A Gaussian process based model for air-jet cooling of mild steel plate in run out table

Bachhar, P., Kundu, A. ORCID: https://orcid.org/0000-0002-8714-4087 and Mandal, P. 2021. A Gaussian process based model for air-jet cooling of mild steel plate in run out table. Presented at: International Conference on Advances in Material Science & Mechanical Engineering (ICAMSME) 2020, Vidyanagar, India, 7-9 February 2020. Published in: Reddy, K. Hemachandra and Chuntamreddy, Vikram Kumar eds. Advances in Material Science and Mechanical Engineering. , vol.106 Switzerland: Trans Tech Publications Ltd, pp. 137-142. 10.4028/www.scientific.net/AST.106.137

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Abstract

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.

Item Type:	Conference or Workshop Item (Paper)
Book Type:	Edited Book
Date Type:	Published Online
Status:	Published
Schools:	Engineering
Subjects:	T Technology > TJ Mechanical engineering and machinery
Publisher:	Trans Tech Publications Ltd
ISBN:	9783035716283
ISSN:	1662-8969
Last Modified:	09 Nov 2022 11:00
URI:	https://orca.cardiff.ac.uk/id/eprint/141423

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