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Finite element analysis of V-ribbed belt/pulley system with pulley misalignment using a neural-network-based material model

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Abstract

Pulley misalignment limits the performance of V-ribbed belt/pulley system as it relates to rib load-sharing and contact pressure distribution for multiple rib belts required in high torque demands of modern automotive applications. In this paper, a three-dimensional dynamic finite element model is built to evaluate the effects of pulley misalignment. The model consists of a pulley and a segment of V-ribbed belt in contact with the pulley. A material model of belt, including rubber compound and reinforcing cord is developed. Multiple rubber layers are each considered hyperelastic with distinct material characterization parameters. A novel neural-network-based hyperelastic material model is implemented to represent properties of nonlinear elastic belt-rib compound. The models are implemented in the commercial code ABAQUS/Explicit to simulate the misalignment of the belt–pulley system. The developed model is first validated by experimental measurements of pulley lateral force due to misalignment. Also, three common types of misalignment in the belt–pulley system are analyzed and results are presented.

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Abbreviations

U :

Strain energy potential

λ :

Principal extension ratio

I :

Strain invariant

J :

Volume ratio

μ :

Friction coefficient

F ξ , F η :

Friction force components

P :

Normal force

RF :

Reaction force

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Acknowledgment

The funding of this project is provided by Mark IV Automotive.

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Authors and Affiliations

  1. Department of Mechanical and Aerospace Engineering, Missouri University of Science and Technology, Rolla, MO, 65409, USA

    J. Hu, J. Chen, S. Sundararaman & K. Chandrashekhara

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  1. J. Hu
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  2. J. Chen
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  3. S. Sundararaman
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  4. K. Chandrashekhara
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Corresponding author

Correspondence to K. Chandrashekhara.

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Hu, J., Chen, J., Sundararaman, S. et al. Finite element analysis of V-ribbed belt/pulley system with pulley misalignment using a neural-network-based material model. Neural Comput & Applic 18, 927–938 (2009). https://doi.org/10.1007/s00521-009-0257-z

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  • DOI: https://doi.org/10.1007/s00521-009-0257-z

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