<div class="csl-bib-body">
<div class="csl-entry">Elgeti, S., Kofler, M., Freinberger, L., & Riegler, M. (2025, December 9). <i>Modeling, Simulation, and Optimization in Plastics Profile Extrusion</i> [Conference Presentation]. The 9th Asian Pacific Congress on Computational Mechanics/The 7th Australasian Conference on Computational Mechanics (APCOM-ACCM 2025), Brisbane, Australia. http://hdl.handle.net/20.500.12708/224223</div>
</div>
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dc.identifier.uri
http://hdl.handle.net/20.500.12708/224223
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dc.description.abstract
Numerical optimization is an important tool in both the design of individualized components and their manufacturing processes. Despite this vital role of numerical optimization, the creative aspects of the design process are still left to the human designer. In fact, for centuries, creativity has been considered a purely human attribute, if not the defining element of a human being. Recent advances in artificial intelligence (AI) have shaken this perception to its roots. Bit by bit, creative, rational agents have made their way into our daily lives. Inspired by these advances, we will explore different ways in which machine learning algorithms can help improve numerical design, e.g., in terms of geometry parameterization, surrogate models for forward simulations and optimization strategies. To this end, we show how machine learning tools can be included into the optimization workflow for the design of both extrusion dies and static mixers. For example, variational autoencoders (VAE) can be used to learn low-dimensional, yet feature-rich, shape representations. A VAE can discover common properties among a variety of shapes without an explicit, human-made parametric representation of the original designs. A VAE is a neural network architecture that learns the underlying structure of a 3D shape in an unsupervised manner. It infers a latent, hierarchical representation of objects. This approach promises significant improvements in both performance and variety of shapes. Furthermore, we will discuss how the resulting shape parameterization can become an input to numerical simulation, including both high-fidelity and reduced-order simulations. In this context, we will present both boundary-conforming methods, e.g., using OpenFOAM, but also immersed methods. In terms of optimization, we touch upon reinforcement learning as a means of learning optimization strategies.
en
dc.language.iso
en
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dc.subject
shape optimization
en
dc.subject
computational fluid dynamics
en
dc.subject
machine learning
en
dc.subject
educed-order modeling
en
dc.subject
lattice structures
en
dc.title
Modeling, Simulation, and Optimization in Plastics Profile Extrusion
en
dc.type
Presentation
en
dc.type
Vortrag
de
dc.contributor.affiliation
RWTH Aachen University, Germany
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dc.type.category
Conference Presentation
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tuw.researchTopic.id
C2
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tuw.researchTopic.id
C6
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tuw.researchTopic.id
C3
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tuw.researchTopic.name
Computational Fluid Dynamics
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tuw.researchTopic.name
Modeling and Simulation
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tuw.researchTopic.name
Computational System Design
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tuw.researchTopic.value
20
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tuw.researchTopic.value
50
-
tuw.researchTopic.value
30
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tuw.publication.orgunit
E317-01-1 - Forschungsgruppe Numerische Analyse- und Designmethoden
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tuw.author.orcid
0000-0002-4474-1666
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tuw.author.orcid
0009-0005-1661-3340
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tuw.event.name
The 9th Asian Pacific Congress on Computational Mechanics/The 7th Australasian Conference on Computational Mechanics (APCOM-ACCM 2025)
en
tuw.event.startdate
07-12-2025
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tuw.event.enddate
10-12-2025
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tuw.event.online
On Site
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tuw.event.type
Event for scientific audience
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tuw.event.place
Brisbane
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tuw.event.country
AU
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tuw.event.presenter
Elgeti, Stefanie
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wb.sciencebranch
Maschinenbau
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wb.sciencebranch
Informatik
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wb.sciencebranch
Mathematik
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wb.sciencebranch.oefos
2030
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wb.sciencebranch.oefos
1020
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wb.sciencebranch.oefos
1010
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wb.sciencebranch.value
50
-
wb.sciencebranch.value
20
-
wb.sciencebranch.value
30
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item.openairecristype
http://purl.org/coar/resource_type/c_18cp
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item.grantfulltext
none
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item.fulltext
no Fulltext
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item.languageiso639-1
en
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item.openairetype
conference paper not in proceedings
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item.cerifentitytype
Publications
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crisitem.author.dept
E317-01 - Forschungsbereich Leichtbau
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crisitem.author.dept
E317-01-1 - Forschungsgruppe Numerische Analyse- und Designmethoden
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crisitem.author.dept
E317-01-1 - Forschungsgruppe Numerische Analyse- und Designmethoden
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crisitem.author.dept
RWTH Aachen University, Germany
-
crisitem.author.orcid
0000-0002-4474-1666
-
crisitem.author.orcid
0009-0005-1661-3340
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crisitem.author.parentorg
E317 - Institut für Leichtbau und Struktur-Biomechanik
