Robust multivariate statistical process control for monitoring a hot dip galvanizing line

• Autores: Juan Carlos García Díaz
• Localización: XXX Congreso Nacional de Estadística e Investigación Operativa y de las IV Jornadas de Estadística Pública: actas, 2007, ISBN 978-84-690-7249-3
• Idioma: inglés
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• Resumen

  • Galvanized steel is a value added product,furnishing e®ective performance by combining the corrosion resistance of zinc with the strength and formability of steel. Measurement and control of the dissolved aluminium levels in zinc hot- dip galvanizing baths, is of great importance to the steel industry. The data set contains the percent weight of Fe, Al total and Al e®ective of 168 samples from the Zn bath of continuous hot dip galvanizing line. Analysis of historical process data revealed the potential for improvements in quality control. The data were ¯rst preprocessed with Matlab software. In this case the preprocessing method is the robust standardization the matrix X columnwise by substracting a robust estimate of the location and by dividing through a robust estimate of the scale. Robust methods for outlier detection in process control are a tool for the comprehensive monitoring of the performance of a manufacturing process. The present paper reports a comparative evaluation of robust multivariate statistical process control techniques for process monitoring in the zinc-pot section of hot dip galvanizing line. The paper proposes a statistical model for the prediction of aluminium concentration in a galvanizing pot of a continuous hot dip galvanizing line. This study investigates the application of statistical process control and times series analysis methods for monitoring and forecasting e®ective aluminium levels.