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Title:

Extending a physics-based constitutive model using genetic programming

Document type:
Zeitschriftenaufsatz
Author(s):
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...     »
Keywords:
Symbolic regression, Genetic programming, Material modelling, Flow stress
Journal title:
Applications in Engineering Science
Year:
2022
Journal volume:
9
Pages contribution:
100080
Fulltext / DOI:
doi:10.1016/j.apples.2021.100080
WWW:
ScienceDirect
Publisher:
Elsevier BV
E-ISSN:
2666-4968
Date of publication:
01.03.2022
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