The International Journal of Advanced Manufacturing Technology

, Volume 22, Issue 11, pp 883–889

A neural network based methodology for the prediction of roll force and roll torque in fuzzy form for cold flat rolling process

Original Article

DOI: 10.1007/s00170-003-1628-8

Cite this article as:
Dixit, U.S. & Chandra, S. Int J Adv Manuf Technol (2003) 22: 883. doi:10.1007/s00170-003-1628-8


Neural network models can be effectively used to predict any type of functional relationship. In this paper, a neural network model is used to predict roll force and roll torque in a cold flat rolling process, as a function of various process parameters. A strategy is developed to obtain a prescribed accuracy of prediction with a minimum number of data for training and testing. The effect of increasing the size of training and testing data set is also examined. After the prediction of most likely value, upper and lower bound estimates are also found with the help of the neural network. With these estimates, the predicted value can be represented as a fuzzy number for use in fuzzy-logic based systems.


Cold flat rollingNeural networksBack propagation algorithmPrediction of roll force and roll torque

Copyright information

© Springer-Verlag London Limited 2003

Authors and Affiliations

  1. 1.Department of Mechanical EngineeringIndian Institute of TechnologyGuwahatiIndia