Modelling of Artificial Neural Network to control the cooling rate of a Laboratory Scale Run-Out Table

Biswas Prabir,Mondal Md Safwan,Mookherjee Saikat,Mandal Pranibesh,M.H.A Shukor

doi:10.1051/matecconf/202030603004

Abstract

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.

Highlights

Run Out Tables (ROTs) have been used for long time to achieving different microstructure of steel in the industries
The microstructure primarily depends on the cooling rate which in turn depends on various factors like the plate velocity, nozzle bank distance, coolant flow rate, and many others of Run Out Tables (ROTs)
In ROT high flow rates of coolants such as air, water, air water mist etc. impinged on a uniformly distributed surface area in motion is apply for Ultra-Fast Cooling (UFC)

Summary

Introduction

Run Out Tables (ROTs) have been used for long time to achieving different microstructure of steel in the industries. Suebsomran et al [1] study to determine the effective cooling parameters for the run-out table (ROT) of strip steel in a hot rolling process. Wang et al.[5] A thermal, micro structural and mechanical coupling analysis model for predicting flatness change of steel strip during the runout table cooling process was established using ABAQUS Finite Element Software. Xiao-dong et al [6] studied Thermal, Micro structural and Mechanical Coupling Analysis Model for Flatness Change Prediction during Run-Out Table Cooling in Hot Strip Rolling. We need apply Artificial Neural Network (ANNs) to train in order to generate more data which will enable to creating an empirical relation between the input and the output In this present work some observed data are trained in ANNs to predict the output i.e., parameter which will determine the cooling rate and validate it. Distance of the nozzle banks from the specimen plate are adjust using simple screw and nut mechanism to have the different cooling rate

Modeling of Artificial Neural Network

System Description

Results & Discussion

Conclusion

Ananya