Cold-rolled sheet shape signal online mode recognition method

A pattern recognition, cold-rolled sheet technology, applied in metal rolling, metal rolling, manufacturing tools, etc., can solve problems such as unsatisfactory accuracy and real-time performance, complex identification model structure, and long network training time.

Inactive Publication Date: 2013-10-09
WISDRI ENG & RES INC LTD
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Problems solved by technology

[0007] The purpose of the present invention is to provide an online pattern recognition method for cold-rolled flatness signals, which can effectively solve the problems of unsatisfactory accuracy and real-time performance, complex identification model structure and network training problems that are

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  • Cold-rolled sheet shape signal online mode recognition method
  • Cold-rolled sheet shape signal online mode recognition method
  • Cold-rolled sheet shape signal online mode recognition method

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Embodiment Construction

[0053] The present invention will be further described below in conjunction with specific examples and accompanying drawings.

[0054] figure 1 It is a flowchart of an embodiment of the present invention, and it includes the following steps:

[0055] 1) Receive the shape measurement value of each measurement section in the width direction of the cold-rolled strip measured by the shapemeter on-line, and compare it with the corresponding target shape distribution value set to obtain the shape deviation value of each measurement section; The number is m, and the plate shape measurement value of the i-th measurement section is F i , the target shape distribution value of the i-th measurement segment is T i , the shape deviation value of the i-th measurement section is ΔF i ;

[0056] 2) Determine ΔF i The absolute value of the maximum value: ΔF max =max|ΔF i |, and normalize the shape deviation value of each measurement section, the shape deviation value Δf of the i-th meas...

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Abstract

The invention provides a cold-rolled sheet shape signal online mode recognition method. The method includes the steps of receiving sheet shape measurement values of measurement segments in the width direction of a cold-rolled strip steel measured by a sheet shape instrument in an online mode, comparing the measurement values with a set distribution value of a corresponding target sheet shape, obtaining sheet shape deviation values of all measurement segments, determining the largest value of the absolute values of the sheet shape deviation values, carrying out the normalization processing on the sheet shape deviation values of all measurement segments, carrying out the rough-filtering processing on deviation values, after the normalization processing, of the measurement segments, recognizing sheet shape modes through a radial basis function neural network based on the differential evolution intelligent optimization algorithm, and judging a sheet shape mode recognition result. Through the normalization processing and the rough-filtering processing of the sheet shape deviation values of all measurement segments, the adverse influence of bad point data of the sheet shape measurement values on sheet shape mode recognition can be obviously eliminated, and recognition accuracy of the sheet shape mode is improved. Molding accuracy and network training efficiency are obviously improved due to the fact that the differential evolution intelligent optimization algorithm is applied to the sheet shape mode recognition based on the radial basis function neural network.

Description

technical field [0001] The invention belongs to the field of cold-rolled steel strips, in particular to an online pattern recognition method for cold-rolled strip shape signals. Background technique [0002] In the cold-rolled strip production process, cold-rolled flatness pattern recognition is an important part of the cold-rolled flatness control system. Shape pattern recognition is to identify and classify the measured shape signal output by the shape meter, so as to accurately judge the shape defect type of the strip, provide control basis for the control system, and finally produce high-quality cold-rolled strip material products. [0003] The traditional plate shape signal pattern recognition method is based on the polynomial decomposition method of the least square method and the improved orthogonal polynomial regression decomposition method. These methods have poor anti-interference ability and have theoretical defects. High-precision shape control requirements. T...

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Application Information

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IPC IPC(8): B21B37/28B21B38/02
Inventor 赵昊裔
Owner WISDRI ENG & RES INC LTD
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