Abstract
AbstractThe actual cause of sliver defect is difficult to determine, since the defect usually reveals itself after the rolling process (hot/cold) is complete. The genesis of sliver defect in cold rolled steel sheets is investigated in this work using two popular computational intelligence tools used in data mining, namely, rough set and fuzzy set theories. A substantial amount of data starting from the steelmaking stage to finish rolling of the product has been collected with the aim of extracting useful knowledge about plausible cause(s) of sliver formation. While rough set theory helps to select the important variables to which the cause of the defect can be attributed in the form of rules, these rules are given a linguistic form through fuzzy membership functions. A rule base thus evolves in the form of a fuzzy inference system constituting a few important variables, which serves as a perceptive model for predicting the severity of sliver defects in cold rolled steel. Validation of the fuzzy system is done using actual industrial trials.
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