Machine Vision Approaches for Evaluating Fretting Damage of Components
A special issue of Lubricants (ISSN 2075-4442).
Deadline for manuscript submissions: 30 April 2025 | Viewed by 84
Special Issue Editor
Interests: fretting wear; machine vision; machine learning; coating; friction; surface roughness characterization
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Machine vision is known as a useful technique in tribology. Continuous improvements in computational power have led to remarkable advancements in machine vision. A variety of machine vision systems have been developed in tribology. However, little is found on the development of a machine vision system related to fretting damage to components.
Thus, we invite authors to submit articles and review papers related to machine vision techniques for fretting damage. The proposed machine vision includes the evaluation of fretting damage to components, surface roughness characterization for the deposition of lubricants, crack detection on controlled surfaces, the prediction of the fretting lifetime of lubricants, or vision-based machine learning.
Dr. Kyungmok Kim
Guest Editor
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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.
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Keywords
- machine vision
- fretting fatigue
- fretting wear
- machine learning
- fretting corrosion
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