Document Type	Master's Dissertation
Author	De Beer, Petrus Gerhardus
Email	debeer.gerhard@columbus.co.za
URN	etd-11022007-132916
Document Title	Continuous cast width prediction using a data mining approach
Degree	MEng (Mechanical Engineering)
Department	Mechanical and Aeronautical Engineering
Supervisor	Advisor Name Title Prof K J Craig Committee Chair
Keywords	stainless steel continuous casting statistical regression decision trees fuzzy logic rule based model width change strand width control
Date	2007-04-20
Availability	unrestricted
Abstract In modern times continuous casting is the preferred way to convert molten steel into solid forms to enable further processing. At Columbus Stainless the continuous casting machine cast slabs of constant thickness with varying width. One important aspect of the continuously cast strand that must be controlled, is the strand width. The strand width exiting from the casting machine, has a direct influence on the product yield which in turn influences the profitability of the company. In general, the strand width control on the austentic and ferritic type steels achieved is excellent with the exception of the 12% chrome non stabilised ferritic steel. This steel type exhibited different strand width changes when a sequence of different heats was cast. The strand width changes corresponded to the different heats in the sequence. Each heat has a unique chemistry and a relationship between the austenite and ferrite fraction at high temperature and the resulting strand width change was explained by Siyasiya[27]. The relationship between the heat composition and width change has in the past resulted in the development of a model that enabled the prediction of the expected width change of a specific heat before it is cast to enable preventative action to be taken. This model has been implemented as an on-line prediction model in the production environment with very encouraging results. This study was initiated because it was uncertain if the implemented model was the most accurate for this application. This study is concerned with the development of more models based on different techniques in an attempt to implement a more accurate model. The data mining techniques used include statistical regression, decision trees and fuzzy logic. The results indicated that the existing model was the most accurate and it could not be improved upon. © University of Pretoria
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