Naslov
Inverse Modelling for Material Parameters Identification of Soft Tissues

Autori
Piličić, Stjepan ; Marković, Kristina

Vrsta, podvrsta i kategorija rada
Sažeci sa skupova, sažetak, znanstveni

Izvornik
Book of Extended Abstracts – My First Conference 2018. / Jardas, Mladen ; Glujić, Darko ; Vukelić, Goran ; Čanađija, Marko ; Travaš, Vanja - Rijeka, 2018, 23-23

Skup
2nd edition of annual conference for doctoral students of engineering and technology „MY FIRST CONFERENCE“

Mjesto i datum
Rijeka, Hrvatska, 27.09.2018

Vrsta sudjelovanja
Predavanje

Vrsta recenzije
Podatak o recenziji nije dostupan

Ključne riječi
Soft tissuees, biomaterials, inverse modelling, parameter identification, genetic algorithm

Sažetak
Soft tissue behaviour modelling has become a significant area of interest of numerous researchers in technical materials. Since soft tissues are nonlinear elastic materials that can undergo large deformations when subjected to loading, it is suitable to consider application of hyperelastic material models, like Yeoh, Mooney-Rivlin, Odgen, neo-Hookean, Weronda-Vestmann, Humphrey, Arruda-Boyce, Gent and polynomial models. Models differ by the number of constants which have to be identified as meaningful material parameters. Some material models have few variants in order to capture more or less phenomena in material, which correspond to the number of parameters. Also, there are complex material models which are comprised from several components which originate from simpler models. It is not only crucial to select appropriate model, but also the calibration of the chosen model must be performed. Excluding very simple models, like those with one, like neo-Hookean model, or two, like Mooney and the second order Yeoh model, parameters, calibration of the models is not a trivial task and adequate optimization procedures need to be applied, especially when working with complex material models. The solution for the mentioned calibration is in the application of genetic algorithm with specially developed operators. The algorithm has proved itself to be a suitable tool for automatization of the calibration process and is applicable within the scope of the widely available numerical computing environments. Effective genetic algorithm enables the achievement of appropriate values of parameters for the chosen models and, consequently, the more accurate modelling of the soft tissue behaviour.

Izvorni jezik
Engleski

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