International Journal of Mechanical and Production Engineering (IJMPE)
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Statistics report
May. 2024
Submitted Papers : 80
Accepted Papers : 10
Rejected Papers : 70
Acc. Perc : 12%
Issue Published : 131
Paper Published : 2390
No. of Authors : 6806
  Journal Paper

Paper Title :
Prediction of Coil Breaks in SPM using Artificial Neural Network

Author :Nikhil.V.Naik, Niyati Raut

Article Citation :Nikhil.V.Naik ,Niyati Raut , (2022 ) " Prediction of Coil Breaks in SPM using Artificial Neural Network " , International Journal of Mechanical and Production Engineering (IJMPE) , pp. 10-15, Volume-10,Issue-2

Abstract : Abstract - Coil breaks are persistent menace for almost every Cold Rolling steel plant. The uncertain demand flow pattern combined with extreme competitive environment has made the steel industry Quality driven. The steel industry consists of processes like Iron Making, Steel Making, Casting, Hot Rolling, Cold rolling, etc. Cold rolling being end process considers defects as wastage of all previous processes, costs, and time invested to achieve the product. Quality defects are considered grave problems for any cold rolling production line. The study aims to predict the formation of coil breaks by use of an artificial neural network at Skin pass mill. The study is conducted at the Tata Steel Cold Rolling Complex (CRC-West) at Tarapur Midc, Boisar. At CRC-W the production lines present are Pickling, 4 hi Rolling mill, Cleaning, Annealing, Skin pass mill, Slitting, Multi blanking line, Cut to length. We are concerning ourselves with the formation of coil breaks at the Skin pass mill. The coil breaks occurs as a result of non-uniform yielding behavior post forming. Typically observed in Deep drawn and extra deep drawn material, however it can also occur in under stabilized IF steel. Prediction of the formation of coil breaks can be done by an artificial neural network program. An ANN is computing system that learns to perform tasks by considering examples and data sets, generally without being programmed with task-specific rules. The appropriate ANN model is to be developed. The input and output parameters of each of these cases have been decided based on criteria as discussed later. With the Input and Output parameters decided, now the dataset can be taken from the tracking software at the Skin pass mill. The Artificial neural network must be trained so as to increase reliability. The trained ANN must now be validated and tested using a program called Python. The ANN will start predicting if coil breaks will occur or not after skin passing using parameters. The accuracy of ANN will increase as size of dataset increases so for further applications; the ANN could be upgraded to include real time monitoring and prediction. Keywords - Coil Breaks, Skin Pass Mill, Artificial Neural Network, Cold Rolled Coils, Non Uniform Yielding, Data Sets, Load, Tension, Prediction, Analysis.

Type : Research paper

Published : Volume-10,Issue-2


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