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BP neural network-based prediction method for rolling force of different-thickness plate

A technology of BP neural network and prediction method, which is applied in the field of prediction of differential rolling force, can solve the problems of many assumptions and large error fluctuations, and achieve the effect of small error fluctuations

Pending Publication Date: 2020-06-16
SUZHOU UNIV
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Problems solved by technology

The above three methods for calculating rolling force all have the problems of too many assumptions and large error fluctuations, which cannot meet the industrial demand that the error of rolling force prediction should be within 10%.

Method used

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  • BP neural network-based prediction method for rolling force of different-thickness plate
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  • BP neural network-based prediction method for rolling force of different-thickness plate

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Embodiment Construction

[0021] In order to enable those skilled in the art to better understand the technical solutions in the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described The embodiments are only some of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts shall fall within the protection scope of the present invention.

[0022] Such as figure 1 As shown, the embodiment of the present invention provides a prediction method based on BP neural network for differential thick plate rolling force. The prediction method of rolling force of differential thick plate includes three steps, and the specific content of each step is as follows:

[0023] Step S1: Si...

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Abstract

The embodiment of the invention discloses a method for predicting the rolling force of a different-thickness plate based on a BP neural network. The prediction method comprises the steps of adopting afinite element software for simulation; rolling force data under different technological parameter conditions are obtained; dividing the obtained rolling force data into two groups; taking the two groups respectively as a training set and a test set of a BP neural network; and determining an optimal BP neural network model by a gradual growth method. According to the BP neural network model, therolling force of the different-thickness plate is predicted, and the prediction error of the rolling force is within 10%.

Description

technical field [0001] The invention relates to the technical field of metallurgy, in particular to a method for predicting the rolling force of differential thick plates based on BP neural network. Background technique [0002] The poor thickness plate manufacturing technology is not only a symbol of the national steel technology level, but also a reflection of the country's industrialization level. In the manufacturing process of differential thick plate, the rolling force is the basis for checking the strength of the rolling mill and optimizing the process. During the rolling of poor thick plates, the speed of the rolls in the vertical direction changes. Therefore, parameters such as the contact arc length and rolling force distribution in the rolling deformation zone are dynamically changed. Traditional rolling theory and geometric relations no longer apply. When rolling a thick plate, the rolling force needs to be calculated accurately, so as to provide a scientific b...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F30/23G06F30/27G06F30/17G06N3/08G06F119/14
CPCG06N3/084
Inventor 章顺虎邓磊姜兴睿
Owner SUZHOU UNIV
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