Papers by Keyword: Zinc Coating Weight

Abstract: In order to predict product quality and optimize production process, the product quality model needs to be built. According to the fact that the common methods always cost long training time and can not realize real-time update, an online product quality model based on the online support vector regression is here proposed. The real field data of zinc coating weights from strip hot-dip galvanizing are used for validation. The results show that the models based on the online support vector regression have a higher prediction precision and shorter training time than traditional support vector regression, which is convenient to complete the real-time update. The zinc coating weights forecasting model based on the online support vector regression for multi-group data has an average of the relative prediction error of 4.35%, thus for the model will be used as an analysis tools for the quality control.

Abstract: The BP network model is established to predict the Zinc Coating Weight for C608/708 hot-dip galvanizing line. The model develops as the simulation function of jet pressure, nozzle to strip distance, nozzle to zinc bath distance, strip velocity and thickness. Compare the prediction precision of different models when the number of neurons in network is five, ten or fifteen. The models have better effect in use after put into production and can be used for other hot-dip galvanizing lines.