MATEC Web Conf.
Volume 306, 2020The 6th International Conference on Mechatronics and Mechanical Engineering (ICMME 2019)
|Number of page(s)||5|
|Section||Control Theory and Control Engineering|
|Published online||14 January 2020|
Modelling of Artificial Neural Network to control the cooling rate of a Laboratory Scale Run-Out Table
Jadavpur University, Mechanical Engineering Department, Kolkata - 700032, India
* Corresponding author: email@example.com
Run Out Tables (ROTs) have been used for long time in order to achieve different microstructure of steel in the industries. The microstructure of steel controlled by the cooling rate which in turn depends on various factors like the plate velocity, nozzle bank distance, coolant flow rate, and many others. Achieving new steel grade thus demand a proper combination setting of all such parameters. The observed data like upper nozzle distance, lower nozzle distance and mass flow rate of coolant from the laboratory scale ROTs are used to find out the cooling rate which is important parameter for achieving desired properties in steel. An Artificial Neural Network has been used here to creating an empirical relation between the observed data and thermodynamics parameter which will determine the cooling rate and validate it.
© The Authors, published by EDP Sciences 2020
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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