Experimental Modelling of Tribosystems

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

Deadline for manuscript submissions: 30 June 2025 | Viewed by 809

Special Issue Editors


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Guest Editor
V-Research GmbH, Industrial Research and Development, Stadtstrasse 33, 6850 Dornbirn, Austria
Interests: friction and wear; experimental methods; surface engineering; coatings; tribology of snow and ice; gas tribology; polymer tribology
Special Issues, Collections and Topics in MDPI journals
Department of Mechanical and Metallurgical Engineering, School of Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile
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, the science of friction, wear, and lubrication, plays a crucial role in numerous industries, ranging from automotive and aerospace to manufacturing and biomedical engineering. Understanding and optimizing tribological behavior is essential for enhancing the efficiency, durability, and reliability of mechanical systems. Experimentation serves as the cornerstone of tribology research, providing invaluable insights into the complex interactions occurring at contact interfaces.

Despite the significance of experimental tribology, there is no one-size-fits-all approach. Each application demands tailored testing methodologies to accurately simulate real-world operating conditions and address specific performance criteria. For instance, while conventional ball-on-disc tribometers are well suited for simulating highly loaded and lubricated contacts, they may not adequately represent scenarios involving low contact pressures or extreme environmental conditions, such as sub-zero and cryogenic conditions, gas atmospheres, and electrical currents.

The shift towards more intricate and application-specific testing methodologies reflects the evolving needs of the field. Researchers are increasingly exploring novel sample geometries, advanced instrumentation, and innovative testing protocols to mimic real-world tribological phenomena with greater fidelity. This evolution is driven by a dual commitment to academic rigor and industrial relevance, aiming to bridge the gap between fundamental research and practical applications.

This Special Issue on "Experimental Modeling of Tribosystems" serves as a platform to showcase the latest advancements in experimental tribology. By bringing together contributions from leading researchers and industry professionals, this Special Issue aims to offer a comprehensive overview of the cutting-edge methodologies, innovative techniques, and insightful findings shaping the future of experimental tribological research. Topics of interest include, but are not limited to, the following:

  • Development of novel tribological testing setups and instrumentation to measure friction and wear as well as other relevant parameters, such as lubricant film thickness, electrical contact resistance, and number of wear particles.
  • Advances in surface characterization techniques for tribological analysis.
  • Investigation of tribological phenomena under extreme operating conditions, such as high temperatures, sub-zero temperatures, cryogenic testing, and gas atmospheres (e.g. nitrogen, hydrogen, ammonia).
  • Application-driven tribological testing methodologies for specific industries or applications.
  • Case studies highlighting the practical implications of experimental tribology research.

Dr. Igor Velkavrh
Dr. Max Marian
Guest Editors

Manuscript Submission Information

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Keywords

  • experimental methods
  • tribological testing
  • friction
  • wear
  • tribometry
  • extreme operating conditions

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Published Papers (1 paper)

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22 pages, 3248 KiB  
Article
Machine Learning-Based Predictions of Metal and Non-Metal Elements in Engine Oil Using Electrical Properties
by Mohammad-Reza Pourramezan, Abbas Rohani and Mohammad Hossein Abbaspour-Fard
Lubricants 2024, 12(12), 411; https://doi.org/10.3390/lubricants12120411 - 26 Nov 2024
Viewed by 485
Abstract
This study investigates the influence of six metallic and non-metallic elements (Fe, Cr, Pb, Cu, Al, Si) on the quality of engine oil under normal, cautious, and critical conditions. To achieve this, the research employs the Design of Experiments (DoE) approach, specifically the [...] Read more.
This study investigates the influence of six metallic and non-metallic elements (Fe, Cr, Pb, Cu, Al, Si) on the quality of engine oil under normal, cautious, and critical conditions. To achieve this, the research employs the Design of Experiments (DoE) approach, specifically the Box–Behnken Design (BBD) method, for designing experiments. The electrical properties of 70 engine oil samples prepared under varying conditions were analyzed. Machine learning models, including RBF, ANFIS, MLP, GPR, and SVM, were utilized to predict the concentrations of the six pollutants in the lubricant oil samples based on their electrical characteristics. The models’ performance was assessed using RMSE and R2 indicators during train, test, and All stages. The results revealed that the Radial Basis Function (RBF) model exhibited the best overall performance (RMSE = 0.01, R2 = 0.99). The study proceeds with optimizing RBF model parameters, such as hidden size (best = 17), spread (best = 0.4 or higher), and training algorithm (best = trainlm), to estimate each pollutant individually. The generalizability of the model was assessed by reducing the training data percentage and increasing the testing data percentage. The results demonstrated the model’s proper performance for all pollutants in various training sizes (RMSE = 0.01, R2 = 0.99). However, as the training data ratio reduced to 60:40 and 50:50, the model’s performance in estimating Cu deteriorated, resulting in increased RMSE values (10.76 or 11.85) and decreased R2 values (0.89 or 0.87) across the All step. This academic research hopes to contribute to the field of applied studies, considering the inherent complexities of lubricants and the challenges in measuring small-scale electrical properties. Full article
(This article belongs to the Special Issue Experimental Modelling of Tribosystems)
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