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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