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A Prediction Model for Coating Thickness Based on PLS Model and Variable Selection
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 Title & Authors
A Prediction Model for Coating Thickness Based on PLS Model and Variable Selection
Lee, Hye-Seon; Lee, Young-Rok; Jun, Chi-Hyuck; Hong, Jae-Hwa;
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 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;
 Language
Korean
 Cited by
 References
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