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Lubricants
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Machine Learning in Tribology
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Machine Learning in Tribology

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

Deadline for manuscript submissions: closed (15 December 2021) | Viewed by 64171

Printed Edition Available!
A printed edition of this Special Issue is available here.

Special Issue Editors

Prof. Dr. Stephan Tremmel

E-Mail Website
Guest Editor
Engineering Design and CAD, University of Bayreuth, Bayreuth, Germany
Interests: engineering design; computer-aided engineering; finite element analysis; machine elements; drive technology; rolling bearings; tribology; PVD/PACVD coatings; elastohydrodynamic lubrication; machine learning
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Max Marian

E-Mail Website
Guest Editor
1. Department of Mechanical and Metallurgical Engineering, School of Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile
2. Institute of Machine Design and Tribology (IMKT), Leibniz University Hannover, Germany
Interests: tribology; elastohydrodynamic lubrication; hydrodynamic lubrication; micro-texturing; biotribology; synovial joint tribology; additive manufacturing; DLC coating; 2D materials; MXenes; solid lubricants; composite materials; machine learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Tribology has been and continues to be one of the most relevant fields, being present in almost all aspects of our lives. The understanding of tribology provides us with solutions for future technical challenges. At the root of all advances made so far are multitudes of precise experiments and advanced computer simulations across different scales and multiple physical disciplines. Based upon this sound and data-rich foundation, advanced data handling, analysis and learning methods can be developed and employed to expand existing knowledge. Therefore, modern machine learning (ML) or artificial intelligence (AI) methods provide opportunities to explore the complex processes in tribological systems and to classify or quantify their behavior in an efficient or even real-time way. Thus, their potential also goes beyond purely academic aspects into actual industrial applications.

To help pave the way, this Special Issue aims to present the latest research on ML or AI approaches for solving tribology-related issues. Contributions from both academic and industrial researchers are welcome. Considered papers should either present new findings in the field or provide deep insights into the development or the application of sophisticated ML or AI approaches to resolve problems broadly related to friction, lubrication and wear.

Prof. Dr. Stephan Tremmel
Dr. Max Marian
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 learning
  • artificial intelligence
  • knowledge discovery in databases, data-mining and big data
  • metamodels and artificial neural networks
  • classification and performance prediction
  • friction, lubrication and wear
  • rheology
  • machine elements and machine systems
  • condition monitoring
  • materials
  • surface modifications
  • lubricants and additives

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Published Papers (11 papers)

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Editorial

Jump to: Research, Review, Other

3 pages, 173 KiB  
Editorial
Machine Learning in Tribology—More than Buzzwords?
by Stephan Tremmel and Max Marian
Lubricants 2022, 10(4), 68; https://doi.org/10.3390/lubricants10040068 - 15 Apr 2022
Cited by 10 | Viewed by 3261
Abstract
Tribology has been and continues to be one of the most relevant fields, being present in almost all aspects of our lives [...] Full article
(This article belongs to the Special Issue Machine Learning in Tribology)

Research

Jump to: Editorial, Review, Other

12 pages, 1799 KiB  
Article
On the Importance of Temporal Information for Remaining Useful Life Prediction of Rolling Bearings Using a Random Forest Regressor
by Christoph Bienefeld, Eckhard Kirchner, Andreas Vogt and Marian Kacmar
Lubricants 2022, 10(4), 67; https://doi.org/10.3390/lubricants10040067 - 14 Apr 2022
Cited by 15 | Viewed by 3215
Abstract
Rolling bearings are frequently subjected to high stresses within modern machines. To prevent bearing failures, the topics of condition monitoring and predictive maintenance have become increasingly relevant. In order to efficiently and reliably maintain rolling bearings in a predictive manner, an estimate of [...] Read more.
Rolling bearings are frequently subjected to high stresses within modern machines. To prevent bearing failures, the topics of condition monitoring and predictive maintenance have become increasingly relevant. In order to efficiently and reliably maintain rolling bearings in a predictive manner, an estimate of the remaining useful life (RUL) is of great interest. The RUL prediction quality achieved when using machine learning depends not only on the selection of the sensor data used for condition monitoring, but also on its preprocessing. In particular, the execution of so-called feature engineering has a major impact on prediction quality. Therefore, in this paper, various methods of feature engineering are presented based on rolling–bearing endurance tests and recorded structure-borne sound signals. The performance of these methods is evaluated in the context of a regression-based RUL model. Furthermore, the way in which the quality of RUL prediction can be significantly improved is demonstrated, by adding further processed, time-considering features. Full article
(This article belongs to the Special Issue Machine Learning in Tribology)
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23 pages, 8942 KiB  
Article
Using Machine Learning Methods for Predicting Cage Performance Criteria in an Angular Contact Ball Bearing
by Sebastian Schwarz, Hannes Grillenberger, Oliver Graf-Goller, Marcel Bartz, Stephan Tremmel and Sandro Wartzack
Lubricants 2022, 10(2), 25; https://doi.org/10.3390/lubricants10020025 - 11 Feb 2022
Cited by 7 | Viewed by 4629
Abstract
Rolling bearings have to meet the highest requirements in terms of guidance accuracy, energy efficiency, and dynamics. An important factor influencing these performance criteria is the cage, which has different effects on the bearing dynamics depending on the cage’s geometry and bearing load. [...] Read more.
Rolling bearings have to meet the highest requirements in terms of guidance accuracy, energy efficiency, and dynamics. An important factor influencing these performance criteria is the cage, which has different effects on the bearing dynamics depending on the cage’s geometry and bearing load. Dynamics simulations can be used to calculate cage dynamics, which exhibit high agreement with the real cage motion, but are time-consuming and complex. In this paper, machine learning algorithms were used for the first time to predict physical cage related performance criteria in an angular contact ball bearing. The time-efficient prediction of the machine learning algorithms enables an estimation of the dynamic behavior of a cage for a given load condition of the bearing within a short time. To create a database for machine learning, a simulation study consisting of 2000 calculations was performed to calculate the dynamics of different cages in a ball bearing for several load conditions. Performance criteria for assessing the cage dynamics and frictional behavior of the bearing were derived from the calculation results. These performance criteria were predicted by machine learning algorithms considering bearing load and cage geometry. The predictions for a total of 10 target variables reached a coefficient of determination of R2≈0.94 for the randomly selected test data sets, demonstrating high accuracy of the models. Full article
(This article belongs to the Special Issue Machine Learning in Tribology)
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15 pages, 1358 KiB  
Article
Design of Amorphous Carbon Coatings Using Gaussian Processes and Advanced Data Visualization
by Christopher Sauer, Benedict Rothammer, Nicolai Pottin, Marcel Bartz, Benjamin Schleich and Sandro Wartzack
Lubricants 2022, 10(2), 22; https://doi.org/10.3390/lubricants10020022 - 7 Feb 2022
Cited by 5 | Viewed by 4496
Abstract
In recent years, an increasing number of machine learning applications in tribology and coating design have been reported. Motivated by this, this contribution highlights the use of Gaussian processes for the prediction of the resulting coating characteristics to enhance the design of amorphous [...] Read more.
In recent years, an increasing number of machine learning applications in tribology and coating design have been reported. Motivated by this, this contribution highlights the use of Gaussian processes for the prediction of the resulting coating characteristics to enhance the design of amorphous carbon coatings. In this regard, by using Gaussian process regression (GPR) models, a visualization of the process map of available coating design is created. The training of the GPR models is based on the experimental results of a centrally composed full factorial 23 experimental design for the deposition of a-C:H coatings on medical UHMWPE. In addition, different supervised machine learning (ML) models, such as Polynomial Regression (PR), Support Vector Machines (SVM) and Neural Networks (NN) are trained. All models are then used to predict the resulting indentation hardness of a complete statistical experimental design using the Box–Behnken design. The results are finally compared, with the GPR being of superior performance. The performance of the overall approach, in terms of quality and quantity of predictions as well as in terms of usage in visualization, is demonstrated using an initial dataset of 10 characterized amorphous carbon coatings on UHMWPE. Full article
(This article belongs to the Special Issue Machine Learning in Tribology)
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25 pages, 6926 KiB  
Article
A Semantic Annotation Pipeline towards the Generation of Knowledge Graphs in Tribology
by Patricia Kügler, Max Marian, Rene Dorsch, Benjamin Schleich and Sandro Wartzack
Lubricants 2022, 10(2), 18; https://doi.org/10.3390/lubricants10020018 - 25 Jan 2022
Cited by 6 | Viewed by 4581
Abstract
Within the domain of tribology, enterprises and research institutions are constantly working on new concepts, materials, lubricants, or surface technologies for a wide range of applications. This is also reflected in the continuously growing number of publications, which in turn serve as guidance [...] Read more.
Within the domain of tribology, enterprises and research institutions are constantly working on new concepts, materials, lubricants, or surface technologies for a wide range of applications. This is also reflected in the continuously growing number of publications, which in turn serve as guidance and benchmark for researchers and developers. Due to the lack of suited data and knowledge bases, knowledge acquisition and aggregation is still a manual process involving the time-consuming review of literature. Therefore, semantic annotation and natural language processing (NLP) techniques can decrease this manual effort by providing a semi-automatic support in knowledge acquisition. The generation of knowledge graphs as a structured information format from textual sources promises improved reuse and retrieval of information acquired from scientific literature. Motivated by this, the contribution introduces a novel semantic annotation pipeline for generating knowledge in the domain of tribology. The pipeline is built on Bidirectional Encoder Representations from Transformers (BERT)—a state-of-the-art language model—and involves classic NLP tasks like information extraction, named entity recognition and question answering. Within this contribution, the three modules of the pipeline for document extraction, annotation, and analysis are introduced. Based on a comparison with a manual annotation of publications on tribological model testing, satisfactory performance is verified. Full article
(This article belongs to the Special Issue Machine Learning in Tribology)
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18 pages, 2683 KiB  
Article
Collaborative Optimization of CNN and GAN for Bearing Fault Diagnosis under Unbalanced Datasets
by Diwang Ruan, Xinzhou Song, Clemens Gühmann and Jianping Yan
Lubricants 2021, 9(10), 105; https://doi.org/10.3390/lubricants9100105 - 15 Oct 2021
Cited by 29 | Viewed by 4441
Abstract
Convolutional Neural Network (CNN) has been widely used in bearing fault diagnosis in recent years, and many satisfying results have been reported. However, when the training dataset provided is unbalanced, such as the samples in some fault labels are very limited, the CNN’s [...] Read more.
Convolutional Neural Network (CNN) has been widely used in bearing fault diagnosis in recent years, and many satisfying results have been reported. However, when the training dataset provided is unbalanced, such as the samples in some fault labels are very limited, the CNN’s performance reduces inevitably. To solve the dataset imbalance problem, a Generative Adversarial Network (GAN) has been preferably adopted for the data generation. In published research studies, GAN only focuses on the overall similarity of generated data to the original measurement. The similarity in the fault characteristics is ignored, which carries more information for the fault diagnosis. To bridge this gap, this paper proposes two modifications for the general GAN. Firstly, a CNN, together with a GAN, and two networks are optimized collaboratively. The GAN provides a more balanced dataset for the CNN, and the CNN outputs the fault diagnosis result as a correction term in the GAN generator’s loss function to improve the GAN’s performance. Secondly, the similarity of the envelope spectrum between the generated data and the original measurement is considered. The envelope spectrum error from the 1st to 5th order of the Fault Characteristic Frequencies (FCF) is taken as another correction in the GAN generator’s loss function. Experimental results show that the bearing fault samples generated by the optimized GAN contain more fault information than the samples produced by the general GAN. Furthermore, after the data augmentation for the unbalanced training sets, the CNN’s accuracy in the fault classification has been significantly improved. Full article
(This article belongs to the Special Issue Machine Learning in Tribology)
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23 pages, 5374 KiB  
Article
A Digital Twin for Friction Prediction in Dynamic Rubber Applications with Surface Textures
by Valentina Zambrano, Markus Brase, Belén Hernández-Gascón, Matthias Wangenheim, Leticia A. Gracia, Ismael Viejo, Salvador Izquierdo and José Ramón Valdés
Lubricants 2021, 9(5), 57; https://doi.org/10.3390/lubricants9050057 - 20 May 2021
Cited by 15 | Viewed by 3990
Abstract
Surface texturing is an effective method to reduce friction without the need to change materials. In this study, surface textures were transferred to rubber samples in the form of dimples, using a novel laser surface texturing (LST)—based texturing during moulding (TDM) production process, [...] Read more.
Surface texturing is an effective method to reduce friction without the need to change materials. In this study, surface textures were transferred to rubber samples in the form of dimples, using a novel laser surface texturing (LST)—based texturing during moulding (TDM) production process, developed within the European Project MouldTex. The rubber samples were used to experimentally determine texture-induced friction variations, although, due to the complexity of manufacturing, only a limited amount was available. The tribological friction measurements were hence combined with an artificial intelligence (AI) technique, i.e., Reduced Order Modelling (ROM). ROM allows obtaining a virtual representation of reality through a set of numerical strategies for problem simplification. The ROM model was created to predict the friction outcome under different operating conditions and to find optimised dimple parameters, i.e., depth, diameter and distance, for friction reduction. Moreover, the ROM model was used to evaluate the impact on friction when manufacturing deviations on dimple dimensions were observed. These results enable industrial producers to improve the quality of their products by finding optimised textures and controlling nominal surface texture tolerances prior to the rubber components production. Full article
(This article belongs to the Special Issue Machine Learning in Tribology)
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18 pages, 5555 KiB  
Article
Semi-Supervised Classification of the State of Operation in Self-Lubricating Journal Bearings Using a Random Forest Classifier
by Josef Prost, Ulrike Cihak-Bayr, Ioana Adina Neacșu, Reinhard Grundtner, Franz Pirker and Georg Vorlaufer
Lubricants 2021, 9(5), 50; https://doi.org/10.3390/lubricants9050050 - 4 May 2021
Cited by 17 | Viewed by 3791
Abstract
For a tribological experiment involving a steel shaft sliding in a self-lubricating bronze bearing, a semi-supervised machine learning method for the classification of the state of operation is proposed. During the translatory oscillating motion, the system may undergo different states of operation from [...] Read more.
For a tribological experiment involving a steel shaft sliding in a self-lubricating bronze bearing, a semi-supervised machine learning method for the classification of the state of operation is proposed. During the translatory oscillating motion, the system may undergo different states of operation from normal to critical, showing self-recovering behaviour. A Random Forest classifier was trained on individual cycles from the lateral force data from four distinct experimental runs in order to distinguish between four states of operation. The labelling of the individual cycles proved to be crucial for a high prediction accuracy of the trained RF classifier. The proposed semi-supervised approach allows choosing within a range between automatically generated labels and full manual labelling by an expert user. The algorithm was at the current state used for ex post classification of the state of operation. Considering the results from the ex post analysis and providing a sufficiently sized training dataset, online classification of the state of operation of a system will be possible. This will allow taking active countermeasures to stabilise the system or to terminate the experiment before major damage occurs. Full article
(This article belongs to the Special Issue Machine Learning in Tribology)
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Review

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32 pages, 6557 KiB  
Review
Current Trends and Applications of Machine Learning in Tribology—A Review
by Max Marian and Stephan Tremmel
Lubricants 2021, 9(9), 86; https://doi.org/10.3390/lubricants9090086 - 1 Sep 2021
Cited by 122 | Viewed by 11826
Abstract
Machine learning (ML) and artificial intelligence (AI) are rising stars in many scientific disciplines and industries, and high hopes are being pinned upon them. Likewise, ML and AI approaches have also found their way into tribology, where they can support sorting through the [...] Read more.
Machine learning (ML) and artificial intelligence (AI) are rising stars in many scientific disciplines and industries, and high hopes are being pinned upon them. Likewise, ML and AI approaches have also found their way into tribology, where they can support sorting through the complexity of patterns and identifying trends within the multiple interacting features and processes. Published research extends across many fields of tribology from composite materials and drive technology to manufacturing, surface engineering, and lubricants. Accordingly, the intended usages and numerical algorithms are manifold, ranging from artificial neural networks (ANN), decision trees over random forest and rule-based learners to support vector machines. Therefore, this review is aimed to introduce and discuss the current trends and applications of ML and AI in tribology. Thus, researchers and R&D engineers shall be inspired and supported in the identification and selection of suitable and promising ML approaches and strategies. Full article
(This article belongs to the Special Issue Machine Learning in Tribology)
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Other

9 pages, 261 KiB  
Technical Note
Fundamentals of Physics-Informed Neural Networks Applied to Solve the Reynolds Boundary Value Problem
by Andreas Almqvist
Lubricants 2021, 9(8), 82; https://doi.org/10.3390/lubricants9080082 - 19 Aug 2021
Cited by 25 | Viewed by 5167
Abstract
This paper presents a complete derivation and design of a physics-informed neural network (PINN) applicable to solve initial and boundary value problems described by linear ordinary differential equations. The objective with this technical note is not to develop a numerical solution procedure which [...] Read more.
This paper presents a complete derivation and design of a physics-informed neural network (PINN) applicable to solve initial and boundary value problems described by linear ordinary differential equations. The objective with this technical note is not to develop a numerical solution procedure which is more accurate and efficient than standard finite element- or finite difference-based methods, but to give a fully explicit mathematical description of a PINN and to present an application example in the context of hydrodynamic lubrication. It is, however, worth noticing that the PINN developed herein, contrary to FEM and FDM, is a meshless method and that training does not require big data which is typical in machine learning. Full article
(This article belongs to the Special Issue Machine Learning in Tribology)
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11 pages, 4581 KiB  
Perspective
The Use of Artificial Intelligence in Tribology—A Perspective
by Andreas Rosenkranz, Max Marian, Francisco J. Profito, Nathan Aragon and Raj Shah
Lubricants 2021, 9(1), 2; https://doi.org/10.3390/lubricants9010002 - 26 Dec 2020
Cited by 116 | Viewed by 9829
Abstract
Artificial intelligence and, in particular, machine learning methods have gained notable attention in the tribological community due to their ability to predict tribologically relevant parameters such as, for instance, the coefficient of friction or the oil film thickness. This perspective aims at highlighting [...] Read more.
Artificial intelligence and, in particular, machine learning methods have gained notable attention in the tribological community due to their ability to predict tribologically relevant parameters such as, for instance, the coefficient of friction or the oil film thickness. This perspective aims at highlighting some of the recent advances achieved by implementing artificial intelligence, specifically artificial neutral networks, towards tribological research. The presentation and discussion of successful case studies using these approaches in a tribological context clearly demonstrates their ability to accurately and efficiently predict these tribological characteristics. Regarding future research directions and trends, we emphasis on the extended use of artificial intelligence and machine learning concepts in the field of tribology including the characterization of the resulting surface topography and the design of lubricated systems. Full article
(This article belongs to the Special Issue Machine Learning in Tribology)
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