Abstract— There are various methods of power requirements on the basis of which motor rating and flywheel size of the rolling mill depend on three high rolling mills. These methods involve a lot of mathematical calculation useful in carrying out the static power energy requirement in rolling mill. The method selected here is based on mechanics of rolling which is widely used but this method also the tremendous mathematical work. Hence in this paper two software’s were developed independently one for static analysis and other for the dynamic analysis of the rolling mill and both can be run independently or jointly for standard 8 hours working of the mill. A curve fitting algorithm was first utilized for approximating the actual load speed curve of a given motor as supplied by the manufacture and by using ANN simulation of the motor characteristics. The software can also invoke the ANN for the calculation of load speed characteristics. The conclusion obtained showed that the percentage of full load shared by induction motor a percentage of full load rating is higher for motor having higher rating. Based on this concept a design approach can be developed. For carrying out the dynamic analysis of the rolling process, first the static analysis was done where intensity of loading during a particular pass was studied. The energy requirement pattern and number of total passes in a fixed span of time is also ascertained. This static analysis forms the basis for dynamic analysis. The analysis was carried out by using Monte Carlo simulation approach.

Index Terms— Artificial Neural Network (ANN), Flywheel, Rolling mill, Monte carlo simulation

Cite: A M Bisen, P M Bapat, S K Ganguly, and P S Agrawal, " Artificial Neural Network Simulation of Prime Mover for the Rolling Process in the Three High Rolling Mills," International Journal of Mechanical Engineering and Robotics Research, Vol. 1, No. 2, pp. 207-219, July 2012.