| People | Locations | Statistics |
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| Naji, M. |
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| Motta, Antonella |
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| Aletan, Dirar |
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| Mohamed, Tarek |
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| Ertürk, Emre |
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| Taccardi, Nicola |
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| Kononenko, Denys |
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| Petrov, R. H. | Madrid |
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| Alshaaer, Mazen | Brussels |
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| Bih, L. |
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| Casati, R. |
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| Muller, Hermance |
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| Kočí, Jan | Prague |
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| Šuljagić, Marija |
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| Kalteremidou, Kalliopi-Artemi | Brussels |
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| Azam, Siraj |
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| Ospanova, Alyiya |
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| Blanpain, Bart |
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| Ali, M. A. |
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| Popa, V. |
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| Rančić, M. |
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| Ollier, Nadège |
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| Azevedo, Nuno Monteiro |
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| Landes, Michael |
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| Rignanese, Gian-Marco |
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Kuhlenkötter, Bernd
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (14/14 displayed)
- 2024Test rig for investigating the functional and structural fatigue of shape memory alloy wires based on different activation profilescitations
- 2024A Two-Finger Gripper Actuated by Shape Memory Alloy for Applications in Automation Technology with Minimized Installation Space
- 2023Transfer Learning Approaches In The Domain Of Radial-Axial Ring Rolling For Machine Learning Applications
- 2023Transfer Learning Approaches In The Domain Of Radial-Axial Ring Rolling For Machine Learning Applications
- 2023Complementary database generation for machine learning in quality prediction of cold ring rolling
- 2023Improvement of Machine Learning Models for Time Series Forecasting in Radial-Axial Ring Rolling through Transfer Learning
- 2023Identification Of Investigation Procedures To Predict Work Roll Fatigue For Developing Machine Learning Applications – A Systematic Literature Review
- 2023Identification Of Investigation Procedures To Predict Work Roll Fatigue For Developing Machine Learning Applications – A Systematic Literature Review
- 2022Influence of the Phase Transformation Behaviour of NiTi Shape Memory Alloy Wires on the Predictability of Strain During Operationcitations
- 2022Investigation of Wire Mark Reading Methods to Support Automatic Quality Control
- 2021Investigation Of Suitable Methods For An Early Classification On Time Series In Radial-Axial Ring Rolling
- 2021Development of an actuator for translatory movement by means of a detented switching shaft based on a shape memory alloy wire for repeatable mechanical positioning
- 2021Improving quality prediction in radial-axial ring rolling using a semi-supervised approach and generative adversarial networks for synthetic data generation
- 2020A Framework for Data Integration and Analysis in Radial-Axial Ring Rolling
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document
Transfer Learning Approaches In The Domain Of Radial-Axial Ring Rolling For Machine Learning Applications
Abstract
Due to increased data accessibility, data-centric approaches, such as machine learning, are getting more represented in the forming industry to improve resource efficiency and to optimise processes. Prior research shows, that a classification of the roundness of shaped rings, using machine learning algorithms, is applicable to radial-axial ring rolling. The accuracy of these predictions nowadays is still limited by the amount and quality of the data. Therefore, this paper will focus on how to make the best use of the limited amount of data, using transfer learning approaches. Since acquiring data for homogenised databases is time, energy and resource consuming, logged data gathered by the industry is often used in research. This paper takes both, industrial data from thyssenkrupp rothe erde Germany GmbH and a smaller dataset of an inhouse research plant, into account. Additionally, a synthetic dataset, created by generative adversarial networks, is considered. To accomplish an improvement of machine learning predictions using accessible data, three transfer learning approaches are investigated in order to extend existing models: (I) transferring from a radial-axial ring rolling mill to a different mill containing less available data with a ratio of 20:1, (II) learning from unlabelled data using an autoencoder and (III) training on synthetic data. The obtained improvements are further evaluated. Based on these results, future possible investigations are elaborated, in particular the consideration of transfer learning from the less complex cold ring rolling process.