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A hot rolled plate convexity prediction method based on data driving

A data-driven, predictive method technology, applied in neural learning methods, electrical digital data processing, special data processing applications, etc., can solve problems such as economic losses

Inactive Publication Date: 2019-04-02
NORTHEASTERN UNIV
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Problems solved by technology

Such a production situation has caused huge economic losses to the enterprise.

Method used

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  • A hot rolled plate convexity prediction method based on data driving
  • A hot rolled plate convexity prediction method based on data driving
  • A hot rolled plate convexity prediction method based on data driving

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[0048] In order to make the purpose, technical solution and advantages of the present invention clearer, the present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments. The specific embodiments described here are only used to explain the present invention, not to limit the present invention.

[0049] In this embodiment, the data-driven hot-rolled sheet convexity prediction method combined with the production line structure is as follows figure 1 As shown, the data of the hot continuous rolling production line composed of six HCw rolling mills are used. The diameter of the roll is 630mm-700mm, the width of the rollable strip is 700mm-2130mm, and the thickness is 1.2mm-25.4mm.

[0050] A data-driven method for predicting the crown of hot-rolled strips, the process is as follows figure 2 shown, including the following steps:

[0051] Step 1: Collect the production data of strip steel, including coil number...

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Abstract

The invention provides a hot rolled plate convexity prediction method based on data driving, and relates to the technical field of rolling process automatic control. The method comprises the steps that production data of strip steel are collected and preprocessed; establishing a single hidden layer neural network, training the single hidden layer neural network, and inputting the preprocessed production data into the trained neural network model for prediction; calculating a training error of the single hidden layer neural network; Encoding the weight and the threshold; initializing a population; performing non-dominated sorting on the individuals in the population; calculating the extracted fitness value and carrying out genetic operation; judging whether a termination condition is met; decoding the weight and the threshold; and outputting the optimized neural network model. According to the method, the plate convexity is predicted by using a neural network in combination with a rapidnon-dominated sorting genetic algorithm, the defects of difficulty in parameter detection and poor precision in the hot rolling production process are overcome, the precision is high, the operation speed is high, investment and use can be realized by directly programming a large amount of production process data on a computer, and the cost is very low.

Description

technical field [0001] The invention relates to the technical field of rolling process automatic control, in particular to a data-driven method for predicting the crown of a hot-rolled sheet. Background technique [0002] The shape of the strip is one of the indicators to measure the quality of the strip product. Usually, the two indicators to measure the quality of the strip are the convexity and the flatness of the strip. Plate convexity, also known as strip transverse thickness difference, refers to the thickness difference of plate and strip along the width direction. Effective control of the strip crown can not only prevent the occurrence of defects such as wedges, but also ensure the flatness of the strip. [0003] For effective control of slab crown, various factors that affect slab crown, such as detection devices, mathematical models, roll profile configurations, load distribution, and roll bending systems, are generally combined organically. However, as the user'...

Claims

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Application Information

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IPC IPC(8): G06F17/50G06N3/08G06N3/12
CPCG06N3/08G06N3/126G06F30/20
Inventor 孙杰邓继飞魏臻单鹏飞胡耀辉彭文丁敬国张殿华
Owner NORTHEASTERN UNIV
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