CN113486587A - Seamless steel pipe perforation process model parameter prediction system based on Matlab - Google Patents

Seamless steel pipe perforation process model parameter prediction system based on Matlab Download PDF

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CN113486587A
CN113486587A CN202110765429.XA CN202110765429A CN113486587A CN 113486587 A CN113486587 A CN 113486587A CN 202110765429 A CN202110765429 A CN 202110765429A CN 113486587 A CN113486587 A CN 113486587A
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王清华
加世滢
胡建华
周新亮
赵铁林
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Abstract

本发明涉及基于Matlab的无缝钢管穿孔工艺模型参数预测系统，属于无缝钢管穿孔工艺模型壁厚预测技术领域，本系统包括数据导入和导出单元实现对工艺参数的计算，BP神经网络对无缝钢管管型参数训练、预测以及训练效果综合评估，该系统结合Matlab编程实现网络模型的建立训练和预测，提供一种设计简单、检测速度快、灵活性高且稳定可靠的基于MATLAB APP的无缝钢管穿孔工艺模型壁厚的预测方法。本发明实现数据导入、性能评估、模型预测以及预测结果导出等功能于一体，减少了工作量，已知关键影响参数可利用BP神经网络预测管型，用以指导穿孔生产,保证钢管生产质量，能保证系统在长时间监测时的预测精度。

Figure 202110765429

The invention relates to a Matlab-based seamless steel pipe perforation process model parameter prediction system, which belongs to the technical field of wall thickness prediction of seamless steel pipe perforation process models. Steel pipe tube shape parameter training, prediction and comprehensive evaluation of training effects. The system combines Matlab programming to realize the establishment, training and prediction of network models, and provides a seamless MATLAB APP-based seamless system with simple design, fast detection speed, high flexibility, stability and reliability. Prediction method of wall thickness of steel pipe perforation process model. The invention realizes the functions of data import, performance evaluation, model prediction, and prediction result export, and reduces the workload. The known key influencing parameters can be used to predict the tube shape by using the BP neural network to guide the perforation production and ensure the production quality of the steel pipe. It can ensure the prediction accuracy of the system during long-term monitoring.

Figure 202110765429

Description

Seamless steel pipe perforation process model parameter prediction system based on Matlab
Technical Field
The invention belongs to the technical field of seamless steel tube perforation process model wall thickness prediction, and particularly relates to a parameter prediction system of a Matlab-based seamless steel tube perforation process model.
Background
With the rapid development of economy in China, the production of seamless steel tubes plays an important role in numerous fields of economic production in China, and the cross piercing process is most widely used in the production of seamless steel tubes. In order to improve the quality of products, reduce the cost required for production and increase the production rate of products, a number of new rolling software and management software have been developed, such as: the software for synchronously monitoring the wall thickness of the steel tube in the technical field of steel rolling can meet the requirement of high-precision seamless steel tube rolling.
In the production of seamless steel pipes by steel rolling science and technology, the former steel rolling production is manually operated by virtue of production experience, and now, due to the mature use of a mathematical model, the steel rolling production tends to be semi-automatically or automatically controlled by a computer. In a seamless steel tube production unit, a puncher punches a solid tube blank into a hollow blank tube, and is one of main production equipment in seamless steel tube production, so that the reasonably planned adjustment of the rolling process of the puncher is the fundamental guarantee for making high-quality steel tubes, and the reasonable process adjustment of the puncher is an important way for reducing the wall thickness quality defect of finished steel tubes. The most suitable parameters are determined, and the uniform wall thickness of the perforated capillary is ensured, which is very important for the steel rolling production.
MATLAB software is a high-level technical computing language and an interactive environment for algorithm development, data visualization, data analysis and numerical calculation, integrates a plurality of powerful functions of numerical analysis, matrix calculation, scientific data visualization, modeling and simulation of a nonlinear dynamic system and the like into an easy-to-use window environment, and provides a comprehensive solution for scientific research, engineering design and a plurality of scientific fields which need to carry out effective numerical calculation. The APP is an interface for information communication between a user and equipment by using a building module on a graphical interface, and a desired effect is achieved. The image identification is an important information processing technology, the digital image processing technology is applied to the prediction of the wall thickness of the seamless steel tube process model, the processing precision is high, the processing content is rich, the complex nonlinear processing can be carried out, the flexible flexibility is realized, and the processing content can be changed only by changing software.
Thus, the present invention was accomplished under the Matlab APP tool. The system is combined with Matlab programming to realize the establishment training and prediction of a network model, and provides a stable and reliable prediction method of the wall thickness of the seamless steel pipe perforation process model based on MATLAB APP, which has the advantages of simple design, high detection speed and high flexibility.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention provides a parameter prediction system of a seamless steel tube perforation process model based on Matlab, a prediction research system of each parameter of the seamless steel tube perforation process model based on a Matlab APP platform and prediction of the tube shape of the seamless steel tube by utilizing Matlab and adopting a BP neural network.
The invention is realized by the following technical scheme.
A parameter prediction system of a seamless steel tube perforation process model based on Matlab comprises a data import and export unit for realizing the calculation of process parameters, and a BP neural network for the training, verification and comprehensive evaluation of prediction effects of the tubular parameters of the seamless steel tube, and specifically comprises the following steps:
(1) establishing a MATLAB APP master-computer interactive interface, wherein the master-computer interactive interface consists of 8 input modules of blank information, roller design, kinematic parameters, top design, guide plate design, process parameters, tube type prediction and force and energy parameters, and can jump to a corresponding interface by writing a callback function of a corresponding menu in a code view and clicking the corresponding menu; according to the invention, a platform product is designed by utilizing Matlab APP, an interface does not need to be redesigned according to the production requirements of different units, the workload is reduced, the system can be used for verification to adjust parameters needing to be changed when finished products with different specifications are produced, and the influence on the control precision caused by errors due to adoption of empirical adjustment is reduced;
(2) each input module respectively establishes a corresponding sub-human-computer interaction interface, each sub-human-computer interaction interface is respectively provided with a corresponding design parameter input and output module, a Matlab function uigetfile () is used for acquiring a file path and a file name of a target Excel form to be imported and exported, a function xlsread () is used for reading data in the target form, and then a system imports the obtained process parameters corresponding to the perforating equipment and the seamless steel pipe type parameters into the target form through the function xlsrrite ();
(3) five parameters affecting the tube type were taken: the method comprises the following steps of taking the forward extension, the roll spacing, the guide plate spacing, the blank diameter and the plug diameter as the input of a BP neural network, training, verifying and predicting the technological parameters of the seamless steel tube by adopting the BP neural network to obtain the tubular prediction result of the perforated tubular billet, and comprises the following steps:
s1: the number of network layers: 1 layer of each of the input layer, the hidden layer and the output layer;
s2: initializing the network: establishing a seamless steel pipe cross-rolling perforated pipe type prediction model by using newff (), determining the number of neurons in an input layer and an output layer according to input and output variables, wherein the network structure is 5-10-2; setting a training function, a training algorithm and a normalization interval;
s3: setting the iteration frequency of the network as 2000 times, the learning rate as 0.1, and setting the allowable deviation of the outer diameter as 0.1 and the allowable deviation of the wall thickness as 0.1 according to the requirement of the national standard on the size deviation;
the BP neural network model can reflect the training effect of the BP neural network in real time by utilizing a relative error histogram of actual values of wall thickness and outer diameter and a predicted value and a comparison line graph of the actual values of the wall thickness and the outer diameter and the predicted value;
(4) and loading and storing the design parameters in the sub human-computer interaction interface in an excel form.
Further, in the step (1), the main human-computer interaction interface can independently open any sub human-computer interaction interface.
Further, in the step (2):
the blank information module in the sub-human-computer interaction interface comprises input windows of the following parameters: date, material, pipe blank diameter, blank length, tapping temperature, capillary diameter, capillary wall thickness, capillary length, and blank information parameters can be generated into a table form.
The roller design module in the sub-human-computer interaction interface comprises an input window of the following parameters: the pipe blank diameter, the roller shape, the roller number, the rolling angle, the roller inlet cone generatrix inclination angle and the roller outlet cone generatrix inclination angle comprise the following parameter output windows: roll diameter, roll length, entrance roll profile taper angle, exit roll profile taper angle, entrance cone length, compression band length, exit cone length, roll end fillet;
the kinematic parameter module in the sub-human-computer interaction interface comprises the following parameter input windows: the tube blank diameter, the tubular billet wall thickness, the roller diameter, the roller rotating speed, the roller number, the feeding angle, the tangential sliding coefficient of the outlet section and the friction coefficient comprise the following parameter output windows: radial rolling reduction, axial slip coefficient, outlet speed, feeding speed, tube blank revolution and screw pitch of the blank before the top;
the top design module in the sub-human-computer interaction interface comprises the following parameter input windows: the device comprises a top material, a pipe blank diameter, a tubular billet wall thickness, a roller inlet cone generatrix inclination angle and a screw pitch, and comprises the following parameter output windows: the diameter of the nose part, the length of the nose part, the diameter of the top head, the length of the arc part of the top head, a perforation cone, a uniform wall cone, a reverse cone and the inclination angle of the generatrix of the cone of the top head;
the guide plate design module in the sub-human-computer interaction interface comprises the following parameter input windows: tube blank diameter, hollow billet wall thickness, feed angle, roll diameter, roll entry cone generating line inclination, top diameter, pitch, including following parameter output window: the guide plate inlet inclined plane inclination angle, the guide plate outlet inclined plane inclination angle, the guide plate inlet cone width, the guide plate outlet cone width, the inlet groove depth and the outlet groove depth;
the process parameter module in the sub-human-computer interaction interface comprises the following parameter input windows: the blank information comprises the diameter of the tube blank, the diameter of the capillary and the wall thickness of the capillary, the roller information comprises an inlet cone angle and an outlet cone angle, and the plug information comprises the diameter of the plug and the length of the plug; the following parameter output windows are included: roll spacing, guide plate spacing, and top extension;
the force parameter module in the sub-human-computer interaction interface comprises the following parameter input windows: the pipe billet diameter, the hollow billet wall thickness, the screw pitch, the feed angle, the rolling angle, the roller inlet cone angle, the roller outlet cone angle, the roller diameter, the inclination angle of the top cone bus, the top extension, the outlet speed, the reduction gearbox transmission ratio, the gear base transmission efficiency, the spindle transmission efficiency, the motor rotating speed and the material deformation resistance comprise the following parameter output windows: the device comprises a rolling force, a top axial force, a total axial force of each roller, a guide plate axial resistance, a torque required for rotating the rollers, a bending torque, a guide plate tangential resistance torque to the rollers, a torque required by one roller, a roller bearing torque, an idling torque, a motor torque and a motor power.
Further, in the step S2, an empirical formula is adopted
Figure BDA0003150856100000031
And determining the number of the hidden layer neurons, wherein m is the number of the hidden layer neurons, a and b are the number of the input layer neurons and the output layer neurons respectively, and c is a regulation constant between 1 and 10.
Further, in the step S2, five parameters of the forward extension, the roll gap, the guide plate gap, the billet diameter and the plug diameter are selected as input variables of the predictive model of the cross-piercing pipe type, and the wall thickness and the outer diameter are selected as output variables. The cross piercing is the first step of metal deformation in seamless steel pipe production, and the geometric dimension of the hollow billet is mainly determined by the outer diameter and the wall thickness. The deformation of a rolled piece is large during punching, according to the geometric relation of a deformation area and considering a bounce formula, the traditional tubular pipe mathematical model is as follows:
the outer diameter value is as follows:
Figure BDA0003150856100000041
wall thickness value:
Figure BDA0003150856100000042
wherein the roll gap b is (1-delta) dzAccording to empirical data, the compression quantity delta at a compression zone is 15-16%, and delta is 15.5%; l is the guide plate spacing in mm; d is the diameter of the roller, and the unit is mm; dz is the diameter of the tube blank, and the unit is mm; lp is the length of the arc part of the plug, and the unit is mm; y is the amount of protrusion in mm; beta is a feed angle, and the unit is degree; alpha is alpha2Is the roller outlet cone angle, and the unit is degree; δ n is the plug diameter in mm.
Figure BDA0003150856100000043
And
Figure BDA0003150856100000044
the values of the outer diameter and the wall thickness, respectively, are in mm.
In the mechanism model, the rigidity and the bounce value of the puncher are considered in the calculation of the outer diameter and the wall thickness, and the bounce value is obtained by experience, so that errors are inevitably generated.
According to the invention, a BP neural network is adopted to establish a tubular prediction model, too many input variables can complicate the network structure, and the selection is not comprehensive enough, so that the model precision is reduced. Selecting key parameters influencing tube shape as input vectors of BP neural network training, and using the outer diameter and the wall thickness as output vectors of the BP neural network training; and determining an ideal BP neural network prediction model according to the number and the output vector of the key parameters and the multiple times of training of the BP neural network and the comprehensive evaluation of the reference training effect.
Further, in the step S2, the transfer function used by each layer is tansig and purelin, and is trained by using logsig function; the training algorithm is set as a back propagation algorithm, a quasi-Newton back propagation algorithm, an elastic back propagation algorithm or a gradient descent back propagation algorithm; the normalization interval is set to [ -1,1 ].
Compared with the prior art, the invention has the beneficial effects that:
1. based on MATLAB APPDESIGER Chinese language environment, design nimble design user operation interface, need not redesign whole APP system to different steel pipe materials, only need compile configuration file can, avoid the transformation to current system in manufacturing and testing process, reduced work load, have actual meaning.
2. The known key influence parameters can utilize a BP neural network to predict the tube shape so as to guide the perforating equipment to adjust process parameters before production, and the tube shape precision during system production can be ensured.
3. The method integrates functions of data import, performance evaluation, model prediction, prediction result export and the like, can meet the requirement of scientific research, and has commercial application value.
Drawings
FIG. 1 is a schematic diagram of a BP neural network topology;
FIG. 2 is a flowchart of a BP neural network based tube type prediction training;
Detailed Description
The following examples are intended to illustrate the invention but are not intended to limit the scope of the invention. Unless otherwise specified, the examples follow conventional experimental conditions.
The Matlab-based seamless steel tube piercing process model parameter prediction system shown in fig. 1 and 2 comprises the following steps:
(1) establishing an MATLAB APP master-computer interactive interface, wherein the master-computer interactive interface consists of 8 input modules of parameter information, roller design, kinematic parameters, top design, guide plate design, process parameters, tube type prediction and force and energy parameters, and can jump to a corresponding interface by writing a callback function of a corresponding menu in a code view and clicking the corresponding menu;
(2) each input module respectively establishes a corresponding sub-human-computer interaction interface, each sub-human-computer interaction interface is respectively provided with a corresponding design parameter input and output module, a Matlab function uigetfile () is used for acquiring a file path and a file name of a target Excel form to be imported and exported, a function xlsread () is used for reading data in the target Excel form, and a system imports the obtained process parameters corresponding to the perforating equipment and the seamless steel pipe type parameters into the target form through the function xlsrrite () after calculation;
(3) five parameters affecting the tube type were taken: the method comprises the following steps of taking the forward extension, the roll spacing, the guide plate spacing, the blank diameter and the plug diameter as the input of a BP neural network, training, verifying and predicting the technological parameters of the seamless steel tube by adopting the BP neural network to obtain the tubular prediction result of the perforated tubular billet, and comprises the following steps:
s1: the number of network layers: 1 layer of each of an input layer, a hidden layer and an output layer is arranged;
s2: initializing the network: establishing a seamless steel pipe cross-rolling perforated pipe type prediction model by using newff (), determining the number of neurons in an input layer and an output layer according to input and output variables, wherein the network structure is 5-10-2; setting a training function, a training algorithm and a normalization interval;
s3: setting the iteration frequency of the network as 2000 times, the learning rate as 0.1, and setting the allowable deviation of the outer diameter as 0.1 and the allowable deviation of the wall thickness as 0.1 according to the requirement of the national standard on the size deviation;
(4) and loading and storing the design parameters in the sub human-computer interaction interface in an excel form.
The invention is based on the prediction of an MATLAB seamless steel pipe perforation process model, utilizes the MATLAB APP and codes of all sub-modules to design and complete a human-computer interface interaction system of the seamless steel pipe perforation process model prediction, and collects data of a certain steel mill for prediction and verification.
The APP Designer comprises buttons, text areas, editing fields and the like, an interface is built by adopting a component library, after interface layout is completed, an MATLAB can automatically create an m-code file according to the control type, and a system can store the fig file and the m file which are required to be in the same folder and are respectively used for storing a design view and a code view of the interface; on the basis, a callback function required to be realized is compiled for the submodule, data obtained by calculation in Matlab is written into a target table by using xlsread, data in an excel file is read by xlsread, a path and a file name of the file are obtained by uiputfile, selected file information is obtained by uigetfile, and fulllfile is created and combined into a complete file name by using information of each part of the file. The calculation result of the output parameter is realized by establishing a traditional formula in the code view.
The general interface mainly comprises a user name, a password and user login. And 7 modules of roll design, kinematic parameters, plug design, guide plate design, process parameters, pipe type prediction and force parameters are arranged above the main interface, and the parameters can be directly entered into the parameter information module after successful login.
And by writing a callback function of the corresponding menu in the code view, clicking the corresponding menu can jump to the corresponding interface.
The parameter information interface inputs the acquired data into the corresponding blank information module and the corresponding capillary information module, a data reading button is arranged, the data is displayed in a product information reading interface, a callback function is compiled for a data storage button, and the known information is stored in an Excel sheet1 form.
The roll design parameter setting interface sets a two-roll cone, sets a load data button to program a callback function to load the data saved in the Excel sheet1 into the roll design parameter module. Setting a calculation button, compiling a traditional formula to calculate the diameter of the roller, the length of the roller, the taper angle of the inlet roller, the taper angle of the outlet roller, the length of the inlet cone, the length of the compression band, the length of the outlet cone and the round angle of the roller end, displaying the calculated data in a roller parameter calculation interface, setting a data storage button, and storing the calculated data and the names of the data in Excel sheet2 in a one-to-one correspondence manner. And setting a return button, and returning to the roller design parameter interface from the interface, wherein the return button can return to the menu interface.
The kinematic parameter setting interface is provided with a data loading button, and parameters required for calculating output parameters are loaded to the kinematic parameter interface from Excel sheet1 and sheet 2. Setting a calculation button, compiling a traditional formula to calculate the radial rolling reduction, the axial slip coefficient, the outlet speed, the feeding speed, the tube blank revolution and the screw pitch of the blank before the plug, displaying the calculated data in a kinematic parameter calculation interface, setting a data storage button, and storing the calculated data and the names of the data in Excel sheet3 in a one-to-one correspondence manner. And setting a return button, and returning to the kinematic parameter interface from the interface, wherein the kinematic parameter interface setting return button can return to the menu interface.
The plug design parameter setting interface is provided with a data loading button, and parameters required by calculating output parameters are loaded to the plug design parameter interface from Excel sheet1, sheet2 and sheet 3. Setting a calculation button, compiling a traditional formula to calculate the nasal diameter, the nasal length, the plug diameter, the length of the arc part of the plug, the inclination angle of the perforation cone, the uniform wall cone, the reverse cone and the bus of the plug cone, displaying the calculated data in a plug design calculation interface, setting a data storage button, and storing the calculated data and the names of the data in Excel sheet4 in a one-to-one correspondence manner. And setting a return button, and returning to the plug design parameter interface from the interface, wherein the plug design parameter interface setting return button can return to the menu interface.
And a data loading button is arranged on the guide plate design parameter setting interface, and parameters required by calculating output parameters are loaded to the guide plate design parameter interface from Excel sheet1, sheet2, sheet3 and sheet 4. Setting a calculation button, compiling a traditional formula to calculate the inclination angle of the guide plate inlet inclined plane, the inclination angle of the guide plate outlet inclined plane, the width of the guide plate inlet cone, the width of the guide plate outlet cone, the depth of the inlet groove and the depth of the outlet groove, displaying the calculated data in a guide plate design calculation interface, setting a data storage button, and storing the calculated data and the names of the data in Excel sheet5 in a one-to-one correspondence manner. And a return button is arranged, the interface can return to the guide plate design parameter interface, and the guide plate design parameter interface can return to the menu interface.
And a data loading button is arranged on the process parameter setting interface, and parameters required by calculating output parameters are loaded into the process parameter interface from an Excel sheet1, a sheet2 and a sheet 4. Setting a calculation button, compiling a traditional formula to calculate the roller distance, the guide plate distance and the plug extension, displaying the calculated data in a technological parameter calculation interface, setting a storage button, and storing the calculated data and the names of the data in an Excel sheet6 in a one-to-one correspondence manner. And setting a return button, returning to the process parameter interface from the interface, and returning to the menu interface by the process parameter interface setting return button.
And a data loading button is arranged on a traditional formula parameter setting interface in the pipe type prediction, and parameters required by calculating output parameters are loaded into the traditional formula parameter interface from an Excel sheet1, a sheet2, a sheet3, a sheet4 and a sheet 6. Setting a calculation button, compiling a traditional formula to calculate the length, the outer diameter and the wall thickness of the pipe, displaying the calculated data in a tubular calculation interface of the traditional formula, setting a data storage button, storing the calculated data and the names of the data in an Excel sheet7 in a one-to-one correspondence manner, setting a return button, and returning to the traditional formula parameter setting interface from the interface, wherein the return button for setting the traditional formula parameter interface can return to a menu interface.
The prediction interface of the tubular prediction BP neural network comprises three modules: loading a data set, and importing training and testing data; setting a network structure 5-10-2; the training functional area sets iteration times, a learning rate, training target settings, a training function and a training algorithm. Setting an emptying button; the network training button displays the training result to a BP neural network prediction result interface, a result export button is arranged, a return button can return to the BP neural network prediction interface from the interface, and the BP neural network prediction interface is provided with a return button which can return to a menu interface.
And predicting the outer diameter and the wall thickness of the capillary according to the trained prediction model. And setting a storage button, and storing the calculated data and the names thereof in an Excel sheet7 in a one-to-one correspondence manner. A return button is provided from which a return to the menu interface is possible.
The uniformity of the wall thickness is one of the main factors influencing the precision of the product, the factors influencing the wall thickness are many in the process of perforating the steel pipe, the relation between each factor and perforation is complex, whether the wall thickness is qualified or not is predicted according to the main parameters, and the parameters which can generate the product disqualification due to manual experience adjustment or unreasonable setting of certain parameters can be adjusted.
And a data loading button is arranged on the force and energy parameter setting interface, and parameters required by calculating output parameters are loaded into the force and energy parameter interface from Excel sheet1, sheet2, sheet3, sheet4 and sheet 6. Setting a calculation button, compiling a traditional formula to calculate the rolling force, the axial force of a top head, the total axial force of each roller, the axial resistance of a guide plate, the torque required by rotating the roller, the bending torque, the tangential resistance torque of the guide plate to the roller, the torque required by one roller, the torque of a roller bearing, the idle torque, the motor torque and the motor power, displaying the calculated result in the calculation of force energy parameters, setting a storage button, and storing the calculated data and the name thereof in an Excel sheet8 in a one-to-one correspondence manner. And setting a return button, and returning to the force and energy parameter interface from the interface, wherein the force and energy parameter interface setting return button can return to the menu interface.
The above description is only for the specific embodiments of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the appended claims.

Claims (6)

1.基于Matlab的无缝钢管穿孔工艺模型参数预测系统，其特征在于包括以下步骤：1. based on the seamless steel pipe perforation process model parameter prediction system of Matlab, it is characterized in that comprising the following steps: (1)建立MATLAB APP主人机交互界面，主人机交互界面由参数信息、轧辊设计、运动学参数、顶头设计、导板设计、工艺参数、管型预测、力能参数8个输入模块组成，通过在代码视图中编写相应菜单的回调函数，点击相应菜单可跳转到相应界面；(1) Establish a MATLAB APP host computer interface, which consists of eight input modules: parameter information, roll design, kinematic parameters, plug design, guide plate design, process parameters, tube shape prediction, and force parameters. Write the callback function of the corresponding menu in the code view, and click the corresponding menu to jump to the corresponding interface; (2)每个所述输入模块分别建立对应的子人机交互界面，每个子人机交互界面分别设置对应的设计参数输入输出模块，使用Matlab函数uigetfile()获取需要导入和导出的目标Excel表格的文件路径和文件名，使用函数xlsread()读取目标Excel表格内的数据，系统通过计算后将得出的穿孔设备对应的工艺参数以及无缝钢管管型参数，用函数xlswrite()导入到目标表格中；(2) Each of the input modules establishes a corresponding sub-human-computer interaction interface, and each sub-human-computer interaction interface is respectively set with a corresponding design parameter input and output module, and the Matlab function uigetfile() is used to obtain the target Excel table that needs to be imported and exported. the file path and file name, use the function xlsread() to read the data in the target Excel table, and the system will import the corresponding process parameters of the perforation equipment and the pipe type parameters of the seamless steel pipe with the function xlswrite() after calculation. in the target table; (3)采取影响管型的五个参数：前伸量、轧辊距、导板距、坯料直径、顶头直径作为BP神经网络的输入，采用BP神经网络对无缝钢管工艺参数进行训练、验证及预测，得到穿孔毛管的管型预测结果，包括以下步骤：(3) The five parameters that affect the tube shape are taken as the input of the BP neural network: the forward extension, the roll distance, the guide plate distance, the blank diameter and the plug diameter, and the BP neural network is used to train, verify and predict the process parameters of the seamless steel pipe. , to obtain the cast prediction result of the perforated capillary, including the following steps: S1：网络层数：设置输入层、隐藏层、输出层各1层；S1: Number of network layers: set the input layer, hidden layer, and output layer to 1 layer each; S2：初始化网络：使用newff()建立无缝钢管斜轧穿孔管型预测模型，根据输入输出变量确定输入输出层神经元数目，网络结构为5-10-2；设置训练函数、训练算法、归一化区间；S2: Initialize the network: use newff() to establish a model for predicting the shape of seamless steel pipe skew rolling and perforation, determine the number of neurons in the input and output layers according to the input and output variables, and the network structure is 5-10-2; set the training function, training algorithm, normalization unification interval; S3：设置网络的迭代次数为2000次，学习率为0.1，根据国标对尺寸偏差的要求，设置外径允许偏差为0.1，壁厚允许偏差为0.1；S3: Set the number of iterations of the network to 2000 and the learning rate to 0.1. According to the requirements of the national standard for size deviation, set the allowable deviation of the outer diameter to 0.1 and the allowable deviation of the wall thickness to be 0.1; (4)子人机交互界面中的设计参数通过excel形式加载和保存。(4) The design parameters in the sub-human-computer interaction interface are loaded and saved in the form of excel. 2.根据权利要求1所述的基于Matlab的无缝钢管穿孔工艺模型参数预测系统，其特征在于：在所述步骤(1)中，主人机交互界面可以独立打开任一子人机交互界面。2. The Matlab-based seamless steel pipe perforation process model parameter prediction system according to claim 1, characterized in that: in the step (1), the main-computer interaction interface can independently open any sub-human-computer interaction interface. 3.根据权利要求1所述的基于Matlab的无缝钢管穿孔工艺模型参数预测系统，其特征在于：在所述步骤(2)中：3. the seamless steel pipe perforation process model parameter prediction system based on Matlab according to claim 1, is characterized in that: in described step (2): 子人机交互界面中的坯料信息模块包括以下参数的输入窗口：日期、材料、管坯直径、坯料长度、出钢温度、毛管直径、毛管壁厚、毛管长度，坯料信息参数可生成表格形式。The blank information module in the sub-man-machine interface includes the input window for the following parameters: date, material, tube blank diameter, blank length, tapping temperature, capillary diameter, capillary wall thickness, capillary length, and blank information parameters can be generated in tabular form . 子人机交互界面中的轧辊设计模块包括以下参数的输入窗口：管坯直径、毛管直径、辊型、轧辊数、碾轧角、轧辊入口锥母线倾角、轧辊出口锥母线倾角，包括以下参数输出窗口：轧辊直径、轧辊长度、入口辊型锥角、出口辊型锥角、入口锥长度、压缩带长度、出口锥长度、辊端圆角；The roll design module in the sub-man-machine interface includes the input window for the following parameters: tube blank diameter, capillary diameter, roll shape, number of rolls, rolling angle, inclination angle of the inlet cone of the roll, and inclination of the outlet cone of the roll, including the output of the following parameters Window: roll diameter, roll length, inlet roll cone angle, outlet roll cone angle, inlet cone length, compression belt length, outlet cone length, roll end fillet; 子人机交互界面中的运动学参数模块包括以下参数输入窗口：管坯直径、毛管直径、毛管壁厚、轧辊直径、轧辊转速、轧辊个数、送进角、出口断面切向滑动系数、摩擦系数，包括以下参数输出窗口：顶头前坯料径向压下量、轴向滑移系数、出口速度、送进速度、管坯转数、螺距；The kinematic parameter module in the sub-human-machine interface includes the following parameter input windows: tube blank diameter, capillary diameter, capillary wall thickness, roll diameter, roll speed, number of rolls, feed angle, tangential sliding coefficient of exit section, Friction coefficient, including the output window of the following parameters: radial reduction of billet before plug, axial slip coefficient, exit speed, feeding speed, tube blank revolutions, thread pitch; 子人机交互界面中的顶头设计模块包括以下参数输入窗口：顶头材料、管坯直径、毛管直径、毛管壁厚、轧辊入口锥母线倾角、螺距，包括以下参数输出窗口：鼻部直径、鼻部长度、顶头直径、顶头圆弧部分长度、穿孔锥、均壁锥、反锥、顶头锥体母线的倾斜角；The plug design module in the sub-human-machine interface includes the following parameter input windows: plug material, tube blank diameter, capillary diameter, capillary wall thickness, inclination angle of the roll inlet cone generatrix, and pitch, including the following parameter output windows: nose diameter, nose diameter part length, plug diameter, plug arc part length, perforated cone, uniform wall cone, reverse cone, and the inclination angle of the plug cone busbar; 子人机交互界面中的导板设计模块包括以下参数输入窗口：管坯直径、毛管直径、毛管壁厚、送进角、碾轧角、轧辊直径、轧辊入口锥母线倾角、顶头直径、螺距，包括以下参数输出窗口：导板入口斜面倾角、导板出口斜面倾角、导板入口锥宽度、导板出口锥宽度、入口处槽深、出口处槽深；The guide plate design module in the sub-man-machine interface includes the following parameter input windows: tube blank diameter, capillary diameter, capillary wall thickness, feed angle, rolling angle, roll diameter, inclination angle of the entrance cone of the roll, plug diameter, pitch, Including the following parameter output window: guide plate inlet slope angle, guide plate exit slope angle, guide plate inlet cone width, guide plate outlet cone width, groove depth at the entrance, groove depth at the exit; 子人机交互界面中的工艺参数模块包括以下参数输入窗口：坯料信息包括管坯直径、毛管直径、毛管壁厚，轧辊信息包括入口锥角和出口锥角，顶头信息包括顶头直径和顶头长度；包括以下参数输出窗口：轧辊间距、导板间距、顶头伸处量；The process parameter module in the sub-human-machine interface includes the following parameter input windows: billet information includes tube blank diameter, capillary diameter, capillary wall thickness, roll information includes inlet cone angle and outlet cone angle, plug information includes plug diameter and plug length ;Include the following parameter output windows: roll spacing, guide plate spacing, head extension; 子人机交互界面中的力能参数模块包括以下参数输入窗口：管坯直径、毛管直径、毛管壁厚、螺距、送进角、碾轧角、轧辊入口锥角、轧辊出口锥角、轧辊直径、顶头锥体母线的倾斜角、顶头伸出量、出口速度、减速箱传动比、齿轮机座传动效率、接轴传动效率、电机转速、材料变形抗力，包括以下参数输出窗口：轧制力、顶头轴向力、每个轧辊的总轴向力、导板轴向阻力、转动轧辊所需的力矩、弯曲力矩、导板对轧辊切向阻力矩、一个轧辊所需要力矩、轧辊轴承力矩、空转力矩、电机力矩、电机功率。The force energy parameter module in the sub-human-machine interface includes the following parameter input windows: tube blank diameter, capillary diameter, capillary wall thickness, pitch, feed angle, rolling angle, roll entry taper angle, roll exit taper angle, roll Diameter, inclination angle of the head cone busbar, head extension, outlet speed, gear box transmission ratio, gear frame transmission efficiency, shaft transmission efficiency, motor speed, material deformation resistance, including the following parameters output window: rolling force , Axial force of the head, total axial force of each roll, axial resistance of guide plate, torque required to rotate the roll, bending moment, tangential resistance moment of guide plate to roll, torque required by one roll, roll bearing torque, idle torque , motor torque, motor power. 4.根据权利要求1所述的基于Matlab的无缝钢管穿孔工艺模型参数预测系统，其特征在于：在所述步骤S2中，采用经验公式
Figure FDA0003150856090000021
确定隐藏层神经元的个数，其中，m为隐藏层神经元数目，a和b分别为输入、输出层神经元数目，c为1～10之间的调节常数。4. the seamless steel pipe perforation process model parameter prediction system based on Matlab according to claim 1, is characterized in that: in described step S2, adopt empirical formula
Figure FDA0003150856090000021
Determine the number of neurons in the hidden layer, where m is the number of neurons in the hidden layer, a and b are the number of neurons in the input and output layers, respectively, and c is an adjustment constant between 1 and 10. 5.根据权利要求1所述的基于Matlab的无缝钢管穿孔工艺模型参数预测系统，其特征在于：在所述步骤S2中，选择前伸量、轧辊距、导板距、坯料直径、顶头直径五个参数作为斜轧穿孔管型预测模型的输入变量，壁厚和外径作为输出变量。5. the seamless steel pipe perforation process model parameter prediction system based on Matlab according to claim 1, is characterized in that: in described step S2, selects the amount of overhang, roll distance, guide plate distance, billet diameter, plug diameter five These parameters are used as the input variables of the skew-rolled piercing tube shape prediction model, and the wall thickness and outer diameter are used as output variables. 6.根据权利要求1所述的基于Matlab的无缝钢管穿孔工艺模型参数预测系统，其特征在于：在所述步骤S2中，每层使用的传输函数为tansig和purelin,使用logsig函数训练，训练算法设置为反向传播算法、拟牛顿反向传播算法、弹性反向传播算法或者梯度下降反向传播算法；归一化区间设置为[-1,1]。6. the seamless steel pipe perforation process model parameter prediction system based on Matlab according to claim 1, is characterized in that: in described step S2, the transfer function that every layer uses is tansig and purelin, uses logsig function training, training The algorithm is set to backpropagation algorithm, quasi-Newton backpropagation algorithm, elastic backpropagation algorithm or gradient descent backpropagation algorithm; the normalization interval is set to [-1,1].
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