Improving Lubricant Formulations Using Machine Learning

Purpose This paper aims to find the best method to predict the friction coefficient of textured 45# steel by comparing different machine learning algorithms and analytical calculations. Design/methodology/approach Five machine learning algorithms, including K-nearest neighbor, random forest, support vector machine (SVM), gradient boosting decision tree (GBDT) and artificial neural network (ANN), are applied to predict friction coefficient of textured 45# steel surface under oil lubrication. The superiority of machine learning is verified by comparing it with analytical calculations and experimental results. Findings The results show that machine learning methods can accurately predict friction coefficient between interfaces compared to analytical calculations, in which SVM, GBDT and ANN methods show close prediction performance. When texture and working parameters both change, sliding speed plays the most important role, indicating that working parameters have more significant influence on friction coefficient than texture parameters. Originality/value This study can reduce the exper... Read More

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Purpose The lubrication performance of static and dynamic pressure thrust bearings is improved by introducing texture on the sealing edge. Design/methodology/approach Through model building, meshing and boundary condition setting, the influence of square texture on oil film lubrication performance was simulated and analyzed, and an improved algorithm was applied to perform optimization of lubrication performance. Findings The findings of this study reveal that the optimum lubrication performance is attained when adjusting the parameters of the square texture to 0.12 mm, 0.1 mm, 1 mm and 34 mm. In such circumstances, the thrust bearing with square textures demonstrates an increase in loading capacity of around 19% and a temperature reduction of about 2C compared to a smooth thrust bearing. Originality/value The original Reynolds equation is revised, and the influence of square texture on the physical field of oil film is analyzed, considering the turbulence state and cavitation phenomenon. The multi-objective function under square texture parameters was established using BP neural ne... Read More

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This paper investigates the use of neural networks to predict characteristic parameters of the grease application process pressure curve. A combination of two feed-forward neural networks was used to estimate both the value and the standard deviation of selected features. Several neuron configurations were tested and evaluated in their capability to make a probabilistic estimation of the lubricants parameters. The value network was trained with a dataset containing the full set of features and with a dataset containing its average values. As expected, the full network was able to predict noisy features well, while the average network made smoother predictions. This is also represented by the networks R2 values which are 0.781 for the full network and 0.737 for the mean network. Several further neuron configurations were tested to find the smallest possible configuration. The analysis showed that three or more neurons deliver the best fit over all features, while one or two neurons are not sufficient for prediction. The results showed that the grease application process pressure cur... Read More

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The chemical properties of oils are vital in the design of microemulsion systems. The hydrophilic-lipophilic difference equation used to predict microemulsions' phase behavior expresses the oils' physiochemical properties as the equivalent alkane carbon number (EACN). The experimental determination of EACN requires knowledge of the temperature dependence of the microemulsion system and the effects of different surfactant concentrations. Thus, the experimental determination is time-intensive and tedious, requiring days to months for proper separations. Furthermore, the experiments require high purity of chemicals because microemulsions are sensitive to impurities. Our work focuses on the quick and reliable predictions of the EACN with machine learning (ML) models. Due to the immaturity of ML chemical predictions, we compare three graph neural networks (GNNs) and a gradient-boosted tree algorithm, known as XGBoost. The GNNs use the molecular structures represented as simplified molecular-input line-entry system (SMILES) codes for the initial input, which allows us to assess whether geo... Read More

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Abstract This study explored the capability of semi-empirical and neural network approaches for correlating and predicting some equilibrium and non-equilibrium thermophysical properties of liquid lubricants. The equilibrium properties, including the densities and several thermodynamic coefficients for 12 liquid lubricants, were correlated and predicted through a perturbed hard-chain equation of state (PHC EoS) by an attractive term of Yukawa tail. The molecular parameters of PHC EoS were obtained by correlating them with 935 data points for the densities and isothermal compressibilities of studied systems in the 278353 K range and pressure up to 70 MPa with the average absolute relative deviations (AARDs) of 0.36 % and 5.25 %, respectively. Then, that EoS was employed to predict the densities of other literature sources (with an AARD of 0.81 %) along with several thermodynamic coefficients, including isobaric expansivities (with an AARD of 12.92 %), thermal pressure coefficients (with the AARD of 12.93 %), and internal pressure (with the AARD of 13.67 %), for which the reference val... 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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Abstract Liquid formulations are ubiquitous yet have lengthy product development cycles owing to the complex physical interactions between ingredients making it difficult to tune formulations to customer-defined property targets. Interpolative ML models can accelerate liquid formulations design but are typically trained on limited sets of ingredients and without any structural information, which limits their out-of-training predictive capacity. To address this challenge, we selected eighteen formulation ingredients covering a diverse chemical space to prepare an open experimental dataset for training ML models for rinse-off formulations development. The resulting design space has an over 50-fold increase in dimensionality compared to our previous work. Here, we present a dataset of 812 formulations, including 294 stable samples, which cover the entire design space, with phase stability, turbidity, and high-fidelity rheology measurements generated on our semi-automated, ML-driven liquid formulations workflow. Our dataset has the unique attribute of sample-specific uncertainty measurem... 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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Minimum quantity lubrication has proven to be more effective and efficient than dry and wet lubrication during machining of materials. However, components surface roughness and tool wear are still of the major concerns affecting the implementation of the said technology in industry. Prediction of these parameters helps to optimise cutting conditions and/or MQL specific parameters. Although various methods are used in the prediction of surface roughness and tool wear such as dimensional analysis, ANOVA etc., present work aims to develop the model for surface roughness and tool wear using artificial neural network. The proposed estimator is based on a neural network with cutting conditions and MQL specific parameters as the input parameters and surface roughness and tool wear as the predicted parameters. The feed forward network that provides ten hidden neurons has been selected. The coefficient of correlation shows good fit with the training, testing, focused and confirmation data sets. The results give encouraging results while using neural network model for the specified conditions ... 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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Machine learning-based predictive models allow rapid and reliable prediction of material properties and facilitate innovative materials design. Base oils used in the formulation of lubricant products are complex hydrocarbons of varying sizes and structure. This study developed Gaussian process regression-based models to accurately predict the temperature-dependent density and dynamic viscosity of 305 complex hydrocarbons. In our approach, strongly correlated/collinear predictors were trimmed, important predictors were selected by least absolute shrinkage and selection operator (LASSO) regularization and prior domain knowledge, hyperparameters were systematically optimized by Bayesian optimization, and the models were interpreted. The approach provided versatile and quantitative structure-property relationship (QSPR) models with relatively simple predictors for determining the dynamic viscosity and density of complex hydrocarbons at any temperature. In addition, we developed molecular dynamics simulation-based descriptors and evaluated the feasibility and versatility of dynamic descri... Read More

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Abstract Machine learnings prowess in extracting insights from data has significantly advanced fluid rheological behavior prediction. This machine-learning-based approach, adaptable and precise, is effective when the strategy is appropriately selected. However, a comprehensive review of machine learning applications for predicting fluid rheology across various fields is rare. This article aims to identify and overview effective machine learning strategies for analyzing and predicting fluid rheology. Covering flow curve identification, yield stress characterization, and viscosity prediction, it compares machine learning techniques in these areas. The study finds common objectives across fluid models: flow curve correlation, rheological behavior dependency on variables, soft sensor applications, and spatialtemporal analysis. It is noted that models for one type can often adapt to similar behaviors in other fluids, especially in the first two categories. Simpler algorithms, such as feedforward neural networks and support vector regression, are usually sufficient for cases with narrow ... Read More

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Choosing an appropriate lubricating oil replacement strategy is crucial for the machine's operation and maintenance. Based on the concept of condition-based maintenance (CBM), this article proposes a method for predicting the remaining useful life (RUL) of lubricating oil using lubrication condition monitoring (LCM) data and machine learning (ML) theory. Firstly, obtain lubricating oil samples through engine bench tests and quantitatively analyze the elemental content of the lubricating oil in use using atomic emission spectroscopy (AES). Then, a method for finding the optimal back propagation (BP) neural network was proposed to construct a lubricating oil RUL prediction model. The content of 12 elements in lubricating oil is used as the input variable, and the three states of lubricating oil are used as the output variable. Finally, by comparing with the lubricating oil RUL prediction model based on support vector machine (SVM), it is shown that the proposed optimal BP neural network model has better accuracy and robustness.

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Opportunities and challenges in data-driven chemical engineering thermodynamics, statistical mechanics and molecular simulation are discussed, and new possibilities offered by machine learning in these areas are assessed. Examples suggest how integration of data science and molecular simulation can prove impactful for the future of chemical engineering.

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Friction behaviour is an important characteristic of dynamic seals. Surface texturing is an effective method to control the friction level without the need to change materials or lubricants. However, it is difficult to put the manual prediction of optimal friction reducing textures as a function of operating conditions into practice. Therefore, in this paper, we use machine learning techniques for the prediction of optimal texture parameters for friction optimisation. The application of pneumatic piston seals serves as an illustrative example to demonstrate the machine learning method and results. The analyses of this work are based on experimentally determined data of surface texture parameters, defined by the dimple diameter, distance, and depth. Furthermore friction data between the seal and the pneumatic cylinder are measured in different friction regimes from boundary over mixed up to hydrodynamic lubrication. A particular innovation of this work is the definition of a generalised method that guides the entire machine learning process from raw data acquisition to model predictio... Read More

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The viability of employing soft computing models for predicting the viscosity of engine lubricants is assessed in this paper. The dataset comprises 555 reports on engine oil analysis, involving two oil types (15W40 and 20W50). The methodology involves the development and evaluation of six distinct models (SVM, ANFIS, GPR, MLR, MLP, and RBF) to predict viscosity based on oil analysis results, incorporating metallic and nonmetallic elements and engine working hours. The primary findings indicate that the radial basis function (RBF) model excels in accuracy, consistency, and generalizability compared with other models. Specifically, a root mean square error (RMSE) of 0.20 and an efficiency (EF) of 0.99 were achieved during training and a RMSE of 0.11 and an EF of 1 during testing, utilizing a 35-network topology and an 80/20 data split. The model demonstrated no significant differences between actual and predicted datasets for average and distribution indices (with P-values of 1.00). Additionally, robust generalizability was exhibited across various training sizes (ranging from 50 to 80... 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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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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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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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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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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Abstract This work conducts a comprehensive modeling study on the viscosity, density, heat capacity, and surface tension of ionic liquid (IL)IL binary mixtures by combining the group contribution (GC) method with three machine learning algorithms: artificial neural network, XGBoost, and LightGBM. A large number of experimental data from reliable open sources is exhaustively collected to train, validate, and test the proposed MLbased GC models. Furthermore, the Shapley Additive Explanations technique is employed to quantify the influential factors behind all the studied properties. Finally, these MLbased GC models are sequentially integrated into computeraided mixed solvent design, process design, and optimization through an industrial case study of recovering hydrogen from raw coke oven gas. Optimization results demonstrate their high computational efficiency and integrability in solvent and process design, while also highlighting the significant potential of ILIL binary mixtures in practical applications.

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Petroleum waxes are products derived from lubricating oils with a wide spectrum of industrial and consumer applications that depend on their composition. In addition, the intended applications of this product are also subject to the practice of blending petroleum waxes with different chemical characteristics (e.g., paraffin waxes and microwaxes) to achieve the appropriate physicochemical properties. This study introduces a novel method based on visible and near-infrared spectroscopy (Vis-NIR) combined with machine learning (ML) for the characterization of blends of the two types of commonly marketed petroleum waxes (paraffin waxes and microwaxes). With spectroscopic data, Partial Least Squared Regression (PLSR), Support Vector Regression (SVR), and Random Forest (RF) Regression-based regression ML models have been developed, obtaining satisfactory results for the characterization of the percentage of blending in petroleum waxes. Moreover, strategies using wrapper variable selection methods like the Boruta algorithm and Genetic Algorithm (GA) have been implemented to assess if fewer p... Read More

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When modeling bearings in the context of entire transmissions or drivetrains, there are practical limits to the calculation resources available to calculate single bearings or even contacts. In settings such as these, curve-fitting methods have historically been deployed to estimate the elastohydrodynamic lubrication conditions. Machine learning methods have the potential to enable more sophisticated physical modeling in the context of larger computation environments, as the evaluation time of a trained model is typically negligible. We present a neural network that accurately evaluates the locally variable elastohydrodynamic film pressure and film thickness distributions and explore its application to (e.g.) cylindrical roller bearings. Employing a neural network for the EHL film thickness calculations rather than the curve-fitted, simplified methods that are today's standard can enable a more physically precise modeling strategy at almost no additional computational cost.

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Lubrication models are essential tools for studying lubricant distribution and failure mechanisms of conformal contacts in various mechanical components. The numerical solution of lubrication problems is a major obstacle in the practical application, where the key challenges arise from the strong nonlinearity and ill-conditioning in the equilibrium equations under severe mixed lubrication. However, corresponding effective numerical methods are still rare. For that, this study proposes a supervised adaptive method (SAM) for rapidly and robustly solving lubrication problems, especially under severe mixed lubrication. First, the SAM adopted the rewritten form of the equilibrium equations, forced decoupling, and error truncation strategies to deal with possible ill-conditioning of the Jacobian matrix. Then, based on the affine covariant principle, an auto-tuning strategy is designed to self-adaptively adjust the stepsizes to cope with the high nonlinearity, which also fuses the feasible region, boundary rebound, and cross-region stepsize attenuation strategies to supervise the iteration ... 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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Abstract Grease in the normal operation of the rotate vector (RV) reducer has a role that cannot be ignored, for the variable working conditions of the RV reducer, the performance of the lubricant changes directly affect its reliable operation. Therefore, the study of the rheological properties of the grease has become the focus of the study of RV reducer performance. Here, SK1A grease is taken as the research object, and its rheological characteristics under wide temperature range working conditions (2040C) are investigated through rheological experiments to analyze the potential influence of the performance of RV reducer. However, the ordinary way of research is too complicated to better research the rheological properties of grease for a variety of working conditions. The Elman neural network (ENN) model was used to predict the rheological properties, and the results were compared with those of back propagation (BP) and radial basis function (RBF) neural networks. The results demonstrate that the ENN model demonstrates high prediction accuracy for grease rheological property p... Read More

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Abstract To resolve the contradiction between the method used to design bearings based on traditional lubrication theory and the actual state of service of water-lubricated bearings (WLBs), this paper proposes a data-driven method for the model of the distribution of lubrication on WLBs. A full-sized WLB test bench featuring multi-sectional pressure due to the film of water and a system to measure the axis of the orbit was built to perform tests under severe operating conditions (75 kN, 25220 rpm). A dataset of the operating parameters of the bearings was obtained based on the results of tests under varying operating conditions. An artificial neural network algorithm was applied to train the proposed model, and its capabilities of prediction and extrapolation were systematically analyzed by using samples with different ranges of values. The proposed model was then used to examine the distributed characteristics of lubrication of the WLB to investigate the effects of variations in speed and elevation on bearing performance. The results showed that it has satisfactory capabilities of ... Read More

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Although most petroleum oil species can be identified by their fluorescence spectra, overlapping fluorescence spectra make identification difficult. This study aims to address the issue that fluorescence spectroscopy is ineffective in identifying overlapping oil species. In this study, an equivalent model of overlapping oil species with fluorescence spectra was established. The linear discriminant analysis (LDA)-assisted machine learning (ML) algorithms K nearest neighbor (KNN), decision tree (DT), and random forest (RF) improved the identification of fluorescent spectrally overlapping oil species for diesel-lubricant oils. The identification accuracies of two-dimensional convolutional neural network (2DCNN), LDA combined with the ML algorithms effectively all 100 %. Furthermore, Partial Least Squares Regression (PLSR) algorithm, Support Vector Regression (SVR) algorithm, DT regression algorithm, and RF regression algorithm were also used to identify the lubricant concentration in diesel-lubricant oils. The coefficient of determination of the DT was 1, and the root-mean-square error ... Read More

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Rough surface lubrication simulation is crucial for designing and optimizing tribological performance. Despite the growing application of Physical Information Neural Networks (PINNs) in hydrodynamic lubrication analysis, their use has been primarily limited to smooth surfaces. This is due to traditional PINN methods suffer from spectral bias, favoring to learn low-frequency features and thus failing to analyze rough surfaces with high-frequency signals. To date, no PINN methods have been reported for rough surface lubrication. To overcome these limitations, this work introduces a novel multi-scale lubrication neural network architecture that utilizes a trainable Fourier feature network. By incorporating learnable feature embedding frequencies, this architecture automatically adapts to various frequency components, thereby enhancing the analysis of rough surface characteristics. This method has been tested across multiple surface morphologies, and the results have been compared with those obtained using the finite element method (FEM). The comparative analysis demonstrates that this a... Read More

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Formulations, or mixtures of chemical ingredients, are ubiquitously found across material science applications, such as themoplastics, consumer packaged goods, and energy storage devices. However, finding formulations with optimal properties is difficult because of the non-obvious connection between the individual ingredient structures and compositions to downstream mixture properties. Computational approaches that could traverse the expansive design space offer a promising solution to finding formulations with improved properties while minimizing the number of experiments. In this work, we generated a large formulation dataset using high-throughput classical molecular dynamics simulations that resulted in more than 30,000 solvent mixtures ranging between pure component to five component systems. We developed three formulation-property relationship approaches to create machine learning models which use the ingredient structure and composition as input to predict a formulation property: formulation descriptor aggregation (FDA), formulation descriptor Set2Set (FDS2S), and formulation g... Read More

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In this paper, an attempt was made to develop several quantitative structureproperty relationship (QSPR) models for predicting the viscosity of ether-functionalized diesters using linear (ordinary least squares, OLS; ridge regression, RR) and non-linear (extra trees, ET) machine learning methods. Performance evaluation demonstrated the successful application of QSPR model in predicting viscosity, and interpretability analysis identified the key molecular descriptors that contributed to reducing viscosity. In addition, molecular dynamics (MD) simulations were conducted to evaluate the impact of ether functionalization on the tribological properties of diesters. The results have significant implications for the molecular design and optimization of low-viscosity synthetic ester oils.

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In this work, we propose a Multi-dot Ensemble Regression AI detector (MER) based on the Lambert-Beer law. We pre-trained a model using the infrared spectral data of target additives collected in advance to detect the target additives in unknown oil samples. The algorithm's feasibility was validated by assessing the content of additives in a series of simulated commercial oil samples that were not part of the training set. We established models for three common lubricating oil additives (anti-friction, anti-wear, and antioxidant agents), demonstrating their effectiveness in oil sample detection. Additionally, by comparing with other algorithms, we established the superiority of MER in small-sample learning scenarios.

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In the present study, a backpropagation neural network combined with genetic algorithm (GA-BP) model was constructed for prediction the solubility of refrigerants in linear chained precursors of POE lubricants (PECs). A total of 2248 experimental solubility data of refrigerants in PECs reported in literature were collected with temperatures from 243.15 K to 363.15 K and pressures up to 10 MPa. The input variables of the model were optimized using non-dominated sorting genetic algorithm with elite strategy (NSGA-II). The optimized inputs include temperature, pressure, molecular weight, critical temperature, and acentric factor. Results indicate that the GA-BP model using the optimized inputs can correlate the solubility data with good accuracy, the average absolute relative deviation between calculated results from the model and the literature is 0.98 %. Moreover, in order to validate the predictive ability of the established GA-BP model, the solubility of R1243zf in PEC4 and PEC5 was measured at the temperature range from 278.15 K to 343.15 K. The calculated values from the GA-BP mod... Read More

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Machine learning (ML) is a subfield of Artificial Intelligence (AI) that uses data, learns the hidden pattern from the data, and makes predictions for future instances with greater accuracy and prediction capabilities, not hallucination (overfitting) with the current data. The application of ML is quite popular in the machining sector because the ML models can be used to predict tool wear, surface roughness and other important machining aspects. With this aim, the present work evaluates the tool wear and class separation by predicting the variation of flank wear (Vb) as a test dataset. A predictive model is proposed for utilizing minimum quantity lubrication (MQL), cryogenic, and MoS2+MQL conditions. Predictions are made using several machine learning (ML) methods, including linear regression, support vector machine, random forest, and multilayer perceptron. The study's findings show that Multi-layer perceptron (MLP) is superior to other methods in classification, with a prediction accuracy of over 95% on average across both training and testing datasets. Even with limited informatio... Read More

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This study extends the use of Machine Learning (ML) approaches for lubricant film thickness predictions to the general case of elliptical elastohydrodynamic (EHD) contacts, by considering wide and narrow contacts over a wide range of ellipticity and operating conditions. Finite element (FEM) simulations are used to generate substantial training and testing datasets that are used within the proposed ML framework. The complete dataset entails 915 samples; split into an 823-sample training dataset and a 92-sample testing dataset, corresponding to 90% and 10% of the combined dataset samples, respectively. The proposed ML model consists of a pre-processing stage in which conventional EHD dimensionless groups are used to minimize the number of inputs into the model, reducing them to only three. The core of the model is based on Gaussian Process Regression (GPR), a powerful ML regression tool, well-suited for small-sized datasets, producing output central and minimum film thicknesses, also in dimensionless form. The last stage is a post-processing one, in which the output film thicknesses a... Read More

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Purpose This study aims to use a machine learning (ML) model for the prediction of traction coefficient and asperity load ratio for different surface topographies of non-conformal rough contacts. Design/methodology/approach The input data set for the ML model is generated using a mixed-lubrication model. Surface topography parameters (skewness, kurtosis and pattern ratio), rolling speed and hardness are used as input features in the multi-layer perceptron (MLP) model. The hyperparameter tuning and fivefold cross-validation are also performed to minimize the overfitting. Findings From the results, it is shown that the MLP model shows excellent accuracy ( R 2 > 90%) on the test data set for making the prediction of mixed lubrication parameters. It is also observed that engineered rough surfaces with high negative skewness, low kurtosis and isotropic surface patterns exhibit a significant low traction coefficient. It is also concluded that the MLP model gives better accuracy in comparison to the random forest regression model based on the training and testing data sets. Originality/v... Read More

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The article analyzes the application of various machine learning methods for solving the problem of approximating the forces of fluid film bearing lubricating layer in static formulation. The initial data on the values of lubricating layer forces for different shaft positions were obtained using a model of a rotor-bearing system based on the numerical solution of the Reynolds equation, with account for the cavitation effect. Methods for reducing the amount of calculation required to obtain the necessary data set are determined on the basis of analyzing solution approximation accuracy with artificial neural networks. After that, approximation models were constructed using a number of other machine learning methods, and the accuracy of predictions as well as the duration of the training process were analyzed. Finally, conclusions were drawn about the most effective approaches to building such models.

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This chapter covers the applications of machine learning (ML) to optimizing chemical and polymer processes, particularly polyolefin manufacturing. It presents an introduction with the historical developments of artificial intelligence (AI) and ML in chemical process industries (CPIs), and suggests the time for actively adopting AI and ML in CPIs has arrived. The chapter continues with three key components of ML applications, namely data, representations, and learning, and explains the concepts of supervised learning, semi-supervised learning, unsupervised learning, and reinforcement learning. It then provides an overview of selected ML methods and their applications to regression and classification problems. The chapter also presents enhanced learning by ensemble methods, including bagging, boosting, and stacking, and introduces the popular methods of random forest, adaptive boosting (AdaBoost) and eXtreme Gradient Boosting (XGBoost), among others. It discusses enhanced learning by deep neural networks.

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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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This article compares machine learning methods and a numerical method of determination of the doped lubricating layer with experimental data. Based on the sweep method, the one-dimensional Fourier heat equation with boundary and initial conditions is solved. As a result of comparing numerical and predictive data with experiments, it can be concluded that machine learning models are better at predicting results compared to numerical data

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Material innovation plays a very important role in technological progress and industrial development. Traditional experimental exploration and numerical simulation often require considerable time and resources. A new approach is urgently needed to accelerate the discovery and exploration of new materials. Machine learning can greatly reduce computational costs, shorten the development cycle, and improve computational accuracy. It has become one of the most promising research approaches in the process of novel material screening and material property prediction. In recent years, machine learning has been widely used in many fields of research, such as superconductivity, thermoelectrics, photovoltaics, catalysis, and high-entropy alloys. In this review, the basic principles of machine learning are briefly outlined. Several commonly used algorithms in machine learning models and their primary applications are then introduced. The research progress of machine learning in predicting material properties and guiding material synthesis is discussed. Finally, a future outlook on machine learn... Read More

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Reducing the viscosity of the lubricant is an effective way to improve the energy efficiency of automobiles. However, designing a lubricant with the desired properties requires elucidating the relationship between viscosity and molecular structure. In this study, we measured the temperature dependence of the dielectric relaxation of model lubricants with different molecular structures. Dielectric relaxation measurements were used to evaluate the influence of ambient viscosity on the motility of single molecules. In addition, we measured the temperature dependence of the lubricant viscosity using a rotational viscometer. By comparing the flow viscosity and dielectric relaxation measurement results, we showed that the activation volume and energy of the luburicant, which determine viscosity, can be resolved. As a result, we succeeded in quantitatively evaluating the contribution of molecular structure to changes in the activation energy, and elucidated the effect of the density of polar groups per molecule on changes in the activation volume.

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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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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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The possibility to control friction through surface microtexturing can offer invaluable advantages in many fields, from wear and pollution reduction in the transportation industry to improved adhesion and grip. Unfortunately, the texture optimization problem is very hard to solve using traditional experimental and numerical methods, due to the complexity of the texture configuration space. Here, we apply machine learning techniques to perform the texture optimization, by training a deep neural network to predict, with extremely high accuracy and speed, the Stribeck curve of a textured surface in lubricated contact. The deep neural network is used to completely resolve the mapping between textures and Stribeck curves, enabling a simple method to solve the texture optimization problem. This work demonstrates the potential of machine learning techniques in texture optimization for friction control in lubricated contacts.

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The design of fluid machinery is a complex task that requires careful consideration of various factors that are interdependent. The correlation between performance parameters and geometric parameters is highly intricate and sensitive, displaying strong nonlinear characteristics. Machine learning techniques have proven to be effective in assisting with optimal fluid machinery design. However, there is a scarcity of literature on this subject. This study aims to present a state-of-the-art review on the optimal design of fluid machinery using machine learning techniques. Machine learning applications primarily involve constructing surrogate models or reduced-order models to explore the correlation between design variables or the relationship between design variables and performance. This paper provides a comprehensive summary of the research status of fluid machinery optimization design, machine learning methods, and the current application of machine learning in fluid machinery optimization design. Additionally, it offers insights into future research directions and recommendations for... Read More

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A method for producing a stable, oil-soluble organometallic salt composition comprising copper, comprising reacting copper carbonate particles with a carboxylic acid at a lower temperature and pressure than conventional methods, resulting in a faster reaction rate and higher conversion of the metal carbonate to the organometallic salt. The resulting composition is useful as a lubricant additive that reduces friction and provides wear protection, and is soluble in a wide variety of hydrocarbon oils.

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This paper studies the remaining useful life (RUL) of lubricating oil based on condition monitoring (CM). Firstly, the element composition and content of the lubricating oil in use were quantitatively analyzed by atomic emission spectrometry (AES). Considering the large variety of oil data obtained through AES, the accuracy and efficiency of the RUL prediction model may be reduced. To solve this problem, a comprehensive parameter selection method based on information entropy, correlation analysis, and lubricant deterioration analysis is proposed to screen oil data. Then, based on a support vector machine (SVM), the RUL prediction model of lubricant was established. By comparing the experimental results with the output data of the prediction model, it is shown that the accuracy and efficiency of the SVM prediction model established after parameter screening have been significantly improved.

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