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Lubricants
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Fundamentals in Building Tribological Digital Twins of Machine Elements
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Fundamentals in Building Tribological Digital Twins of Machine Elements

A special issue of Lubricants (ISSN 2075-4442).

Deadline for manuscript submissions: closed (22 December 2023) | Viewed by 6410

Special Issue Editors

Prof. Dr. Jianjun Du

E-Mail Website
Guest Editor
School of Mechanical Engineering and Automation, Harbin Institute of Technology (Shenzhen), Shenzhen 518055, China
Interests: gas lubrication; magnetic bearing; flexible electronics
Special Issues, Collections and Topics in MDPI journals
Dr. Yuechang Wang

E-Mail Website
Guest Editor
School of Mechanical Engineering and Automation, Harbin Institute of Technology (Shenzhen), Shenzhen 518055, China
Interests: tribology of key machine elements; tribochemistry

Special Issue Information

Dear Colleagues,

Machine elements, such as bearings, seals, and gears, are critical in many essential applications, such as high-speed trains and aeroengines. Tribological characteristics, such as frictional energy loss, wear loss, and contact stiffness, plays essential roles in the design, manufacturing, and life prediction of most machine elements. A better understanding of these tribological characteristics can help researchers and engineers to develop and maintain machine elements with higher standards, such as working under extreme conditions, aiming for longer life, and reducing frictional energy loss. Such breakthroughs in machine elements are crucial in pursuing sustainable development in the field of mechanical equipment.

Many modeling and experimental efforts have been made to reveal the mechanism of tribological phenomena in machine elements. Tribologists have proposed many multiphysics, empirical, or hybrids of both models to evaluate and predict lubrication, friction, and wear across length scales and time scales. Physics-based and data-driven approaches are used widely. However, there is still no widely accepted tool that can reflect the tribological performance of machine elements in the full life cycle.

A digital twin is usually defined as a virtual duplicate of a complex system built from models and data fusion, emphasizing the real-time reflection of the physical system with high synchronization and fidelity. The real-time reflection characteristic is a crucial factor needed to improve the current studies of the tribological performance of machine elements. Therefore, building tribological digital twins of machine elements could be a way to push loads of tribological knowledge and techniques toward real-world applications.

Building tribological digital twins requires improvements in the fundamental understanding of all the multiscale/multiphysics processes occurring in tribological systems. Multiscale modeling techniques are required, including atomistic simulation, such as molecular dynamics (MD) and density functional theory (DFT), and continuum methods, such as the finite element method (FEM). Multiphysics modeling, such as the coupling of fluid, solid, and thermal fields, and irreversible time-related phenomena, such as plastic deformation and wear, are also crucial factors. Besides the physics-based techniques, data-driven approaches, such as machine learning, are potential tools for predicting tribological performance.

The real-time digital twin must have real-time measured data from its physical system, which is used to update the digital twin to ensure high synchronization and fidelity. Building tribological digital twins also requires experimental techniques to continuously collect tribological data. The data must be well-chosen to assimilate with those simulated ones. Data assimilation and updating of digital twin methods are essential tasks in building tribological digital twins.

This Special Issue addresses all studies on fundamentals in building tribological digital twins of machine elements. Contributions are welcome from all scientists working in tribology and related areas.

Prof. Dr. Jianjun Du
Dr. Yuechang Wang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Lubricants is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • machine elements
  • bearings
  • seals
  • gears
  • hydrodynamic lubrication
  • EHL
  • mixed lubrication
  • roughness
  • digital twin
  • real-time measurement
  • data assimilation
  • life prediction

Published Papers (4 papers)

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Research

20 pages, 3893 KiB  
Article
Bearing Digital Twin Based on Response Model and Reinforcement Learning
by Zhaorong Li, Jiaoying Wang, Diwang Ruan, Jianping Yan and Clemens Gühmann
Lubricants 2023, 11(12), 502; https://doi.org/10.3390/lubricants11120502 - 27 Nov 2023
Viewed by 1243
Abstract
In recent years, research on bearing fault modeling has witnessed significant advancements. However, the modeling of bearing faults using digital twins (DTs) remains an emerging area of exploration. This paper introduces a bearing digital twin developed by integrating a signal-based response model with [...] Read more.
In recent years, research on bearing fault modeling has witnessed significant advancements. However, the modeling of bearing faults using digital twins (DTs) remains an emerging area of exploration. This paper introduces a bearing digital twin developed by integrating a signal-based response model with reinforcement learning techniques. Initially, a signal-based model is constructed, comprising a unit fault impulse function and a decay oscillation function. This model illustrates the bearing’s acceleration response under fault conditions and acts as the environmental component within the bearing digital twin. Subsequently, a parameter estimation process identifies two critical parameters from the signal-based model: the load proportional factor and the decaying constant. The Deep Deterministic Policy Gradient (DDPG) algorithm is employed as the agent for online learning of these parameters. The cosine similarity metric is employed to define the state and reward by comparing the real acceleration measurements with the simulation data generated by the digital twin. To validate the effectiveness of the digital twin, experimental data sourced from the three datasets are utilized. The results underscore the digital twin’s capacity to faithfully replicate the bearing’s acceleration response under diverse conditions, demonstrating a high degree of similarity in both the time and frequency domains. Full article
(This article belongs to the Special Issue Fundamentals in Building Tribological Digital Twins of Machine Elements)
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17 pages, 7248 KiB  
Article
A Hydraulic Reciprocating Rod Seal’s Life Evaluation Method Incorporating Failure Mechanism Analysis and Test Observation Data
by Xiaochuan Duan, Di Liu, Shaoping Wang and Yaoxing Shang
Lubricants 2023, 11(8), 319; https://doi.org/10.3390/lubricants11080319 - 28 Jul 2023
Cited by 1 | Viewed by 970
Abstract
Reciprocating rod seals are widely used in hydraulic systems. Their useful life and reliability affect that of the system. Degradation modeling is necessary to evaluate the useful life of the seal. Seal wear is one of the important forms of hydraulic reciprocating rod [...] Read more.
Reciprocating rod seals are widely used in hydraulic systems. Their useful life and reliability affect that of the system. Degradation modeling is necessary to evaluate the useful life of the seal. Seal wear is one of the important forms of hydraulic reciprocating rod seal degradation, yet it is difficult to measure through direct methods. Because seal wear determines the leakage of the seal, we therefore consider the seal leakage as the performance degradation index. Furthermore, the degradation of the seal is always associated with random effects, which cannot be considered by theoretical failure mechanism analysis. Hence, stochastic processes are applied to consider the random effects. Considering the error between the measured value and its real degradation state caused by the measurement environment or other factors, we introduce the measurement error term into the Wiener process model and develop the corresponding Wiener process life prediction model. Finally, the failure mechanism analysis and test measurement data are fused to predict the life cycle of the hydraulic reciprocating rod seals. The effectiveness of the proposed method is verified by comparing the predicted degradation and the experimental observations. Full article
(This article belongs to the Special Issue Fundamentals in Building Tribological Digital Twins of Machine Elements)
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23 pages, 8619 KiB  
Article
Establishment and Calibration of a Digital Twin to Replicate the Friction Behaviour of a Pin-on-Disk Tribometer
by Erik Hansen, Gerda Vaitkunaite, Johannes Schneider, Peter Gumbsch and Bettina Frohnapfel
Lubricants 2023, 11(2), 75; https://doi.org/10.3390/lubricants11020075 - 10 Feb 2023
Cited by 4 | Viewed by 1737
Abstract
While the modification of surface contacts offers significant potential for friction reduction, obtaining an underlying consistent friction behaviour of real-life experiments and virtual simulations is still an ongoing challenge. In particular, most works in the literature only consider idealised geometries that can be [...] Read more.
While the modification of surface contacts offers significant potential for friction reduction, obtaining an underlying consistent friction behaviour of real-life experiments and virtual simulations is still an ongoing challenge. In particular, most works in the literature only consider idealised geometries that can be parametrised with simple analytical functions. In contrast to this approach, the current work describes the establishment of a digital twin of a pin-on-disk tribometer whose virtual geometry is completely replicated from real-life post-test topography measurements and fed into a two-scale mixed lubrication solver. Subsequently, several calibration steps are performed to identify the sensitivities of the friction behaviour towards certain geometry features and enable the digital twin to robustly represent the Stribeck curve of the physical experiments. Furthermore, a derivation of the Hersey number is used to generalise the obtained friction behaviour for different dynamic viscosities and allow the validation of the presented method. Full article
(This article belongs to the Special Issue Fundamentals in Building Tribological Digital Twins of Machine Elements)
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26 pages, 4166 KiB  
Article
Influence of Manufacturing Error Tolerances on Thermal EHL Behavior of Gears
by Rikard Hjelm and Jens Wahlström
Lubricants 2022, 10(11), 323; https://doi.org/10.3390/lubricants10110323 - 21 Nov 2022
Cited by 2 | Viewed by 1521
Abstract
Due to the electrification of vehicles, new demands are being imposed on gears, which translates to the tolerances of manufacturing errors. However, not many studies treat the impact of manufacturing error combinations on the lubricant behavior of gear sets. Therefore, a simulation method [...] Read more.
Due to the electrification of vehicles, new demands are being imposed on gears, which translates to the tolerances of manufacturing errors. However, not many studies treat the impact of manufacturing error combinations on the lubricant behavior of gear sets. Therefore, a simulation method is developed, including its derivation, discretization, and implementation. The method solves the thermal elasto-hydrodynamic lubrication (TEHL) problem, taking into account the varying temperature, viscosity, density, and cavitation of the lubricant. To account for manufacturing errors, the load distribution from a loaded tooth contact analysis (LTCA), developed by the authors, is used as input to the TEHL method. Comparison is made with a standard load distribution assumption, and a numerical example is used to show some preliminary results. The results show good agreement with results from other studies. It is shown that there is a great effect of manufacturing errors on the TEHL behavior, such as temperature, due to the change in load distribution such errors impose. It can be concluded that manufacturing errors of different tolerances have a great impact and that they should therefore be taken into consideration when analyzing gear set behavior and constructing gear sets for new applications. Full article
(This article belongs to the Special Issue Fundamentals in Building Tribological Digital Twins of Machine Elements)
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