Intelligent Algorithms for Triboinformatics
A special issue of Lubricants (ISSN 2075-4442).
Deadline for manuscript submissions: 31 July 2025 | Viewed by 2069
Special Issue Editor
Special Issue Information
Dear Colleagues,
Ever since the term "tribology" was introduced in 1966, this field has evolved and given rise to various new domains of study, including nanotribology, biotribology, ecotribology, biomimetic tribology, and more. However, a significant challenge in the realm of surface engineering and tribology persists. While there exists a wealth of data concerning the surface characteristics of diverse materials, systems, and engineering components, this interdisciplinary field heavily relies on empirical methods. Despite numerous attempts to formulate tribological laws and rules, the discipline often lacks derivation from physical or chemical first principles. Consequently, tribology remains a data-driven inductive science.
In addressing this challenge, there has recently been the emergence of a new subfield within tribology known as "Triboinformatics". This development has been made possible by the latest advancements in the field of informatics, particularly artificial intelligence and machine learning. Informatics leverages digital technology's potential to transform data and information into knowledge. It utilizes inductive statistics to deduce laws, nonlinear relationships, and causal effects from extensive datasets with relatively low information density. Techniques like machine learning are employed to uncover relationships, dependencies, and predictions related to outcomes and behaviors.
In Triboinformatics, informatics techniques and tribology are integrated to gain insights into systems that do not adhere to established physical and chemical first principles. This Special Issue is focused on recent advancements in algorithm developments and their applications in the field of tribology. Specifically, it hones in on intelligent algorithms that have been employed to analyze and extract knowledge from diverse tribological data sources, including tabular data, time series, spectra, images, and videos. The emphasis lies in studies where intelligent algorithms have been utilized to predict tribological properties or design systems and materials with optimized tribological characteristics.
Dr. Amir Kordijazi
Guest Editor
Manuscript Submission Information
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Keywords
- tribology
- triboinformatics
- artificial intelligence
- machine learning
- intelligent algorithms
- multimodal data
- friction
- wear
- lubrication
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