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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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Kundu, Abhishek
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (10/10 displayed)
- 2023Elastic modulus of self-compacting fibre reinforced concrete: Experimental approach and multi-scale simulationcitations
- 2023Deep learning for automatic assessment of breathing-debonds in stiffened composite panels using non-linear guided wave signalscitations
- 2022Acoustic emission data based deep learning approach for classification and detection of damage-sources in a composite panelcitations
- 2021A Gaussian Process Based Model for Air-Jet Cooling of Mild Steel Plate in Run Out Table
- 2019Nondestructive Analysis of Debonds in a Composite Structure under Variable Temperature Conditionscitations
- 2019Nondestructive analysis of debonds in a composite structure under variable temperature conditionscitations
- 2019A generic framework for application of machine learning in acoustic emission-based damage identificationcitations
- 2018Probabilistic method for damage identification in multi-layered composite structures
- 2018Online detection of barely visible low-speed impact damage in 3D-core sandwich composite structurecitations
- 2017Acoustic emission based damage localization in composites structures using Bayesian identificationcitations
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article
A Gaussian Process Based Model for Air-Jet Cooling of Mild Steel Plate in Run Out Table
Abstract
<jats:p>Controlled cooling rate is essential in steel production in order to obtain the desired grades for specific mechanical properties. Optimal control of cooling process parameters is important to obtain the desired cooling rate. The system level uncertainty around the cooling process, the model form error around the generative model for the cooling process as well as the measurement noise make the problem of optimal cooling even more challenging. Machine learning approaches have been used in the recent past to solve optimization and optimal control problems. The present study sets out to design an optimal and robust cooling rate controller using a data-driven approach within a machine learning framework which accounts for the uncertainties inherent in the system. A Gaussian process regression model is developed to predict the cooling rate using temperate-time data and two simulated latent parameters with a suitable confidence interval. The experiments have been undertaken on a laboratory scale Run Out Table setup. The results show the suitability of the proposed approach to obtain a robust response surface of the cooling rate with the process parameters.</jats:p>