TY - JOUR
T1 - Experimental study on the factors affecting squeak noise occurrence in automotive suspension bushings
AU - Kang, Byunghyun
AU - Choi, Cheol
AU - Sung, Daeun
AU - Yoon, Seongho
AU - Choi, Byoung Ho
N1 - Funding Information:
The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was financially supported by the Hyundai Motor Company.
Publisher Copyright:
© IMechE 2021.
PY - 2022/3
Y1 - 2022/3
N2 - In 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.
AB - In 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.
KW - Squeak noises
KW - friction test
KW - multiple logistics regression model
KW - natural rubbers
KW - neural network model
KW - stick-slip
UR - http://www.scopus.com/inward/record.url?scp=85107403966&partnerID=8YFLogxK
U2 - 10.1177/09544070211024109
DO - 10.1177/09544070211024109
M3 - Article
AN - SCOPUS:85107403966
SN - 0954-4070
VL - 236
SP - 655
EP - 664
JO - Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering
JF - Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering
IS - 4
ER -