“Lubrication Brain” ― A machine learning framework of lubrication oil molecule design

Zhou, Rui; Ma, Rui; Bao, Luyao; Zhou, Feng; Liu, Weimin; Wang, Xiaobo

doi:10.1016/j.triboint.2023.108381

Cited by 5 publications

(1 citation statement)

References 25 publications

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“…While some have used lubricant parameters as descriptors to predict the tribological performance through regression models, the small size of the data set, limited by experimental constraints, can lead to the phenomenon of overfitting, which often results in poor performances in practical applications. Different lubricants possess varying antifriction and antiwear properties.…”

Section: Introductionmentioning

confidence: 99%

GNBoost-Based Ensemble Machine Learning for Predicting Tribological Properties of Liquid-Crystal Lubricants

Shi,

Li,

Guo

et al. 2024

Langmuir

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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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Section: Introductionmentioning

confidence: 99%

GNBoost-Based Ensemble Machine Learning for Predicting Tribological Properties of Liquid-Crystal Lubricants

Shi,

Li,

Guo

et al. 2024

Langmuir

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AI for tribology: Present and future

Yin,

Yang,

Liu

et al. 2024

Friction

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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 interdisciplinary field, which combines tribology with informatics, this review elucidates the future directions and research framework of “AI for tribology”. In this paper, tribo-system information is divided into 5 categories: input information (I), system intrinsic information (S), output information (O), tribological state information (Ts), and derived state information (Ds). Then, a fusion method among 5 types of tribo-system information and different AI technologies (regression, classification, clustering, and dimension reduction) has been proposed, which enables tribo-informatics methods to solve common problems such as tribological behavior state monitoring, behavior prediction, and system optimization. The purpose of this review is to offer a systematic comprehension of tribo-informatics and to inspire new research ideas of tribo-informatics. Ultimately, it aspires to enhance the efficiency of problem-solving in tribology.

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Influence of ether group on viscosity and film lubrication of diester lubricants: Integrated quantitative structure–property relationship and molecular dynamics simulation methods

Wang,

Zhang,

Chen

et al. 2024

Journal of Molecular Liquids

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“Lubrication Brain” ― A machine learning framework of lubrication oil molecule design

Cited by 5 publications

References 25 publications

GNBoost-Based Ensemble Machine Learning for Predicting Tribological Properties of Liquid-Crystal Lubricants

GNBoost-Based Ensemble Machine Learning for Predicting Tribological Properties of Liquid-Crystal Lubricants

AI for tribology: Present and future

Influence of ether group on viscosity and film lubrication of diester lubricants: Integrated quantitative structure–property relationship and molecular dynamics simulation methods

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