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Friction, Wear and Lubrication of Advanced Materials

A special issue of Materials (ISSN 1996-1944). This special issue belongs to the section "Mechanics of Materials".

Deadline for manuscript submissions: 20 September 2026 | Viewed by 438

Editors


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Guest Editor
Department of Computer Science, Faculty of Mechanical Engineering and Aeronautics, Rzeszow University of Technology, 35-959 Rzeszow, Poland
Interests: metrology; lubrication; wear; friction; coatings; surface treatment
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Laboratoire de Tribologie et Dynamique des Systemes (LTDS), Ecole Centrale de Lyon, Centre National de la Recherche Scientifique, 69134 Lyon, France
Interests: tribology; rheology; metrology; surface technology; nano-technology; machining; surface characterizations; surface topography; instrumentation design
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Friction, wear and lubrication play a decisive role in the performance, reliability and energy efficiency of almost all modern technologies. From aerospace, transportation and manufacturing to space technologies, micro- and nanosystems and biomedical applications, increasingly demanding operating conditions require new tribological solutions and advanced material concepts. This Special Issue, “Friction, Wear and Lubrication of Advanced Materials”, aims to provide an open platform for contributions covering a wide spectrum of materials, length scales and applications.

We welcome experimental, theoretical and numerical studies dealing with any class of materials, including metals, polymers, ceramics, composites, coatings, thin films, soft matter and hybrid systems. Topics of interest include, but are not limited to, the following: friction and wear mechanisms, surface engineering and texturing, environmentally friendly lubricants and additives, solid and liquid lubrication, bio-inspired and multifunctional surfaces, as well as in situ and advanced characterization methods, quantitative wear assessment, probabilistic or statistical descriptions of surface topography, modelling and data-driven or AI-assisted approaches.

By bringing together results from different disciplines and application areas, this Special Issue seeks to stimulate cross-fertilization of ideas and support the development of next-generation tribological systems for sustainable engineering.

Dr. Wiesław A. Graboń
Dr. Thomas G. Mathia
Dr. Gilmar Ferreira Batalha
Guest Editors

Manuscript Submission Information

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Keywords

  • friction
  • wear
  • lubrication
  • advanced materials
  • surface engineering
  • surface topography
  • quantitative wear assessment
  • statistical surface characterization
  • environmentally friendly lubricants
  • data-driven tribology

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Published Papers (1 paper)

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Research

20 pages, 5809 KB  
Article
Data-Driven Modeling of Friction in Drawbead Test Through Advanced Machine Learning
by Tomasz Trzepieciński, Romuald Fejkiel and Marek Kowalik
Materials 2026, 19(12), 2641; https://doi.org/10.3390/ma19122641 - 18 Jun 2026
Viewed by 32
Abstract
Friction at the drawbead in metal forming operations directly affects the quality of drawpieces. However, identifying the complex effect of friction process parameters on the coefficient of friction (CoF) is difficult based on experimental results. The aim of this paper is to analyze [...] Read more.
Friction at the drawbead in metal forming operations directly affects the quality of drawpieces. However, identifying the complex effect of friction process parameters on the coefficient of friction (CoF) is difficult based on experimental results. The aim of this paper is to analyze the results of a drawbead simulator test using various machine learning (ML) methods to select the most appropriate algorithm and to analyze in detail the feature importance, permutation importance, and cumulative Shapley additive explanation values of predictors. The test material was DC04 low-carbon steel sheet. Experimental tests were conducted for varying friction process conditions. Of the three different ML algorithms (support vector machine, regression trees, and ensemble tress), the support vector machine (SVM) algorithm with a cubic kernel function provided the lowest root mean square error (0.0085) and the highest correlation coefficient R2 (0.9657) for the test data. The predictors in descending order of permutation importance are friction conditions, drawbead height, sample width, Sa of countersamples, and sample orientation. A combined swarm-box chart presenting Shapley values for an SVM model with a cubic kernel function indicates that a low value of the drawbead height predictor has a strong, increasing effect on CoF. However, low values of the remaining explanatory parameters (sample width, mean roughness of countersamples, and sample orientation) have a decreasing effect on CoF. Full article
(This article belongs to the Special Issue Friction, Wear and Lubrication of Advanced Materials)
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