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Knowledge and Information Systems

, Volume 41, Issue 2, pp 355–378 | Cite as

Data mining-based flatness pattern prediction for cold rolling process with varying operating condition

  • Ningyun Lu
  • Bin Jiang
  • Jianhua Lu
Regular Paper

Abstract

Data-rich environments in modern rolling processes provide a great opportunity for more effective process control and more total quality improvement. Flatness is a key geometrical feature of strip products in a cold rolling process. In order to achieve good flatness, it is necessary to reveal the factors that often influence the flatness quality, to develop a general flatness pattern prediction model that can handle the varying operating condition during the rolling of products with different specifications and to realize an effective flatness feedback control strategy. This paper develops a practical data mining-based flatness pattern prediction method for cold rolling process with varying operating condition. Firstly, the high-dimensional process measurements are projected onto a low-dimensional space (i.e., the latent variable space) using locality preserving projection method; at the same time, the Legendre orthogonal polynomials are used to extract the basic flatness patterns by projecting the high-dimensional flatness measurements into several flatness characteristic coefficients. Secondly, a mixture probabilistic linear regression model is adopted to describe the relationships between the latent variables and the flatness characteristic coefficients. Case study is conducted on a real steel rolling process. Results show that the developed method has not only the satisfactory prediction performance, but good potentials to improve process understanding and strip flatness quality.

Keywords

Data mining Flatness pattern prediction Locality preserving projection (LPP) Mixture probabilistic linear regression model (MPLR) Varying operating condition 

Notes

Acknowledgments

We gratefully acknowledge the financial support of National Natural Science Foundation of China (Nos. 61073059, 61374141 and 61034005), Jiangsu Provincial Natural Science Foundation of China (BK2010409), and the Fundamental Research Funds for the Central Universities (NS2012039).

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Copyright information

© Springer-Verlag London 2014

Authors and Affiliations

  1. 1.College of Automation EngineeringNanjing University of Aeronautics and AstronauticsNanjingChina
  2. 2.School of Computer Science and EngineeringSoutheast UniversityNanjingChina

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