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Advances in Friction, Wear-Resistant and Solid-Lubricating Properties of Materials

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

Deadline for manuscript submissions: 20 March 2025 | Viewed by 473

Special Issue Editors


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Guest Editor
Institute of Technology for Carbon Neutralization, Yangzhou University, Yangzhou, 225127, China
Interests: wear-resistant material; high temperature friction and wear mechanism

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Guest Editor
School of Mechanical and Electronic Control Engineering, Beijing Jiaotong University, Beijing, China
Interests: wear; lubrication
School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China
Interests: solid lubrication; 2D materials; composites; nanoadditives; tribochemical reaction
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Special Issue Information

Dear Colleagues,

Wear-resistant lubrication are a viable option for reducing friction and wear in a variety of environments. With rapid advances in science and technology, techniques in more modern industrial tribo-systems rely on lubricating materials for high performance, efficiency, and durability, especially to design and produce materials that possess a high wear resistance and a low friction coefficient over wide load, speed, and temperature ranges. However, a lack of understanding of tribological mechanisms hinders the optimization design of lubricating materials and their applications in other fields.

This Special Issue on the “Advances in wear-resistant lubrication materials” hopes to attract both academic and industrial researchers to promote innovation in the application of wear-resistant lubrication materials, thereby fostering new ideas for future research and expanding knowledge in this field. We sincerely hope that you will accept our invitation to contribute to this Special Issue.

Potential topics of interest include, but are not limited to, the following:

  • Friction and wear properties of traditional wear-resistant lubrication materials;
  • Lubrication mechanisms of wear-resistant lubrication;
  • Research and development of new-type solid-lubricating materials.

Dr. Xiangli Wen
Dr. Bin Wang
Dr. Yanfei Liu
Guest Editors

Manuscript Submission Information

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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

  • tribology
  • wear
  • lubrication
  • solid lubrication
  • wear rate

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Published Papers (1 paper)

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Research

16 pages, 6128 KiB  
Article
Wear Resistance Design of Laser Cladding Ni-Based Self-Fluxing Alloy Coating Using Machine Learning
by Jiabo Fu, Quanling Yang, Oleg Devojno, Marharyta Kardapolava, Iryna Kasiakova and Chenchong Wang
Materials 2024, 17(22), 5651; https://doi.org/10.3390/ma17225651 - 19 Nov 2024
Viewed by 227
Abstract
To improve the collaborative design of laser cladding Ni-based self-fluxing alloy (SFA) wear-resistant coatings, machine learning methods were applied. A comprehensive database was constructed from the literature, linking alloy composition, processing parameters, testing conditions, and the wear properties of Ni-based SFA coatings. Feature [...] Read more.
To improve the collaborative design of laser cladding Ni-based self-fluxing alloy (SFA) wear-resistant coatings, machine learning methods were applied. A comprehensive database was constructed from the literature, linking alloy composition, processing parameters, testing conditions, and the wear properties of Ni-based SFA coatings. Feature correlation analysis using Pearson’s correlation coefficient and feature importance assessment via the random forest (RF) model highlighted the significant impact of C and B elements. The predictive performance of five classical machine learning algorithms was evaluated using metrics such as the squared correlation coefficient () and mean absolute error (MAE). The RF model, which exhibited the best overall performance, was further combined with a genetic algorithm (GA) to optimize both composition and processing parameters collaboratively. This integrated RF-GA optimization system significantly enhanced efficiency and successfully designed multiple composition and process plans. The optimized alloy demonstrated superior wear resistance with an average friction coefficient of only 0.34, attributed to an enhanced solid solution strengthening effect (110 MPa) and increased hard phase content (52%), such as Ni₃Si, CrB, and NbC. These results provide valuable methodological insights and theoretical support for the preparation of laser cladding coatings and enable efficient process optimization for other laser processing applications. Full article
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