Journal ArticleDOI
Finite element analysis of V-ribbed belts using neural network based hyperelastic material model
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TLDR
In this article, a three-dimensional finite element model was built to study V-ribbed belt pulley contact mechanics, which consists of a pulley and a segment of Vribbed belts in contact with the pulley.Abstract:
A three-dimensional finite element model was built to study V-ribbed belt pulley contact mechanics. The model consists of a pulley and a segment of V-ribbed belt in contact with the pulley. A material model for the belt, including the rubber compound and the reinforcing cord is developed. Rubber is modeled as hyperelastic material. The hyperelastic strain energy function is approximated by neural network trained by rubber test data. Reinforcing cord is modeled as elastic rebar. The material model developed is implemented in the commercial finite element code ABAQUS to simulate the V-ribbed belt-pulley system. A study is then conducted to investigate the effect of belt pulley system parameters on the contact mechanics. The effects of temperature and aging on belt materials are also investigated. The information gained from the analysis can be applied to optimize V-ribbed belt and pulley design.read more
Citations
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Machine learning strategies for systems with invariance properties
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Learning constitutive relations using symmetric positive definite neural networks
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A review of artificial neural networks in the constitutive modeling of composite materials
TL;DR: A state-of-the-art literature review of ANN models in the constitutive modeling of composite materials, focusing on discovering unknown constitutive laws and accelerating multiscale modeling is given.
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Learning constitutive relations from indirect observations using deep neural networks
TL;DR: In this paper, a neural network is used to represent the unknown constitutive relations, and neural networks are compared with piecewise linear functions, radial basis functions, and radial basis function networks, and the neural network outperforms the others in certain cases.
Journal ArticleDOI
Unsupervised discovery of interpretable hyperelastic constitutive laws
TL;DR: The proposed approach is unsupervised, it requires no stress data but only displacement and global force data, and it delivers interpretable models, i.e., models that are embodied by parsimonious mathematical expressions discovered through sparse regression of a large catalogue of candidate functions.
References
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Book
The physics of rubber elasticity
TL;DR: In this paper, the Elasticity of Long-Chain Molecules (LCHs) and Elasticity in a Molecular Network (MNNs) is investigated. But the authors focus on the elasticity of the long chain Molecules.
Book
Nonlinear Finite Elements for Continua and Structures
TL;DR: In this paper, the authors present a list of boxes for Lagrangian and Eulerian Finite Elements in One Dimension (LDF) in one dimension, including Beams and Shells.
Book
Non-Linear Elastic Deformations
TL;DR: In this paper, the influence of non-linear elastic systems on a simple geometric model for elastic deformations is discussed, and the authors propose a planar and spatial euler introduction to nonlinear analysis.
Book
History of Tribology
TL;DR: The History of Tribology as mentioned in this paper provides a comprehensive account of the subject from the earliest times to the present day, including the latest developments up to and including the current day. But it is not a complete history of the field.
Journal ArticleDOI
Mechanics of the belt drive
TL;DR: In this paper, the mechanics of the belt drive is considered when the belt possesses a soft pliable envelope to grip the pulley and strong tension members to transmit the power, and it is concluded that shear strains in the belt envelope are a large factor in determining drive behaviour.