Thumbnail
Access Restriction
Open

Author Serafińska, Aleksandra ♦ Graf, Wolfgang ♦ Kaliske, Michael
Editor Montáns, Francisco J.
Source Hindawi
Content type Text
Publisher Hindawi
File Format PDF
Copyright Year ©2018
Language English
Abstract 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.
ISSN 10762787
Learning Resource Type Article
Publisher Date 2018-11-19
Rights License This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
e-ISSN 10990526
Journal Complexity
Volume Number 2018
Page Count 15


Open content in new tab

   Open content in new tab