Detektion von Bindefehlern beim DED-Arc
Abstract Based on artificial intelligence (AI) developed for monitoring arc welding, this article presents a deep neural network for monitoring lack of fusion defects in wire arc additive manufacturing of aluminium. The aim is to detect defects in built-up volumes on the basis of weld source data. These can be successfully processed by the algorithm presented and a trained AI. The achieved accuracy of the network is > 90 percent.
Authors
- Max Biegler (ORCID: https://orcid.org/0000-0001-8665-5347)
- Michael Rethmeier (ORCID: https://orcid.org/0000-0001-8123-6696)
- Benedikt Neumann
- Sven-Frithjof Goecke
Institutions
- Federal Institute For Materials Research and Testing (DE)
- Brandenburg University of Applied Sciences (DE)
- Fraunhofer Institute for Production Systems and Design Technology (DE)
- Technische Universität Berlin (DE)
Publication Details
- Journal
- Zeitschrift für wirtschaftlichen Fabrikbetrieb
- Published
- 2024-08-16
- DOI
- https://doi.org/10.1515/zwf-2024-1107
- Primary Topic
- Welding Techniques and Residual Stresses
- Type
- article
- Field-Weighted Citation Impact
- 0.00