KNN-Entwicklung in der Halbwarmumformung
- authored by
- D. Vasquez Ramirez, H. Wester, J. Uhe, B. A. Behrens
- Abstract
The numerical representation of thermomechanical forming processes requires high computing power. This can be reduced by combining FE simulation and artificial neural networks (KNN), especially for processes involving forming and heat treatment. The article presents the development of a KNN to be used for predicting the material properties of an EN AW-7075 T6 alloy after cathodic dip painting depending on the forming history.
- Organisation(s)
-
Institute of Metal Forming and Metal Forming Machines
- Type
- Article
- Journal
- WT Werkstattstechnik
- Volume
- 113
- Pages
- 407-412
- No. of pages
- 6
- ISSN
- 1436-5006
- Publication date
- 2023
- Publication status
- Published
- Peer reviewed
- Yes
- ASJC Scopus subject areas
- Control and Systems Engineering, Automotive Engineering
- Electronic version(s)
-
https://doi.org/10.37544/1436-4980-2023-10-29 (Access:
Open)
-
Details in the research portal "Research@Leibniz University"
