Browse > Article
http://dx.doi.org/10.5351/KJAS.2010.23.2.295

A Prediction Model for Coating Thickness Based on PLS Model and Variable Selection  

Lee, Hye-Seon (Department of Industrial and Management Engineering, POSTECH)
Lee, Young-Rok (Department of Industrial and Management Engineering, POSTECH)
Jun, Chi-Hyuck (Department of Industrial and Management Engineering, POSTECH)
Hong, Jae-Hwa (Instrumentation Research Group, Technical Research Laboratory, POSCO)
Publication Information
The Korean Journal of Applied Statistics / v.23, no.2, 2010 , pp. 295-304 More about this Journal
Abstract
Coating thickness is one of target variables in quality control process in steel industry. To predict coating thickness and to control quality of anti-fingerprint steel coils, ultraviolet-visible spectra are measured. We propose a variable-interval selection procedure based on the variable importance in projection in partial least square model. Using the proposed variable interval selection method, prediction performance gets better in the reduced model than the full model with full spectra absorbance. It is also shown that the first differencing as a data preprocessing technique does work well for the prediction of coating thickness.
Keywords
Partial least square; variable importance in projection; data preprocessing;
Citations & Related Records
연도 인용수 순위
  • Reference
1 Chong, I. and Jun, C. (2005). Performance of some variable selection methods when multicollinearity is present, Chemometrics and Intelligent Laboratory Systems, 78, 103-112.   DOI   ScienceOn
2 Cramer, J. A., Kramer, K. E., Johnson, K. J., Morris, R. E. and Rose-Pehrsson, S. L. (2008). Automated wavelength selection for spectroscopic fuel models by symmetrically contracting repeated unmoving window partial least squares, Chemometrics and Intelligent Laboratory Systems, 92, 13-21.   DOI   ScienceOn
3 Heise, H. M., Damm, U., Lampen, P., Davies, A. N. and Mclntyre, P. S. (2005). Spectral variable selection for partial least squares calibration applied to authentication and quantification of extra virgin olive oils using fourier transform raman spectroscopy, Applied Spectroscopy, 59,1286-1294.   DOI   ScienceOn
4 Lee, D., Lee, H., Jun, C-H. and Chang, C. H. (2007). A variable selection procedure for X-ray diffraction phase analysis, Applied Spectroscopy, 61, 1398-1403.   DOI   ScienceOn
5 Wold, S., Ruhe, A., Wold, H. and Dunn III, W. J. (1984). The collinearity problem in linear regression. The partial least squares(PLS) approach to generalized inverses, SIAM Journal on Scientific and Statistical Computing, 5, 735-743.   DOI
6 Wold, S., Sjostrom, M. and Eriksson, L. (2001). PLS-regression: A basic tool of chemometrics, Chemometrics and Intelligent Laboratory System, 58, 109-130.   DOI   ScienceOn