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Identification of mechanical models and parameters for alginate‐based hydrogels as proxy materials for brain tissue
Author(s) -
Kaessmair Stefan,
Distler Thomas,
Schaller Emely,
Boccaccini Aldo R.,
Steinmann Paul,
Budday Silvia
Publication year - 2021
Publication title -
pamm
Language(s) - English
Resource type - Journals
ISSN - 1617-7061
DOI - 10.1002/pamm.202000338
Subject(s) - self healing hydrogels , viscoelasticity , finite element method , brain tissue , identification (biology) , system identification , least squares function approximation , biological system , computer science , proxy (statistics) , materials science , biomedical engineering , mathematics , composite material , data modeling , structural engineering , engineering , machine learning , statistics , polymer chemistry , biology , botany , database , estimator
Due to their qualitatively similar mechanical behavior to brain tissue, the present study focuses on the modeling and identification of material parameters for alginate‐gelatine hydrogels. A generalized Maxwell model is used to describe its finite viscoelastic response. By comparing the experimentally recorded data with finite element simulations, we define a least squares problem to identify appropriate material parameters. We show that we can parameterize the model to well fit each loading mode individually.

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