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ETH Zurich
Deep Learning-based deposition modelling and path planning in Wire Arc Additive Manufacturing
Degree
thesis:*- Grantor dc:publisher
- ETH Zurich
- Year dc:date
- 2024
Author and committee
dc:creator, dc:contributor.*- Author dc:creator
-
- Glasder, Magnus; id_orcid0000-0002-1071-044X
- Contributors dc:contributor
-
- Wegener, Konrad
- Bambach, Markus; id_orcid0000-0002-1071-044X
- Flügge, Wilko
Subjects
dc:subject × 7- Additive Manufacturing; Wire Arc Additive Manufacturing (WAAM); Deep Learning; Deposition model; Wire arc directed energy deposition (DED-Arc); Artificial Intelligence; Machine Learning; WELDING AND ALLIED TECHNIQUES (JOINING OF MATERIALS)
- info:eu-repo/classification/ddc/670
- info:eu-repo/classification/ddc/620
- info:eu-repo/classification/ddc/004
- Manufacturing
- Engineering & allied operations
- Data processing, computer science
Rights
dc:rights- Statement dc:rights
-
- info:eu-repo/semantics/openAccess
- In Copyright - Non-Commercial Use Permitted
- Language dc:language
- en
Identifiers
dc:identifier.*- Identifier
- https://doi.org/10.3929/ethz-b-000712309
- OAI identifier oai:identifier
- oai:www.research-collection.ethz.ch:20.500.11850/712309