~30 m and loads below ~25 n. Hence, the cryo+MQL conditions are beneficial in enhancing the tribological features due to the dual cooling and lubricating effects.

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A deterministic mixed lubrication model for parallel surfaces is proposed. The oil film force is solved by the Reynolds equation with a mass-conserving cavitation model. The contact force is predicted by using a neural network trained on a database, which was built by conducting finite element analysis on a single asperity. In addition, an extended Archard equation is introduced to predict the transient running-in wear. The contact model is proved to be suitable for Gaussian and non-Gaussian surfaces. The Stribeck curves calculated under the different wear coefficients and wear steps are compared with the experimental results. The influence of initial surface topography on the running-in behavior has also been studied. The contact database for GCr15 is provided.

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Abstract The tribological properties of self-lubricating composites are influenced by many variables and complex mechanisms. Data-driven methods, including machine learning (ML) algorithms, can yield a better comprehensive understanding of complex problems under the influence of multiple parameters, typically for how tribological performances and material properties correlate. Correlation of friction coefficients and wear rates of copper/aluminum-graphite (Cu/Al-graphite) self-lubricating composites with their inherent material properties (composition, lubricant content, particle size, processing process, and interfacial bonding strength) and the variables related to the testing method (normal load, sliding speed, and sliding distance) were analyzed using traditional approaches, followed by modeling and prediction of tribological properties through five different ML algorithms, namely support vector machine (SVM), K-Nearest neighbor (KNN), random forest (RF), eXtreme gradient boosting (XGBoost), and least-squares boosting (LSBoost), based on the tribology experimental data. Results d... Read More

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Abstract For training artificial neural network (ANN), big data either generated by machine or measured from experiments are used as input to learn the unspecified functions defining the ANN. The experimental data are fed directly into the optimizer allowing training to be performed according to a predefined loss function. To predict sliding friction and wear at mixed lubrication conditions, in this study a specific ANN structure was so designed that deep learning algorithms and data-driven optimization models can be used. Experimental ball-on-plate friction and wear data were analyzed using the specific training procedure to optimize the weights and biases incorporated into the neural layers of the ANN, and only two independent experimental data sets were used during the ANN optimization procedure. After the training procedure, the ANN is capable to predict the contact and hydrodynamic pressure by adapting the output data according to the tribological condition implemented in the optimization algorithm.

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The forthcoming revolution in mobility and the use of lubricants to ensure ecological friendliness intensifies the pressure on tribology for predictors in new life cycles, mainly addressing wear. The current paper aims to obtain such predictors by studying how the wear processes that occur in a standard FE8 bearing test rig under thin film lubrication are conducted by the properties of the lubricant rather than simple viscosity parameters. Assuming that the activity of a lubricant with respect to the temperature, surface, and chemicals is a matter of its chemical potential, the results show that the nature of the base oil is a key parameter, apart from the chemical structure of the additives. Moreover, it becomes clear that chemical predictors are changing by altering the conditions they are exposed to. As an important result, the lubricant is effective in the prevention of wear if it has the capacity to uptake and transmit electrical charges due to its polarisability during a wear process.

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Physics-informed machine learning (PIML) has gained significant attention in various scientific fields and is now emerging in the area of tribology. By integrating physics-based knowledge into machine learning models, PIML offers a powerful tool for understanding and optimizing phenomena related to friction, wear, and lubrication. Traditional machine learning approaches often rely solely on data-driven techniques, lacking the incorporation of fundamental physics. However, PIML approaches, for example, Physics-Informed Neural Networks (PINNs), leverage the known physical laws and equations to guide the learning process, leading to more accurate, interpretable and transferable models. PIML can be applied to various tribological tasks, such as the prediction of lubrication conditions in hydrodynamic contacts or the prediction of wear or damages in tribo-technical systems. This review primarily aims to introduce and highlight some of the recent advances of employing PIML in tribological research, thus providing a foundation and inspiration for researchers and R&amp;D engineers in the sea... Read More

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This study aims to evaluate the tribological performance of various commercially available perfluoropolyether (PFPE)-based greases in order to assess their potential application in automotive bearings. The composition and specification of the greases are taken into consideration for the analysis. The thermal oxidative stability of the selected grease is determined through oxidative thermo-gravimetric analysis test. Subsequently, the greases are evaluated for their frictional and wear behaviour using a ball-on-disk tribometer. To assess the wear condition of the ball and disk, a digital microscope with Amscope software is employed. The study reveals a scarcity of PFPE lubricant applications in the automotive sector. Among the greases tested, GPL 205 exhibits the highest thermal oxidative stability, characterized by the highest onset temperature of 321C. Conversely, GPL 215 demonstrates superior friction reduction and anti-wear properties.

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Even today, there is no full understanding of the relationship between the physical, chemical, and mechanical properties and the behavior of the lubricating films formed in tribological systems. Most of the published scientific research measures and reports the overall values of friction and wear, but the information given via statistical signals in the tribological tests is, in general, dismissed, leaving a hole in the study of the dynamics of tribological systems. In this work, an experimental study of statistical friction data, coating characteristics, and tribological performance is carried out using a pin-on-disk tribometer to test some metallic samples coated with lubricant films under several experimental conditions. The results indicate that long deposition times at high deposition temperatures of coatings from engine oil develop low-friction intervals, which fall until 20% of the uncoated coefficient friction. However, an unexpected and unfavorable behavior of the coatings was observed for the short deposition times and high temperature. In these conditions, the developed fr... Read More

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The purpose of this research is to forecast the tribological behavior of the materials used in the field of braking systems using an Artificial Neural Network (ANN) based on the experimental data obtained by measuring the friction between the friction linings and the brake disc of a bicycle in the translational movement. The data analysis results from this research show that the estimates and forecasts with the proposed model (ANFIS) of the dynamic friction coefficient (COF) between the pads and the disc in translational motion using the ANN have been confirmed to be powerful and useful. The experimentally determined average value of the dynamic COF was 0.2003 with a standard deviation of 0.0233 in the range of values of 0.1244-0.3013.

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Lubricants play a central role in many technical applications, e.g. in bearings and gears as well as in machining processes. In such applications, lubricants are exposed to extreme conditions in the contact area. In lubrication gaps, the pressure can reach values up to 5 GPa. The thermophysical properties of lubricants, and in particular the viscosity, at such extreme conditions have an important influence on the friction and wear behavior of a tribosystem. Accordingly, reliable lubricant property models are a prerequisite for accurate tribological simulations, e.g. elastohydrodynamic lubrication (EHL) simulations. Presently, the vast majority of experimental thermophysical property data are only available up to 1 GPa. Thus, reliable and robust models with strong extrapolation capabilities to higher pressure are required. In this work, viscosity measurements of squalane in a temperature range be tween 20 C and 100 C and pressures up to 1 GPa were carried out. Based on that data, a physical model for the viscosity was developed. The model is built by combining a molecular-based equa... Read More

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In this study, a systematic test of 36 organic liquid compounds as lubricants in the SiC/PI friction pair was conducted to investigate their friction-reducing performance. The back propagation neural network (BPNN) method was employed to establish a quantitative structure tribo-ability relationship (QSTR) model for the friction performance of these lubricants. The developed BPNN-QSTR model exhibited excellent fitting and predictive accuracy, with R2 = 0.9700, R2 (LOO) = 0.6570, and q2 = 0.8606. The impact of different descriptors in the model on the friction-reducing performance of the lubricants was explored. The results provide valuable guidance for the design and optimization of lubricants in SiC/PI friction systems, contributing to the development of high-performance lubrication systems.

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Tribology is the study of interacting surfaces in relative motion, and it includes the study and application of the principles of friction, lubrication, and wear. A tribology system is a complex time-dependent system consisting of tribo-pairs, lubricants, and external environmental conditions. In order to study the tribo-system, a tremendous amount of information is generated, including contact conditions (e.g., load, speed, and time), direct signals (e.g., coefficient of friction and wear-rate), and indirect signals (e.g., images, noise, and temperature). Tribologists analyze the information to understand, predict, and optimize the system. Although breakthrough in tribology has been achieved in the past decades, it is more difficult to gain a comprehensive understanding and an accurate prediction of a tribology system (e.g., coefficient of friction, wear-rate, vibration, and temperature) based on pure physical models. The main reason is that the tribology system is more complex since cross-scale problems and multi-disciplinary knowledge are involved.On the other hand, information te... Read More

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Many laboratory tribology test machines are available for evaluating the effect of different lubricants and different operating conditions on friction. For the Mini Traction Machine (MTM) there is much published data that shows how the measured friction coefficient varies with operating conditions and lubricant type. Fully formulated lubricants containing the anti-wear additive ZDDP have often been found to have a significantly higher friction coefficient, which persists to higher speeds, compared to base oils (lubricants with no additives). Recent work has found that the surface roughness of ZDDP tribo-films can evolve to become significantly higher than that of the surfaces they are deposited on. When the measured friction coefficients of lubricants tested in the MTM machine are suitably normalized and plotted against the ratio (which is equal to the oil film thickness separating the moving surfaces divided by the combined surface roughness) then the curves for various different lubricants lie on a master curve which enables reliable friction estimates to be made for lubricated... Read More

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A computing system for developing specialty chemicals for oil and gas production that uses machine learning to optimize formulation selection and prediction of performance indicators. The system receives a test description of an oil field process parameter, searches a database of historical test results to identify similar parameters, and generates candidate chemical formulations. It then clusters the formulations using unsupervised learning, selects representative formulations, and trains a predictor model using supervised learning to predict performance indicators. The system iteratively refines the predictor model using new test results to improve formulation prediction accuracy.

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Machine learning (ML) algorithms have brought about a revolution in many industries where otherwise operation time, cost, and safety would have been compromised. Likewise, in lubrication research, ML has been utilized on many occasions. This review provides an in-depth understanding of seven ML algorithms from a tribological perspective. More specifically, it presents a comprehensive overview of recent advancements in ML applied to lubrication research, organized into four distinct categories. The first category, experimental parameter prediction, highlights the significant contributions of artificial neural networks (ANNs) in accurately forecasting operating conditions related to friction and wear. These predictions offer valuable insights that aid in forensic preparation. Discriminant analysis, Bayesian modeling, and transfer learning approaches have also been used to predict experimental parameters. Second, to predict the lubrication film thickness and identify the lubrication regime, algorithms such as logistic regression and ANN were useful. Such predictions provide up to 99.25%... Read More

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A method, system, and medium for lubrication assessment that integrates working condition data, condition monitoring data, and lubrication assessment data to provide a comprehensive and multi-dimensional assessment of equipment lubrication status. The method preprocesses the data, performs data integration, extracts features, establishes a lubrication analysis model, and generates a lubrication assessment result based on the model's outputs.

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Machine learning-based system for real-time drilling mud characterization, property prediction, and optimization at the rig site. The system predicts rheological properties from compositional data and sensor responses, enabling automated mud formulation and optimization. It combines materials-mud-properties relationships, sensor-mud-properties relationships, and artificial intelligence to deliver a drilling mud formulation in real-time at the rig site, reducing non-productive time and improving drilling efficiency.

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Improving the frictional response of a functional surface interface has been a significant research concern. During the last couple of decades, lubricant oils have been enriched with several additives to obtain formulations that can meet the requirements of different lubricating regimes from boundary to full-film hydrodynamic lubrication. The possibility to improve the tribological performance of lubricating oils using various types of nanoparticles has been investigated. In this study, we proposed a data-driven approach that utilizes machine learning (ML) techniques to optimize the composition of a hybrid oil by adding ceramic and carbon-based nanoparticles in varying concentrations to the base oil. Supervised-learning-based regression methods including support vector machines, random forest trees, and artificial neural network (ANN) models are developed to capture the inherent non-linear behavior of the nano lubricants. The ANN hyperparameters were fine-tuned with Bayesian optimization. The regression performance is evaluated with multiple assessment metrics such as the root mean s... Read More

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Abstract This study aims at introducing the potential to utilise transfer learning methods in the training of artificial neural networks for tribological applications. Artificially enhanced surfaces through surface texturing, as an example, are investigated under hydrodynamic regime of lubrication. The performance of these surface features is assessed in terms of load carrying capacity and friction. A large performance dataset including bearing load carrying capacity and friction is initially obtained for a specific category of textures with rectangular cross-sectional profile through analytical methods. The produced bearing performance are used to train a neural network. This neural network was then trained further by a minimal set of performance measure data from an intended category of textures with triangular cross-sectional profiles. It is shown that the resulting neural network performs with acceptable level of confidence for those intended texture profiles when trained with such relatively low number of performance data points. The results indicate that fast analytical methods... Read More

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It is possible to solve challenges in the global automotive and manufacturing industries by using a multidisciplinary approach to advanced industrial lubricants, their tribological performance evaluation, and new surface engineering techniques for prospective tribo-systems [...]

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Abstract Wear is one of the major causes that affect the performance and reliability of tribo-systems. To mitigate its adverse effects, it is necessary to monitor the wear progress so that preventive maintenance can be timely scheduled. An online visual ferrograph (OLVF) apparatus is used to obtain online measurements of wear particle quantities, and monitor the wearing of a four-ball tribometer under different lubrication conditions, and several popular deep learning algorithms are evaluated for their effectiveness in providing maintenance decisions. The obtained data are converted to the cross-sectional time series (CSTS), for its effectiveness in representing the variation trends of multiple variables, and the data are used as the input to the deep learning algorithms. Experimental results indicate that the CSTS together with the bidirectional long short-term memory (Bi-LSTM) architecture outperforms other tested settings in terms of the mean-squared error (MSE). Increased prediction accuracy is observed for tribological pairs with a stochastically changing coefficient of friction... Read More

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The replacement of conventional lubricants with esters is an alternative to provide a low environmental impact and at the same time excellent lubricity features, the high solubility of additives, good viscosity index, low volatility, and high thermal stability. Friction modifiers and antiwear/extreme pressure additives are extensively used to save energy and increase operational life in machine components. In this study, the lubricity of a Group IV base oil containing ester and various benchmark friction modifiers and/or antiwear/extreme pressure additives is measured to evaluate the influence of the ester on the tribological performance of the mixture components. The tribological performance is discussed based on the tabulation of the traction coefficient using a Mini-Traction-Machine and on the measurement of the specific wear rate from the wear scar of the experimental studies using an optical profilometer. In general, results show synergies between the ester and the additive formulations, reducing the wear rate to 75% and decreasing the traction coefficient a 20 to 50%, depending... Read More

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In recent years, lubricant research has developed from empirical to theoretical, from descriptive to rational, from qualitative to quantitative, and from macroscopic to microscopic studies. This review presents the new concept of the quantitative structure tribo-ability relationship (QSTR) derived from the basic principles of quantitative structure activity relationship (QSAR) theory and reviews the latest advances in research on basic problems of the QSTR of lubricants. Specifically, it highlights a series of recent studies conducted by Chinese scholars and future prospects related to these works. It is noted that the study of lubricants involves many related issues, such that there may be omissions in this review. Additionally, the research topics of the quantitative tribo-ability relationship of lubricants covered in this review are mainly mentioned to introduce various modeling methods, and there may be many similar works that are not covered in this review. Despite these limitations, it is hoped that the described QSTR method will become a useful tool and serve as a reference fo... Read More

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The application of data mining technology has intensively advanced tribology research. While recent lubrication studies have highlighted the importance of data mining, researchers have not fully bridged the gap between massive lubrication data and intrinsic lubrication mechanisms. Thus, by revisiting lubrication modelling from the data-driven and physics-informed perspectives, we aim to construct a hybrid approach for hydrodynamic lubrication classification and prediction, where data-driven methods are combined with physics-informed approaches to achieve a fast and accurate prediction of the hydrodynamic lubrication scenario. Our approach will spur the application of data mining methods in lubrication studies.

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