Hot-rolled strip steel plate convexity prediction method based on deep learning

A deep learning, hot-rolled strip technology, applied in the field of metallurgy, can solve the problems of low model prediction accuracy and generalization ability, and achieve the effect of avoiding a huge amount of calculation and improving prediction accuracy.

Active Publication Date: 2019-11-08
东北大学秦皇岛分校
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Problems solved by technology

[0004] The purpose of the present invention is to provide a method for predicting the crown of hot-rolled strip steel plate based on deep learning, which can effectively solve the probl

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  • Hot-rolled strip steel plate convexity prediction method based on deep learning
  • Hot-rolled strip steel plate convexity prediction method based on deep learning
  • Hot-rolled strip steel plate convexity prediction method based on deep learning

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experiment example

[0107] Experimental example: A method for predicting the convexity of hot-rolled strip steel plates based on deep learning, such as figure 1 shown, including the following steps:

[0108] (1) Data collection and recording: Collect historical rolling data of a 2160 hot continuous rolling mill, including: the speed of each stand of the finishing mill, rolling force, roll bending force, roll shifting amount, side press press quantity, rough rolling centerline position, and strip steel type, temperature, length, width, thickness, weight, etc., each strip collects the above 50 production variable data and convexity data as a sample, and uses a 51-dimensional vector To represent;

[0109] (2) Data preprocessing: data cleaning and transformation of the original data, including deletion of missing values, deletion of outliers using the 3σ criterion, and normalization, to obtain production parameters of 11,544 steel strips for training and testing of the prediction model . The datas...

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Abstract

The invention discloses a hot-rolled strip steel plate convexity prediction method based on deep learning, and the method comprises the following steps: S1, collecting and recording strip steel production data, and carrying out the preprocessing, including missing value deletion, abnormal value deletion and normalization, of the collected data; S2, according to the strip steel production data, removing redundant and irrelevant attributes in the collected data through an attribute selection method based on a Morita index, and screening a minimum number of attributes capable of representing plate convexity changes to form an input variable set of a forecasting model; and S3, establishing a strip steel outlet plate convexity prediction model based on the deep and wide convolutional neural network based on the input variable set so as to obtain the hot rolled strip steel outlet plate convexity. According to the method, high-order features and invariant features of the data are extracted byusing the convolutional layer in the convolutional neural network, the local correlation between the variables is learned, and the global feature learning ability of the deep neural network is combined, so that the plate convexity prediction precision is remarkably improved.

Description

technical field [0001] The invention relates to a method for predicting the crown of a hot-rolled strip steel plate based on deep learning, which belongs to the field of metallurgy. Background technique [0002] The iron and steel industry is a pillar industry for national production and social development. Among iron and steel products, strip steel is known as general-purpose steel. It is an important basic material for high-end products and is widely used in construction, transportation, national defense and other fields. With the continuous improvement of the manufacturing industry, the demand for high-precision plates and strips has increased sharply, and the requirements for the dimensional accuracy of plates and strips have become more stringent. As an intermediate step in strip rolling, hot rolling has a significant impact on the product quality of downstream processes such as cold rolling. Therefore, it is of great significance to achieve precise control of hot-rolle...

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Application Information

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IPC IPC(8): G06Q10/06G06Q50/04G06F16/215G06F16/2458G06N3/04
CPCG06Q10/0639G06Q50/04G06F16/215G06F16/2465G06N3/045Y02P90/30
Inventor 赵强苏帆帆汪晋宽韩英华
Owner 东北大学秦皇岛分校
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