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Tensile strength prediction for hot rolled steels by Bayesian neural network model

Yang, Yong Y., Mahfouf, Mahdi, Linkens, Derek A., Zhang, Qian (2009) Tensile strength prediction for hot rolled steels by Bayesian neural network model. In: Automation in Mining, Mineral and Metal Processing. 1 (1). pp. 255-260. (doi:10.3182/20091014-3-CL-4011.00046) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided) (KAR id:50550)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided.
Official URL
http://doi.org/10.3182/20091014-3-CL-4011.00046

Abstract

A neural network model under the Bayesian framework (referred to as Bayesian neural network hereafter) is developed to predict the tensile strength of hot rolled products. The motivation of using a data-driven model is that in a modern steel mill, there exist huge online measurements and offline data, and can be exploited to extract the underlying relationships. The main advantage of using Bayesian neural network (BNN) is the robustness against over-fitting, thanks to the probabilistic reasoning behind the BNN. Preliminary modelling results are encouraging, and the properly trained BNN model can be used for online control and optimisation of the rolling mill to achieve desired mechanical properties.

Item Type: Conference or workshop item (Paper)
DOI/Identification number: 10.3182/20091014-3-CL-4011.00046
Subjects: Q Science > Q Science (General) > Q335 Artificial intelligence
T Technology > TA Engineering (General). Civil engineering (General) > TA168 Systems engineering
T Technology > TA Engineering (General). Civil engineering (General) > TA401 Materials engineering and construction
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Engineering and Digital Arts
Depositing User: Qian Zhang
Date Deposited: 18 Sep 2015 16:17 UTC
Last Modified: 16 Nov 2021 10:21 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/50550 (The current URI for this page, for reference purposes)
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