Wiesheier S, Moreno Mateos MA, Steinmann P (2024)
Publication Type: Journal article
Publication year: 2024
Book Volume: 430
Pages Range: 117208
Article Number: 117208
DOI: 10.1016/j.cma.2024.117208
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Outline
Abstract
Keywords
1. Introduction
2. Hyperelasticity based on a approximation of the strain energy function
3. Inverse identification of the parameters of the approximated strain energy function
4. Experimental insights: threefold application and validation of the model
5. Conclusion and outlook
CRediT authorship contribution statement
Declaration of competing interest
Acknowledgment
Appendix A. Normalized error plots of forces and vertical displacement fields
Appendix B. Sensitivity matrix
Data availability
References
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Figures (10)
Fig. 1. Graphical comparison of linear basis functions (above, k=2) and cubic basis…
Fig. 2. Sampled invariants Ī1 at the quadrature points (c) across all load steps of a…
Fig. 3. Deformed samples featuring holes of different dimensions for three elastomers
Fig. 4. Identification of the strain energy function W̄(I,Ī1) for the material…
Fig. 5. Identification of the strain energy function W̄(I,Ī1) for the material DOWSIL
Fig. 6. Identification of the strain energy function W̄(I,Ī1)+W̄(I,Ī2) for the…
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Elsevier
Computer Methods in Applied Mechanics and Engineering
Volume 430, 1 October 2024, 117208
Computer Methods in Applied Mechanics and Engineering
Versatile data-adaptive hyperelastic energy functions for soft materials
Author links open overlay panelSimon Wiesheier a, Miguel Angel Moreno-Mateos a, Paul Steinmann a b
a
Institute of Applied Mechanics, Friedrich-Alexander-Universität Erlangen–Nürnberg, Egerlandstr. 5, 91058, Erlangen, Germany
b
Glasgow Computational Engineering Centre, School of Engineering, University of Glasgow, G12 8QQ, United Kingdom
Received 21 May 2024, Revised 25 June 2024, Accepted 30 June 2024, Available online 11 July 2024, Version of Record 11 July 2024.
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Abstract
Applications of soft materials are customarily linked to complex deformation scenarios and material nonlinearities. In the bioengineering field, soft materials typically mimic the low stiffness of biological matter subjected to extreme deformations. Computational frameworks surge as a versatile tool to assist the design of functional applications. The constitutive model lies at the core of such frameworks. In this regard, the customary extreme non-linear behavior of elastomers poses an additional challenge to thoroughly capture the material behavior. Here, data-driven methodologies hold considerable promise for enhancing constitutive modeling when contrasted with phenomenological approaches. In this investigation, we introduce a versatile data-adaptive method tailored to the modeling of hyperelastic soft materials at finite strains. Specifically, our method substitutes an a priori chosen model for the strain energy function by a flexible interpolant defined on a discretized invariant space. Within this framework, the interpolation values assume the role of material parameters and are determined through finite element model updating to conform to measured experimental data — comprising full-field displacements coming from Digital-Image-Correlation and global reaction forces. We validate the method on uniaxial experimental tests of soft elastomers, encompassing
,
, and
. Overall, we aim to establish a new route for the construction of hyperelastic energy functions, untethered from any predefined existing models or assumptions regarding the shape of the energy.
APA:
Wiesheier, S., Moreno Mateos, M.A., & Steinmann, P. (2024). Versatile data-adaptive hyperelastic energy functions for soft materials. Computer Methods in Applied Mechanics and Engineering, 430, 117208. https://doi.org/10.1016/j.cma.2024.117208
MLA:
Wiesheier, Simon, Miguel Angel Moreno Mateos, and Paul Steinmann. "Versatile data-adaptive hyperelastic energy functions for soft materials." Computer Methods in Applied Mechanics and Engineering 430 (2024): 117208.
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