Friction-induced vibration of a slider on an elastic disc spinning at variable speeds

Liu, Ningyu; Ouyang, Huajiang

doi:10.1007/s11071-019-05169-1

Cited by 21 publications

(9 citation statements)

References 42 publications

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“…Instead, combinations of various parameters, their instantaneous changes as well as their past values, are more likely to drive the system dynamics into different stability regimes. The rich corpus of research on frictional systems has identified various mechanisms for instability [75], such as stick-slip [75,76], a negative friction slope [77,78,79] and mode-coupling [5,80,7]. However, most of these mechanisms are limited to single-point contacts in idealized model systems.…”

Section: Input-output Behavior Of Dynamical Systemsmentioning

confidence: 99%

Deep learning for brake squeal: vibration detection, characterization and prediction

Stender,

Tiedemann,

Spieler

et al. 2020

Preprint

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Despite significant advances in numerical modeling of brake squeal, the majority of industrial research and design is still conducted experimentally. In this work we report on novel strategies for handling data-intensive vibration testings and gaining better insights into brake system vibrations. To this end, we propose machine learning-based methods to detect and characterize vibrations, understand sensitivities and predict brake squeal. Our aim is to illustrate how interdisciplinary approaches can leverage the potential of data science techniques for classical mechanical engineering challenges. In the first part, a deep learning brake squeal detector is developed to identify several classes of typical sounds in vibration recordings. The detection method is rooted in recent computer vision techniques for object detection. It allows to overcome limitations of classical approaches that rely on spectral properties of the recorded vibrations. Results indicate superior detection and characterization quality when compared to state-of-the-art brake squeal detectors. In the second part, deep recurrent neural networks are employed to learn the parametric patterns that determine the dynamic stability of the brake system during operation. Given a set of multivariate loading conditions, the models learn to predict the vibrational behavior of the structure. The validated models represent virtual twins for the squeal behavior of a specific brake system. It is found that those models can predict the occurrence and onset of brake squeal with high accuracy. Hence, the deep learning models can identify the complicated patterns and temporal dependencies in the loading conditions that drive the dynamical structure into regimes of instability. Large data sets from commercial brake system testing are used to train and validate the deep learning models.

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Section: Input-output Behavior Of Dynamical Systemsmentioning

confidence: 99%

Deep learning for brake squeal: vibration detection, characterization and prediction

Stender,

Tiedemann,

Spieler

et al. 2020

Preprint

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“…However, there are also some disadvantages, due to the large contact area, the frictional resistance is relatively large, the wear is fast, and the moving speed is limited. At the same time, hard guideway machines are easy to occur the phenomenon of fast-and-slow, and stop-and-go, which is called stick-slip [1][2][3][4][5]. In engineering systems, stick-slip phenomena in friction-induced vibrating systems can be observed in real systems, for example, the sound of the bowed instrument, machine tools [6][7][8][9], drill string systems [10],…”

Section: Introductionmentioning

confidence: 99%

Analysis of the Critical Stick-slip Velocity of CNC Machine Tool Combining Friction Parameters Identification and Kinetic Model

Yang¹,

Zhang²,

Li³

et al. 2022

Preprint

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Stick-slip is a negative phenomenon caused by friction in servo feed systems, which is particularly prominent in low-speed and heavy-load conditions. At present, most research on the critical Stick-slip velocity (CSSV) ignores higher-order terms of the equivalent damping ratio, the calculation accuracy is greatly reduced when the system has a large equivalent damping ratio. Firstly, an Improved Stribeck Model based on Least Squares Genetic algorithm (ISM-LSG) is proposed for friction identification, At the same time, the identification of shape parameters of the traditional Stribeck model is extended instead of empirical values. Then, an improved Critical Velocity solving method based on the kinetic model is constructed, and the influence of the higher-order term of the equivalent damping ratio on the critical velocity identification accuracy is analyzed. Finally, this method is verified by the CNC end-face cylindrical grinder with a hard guideway. The friction force identification error with ISM-LSG is reduced 10% compared with the traditional method, and the error of the critical Stick-slip velocity with retaining higher-order term (CSSV-RHT) is reduced 37% compared with the critical Stick-slip velocity by ignoring higher-order term (CSSV-iHT).

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“…Li et al [26] examined the dynamics of a 2-DoF model with nonlinear contact stiffness and the effects of separation and reattachment on the vibration amplitudes of dynamic responses were studied. Liu and Ouyang [27] studied the friction-induced vibration of a slider on an elastic disc spinning at time-varying speeds and observed that the time-varying disc speed could be a cause for unstable vibration. Sinou [28] studied the transient and stationary self-excited vibration in a nonlinear finite element model of a disc brake.…”

Section: Introductionmentioning

confidence: 99%

“…The nonlinearities in friction-induced-vibration problems can originate from multiple sources, e.g. nonlinear contact stiffness [26,28,[31][32][33], nonsmooth behaviours including stick-slip and contact/ separation [6,27,[34][35][36][37]. Although quite a few published papers on friction-induced vibration took the nonlinearities into account, a comprehensive analysis of the effects of multiple types of nonlinearities on the friction-induced self-excited vibration is still lacking.…”

Section: Introductionmentioning

confidence: 99%

Friction-induced vibration considering multiple types of nonlinearities

Liu

Ouyang

2020

Nonlinear Dyn

Self Cite

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The friction-induced vibration of a novel 5-DoF (degree-of-freedom) mass-on-oscillating-belt model considering multiple types of nonlinearities is studied. The first type of nonlinearity in the system is the nonlinear contact stiffness, the second is the non-smooth behaviour including stick, slip and separation, and the third is the geometrical nonlinearity brought about by the moving-load feature of the mass slider on the rigid belt. Both the linear stability of the system and the nonlinear steady-state responses are investigated, and rich dynamic behaviours of the system are revealed. The results of numerical study indicate the necessity of the transient dynamic analysis in the study of friction-induced-vibration problems as the linear stability analysis fails to detect the occurrence of self-excited vibration when two stable solutions coexist in the system. The bifurcation behaviour of the steady-state responses of the system versus some parameters is determined. Additionally, the significant effects of each type of nonlinearity on the linear stability and nonlinear steady-state responses of the system are discovered, which underlie the necessity to take multiple types of nonlinearities into account in the research of friction-induced vibration and noise.

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Friction-induced vibration of a slider on an elastic disc spinning at variable speeds

Cited by 21 publications

References 42 publications

Deep learning for brake squeal: vibration detection, characterization and prediction

Deep learning for brake squeal: vibration detection, characterization and prediction

Analysis of the Critical Stick-slip Velocity of CNC Machine Tool Combining Friction Parameters Identification and Kinetic Model

Friction-induced vibration considering multiple types of nonlinearities

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