Tribology has been and continues to be one of the most relevant fields of research, and its understanding 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 our existing knowledge of this field. 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 manner or even real-time way. The first edition of the Special Issue "Machine Learning in Tribology" has already demonstrated the variety of potential applications of these methods, moving beyond purely academic purposes to also encompass industrial applications. This second edition of this Special Issue, entitled "Recent Advances in Machine Learning in Tribology", covers 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.

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