Abstract
AbstractGelatin 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.
Highlights
One of the most popular methods to identify material parameters is to compare experimental results with computer simulations of the same set-up and to adjust the material parameters in the simulations iteratively until the difference between experiments and simulations becomes minimal [2]
[4] suggested using artificial neural networks (ANN) for constructing inverse functions mapping measurement data such as the force-depth curves of spherical indentations directly onto material parameters. We extend this idea so that it will allow us to predict the mechanical properties of gelatin from its concentration even if no experimental data are available for the specific gelatin of interest
The first network ANNF is trained with data from finite element simulations of the fictitious indentation experiments
Summary
One of the most popular methods to identify material parameters is to compare experimental results with computer simulations of the same set-up and to adjust the material parameters in the simulations iteratively until the difference between experiments and simulations becomes minimal [2]. This method has, some well-known disadvantages [2, 3]. We extend this idea so that it will allow us to predict the mechanical properties of gelatin from its concentration even if no experimental data are available for the specific gelatin of interest
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