Quality Improvement in Hot Dip Galvanizing Line through Hybrid Case-Based Reasoning System

The present paper deals with quality improvement of flat steel sheet surface coming from the continuous Hot Dip Galvanizing (HDG) process. The main idea has been to combine a Case-Based Reasoning (CBR) system, which allows to learn from previous experience, and a module exploiting a Cause Induction in Discrimination tree (CID tree), which allows to identify the process variables of the HDG process which mostly affect the formation of surface defects on the steel sheet. This hybrid system is capable to suggest optimal variability ranges for these variables in order to reduce or avoid defects formation, by using a data mining approach. The joint use of the CBR system and the CID tree methodology allows the identification of defects and the detection of possible causes (i.e. values of some HDG process parameters) on their formation, by tracking them in a knowledge base representing a baseline for reduction of defects formation in future manufacturing.

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