Numerical determination of hysteresis friction on different length scales and comparison to experiments

A realistic characterization of the frictional behaviour of materials and mechanical systems is of prime importance for the assessment of their contact interaction properties, especially in the context of undesired temperature rise or intensive wear leading to service life reduction. A characteristic tribological property of elastomeric materials is the dependency of the friction coefficient on the local contact pressure, sliding velocity, and temperature in the contact interface. Thus, the friction coefficient is not constant in the entire contact area but varies according to the magnitudes of the aforementioned three influencing factors. In this contribution, a friction law based on artificial neural networks (ANN) is presented, which is able to capture the nonlinear dependencies of the friction coefficient on the contact pressure, sliding velocity, and temperature. Due to an extraordinary adaptivity of the ANN structure, these nonlinear relations stemming from experimental data can be modelled properly within the introduced friction law, in contrast to other friction formulations, which are limited by the fitting quality of their parameters. The ANN based friction law is implemented into a contact formulation of the finite element method (FEM). Especially, the linearization of contact contributions to the weak form of momentum balance equation, required for the FEM, is developed taking into account the differentiability of the ANN. The applicability of the developed friction law within the finite element analysis of tires as well as within sliding simulations of rubber elements is presented in this paper.

show abstract

“…From the formulation of the friction coefficient given in (9) and from the definition of the neuronal output shown in (7) and (8), the derivative…”

Section: Complexitymentioning

confidence: 99%

“…Analytical approaches introducing friction theories considering surface roughness are proposed by Klüppel and Heinrich [2,3] as well as by Persson [4]. Furthermore, numerical approaches for the modelling of friction are developed, which are based on the finite element method, multiscale analysis, and homogenization; see [5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Artificial Neural Networks Based Friction Law for Elastomeric Materials Applied in Finite Element Sliding Contact Simulations

2018

Self Cite

View full text Add to dashboard Cite

show abstract

“…Modified versions of the Archard model have been used to predict wear rates and contain the related variables and parameters in a system [23]. Moreover, experimental and numerical investigations of friction and wear have been ongoing, from the atomic level [24][25][26][27] to the continuum level [28][29][30][31][32][33], to the environmental level regarding tyre and road wear particles (TRWP) [34,35]. Ultimately, it has been difficult to develop a comprehensive model that concisely expresses multiscale wear phenomena.…”

Section: Introductionmentioning

confidence: 99%

Spectral wear modelling of rubber friction on a hard substrate with large surface roughness

Tanaka,

Yanagihara,

Shiomi

et al. 2023

Proc. R. Soc. A.

View full text Add to dashboard Cite

Soft-hard matter friction is a long-standing tribology problem that remains unclarified, requiring engineers to empirically predict the wear life. To clarify this issue, this study examines the transient running-in regime of rubber friction on a hard rough substrate and models the temporal wear progression using the spectrum curves of surface roughness for both materials. Performing a series of friction tests and three-dimensional surface-height measurements, the time-dependent behaviours of the power spectral densities (PSDs) are divided into two phases, namely the initial non-steady and long-term steady phases. The detailed spectral analyses of worn rubber surfaces in the initial phase lead to a blended PSD function between self-affine and K -correlation surface models, consisting of one variable (the Hurst exponent) that is saturated by the substrate self-affinity. Supported by the Greenwood–Williamson theory concerning rough contact mechanics, the volumetric estimate with the blended PSD function is used to assess the volume rate of wear debris in the steady phase, which is validated experimentally. These findings not only improve the wear predictions of soft materials from the initial measurements of worn surfaces but also help clarify the constrained multiscale mechanism of wear.

show abstract

Multi-physical and Multi-scale Theoretical-Numerical Modeling of Tire-Pavement Interaction

Kaliske

Behnke

Hartung

et al. 2021

Coupled System Pavement - Tire - Vehicle

View full text Add to dashboard Cite

Numerical determination of hysteresis friction on different length scales and comparison to experiments

Cited by 18 publications

References 14 publications

Artificial Neural Networks Based Friction Law for Elastomeric Materials Applied in Finite Element Sliding Contact Simulations

Artificial Neural Networks Based Friction Law for Elastomeric Materials Applied in Finite Element Sliding Contact Simulations

Spectral wear modelling of rubber friction on a hard substrate with large surface roughness

Multi-physical and Multi-scale Theoretical-Numerical Modeling of Tire-Pavement Interaction

Contact Info

Product

Resources

About