Flatness Assessment System of Ultra-Wide Tandem Cold Rolling Mill

Abstract:

Many complex waviness defects occur during the production process of ultra wide TCMs, flatness idex I can not meet the demands of analyzing these problems. Therefore flatness pattern recognition considering cubic patterns is introduced to provide a flatness evaluation parameter. Combining the calculation methods and statistical methods of evaluation parameters, a flatness assessment system is programmed with Matlab GUI, which provides functions such as representing the flatness of previous strips, recognizing the flatness error and making statistics. After verifying of actual flatness measured value, the system is proved to be effective, which provides theoretical basis and data support for mastering the flatness quality and optimizing flatness control parameters.

• Periodical: Applied Mechanics and Materials (Volume 529)
• Pages: 415-419
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.529.415
• Online since: June 2014
• Authors: Ren Ren Bao, Jie Zhang, Hong Bo Li, Jun Wang, Yu Gang Chu, Ming Gang Li
• Keywords: Cubic Flatness Error, Flatness Assessment, Least Squares Regression, Ultra-Wide Mill

References

[1] Q.T. Yan. Flatness Control Performance Analysis of the Super-wide Tandem Cold Rolling Mill [Dissertation]. Beijing: University of Science and Technology Beijing, (2011).

[2] Przywara J, Spreitzhofer G, Dummler A. Innovative Contactless Flatness Measurement System for Cold Rolling Reversing Stands. Stahl und Eisen, Vol. 123 (2003), pp.57-60.

[3] Y. Peng, H. Li, R. Du. A Neural Network-based Shape Control System for Cold Rolling Operations. Journal of Materials Processing Technology, Vol. 202(2003) , pp.54-60.

DOI: 10.1016/j.jmatprotec.2007.08.075

[4] X. Zhang, S. Zhang, G. Tan, et al. A Novel Method for Flatness Pattern Recognition via Least Squares Support Vector Regression. Journal of Iron and Steel Research, International, Vol. 19(2012) , pp.25-30.

DOI: 10.1016/s1006-706x(12)60069-7

[5] H. He, Y. Li. A New Flatness Pattern Recognition Model Based on Cerebellar Model Articulation Controllers Network. Journal of Iron and Steel Research, International, Vol. 15 (2008), pp.32-36.

DOI: 10.1016/s1006-706x(08)60245-9

[6] Y. X. Shan, H. M. Liu, C. Y. Jia. A Recognition Method of New Flatness Pattern Containing the Cubic Flatness. Iron & Steel, Vol. 45 (2010), pp.56-60.

[7] D. J. Xu, C. Y. Jia, Y. C. Cui, Y. N. Ye. Study on BP Network Flatness Pattern Recognition Based on Quantum Particle Swarm Optimization Algorithm: Journal of Yanshan University Vol. 35 (2011), pp.35-39.

[8] X. D. Zhou, G. D. Wang. Orthogonal Polynomial Decomposition Model for the Flatness of Cold-rolled Strip. Iron & Steel, Vol. 32 (1997), pp.46-47.

[9] H. S. Di, X. F. Zhang. Orthogonal Polynomial Decomposition and Mathematical Model for Measured Signals of Thin Strip. Iron & Steel, Vol. 30 (1995), pp.33-36.

[10] Q. D. Zhang, X. M. Zhou, J. T. Liu. Evalution and Statics Methods of Cold-rolled Strip Flatness. 2001 CSM Annual Meeting, Beijing, P. 57-60.

[11] J. B. Dai, W. B. Wu, Q. D. Zhang, et al. Recognition and Evaluation System for Strip Flatness on 2030 mm Cold Tandem Mills. Journal of University of Science and Technology Beijing, Vol. 25(2003), pp.572-574.

[12] L. Hao, H. S. Di, D. Y. Gong, et al. Software Development of Strip Flatness Off-line Display and Pattern Recognition. Journal of Northeastern University (Nature Science) , Vol. 31(2010), pp.1414-1416, 1420.