Real-time prediction of wear morphology and coefficient of friction using acoustic signals and deep neural networks in a tribological system

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dc.contributor.authorTian, Yang
dc.contributor.authorZheng, Bohao
dc.contributor.authorKhan, Muhammad
dc.contributor.authorYang, Yifan
dc.date.accessioned2025-06-10T11:24:04Z
dc.date.available2025-06-10T11:24:04Z
dc.date.freetoread2025-06-10
dc.date.issued2025-06-01
dc.date.pubOnline2025-06-03
dc.description.abstractPredicting real-time wear depth distribution and the coefficient of friction (COF) in tribological systems is challenging due to the dynamic and complex nature of surface interactions, particularly influenced by surface roughness. Traditional methods, relying on post-test measurements or oversimplified assumptions, fail to capture this dynamic behavior, limiting their utility for real-time monitoring. To address this, we developed a deep neural network (DNN) model by integrating experimental tribological testing and finite element method (FEM) simulations, using acoustic signals for non-invasive, real-time analysis. Experiments with brass pins (UNS C38500) of varying surface roughness (240, 800, and 1200 grit) sliding against a 304 stainless steel disc provided data to validate the FEM model and train the DNN. The DNN model predicted wear morphology with accuracy comparable to FEM simulations but at a lower computational cost, and the COF with relative errors below 10% compared to experimental measurements. This approach enables real-time monitoring of wear and friction, offering significant benefits for predictive maintenance and operational efficiency in industrial applications.
dc.description.journalNameProcesses
dc.identifier.citationTian Y, Zheng B, Khan M, Yang Y. (2025) Real-time prediction of wear morphology and coefficient of friction using acoustic signals and deep neural networks in a tribological system. Processes, Volume 13, Issue 6, June 2025, Article number 1762en_UK
dc.identifier.eissn2227-9717
dc.identifier.elementsID673580
dc.identifier.issn2227-9717
dc.identifier.issueNo6
dc.identifier.paperNo1762
dc.identifier.urihttps://doi.org/10.3390/pr13061762
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/24006
dc.identifier.volumeNo13
dc.languageEnglish
dc.language.isoen
dc.publisherMDPIen_UK
dc.publisher.urihttps://www.mdpi.com/2227-9717/13/6/1762
dc.rightsAttribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subject4007 Control Engineering, Mechatronics and Roboticsen_UK
dc.subject40 Engineeringen_UK
dc.subjectBioengineeringen_UK
dc.subject4004 Chemical engineeringen_UK
dc.subjectwearen_UK
dc.subjectfrictionen_UK
dc.subjectDNNen_UK
dc.subjectfriction noiseen_UK
dc.titleReal-time prediction of wear morphology and coefficient of friction using acoustic signals and deep neural networks in a tribological systemen_UK
dc.typeArticle
dcterms.dateAccepted2025-05-29

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