Detailed Information

Cited 0 time in webofscience Cited 0 time in scopus
Metadata Downloads

Experimental study on the factors affecting squeak noise occurrence in automotive suspension bushings

Full metadata record
DC Field Value Language
dc.contributor.authorKang, Byunghyun-
dc.contributor.authorChoi, Cheol-
dc.contributor.authorSung, Daeun-
dc.contributor.authorYoon, Seongho-
dc.contributor.authorChoi, Byoung-Ho-
dc.date.accessioned2022-08-15T06:41:00Z-
dc.date.available2022-08-15T06:41:00Z-
dc.date.created2022-08-12-
dc.date.issued2022-03-
dc.identifier.issn0954-4070-
dc.identifier.urihttps://scholar.korea.ac.kr/handle/2021.sw.korea/143243-
dc.description.abstractIn this study, friction tests are performed, via a custom-built friction tester, on specimens of natural rubber used in automotive suspension bushings. By analyzing the problematic suspension bushings, the eleven candidate factors that influence squeak noise are selected: surface lubrication, hardness, vulcanization condition, surface texture, additive content, sample thickness, thermal aging, temperature, surface moisture, friction speed, and normal force. Through friction tests, the changes are investigated in frictional force and squeak noise occurrence according to various levels of the influencing factors. The degree of correlation between frictional force and squeak noise occurrence with the factors is determined through statistical tests, and the relationship between frictional force and squeak noise occurrence based on the test results is discussed. Squeak noise prediction models are constructed by considering the interactions among the influencing factors through both multiple logistic regression and neural network analysis. The accuracies of the two prediction models are evaluated by comparing predicted and measured results. The accuracies of the multiple logistic regression and neural network models in predicting the occurrence of squeak noise are 88.2% and 87.2%, respectively.-
dc.languageEnglish-
dc.language.isoen-
dc.publisherSAGE PUBLICATIONS LTD-
dc.titleExperimental study on the factors affecting squeak noise occurrence in automotive suspension bushings-
dc.typeArticle-
dc.contributor.affiliatedAuthorChoi, Byoung-Ho-
dc.identifier.doi10.1177/09544070211024109-
dc.identifier.scopusid2-s2.0-85107403966-
dc.identifier.wosid000682561300001-
dc.identifier.bibliographicCitationPROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART D-JOURNAL OF AUTOMOBILE ENGINEERING, v.236, no.4, pp.655 - 664-
dc.relation.isPartOfPROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART D-JOURNAL OF AUTOMOBILE ENGINEERING-
dc.citation.titlePROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART D-JOURNAL OF AUTOMOBILE ENGINEERING-
dc.citation.volume236-
dc.citation.number4-
dc.citation.startPage655-
dc.citation.endPage664-
dc.type.rimsART-
dc.type.docTypeArticle-
dc.description.journalClass1-
dc.description.journalRegisteredClassscie-
dc.description.journalRegisteredClassscopus-
dc.relation.journalResearchAreaEngineering-
dc.relation.journalResearchAreaTransportation-
dc.relation.journalWebOfScienceCategoryEngineering, Mechanical-
dc.relation.journalWebOfScienceCategoryTransportation Science & Technology-
dc.subject.keywordAuthorSqueak noises-
dc.subject.keywordAuthornatural rubbers-
dc.subject.keywordAuthorfriction test-
dc.subject.keywordAuthorstick-slip-
dc.subject.keywordAuthormultiple logistics regression model-
dc.subject.keywordAuthorneural network model-
Files in This Item
There are no files associated with this item.
Appears in
Collections
College of Engineering > Department of Mechanical Engineering > 1. Journal Articles

qrcode

Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.

Related Researcher

Researcher Choi, Byoung Ho photo

Choi, Byoung Ho
공과대학 (기계공학부)
Read more

Altmetrics

Total Views & Downloads

BROWSE