JCIPRNET
Classification optimization method for steel grade family of steel rolling model
Provided is a classification optimization method for steel grade family of a steel rolling model, including: step 1: calculating the deformation rate and deformation resistance of a rolled piece according to rolling process data; step 2: reversing the “equivalently measured” deformation resistance according to the measured roll force; step 3: drawing a scatter plot of data on a graph, and judging whether layer division is reasonable according to distribution characteristics; and step 4: for the unreasonable division of steel grade family, optimizing the existing classification of steel grade family using a new algorithm called line clustering, determining steel grade codes that need to be divided into new family, searching for the shortest distance from a sample point to the curve of each cluster centre based on a Nelder-Mead algorithm, dividing all data points into k subsets according to the principle of proximity, fitting a power curve to each data subset using a least square method, repeating the above steps, and stopping iterating when cluster centrelines obtained by two successive iterations are consistent. The present invention improves the precision of the division of steel grade family of a hot rolled model.
WUHAN UNIVERSITY OF SCIENCE AND TECHNOLOGY
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G06F G06F G06F G06N
inventional additional inventional inventional
Yuntao Weigang Wei Wensheng
Zhao Li Ma Xu