| People | Locations | Statistics |
|---|---|---|
| Naji, M. |
| |
| Motta, Antonella |
| |
| Aletan, Dirar |
| |
| Mohamed, Tarek |
| |
| Ertürk, Emre |
| |
| Taccardi, Nicola |
| |
| Kononenko, Denys |
| |
| Petrov, R. H. | Madrid |
|
| Alshaaer, Mazen | Brussels |
|
| Bih, L. |
| |
| Casati, R. |
| |
| Muller, Hermance |
| |
| Kočí, Jan | Prague |
|
| Šuljagić, Marija |
| |
| Kalteremidou, Kalliopi-Artemi | Brussels |
|
| Azam, Siraj |
| |
| Ospanova, Alyiya |
| |
| Blanpain, Bart |
| |
| Ali, M. A. |
| |
| Popa, V. |
| |
| Rančić, M. |
| |
| Ollier, Nadège |
| |
| Azevedo, Nuno Monteiro |
| |
| Landes, Michael |
| |
| Rignanese, Gian-Marco |
|
Schricker, Klaus
Technische Universität Ilmenau
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (16/16 displayed)
- 2024Recurrent Autoencoder for Weld Discontinuity Predictioncitations
- 2023Understanding the formation of “false friends” (hidden lack of fusion defects) in laser beam welding by means of high-speed synchrotron X-ray imagingcitations
- 2023Understanding the formation of “false friends” (hidden lack of fusion defects) in laser beam welding by means of high-speed synchrotron X-ray imagingcitations
- 2023Effect of partial and global shielding on surface-driven phenomena in keyhole mode laser beam weldingcitations
- 2023Effect of partial and global shielding on surface-driven phenomena in keyhole mode laser beam weldingcitations
- 2022Influence of solidification rate on hot crack behavior in heat conduction laser beam welding of EN AW-6082citations
- 2022Evaluation of the effect of local gas flows on keyhole geometry by means of a half-section setupcitations
- 2022Automatic detection and prediction of discontinuities in laser beam butt welding utilizing deep learningcitations
- 2021Local Shielding Gas Supply in Remote Laser Beam Weldingcitations
- 2020Laser-assisted joining of AISI 304 thin sheets with polymerscitations
- 2020Process control by real-time pulse shaping in laser beam welding of different material combinationscitations
- 2020Strategies for increasing the productivity of pulsed laser cladding of hot-crack susceptible nickel-base superalloy Inconel 738 LCcitations
- 2020In-situ monitoring of hybrid friction diffusion bonded EN AW 1050/EN CW 004A lap joints using artificial neural netscitations
- 2020Acoustic process monitoring in laser beam weldingcitations
- 2018Thermal joining of thermoplastics to metals: surface preparation of steel based on laser radiation and tungsten inert gas arc processcitations
- 2016Laser-based joining of thermoplastics to metals: influence of varied ambient conditions on joint performance and microstructurecitations
Places of action
| Organizations | Location | People |
|---|
article
In-situ monitoring of hybrid friction diffusion bonded EN AW 1050/EN CW 004A lap joints using artificial neural nets
Abstract
<jats:p>In this work, a dissimilar copper/aluminum lap joint was generated by force-controlled hybrid friction diffusion bonding setup (HFDB). During the welding process, the appearing torque, the welding force as well as the plunge depth are recorded over time. Due to the force-controlled process, tool wear and the use of different materials, the resulting data series varies significantly, which makes quality assurance according to classical methods very difficult. Therefore, a Convolutional Neural Network was developed which allows the evaluation of the recorded process data. In this study, data from sound welds as well as data from samples with weld defects were considered. In addition to the different welding qualities, deviations from the ideal conditions due to tool wear and the use of different alloys were also considered. The validity of the developed approach is determined by cross validation during the training process and different amounts of training data. With an accuracy of 88.5%, the approach of using Convolutional Neural Network has proven to be a suitable tool for monitoring the processes.</jats:p>