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

Yang, Yong Y. and Mahfouf, Mahdi and Linkens, Derek A. and Zhang, Qian (2009) Tensile strength prediction for hot rolled steels by Bayesian neural network model. In: Automation in Mining, Mineral and Metal Processing. pp. 255-260. (doi:https://doi.org/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)

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. (Contact us about this Publication)
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)
Subjects: Q Science > Q Science (General) > Q335 Artificial intelligence
T Technology > TA Engineering (General). Civil engineering (General) > TA168 Systems engineering, cybernetics and intelligent systems
T Technology > TA Engineering (General). Civil engineering (General) > TA 403 Materials Science
Divisions: Faculties > Sciences > School of Engineering and Digital Arts > Instrumentation, Control and Embedded Systems
Depositing User: Qian Zhang
Date Deposited: 18 Sep 2015 16:17 UTC
Last Modified: 21 Sep 2015 14:24 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/50550 (The current URI for this page, for reference purposes)
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