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Soft Tissue Hybrid Model for Real-Time Simulations
In this article, a recent formulation for real-time simulation is developed combining the strain energy density of the Spring Mass Model (SMM) with the equivalent representation of the Strain Energy Density Function (SEDF). The resulting Equivalent Energy Spring Model (EESM) is expected to provide i...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9003246/ https://www.ncbi.nlm.nih.gov/pubmed/35406279 http://dx.doi.org/10.3390/polym14071407 |
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author | Moreno-Guerra, Mario R. Martínez-Romero, Oscar Palacios-Pineda, Luis Manuel Olvera-Trejo, Daniel Diaz-Elizondo, José A. Flores-Villalba, Eduardo da Silva, Jorge V. L. Elías-Zúñiga, Alex Rodriguez, Ciro A. |
author_facet | Moreno-Guerra, Mario R. Martínez-Romero, Oscar Palacios-Pineda, Luis Manuel Olvera-Trejo, Daniel Diaz-Elizondo, José A. Flores-Villalba, Eduardo da Silva, Jorge V. L. Elías-Zúñiga, Alex Rodriguez, Ciro A. |
author_sort | Moreno-Guerra, Mario R. |
collection | PubMed |
description | In this article, a recent formulation for real-time simulation is developed combining the strain energy density of the Spring Mass Model (SMM) with the equivalent representation of the Strain Energy Density Function (SEDF). The resulting Equivalent Energy Spring Model (EESM) is expected to provide information in real-time about the mechanical response of soft tissue when subjected to uniaxial deformations. The proposed model represents a variation of the SMM and can be used to predict the mechanical behavior of biological tissues not only during loading but also during unloading deformation states. To assess the accuracy achieved by the EESM, experimental data was collected from liver porcine samples via uniaxial loading and unloading tensile tests. Validation of the model through numerical predictions achieved a refresh rate of 31 fps (31.49 ms of computation time for each frame), achieving a coefficient of determination R(2) from 93.23% to 99.94% when compared to experimental data. The proposed hybrid formulation to characterize soft tissue mechanical behavior is fast enough for real-time simulation and captures the soft material nonlinear virgin and stress-softened effects with high accuracy. |
format | Online Article Text |
id | pubmed-9003246 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-90032462022-04-13 Soft Tissue Hybrid Model for Real-Time Simulations Moreno-Guerra, Mario R. Martínez-Romero, Oscar Palacios-Pineda, Luis Manuel Olvera-Trejo, Daniel Diaz-Elizondo, José A. Flores-Villalba, Eduardo da Silva, Jorge V. L. Elías-Zúñiga, Alex Rodriguez, Ciro A. Polymers (Basel) Article In this article, a recent formulation for real-time simulation is developed combining the strain energy density of the Spring Mass Model (SMM) with the equivalent representation of the Strain Energy Density Function (SEDF). The resulting Equivalent Energy Spring Model (EESM) is expected to provide information in real-time about the mechanical response of soft tissue when subjected to uniaxial deformations. The proposed model represents a variation of the SMM and can be used to predict the mechanical behavior of biological tissues not only during loading but also during unloading deformation states. To assess the accuracy achieved by the EESM, experimental data was collected from liver porcine samples via uniaxial loading and unloading tensile tests. Validation of the model through numerical predictions achieved a refresh rate of 31 fps (31.49 ms of computation time for each frame), achieving a coefficient of determination R(2) from 93.23% to 99.94% when compared to experimental data. The proposed hybrid formulation to characterize soft tissue mechanical behavior is fast enough for real-time simulation and captures the soft material nonlinear virgin and stress-softened effects with high accuracy. MDPI 2022-03-30 /pmc/articles/PMC9003246/ /pubmed/35406279 http://dx.doi.org/10.3390/polym14071407 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Moreno-Guerra, Mario R. Martínez-Romero, Oscar Palacios-Pineda, Luis Manuel Olvera-Trejo, Daniel Diaz-Elizondo, José A. Flores-Villalba, Eduardo da Silva, Jorge V. L. Elías-Zúñiga, Alex Rodriguez, Ciro A. Soft Tissue Hybrid Model for Real-Time Simulations |
title | Soft Tissue Hybrid Model for Real-Time Simulations |
title_full | Soft Tissue Hybrid Model for Real-Time Simulations |
title_fullStr | Soft Tissue Hybrid Model for Real-Time Simulations |
title_full_unstemmed | Soft Tissue Hybrid Model for Real-Time Simulations |
title_short | Soft Tissue Hybrid Model for Real-Time Simulations |
title_sort | soft tissue hybrid model for real-time simulations |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9003246/ https://www.ncbi.nlm.nih.gov/pubmed/35406279 http://dx.doi.org/10.3390/polym14071407 |
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