Plate convexity prediction method based on kernel partial least squares (KPLS) and support vector machine combined

A kernel partial least squares and support vector machine technology, which can be used in length measuring devices, metal rolling, manufacturing tools, etc., and can solve problems such as insufficient accuracy

Active Publication Date: 2020-06-09
TAIYUAN UNIVERSITY OF SCIENCE AND TECHNOLOGY
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Problems solved by technology

[0003] Aiming at the technical problem of insufficient accuracy of the above-mentioned general flatness control method, the present invention provides a rolling strip co...

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  • Plate convexity prediction method based on kernel partial least squares (KPLS) and support vector machine combined
  • Plate convexity prediction method based on kernel partial least squares (KPLS) and support vector machine combined
  • Plate convexity prediction method based on kernel partial least squares (KPLS) and support vector machine combined

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Embodiment Construction

[0075] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some, not all, embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.

[0076] A board convexity prediction method based on kernel partial least squares combined with support vector machine, such as figure 1 shown, including the following steps:

[0077] S1, such as image 3 As shown, the rolling driven plate in the seven-stand continuous rolling production line passes through the roughing mill 1, the flying shear 2, the finishing mill 3, the laminar flow cooling 4, and the coiler 5 in sequence, and collects a large amount of on-...

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Abstract

The invention belongs to the technical field of convexity prediction, and particularly relates to a plate convexity prediction method based on kernel partial least squares (KPLS) and a support vectormachine combined. The plate convexity prediction method comprises the following steps: S1, field data are collected through a high-precision monitoring device; S2, the collected data are preprocessed;S3, a KPLS regression prediction model is established; and S4, a KPLS-SVM plate convexity prediction model is established. By taking a data-driven algorithm as a mathematical tool, abnormal values can be removed from a large quantity of collected field rolling process data, a convexity prediction model for a strip steel continuous-rolling plate based on a KPLS method and the support vector machine combined is established, the convexity of the strip steel continuous-rolling plate is predicted, the established model is optimized through a particle swarm optimization algorithm, and the prediction precision of the convexity of the strip steel continuous-rolling plate is further improved. The plate convexity prediction method is used for predicting the convexity of the strip steel continuous-rolling plate.

Description

technical field [0001] The invention belongs to the technical field of convexity prediction, and in particular relates to a board convexity prediction method based on kernel partial least squares combined with a support vector machine. Background technique [0002] The plate shape refers to the appearance shape of the plate and strip, which is one of the important indicators to measure the geometric accuracy of the plate and strip, just like the thickness and width. The plate shape actually includes two aspects: the geometric shape of the strip steel section and the flatness of the strip in the natural state. Therefore, quantitative description of the plate shape involves multiple indicators such as convexity, wedge shape, edge weakening, local high point and flatness. , where the plate crown is the main factor to describe and measure the hot-rolled plate shape. The problem of flatness quality has always been the focus of researchers. With the increasing requirements for fl...

Claims

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Application Information

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IPC IPC(8): B21B37/28B21B38/02
CPCB21B37/28B21B38/02
Inventor 姬亚锋宋乐宝彭文李华英原浩牛晶
Owner TAIYUAN UNIVERSITY OF SCIENCE AND TECHNOLOGY
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