A Novel Parameter Identification Toolbox for the Selection of Hyperelastic Constitutive Models from Experimental Data

Date

2017-10-13

Author

Dal, Hüsnü
Açıkgöz, Kemal

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This paper presents a novel parameter identification toolbox based on various multi-objective optimization strategies for the selection of the best constitutive models from a given set of homogeneous experiments. The toolbox aims at providing an objective model selection procedure along with the material parameters for the rubber compound at hand. To this end, we utilize the multi-objective optimization using genetic algorithm of MATLAB. For the validation purposes, we use 10 constitutive laws.

URI

http://dx.doi.org/10.18419/opus-9334
https://hdl.handle.net/11511/72684
https://www.gacm2017.uni-stuttgart.de/registration/Upload/ExtendedAbstracts/ExtendedAbstract_0174.pdf

Conference Name

Proceedings of the 7th GACM Colloquium on Computational Mechanics for Young Scientists from Academia and Industry (October 11-13, 2017)

Collections

Department of Civil Engineering, Conference / Seminar

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Citation Formats

H. Dal and K. Açıkgöz, “A Novel Parameter Identification Toolbox for the Selection of Hyperelastic Constitutive Models from Experimental Data,” presented at the Proceedings of the 7th GACM Colloquium on Computational Mechanics for Young Scientists from Academia and Industry (October 11-13, 2017), Stuttgart, Germany, 2017, Accessed: 00, 2021. [Online]. Available: http://dx.doi.org/10.18419/opus-9334.