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Concentration‐Specific Constitutive Modeling of Gelatin Based on Artificial Neural Networks
Author(s) -
Abdolazizi Kian P.,
Linka Kevin,
Sprenger Johanna,
Neidhardt Maximilian,
Schlaefer Alexander,
Cyron Christian J.
Publication year - 2021
Publication title -
pamm
Language(s) - English
Resource type - Journals
ISSN - 1617-7061
DOI - 10.1002/pamm.202000284
Subject(s) - gelatin , artificial neural network , materials science , biological system , constitutive equation , artificial intelligence , computer science , biomedical engineering , engineering , chemistry , structural engineering , biochemistry , finite element method , biology
Gelatin phantoms are frequently used in the development of surgical devices and medical imaging techniques. They exhibit mechanical properties similar to soft biological tissues [1] but can be handled at a much lower cost. Moreover, they enable a better reproducibility of experiments. Accurate constitutive models for gelatin are therefore of great interest for biomedical engineering. In particular it is important to capture the dependence of mechanical properties of gelatin on its concentration. Herein we propose a simple machine learning approach to this end. It uses artificial neural networks (ANN) for learning from indentation data the relation between the concentration of ballistic gelatin and the resulting mechanical properties.