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Advanced combined inverse techniques for the identification of material parameters


Gljušćić, Matej; Franulović, Marina; Lanc, Domagoj
Advanced combined inverse techniques for the identification of material parameters // My first conference 2018 : book of abstracts
Rijeka, 2018. str. 7-7 (ostalo, podatak o recenziji nije dostupan, sažetak, stručni)


Naslov
Advanced combined inverse techniques for the identification of material parameters

Autori
Gljušćić, Matej ; Franulović, Marina ; Lanc, Domagoj

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

Izvornik
My first conference 2018 : book of abstracts / - Rijeka, 2018, 7-7

ISBN
978-953-165-128-8

Skup
My First Confference (2018)

Mjesto i datum
Rijeka, Republika Hrvatska, 27.09.2018

Vrsta sudjelovanja
Ostalo

Vrsta recenzije
Podatak o recenziji nije dostupan

Ključne riječi
Constitutive model, material parameter identification, finite element model updating

Sažetak
The recent advancement of science and engineering has pushed the integration of multidisciplinary discoveries into research of innovative composite and hybrid materials, which brought to attention the necessity for investigation of their performance combining both experimental and numerical studies. The key issue in experimental solid mechanics is the identification of the parameters governing the constitutive equations. In cases where constitutive equations depend on a large number of unknowns, scarce linking assumptions between the unknown parameters and those determinable from mechanical tests result in the necessity to perform a larger number of experiments for accurate parameter identification. Few techniques to overcome these limitations have already been proposed. The virtual fields method [1] was developed on the basis of full-field non-contact measurements, while iFEM [2] combines experimental and numerical analysis using an inverse finite element method. Moreover, constitutive parameters can be extracted by combining finite element method with advanced optimization tools such as genetic algorithms (GA) and artificial neural networks (ANN) [3]. Combining these advanced methods for the material model calibration of materials such as printed composites provides a base for accurate material behaviour modelling of innovative materials.

Izvorni jezik
Engleski

Znanstvena područja
Strojarstvo, Temeljne tehničke znanosti



POVEZANOST RADA


Ustanove
Tehnički fakultet, Rijeka