Acoustic Emission in Friction

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

Deadline for manuscript submissions: closed (31 May 2023) | Viewed by 2198

Special Issue Editor


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Guest Editor
Laboratoire Génie de Production, Université de Toulouse, Ecole Nationale d’Ingénieurs de Tarbes, 47 Avenue d’Azereix, 65000 Tarbes, France
Interests: tribology; third body approach; EHL lubrication

Special Issue Information

Dear Colleagues,

Acoustic emissions (AEs) refer to the transient mechanical waves (ultrasound) produced by the release of elastic energy from irreversible processes occurring in materials under stress. In sliding contacts, AE can be generated from sources associated with various tribological mechanisms, e.g., interfacial plastic deformation, cracks propagation, material transfers, and allotropic transformations.

One challenge in tribology is in obtaining access to the blinded sliding interface. AE technique is a noninvasive and promising technique that could potentially lead to major breakthroughs in the field of friction, wear, and lubrication. Presently, thanks to AE device acquisition rate and calculation capabilities, in situ and real-time monitoring of tribological mechanisms seems attainable. However, this objective needs clearer relationships to be established between tribological mechanisms and AE signals using qualitative or quantitative correlations among AE parameters and tribological, innovative experimental setups and data processing.

For the current Special Issue, “Acoustic Emission in Friction”, original papers dealing with AEs of tribological systems are welcomed. This Special Issue aims to bring new insights concerning in situ measurement in the field of tribology.

Dr. Malik Yahiaoui
Guest Editor

Manuscript Submission Information

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Keywords

  • acoustic emission
  • adhesive wear
  • abrasive wear
  • adhesion
  • ploughing
  • cutting
  • allotropic transformation
  • third body approach
  • interfacial rheology
  • tribological surface transformation

Published Papers (1 paper)

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Research

17 pages, 4145 KiB  
Article
Prediction of Friction Power via Machine Learning of Acoustic Emissions from a Ring-on-Disc Rotary Tribometer
by Christopher Strablegg, Florian Summer, Philipp Renhart and Florian Grün
Lubricants 2023, 11(2), 37; https://doi.org/10.3390/lubricants11020037 - 19 Jan 2023
Cited by 4 | Viewed by 1592
Abstract
Acoustic emissions from tribological contacts have become an interesting field of science in recent years. This study focuses on predicting the friction power of a given system (lubricated ring-on-disc geometry), independently of the used sliding material and lubricant, from the acoustic emissions emitted [...] Read more.
Acoustic emissions from tribological contacts have become an interesting field of science in recent years. This study focuses on predicting the friction power of a given system (lubricated ring-on-disc geometry), independently of the used sliding material and lubricant, from the acoustic emissions emitted from the system. The low-frequency (1 Hz), continuously measured RMS value of the acoustic data is combined with short-duration and high-frequency (850 kHz) signal data in form of the power spectra and hit rate with three prominence levels. The classification system then predicts the friction power of the test system continuously over the whole test time. Prediction is achieved by four different machine learning methods (tree-type, support vector machine, K-nearest-neighbor, neural network) trained with data from 54 ring-on-disc tests with high variation in material and oil combinations. The method allows for the quantifiable and step-free prediction of absolute values of friction power with accuracy of 97.6% on unseen data, with a weighted K-nearest-neighbor classifier, at any point in time during an experiment. The system reacts well to rapid changes in friction conditions due to changes in load and temperature. The study shows the high information degree of acoustic emissions, concerning the actual friction mechanisms occurring and the quantitative, and not only qualitative, information that one can gain about a tribological system by analyzing them. Full article
(This article belongs to the Special Issue Acoustic Emission in Friction)
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