Identification of the concentration‐dependent viscoelastic constitutive parameters of gelatin by combining computational mechanics and machine learning

Since the mechanical properties of gelatin are similar to those of soft biological tissues, gelatin is a commonly used surrogate for real tissues, for example in safety engineering or medical engineering. Additional advantages of gelatin over real tissues are lower costs and better reproducibility of experiments. Therefore, constitutive models of gelatin are of great interest. In particular, it is important to capture the concentration dependence of the mechanical properties since the gelatin mass concentration significantly affects the constitutive behavior. To this end, we propose a hybrid approach linking artificial neural networks (ANN) and classical constitutive modeling to relate the gelatin's concentration to its viscoelastic material properties using indentation data.

Subjects

MLE@TUHH

DDC Class

600: Technik

620: Ingenieurwissenschaften

Funding(s)

I³-Lab - Modell-gestütztes maschinelles Lernen für die Weichgewebsmodellierung in der Medizin

Projekt DEAL

Publication version

acceptedVersion

Lizenz

https://creativecommons.org/licenses/by/4.0/

Name

Proc Appl Math Mech - 2021 - Abdolazizi - Identification of the concentration%u2010dependent viscoelastic constitutive.pdf

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2.31 MB

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Adobe PDF

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Identification of the concentration‐dependent viscoelastic constitutive parameters of gelatin by combining computational mechanics and machine learning