New Horizons in Machine Learning Applications for Tribology

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

Deadline for manuscript submissions: 30 June 2025 | Viewed by 88

Special Issue Editors


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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
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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

Special Issue Information

Dear Colleagues,

Tribology has been and continues to be one of the most relevant fields in engineering science, and its understanding provides us with solutions for future technical challenges in health, energy solutions, mobility, and sustainability. 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. Thereby, machine learning (ML) and 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.

The first two editions of the Special Issue “Machine Learning in Tribology” and “Recent Advances in Machine Learning in Tribology” already demonstrated the variety of potential areas of applications and that the potential goes beyond purely academic aspects into actual industrial applications.

The reception of the first two editions by the readers exceeded our expectations and, together with the Editorial Office of Lubricants, we are now proud to launch the third edition of the Special Issue entitled “New Horizons in Machine Learning Applications for Tribology”, aiming to cover the latest developments from academic and industrial researchers linked to innovations in the broad field of tribology by employing machine learning and artificial intelligence approaches.

Prof. Dr. Max Marian
Prof. Dr. Stephan Tremmel
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
  • metamodels
  • artificial neural networks
  • classification
  • regression
  • friction
  • lubrication
  • wear
  • rheology
  • machine elements
  • condition monitoring
  • composite materials
  • surface modifications
  • lubricants and additives

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Published Papers

This special issue is now open for submission.
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