<div class="csl-bib-body">
<div class="csl-entry">Kofler, M., Peyker, L., Luxner, M. H., & Pettermann, H. (2026). <i>Constitutive Modeling of Anisotropic Elasto-Damage Materials – A Data Driven Machine Learning Approach</i> [Conference Presentation]. 5th Materials Science Colloquium (70. Metallkunde-Kolloquium), Lech am Arlberg, Austria.</div>
</div>
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dc.identifier.uri
http://hdl.handle.net/20.500.12708/227810
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dc.description.abstract
A data driven machine learning based approach is presented to model the constitutive
response of anisotropic elasto-damage materials such as wood and fiber reinforced
composites. Adopting principles of continuum damage mechanics, the nonlinear constitutive
response is formulated by recourse to damage variables or damage tensors. The knowledge
of the damage tensor and the undamaged (initial) linear elasticity determines the nonlinear
relation between the stress and stain tensors. Instead of analytically expressing the relation
between damage and strain state, an artificial neural network (ANN) is employed.
For training, testing, and validation of various ANN architectures data sets are generated by
Finite Element Method simulations. Monotonous increasing, radial strain loads are applied to
sample the strain space with sufficient resolution. Complementary to the strain–stress and
strain–damage response given by the trained ANNs, physics principles are considered outside
the ANN to handle unloading and reloading scenarios. Tension-compression asymmetry and
mesh size dependence are addressed.
Examples are shown for plane stress states for which the nonlinear tensorial constitutive
relation is captured by a surrogate model, i.e. a trained ANN. The latter is implemented as
constitutive material law into a Finite Element Method program to run structural analyses.
en
dc.description.sponsorship
Luxner Engineering ZT GmbH
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dc.language.iso
en
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dc.subject
machine learning
en
dc.subject
surrogate constitutive model
en
dc.subject
continuum damage mechanics
en
dc.subject
composites
en
dc.subject
wood
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dc.title
Constitutive Modeling of Anisotropic Elasto-Damage Materials – A Data Driven Machine Learning Approach
en
dc.type
Presentation
en
dc.type
Vortrag
de
dc.contributor.affiliation
Luxner Engineering ZT GmbH, Austria
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dc.contributor.affiliation
Luxner Engineering ZT GmbH, Austria
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dc.relation.grantno
0
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dc.type.category
Conference Presentation
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tuw.project.title
KI-basierte Materialmodellierung für Holzwerkstoff - Phase 2
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tuw.researchTopic.id
M5
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tuw.researchTopic.id
C6
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tuw.researchTopic.id
C1
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tuw.researchTopic.name
Composite Materials
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tuw.researchTopic.name
Modeling and Simulation
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tuw.researchTopic.name
Computational Materials Science
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tuw.researchTopic.value
30
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tuw.researchTopic.value
40
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tuw.researchTopic.value
30
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tuw.publication.orgunit
E317-01-2 - Forschungsgruppe Struktur- und Werkstoffsimulation
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tuw.publication.orgunit
E317-01-1 - Forschungsgruppe Numerische Analyse- und Designmethoden
