scholarly journals Tuning of friction noise by accessing the rolling-sliding option

2020 ◽  
Vol 2 (1) ◽  
Author(s):  
Soumen Das ◽  
Shankar Ghosh
Keyword(s):  
Friction ◽  
2021 ◽  
Author(s):  
Kevin Lontin ◽  
Muhammad Khan

AbstractPhenomena of friction, wear, and noise in mechanical contacts are particularly important in the field of tribomechanics but equally complex if one wants to represent their exact relationship with mathematical models. Efforts have been made to describe these phenomena with different approaches in past. These efforts have been compiled in different reviews but most of them treated friction, wear mechanics, and acoustic noise separately. However, an in-depth review that provides a critical analysis on their interdependencies is still missing. In this review paper, the interdependencies of friction, wear, and noise are analysed in the mechanical contacts at asperitical level. The origin of frictional noise, its dependencies on contact’s mechanical properties, and its performance under different wear conditions are critically reviewed. A discussion on the existing mathematical models of friction and wear is also provided in the last section that leads to uncover the gap in the existing literature. This review concludes that still a comprehensive analytical modelling approach is required to relate the interdependencies of friction, noise, and wear with mathematical expressions.


2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Bin Zhou ◽  
Jin Ma ◽  
Hongyan Zhou ◽  
Xiaoliang Shi ◽  
Ahmed Mohamed Mahmoud Ibrahim

PurposeThis paper aims to investigate the friction noise properties of M50 matrix curved microporous channel composites filled with solid lubricant Sn-Ag-Cu (MS). Design/methodology/approachPure M50 (MA) and MS are prepared by selective laser melting and vacuum-pressure infiltration technology. The tribological and friction noise properties of MA and MS are tested through dry sliding friction and then the influential mechanism of surface wear sate on friction noise is investigated by analyzing the variation law of noise signals and the worn surface characteristics of MS. FindingsExperimental results show that the friction noise sound pressure level of MS is only 75.6 dB, and it mainly consists of low-frequency noise. The Sn-Ag-Cu improves the surface wear state, which reduces self-excited vibration of the interface caused by fluctuation of friction force, leading to the decrease of friction noise. Originality/valueThis investigation is meaningful to improve the tribological property and suppress the friction noise of M50 bearing steel.


Wear ◽  
2011 ◽  
Vol 271 (3-4) ◽  
pp. 621-624 ◽  
Author(s):  
H. Ben Abdelounis ◽  
H. Zahouani ◽  
A. Le Bot ◽  
J. Perret-Liaudet ◽  
M. Ben Tkaya

2019 ◽  
Vol 6 (7) ◽  
pp. 076510 ◽  
Author(s):  
Yuan Chen ◽  
Xiaoliang Shi ◽  
Guanchen Lu ◽  
Hongyan Zhou ◽  
Zhenyu Yang

2018 ◽  
Vol 141 (1) ◽  
Author(s):  
Cong Ding ◽  
Hua Zhu ◽  
Yu Jiang ◽  
Guodong Sun ◽  
Chunling Wei

To explore the recursive characteristics of a running-in attractor, recurrence plot (RP) and recursive parameters are used to investigate the dynamic features of the structure. The running-in attractor is constructed based on friction noise signals generated from the ring-on-disk wear experiments. The RPs of the running-in attractor are then reproduced in a two-dimensional space. Recursive parameters, recurrence rate (RR), entropy (ENTR), and trend of recurrence (RT) are calculated. Results show that the RP evolves from a disrupted pattern to a homogeneous pattern and then returns to a disrupted pattern in the entire wear process, corresponding to the “formation–stabilization–disappearance” stage of the running-in attractor. The RR and ENTR of the running-in attractor sharply increase at first, remain steady, and then sharply decrease. Moreover, the inclination of RT in the normal wear process is smaller than those in the other two processes. This observation reveals that the running-in attractor exhibits high stability and complexity. This finding may contribute to the running-in state identification, process prediction, and control.


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