Modeling friction coefficient for roll force calculation during hot strip rolling

Modeling friction coefficient for roll force calculation during hot strip rolling A mathematical model of friction coefficient was established to calculate the roll force during hot strip rolling process. Firstly, a linear regression model with four input variables was proposed to describe friction behavior, namely rolling speed, strip temperature, reduction rate, and the number of rolled strips since roll change. Then, the method of principal component regression, which can eliminate the effect of multi-collinearity among the input variables, is used to build the friction model. The obtained model was tested by statistical functions, and the results indicated that the model was valid and showed as well that the cumulative contribution rate of the first two principal components of the regression model was as high as 98% and the first two principal components contained almost all the information of original input variables. The industrial experiment confirmed that the roll force model with the proposed friction model had the higher prediction accuracy than the Sims model, and the proposed model could be used in online calculation for hot-rolled roll force. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png The International Journal of Advanced Manufacturing Technology Springer Journals

Modeling friction coefficient for roll force calculation during hot strip rolling

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Publisher
Springer London
Copyright
Copyright © 2017 by Springer-Verlag London
Subject
Engineering; Industrial and Production Engineering; Media Management; Mechanical Engineering; Computer-Aided Engineering (CAD, CAE) and Design
ISSN
0268-3768
eISSN
1433-3015
D.O.I.
10.1007/s00170-017-0188-2
Publisher site
See Article on Publisher Site

Abstract

A mathematical model of friction coefficient was established to calculate the roll force during hot strip rolling process. Firstly, a linear regression model with four input variables was proposed to describe friction behavior, namely rolling speed, strip temperature, reduction rate, and the number of rolled strips since roll change. Then, the method of principal component regression, which can eliminate the effect of multi-collinearity among the input variables, is used to build the friction model. The obtained model was tested by statistical functions, and the results indicated that the model was valid and showed as well that the cumulative contribution rate of the first two principal components of the regression model was as high as 98% and the first two principal components contained almost all the information of original input variables. The industrial experiment confirmed that the roll force model with the proposed friction model had the higher prediction accuracy than the Sims model, and the proposed model could be used in online calculation for hot-rolled roll force.

Journal

The International Journal of Advanced Manufacturing TechnologySpringer Journals

Published: Mar 1, 2017

References

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