Reduction of tire noise by modifying tread pattern characteristics

Document Type : Research Article

Authors

1 ph.D. Candidate , Mechanical Engineering Department, Amirkabir University of Technology, Tehran,Iran

2 Professor, Acoustics Research Lab., Mechanical Engineering Department, Amirkabir University of Technology,Tehran, Iran

3 Assistant Professor, Faculty of Mechanical Engineering, Sharif University of Technology, Tehran, Iran

Abstract

The complexity of tire/road noise generation and amplification mechanisms has made it challenging for tire builders to reduce emitted sound. Statistical methods help to model complex problems. This paper predicts tire noise level by a superior regression method in machine learning, relevance vector machine, with a total noise prediction error of 0.62 dB(A). The tire’s noise sensitivity to its parameters is analyzed by applying a small central composite design to the developed model. The effect of grooves’ shapes on tire noise is preserved in the results, unlike the previous publications. For a case study, grooves’ depth has been recognized as critical in controlling tire noise. Based on the variance analysis results, the interaction of this parameter with the number, length, and width of transverse grooves has also been identified as significant. According to the parametric study’s striking tips, two sets of tread pattern specifications are proposed for noise reduction, utilizing the response surface method. They reduce the noise level by 1.72 and 1.54 dB(A) for a tire with a measured noise of 75.88 dB(A)

Highlights

  • Tire noise sensitivity to tread pattern parameters is analysed.
  • Remarkable interactions between tire tread pattern parameters are illustrated.
  • Tire noise reduction tips by modifying tread pattern characteristics are provided.
  • Response surface method is used to reduce tire noise.

Keywords

Main Subjects


[1] T. Li, Literature review of tire-pavement interaction noise and reduction approaches, Journal of Vibroengineering, 20 (2018) 2424-2452.
[2] T. Li, Influencing parameters on tire–pavement interaction noise: Review, experiments, and design considerations, Designs, 2 (2018) 38.
[3] X. Pei, G. Wang, H. Zhou, F. Zhao, J. Yang, Influence of tread structure design parameters on tire vibration noise, in:  Proceedings of SAE-China Congress 2015: Selected Papers, Springer, 2016, pp. 325-338.
[4] K. Yum, Control of structural-acoustic radiation from tires by structural modification, in, Purdue University, 2005.
[5] H. Zhou, H. Li, C. Liang, L. Zhang, G. Wang, Relationship between Tire Ground Characteristics and Vibration Noise, Strojniski Vestnik/Journal of Mechanical Engineering, 67 (2021).
[6] M. Becker, H. Szczerbicka, M. Thomas, Neural networks and optimization algorithms applied for construction of low noise tread profiles, Cybernetics and Systems: An International Journal, 38 (2007) 535-548.
[7] E.-Y. Kim, S.-W. Hwang, S.-K. Lee, Image-based approach to optimize the tyre pitch sequence for a reduction in the air-pumping noise based on a genetic algorithm, Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering, 226 (2012) 1171-1184.
[8] S. Mohammadi, A. Ohadi, M. Irannejad-Parizi, A comprehensive study on statistical prediction and reduction of tire/road noise, Journal of Vibration and Control, (2021) 10775463211013184.
[9] Y. Wei, Q. Feng, H. Wang, M. Kaliske, A hybrid numerical-experimental analysis for tire air-pumping noise with application to pattern optimization, Noise Control Engineering Journal, 64 (2016) 56-63.
[10] L. Dorsch, Predicting tire noise and performance interactions, in, SAE Technical Paper, 1976.
[11] Y. Che, W.X. Xiao, L.J. Chen, Z.C. Huang, GA-BP neural network based tire noise prediction, in:  Advanced Materials Research, Trans Tech Publ, 2012, pp. 65-70.
[12] G.M. Uddin, S.G. Niazi, S.M. Arafat, M.S. Kamran, M. Farooq, N. Hayat, S.A. Malik, A. Zeid, S. Kamarthi, S. Saqib, Neural networks assisted computational aero-acoustic analysis of an isolated tire, Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering, 234 (2020) 2561-2577.
[13] H.-Y. Cho, S.-K. Lee, S.-W. Hwang, B.-H. Kim, Improvement of tire pattern noise estimation using adaptive filter and sound quality application research, in:  The 21st International Congress on Sound and Vibration, 2014.
[14] S.-K. Lee, H. Lee, J. Back, K. An, Y. Yoon, K. Yum, S.-U. Hwang, Prediction of tire pattern noise in early design stage based on convolutional neural network, Applied Acoustics, 172 (2021) 107617.
[15] J.-T. Chiu, F.-Y. Tu, Application of a pattern recognition technique to the prediction of tire noise, Journal of Sound and Vibration, 350 (2015) 30-40.
[16] T. Li, R. Burdisso, C. Sandu, An artificial neural network model to predict tread pattern-related tire noise, in, SAE Technical Paper, 2017.
[17] L.D. Spies, Machine-learning based tool to predict tire noise using both tire and pavement parameters, in, Virginia Tech, 2019.
[18] S. Mohammadi, A. Ohadi, A novel approach to design quiet tires, based on multi-objective minimization of generated noise, Applied Acoustics, 175 (2021) 107825.
[19] Y. Nakajima, Theory on pitch noise and its application, J. Vib. Acoust., 125 (2003) 252-256.
[20] C.M. Bishop, N.M. Nasrabadi, Pattern recognition and machine learning, Springer, 2006.
[21] Mohammadi, S., Rezaeian, M., 2018. Reliability evaluation of statistical methods used for design and analysis of experiments. In: The 5th International Reliability and Safety Engineering Conference (IRSEC2018), Shiraz, Iran. Iran: Safety Research Center of Shiraz University.