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2. Data Collection and Data Analysis

Data Source Preparation

Data sources for the work were from both the Level 2 log files and Level 3 business database. Data collection from the Level 3 Informix database was performed with SQL scripts. After data was transferred into the local SQL Server 2000, stored procedures were used to select and process data, for example, to select hard and thin grades, and to round the data.

Flow stress coefficients C1, C2, C3 and C4 for all grades were retrieved by solving the matrices of the intermediate values files through long term learning regression stored in thousands of grade.

In the initial data collection, the data from all passes rolled in the year 2006 were used. In later studies, the data from the first half of 2007 were also included. This involved about 1,500,000 records with data size over 1 gigabyte (GB). Besides the stored procedures, Microsoft .Net was used as a programming environment to process the data, so for example, a tool was developed to create drawings automatically, in order to display influences of major rolling process parameters on the force errors.

Error Source Survey

Though the learning logic was considered the primary source of errors in the early stage of the project, it was still necessary to exclude other potential sources of error. For this reason, an error survey was carried out. General rule to track an influence factor on a parameter is to isolate the influence factor. In other words, while the selected influence factor varies in its range, all the remaining factors should remain unchanged. This was, however, impossible to accomplish due to the shear amount of actual production data. Instead, to isolate a rolling process factor, such as temperature, the data was rounded to reduce the scatter; so for example, data points within 1590F and 1610F were rounded to 1600F.

To view influence factors of various parameters on the force error, graphs were drawn. Because it was too time-consuming to draw thousands of graphs manually, a tool was developed to create drawings. Fig. 1 shows the temperature influence on the force error. It is interesting that for some grades and in some temperature ranges, the temperature had an effect on the error, while in others the effect was unnoticeable. This phenomenon was explained as follows: the different metallurgical effects for different grades in different temperature ranges are different.

Fig. 1: Temperature influence on the force error

Further error surveys confirmed that the force learning process should be the primary focus, at least in the initial stage of the project. In fact, after the learning problem was resolved, other sources of error were then easily identified, such as the error in the resume passes and in the passes with small draft below 10%.

 

<To Be Continued>

 

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