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Titel:

Extending a physics-based constitutive model using genetic programming

Dokumenttyp:
Zeitschriftenaufsatz
Autor(en):
Kronberger, Gabriel; Kabliman, Evgeniya; Kronsteiner, Johannes; Kommenda, Michael
Abstract:
In material science, models are derived to predict emergent material properties (e.g. elasticity, strength,conductivity) and their relations to processing conditions. A major drawback is the calibration of modelparameters that depend on processing conditions. Currently, these parameters must be optimized to fitmeasured data since their relations to processing conditions (e.g. deformation temperature, strain rate) arenot fully understood. We present a new approach that identifies the functional d...     »
Stichworte:
Symbolic regression, Genetic programming, Material modelling, Flow stress
Zeitschriftentitel:
Applications in Engineering Science
Jahr:
2022
Band / Volume:
9
Seitenangaben Beitrag:
100080
Volltext / DOI:
doi:10.1016/j.apples.2021.100080
WWW:
ScienceDirect
Verlag / Institution:
Elsevier BV
E-ISSN:
2666-4968
Publikationsdatum:
01.03.2022
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