CN113182376A - Intelligent mold, control system, control method, data processing terminal, and medium - Google Patents

Intelligent mold, control system, control method, data processing terminal, and medium Download PDF

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CN113182376A
CN113182376A CN202110356670.7A CN202110356670A CN113182376A CN 113182376 A CN113182376 A CN 113182376A CN 202110356670 A CN202110356670 A CN 202110356670A CN 113182376 A CN113182376 A CN 113182376A
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die
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mold
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汪建余
宁海涛
连杰
王长勇
黄锦川
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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B21—MECHANICAL METAL-WORKING WITHOUT ESSENTIALLY REMOVING MATERIAL; PUNCHING METAL
    • B21C—MANUFACTURE OF METAL SHEETS, WIRE, RODS, TUBES OR PROFILES, OTHERWISE THAN BY ROLLING; AUXILIARY OPERATIONS USED IN CONNECTION WITH METAL-WORKING WITHOUT ESSENTIALLY REMOVING MATERIAL
    • B21C31/00—Control devices, e.g. for regulating the pressing speed or temperature of metal; Measuring devices, e.g. for temperature of metal, combined with or specially adapted for use in connection with extrusion presses
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B21—MECHANICAL METAL-WORKING WITHOUT ESSENTIALLY REMOVING MATERIAL; PUNCHING METAL
    • B21C—MANUFACTURE OF METAL SHEETS, WIRE, RODS, TUBES OR PROFILES, OTHERWISE THAN BY ROLLING; AUXILIARY OPERATIONS USED IN CONNECTION WITH METAL-WORKING WITHOUT ESSENTIALLY REMOVING MATERIAL
    • B21C25/00—Profiling tools for metal extruding
    • B21C25/02—Dies
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
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    • G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
    • G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
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    • G06—COMPUTING; CALCULATING OR COUNTING
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Abstract

The invention discloses an intelligent mold, a control system, a control method, a data processing terminal and a medium, and relates to the technical field of intelligent molds. According to the combination of the pressure sensor and the shape of the key part of the die, the pressure of the key part of the die is preliminarily calculated, and the data processing terminal obtains a calculated value of the pressure of the die in real time according to the stamping strength calculation model to obtain the thickness of a finished die product; the temperature of key parts of the die is acquired through a temperature sensor and fed back to the data processing terminal, and a water cooling device is automatically called to perform constant temperature control on the die; and detecting the working state of the die by adding a vibration sensor, and feeding the working state back to the data processing terminal to control the running state of the die. The pressure sensor of the invention obtains the stamping strength and ensures the consistency of the thickness of the finished product. And a temperature sensor is added, a water cooling device is automatically called to ensure the constant temperature of the die, and a vibration sensor is added to detect the working state of the die to ensure the healthy operation of the equipment.

Description

Intelligent mold, control system, control method, data processing terminal, and medium
Technical Field
The invention relates to the technical field of intelligent molds, in particular to an intelligent mold, a control system, a control method, a data processing terminal and a medium.
Background
At present, the prior art provides an aluminum alloy hot extrusion porous die, which comprises an upper die and a lower die, wherein a first diversion groove is arranged on the surface of the upper die, and a second diversion groove is arranged inside the first diversion groove; the lower end of the upper die is provided with a die core, and the second shunting grooves are distributed on the outer side of the die core; a connecting sleeve is arranged on the outer side of the upper die, and an internal thread is arranged on the inner side of the connecting sleeve;
the surface of the lower die is provided with a welding chamber, the bottom of the lower die is provided with a discharge hole, an extrusion cavity is arranged between the welding chamber and the discharge hole, and the welding chamber, the extrusion cavity and the discharge hole are communicated; and the outer side of the lower die is provided with an external thread, and the external thread on the outer side of the lower die is matched with the internal thread on the inner side of the connecting sleeve.
With the aluminum alloy hot extrusion porous die, the embodiment has four first diversion trenches, and each first diversion trench has four second diversion trenches inside; the upper die and the lower die are fixedly connected through the matching of the internal thread on the inner side of the connecting sleeve and the external thread on the outer side of the lower die, the device is simple and convenient to assemble and disassemble, and the device is convenient to maintain; when hot extrusion is carried out, raw materials pass through the first diversion groove and the second diversion groove enter the interior of the welding chamber, the hot extrusion molding of the aluminum alloy section is realized through the cooperation of the mold core and the extrusion cavity, and the formed aluminum alloy section is discharged through the discharge hole.
The traditional die is composed of metal, and numerical values of pressure, vibration and temperature cannot be obtained. The yield can be improved only through experience, the digitization and the standardization cannot be realized, and the control cannot be well realized for improving the level of the whole product quality.
Through the above analysis, the problems and defects of the prior art are as follows: in the prior art, the feedback of the pressure value and the constant temperature control effect of the mold are poor at the key part of the mold, the working vibration state of the mold cannot be detected, and the equipment cannot run healthily.
Disclosure of Invention
In order to overcome the problems in the related art, the disclosed embodiments of the present invention provide an intelligent mold, a control system, a control method, a data processing terminal, and a medium. The technical scheme is as follows:
according to a first aspect of the disclosed embodiments of the present invention, there is provided an intelligent mold control method applied to a data processing terminal, the intelligent mold control method including:
collecting pressure values at key positions of the die through a pressure sensor to obtain stamping strength, feeding the stamping strength back to the data processing terminal, and controlling the thickness of a die finished product;
according to the combination of the pressure sensor and the shape of the key part of the mould, the pressure of the key part of the mould is preliminarily calculated according to a formula:
Figure BDA0003003550390000021
and (3) deducing and calculating to obtain a stamping strength calculation model:
Figure BDA0003003550390000022
in the formula plStamping Strength pa(ii) a d-the inner radius of the key part of the mould is mm; dp-the outer radius of the critical part of the mould is mm; d, mold radius mm; rho-die density kg/m3(ii) a L-the length of the die is mm; f-coefficient of friction resistance;
Figure BDA0003003550390000023
-average press speed of the die in the press, m/s;
the data processing terminal obtains a calculated value of the pressure of the grinding tool in real time according to the stamping strength calculation model to obtain the thickness of the finished product of the die;
the temperature of key parts of the die is acquired through a temperature sensor and fed back to the data processing terminal, and a water cooling device is automatically called to perform constant temperature control on the die;
detecting the working state of the die by adding a vibration sensor, feeding the working state back to the data processing terminal, and controlling the running state of the die; the vibration sensor detects the working state of the die and comprises the following steps:
placing a vibration sensor in a mold to be processed, and carrying out initialization operation on the vibration sensor;
operating a vibration sensor on the die to be processed, and recording data of the vibration sensor;
the data are processed on line by using built-in data processing software, the position and the operation track of the die to be processed are determined, the range and the quality of the moving data are monitored on line, and the moving data are visualized on a display device;
after part or all of the operation areas are finished, uploading the data to a data processing terminal;
processing the acquired data and determining a position track in the operation process;
generating a spatial moving scanning point cloud, and generating two-dimensional and three-dimensional mold model diagrams of the measured area;
processing the ubiquitous signal data, calibrating the position of the mould data by using the obtained position, and generating a ubiquitous signal positioning feature library or the position of a positioning beacon source for ubiquitous signal positioning calculation;
processing the synchronous visual and physical environment data, extracting relevant information from the data, and calibrating the mould data and the space position by using the obtained position;
outputting a mold model diagram, the position of a ubiquitous positioning signal feature or a positioning beacon source, and multi-dimensional spatial information data of visual and physical environment spatial information for calibrating position service application.
Preferably, the data processing terminal obtains a calculated value of the pressure of the grinding tool in real time according to the stamping strength calculation model, and the obtaining of the thickness of the finished product of the die further comprises:
the method comprises the following steps that firstly, the data processing terminal obtains real-time measurement data of the die pressure from a pressure sensor;
secondly, performing polynomial fitting on a calculated value of the mold pressure obtained by real-time measurement, and correcting a measured real-time calculation mold pressure model;
and (4) utilizing the corrected real-time calculation mold pressure model to perform real-time tracking, and comparing the real-time tracking with the mold shape, thereby automatically judging the suitability of the mold pressure along with the measurement data.
The automatic judgment of the suitability of the die pressure along with the measurement data comprises the following steps:
the fitting function is
Figure BDA0003003550390000031
The estimated value of the die pressure and the historical measured values are collated, n data are corrected in total, the fitting times k are determined, and the historical data (L) to be correctedi,Pai) Substituting, tabulating and calculating a linear system of equations:
Figure BDA0003003550390000041
the system of linear equations is a positive definite matrix, so that there is a unique solution to solve for a0,a1,a2....ak；
Figure BDA0003003550390000042
In the formula PaFor mold pressure, L is the total mold length.
The data preprocessing method of the data processing software comprises the following steps of utilizing built-in data processing software to process data on line and determine the position and the operation track of a die to be processed:
step one, training subset selection and generation: each piece of information is called a training sample, and a plurality of training samples form a training set; if the training samples have k types, k is more than or equal to 2; according to the training sample classIs composed of two types of samples
Figure BDA0003003550390000043
A training subset, training subset XnExpressed as:
Xn＝{{xi}，{xj}}；
wherein the content of the first and second substances,
Figure BDA0003003550390000044
i, j ∈ {1, 2, …, n } i, j ∈ {1, 2, …, n } with i ≠ j, { x }iAnd { x }jRespectively representing the set of ith and jth samples in the training set;
step two, utilizing the training subset XnGenerating Fisher discriminant model yn＝fn(x)：
Step three, the nonlinear continuous function mapping method comprises the following steps:
output to a set of classifiers using a non-linear continuous function
Figure BDA0003003550390000045
Carry out mapping to
Figure BDA0003003550390000046
Non-linear mapping for nth classifier output and:
Figure BDA0003003550390000047
wherein a (a)>0) Is a relaxation variable introduced to enhance the generalization performance of the algorithm; if the classifier group consists of k classifiers, then
Figure BDA0003003550390000051
Is the result of data preprocessing.
The second step specifically comprises:
1) finding XnMean value of two kinds of samples of middle i, j
Figure BDA0003003550390000052
And
Figure BDA0003003550390000053
2) solving an intra-class divergence matrix Swn：
Figure BDA0003003550390000054
Wherein
Figure BDA0003003550390000055
Is that
Figure BDA0003003550390000056
The transposed matrix of (2);
3) solving an inter-class divergence matrix Sbn：
Figure BDA0003003550390000057
4) Calculating the projection direction Wn：
Wn＝Swn -1·Sbn；
5) Calculating a discrimination threshold w0n：
Figure BDA0003003550390000058
Then get the training subset XnThe corresponding discrimination model is as follows: y isn＝fn(x)＝Wn·x-w0n；
6) Determining a discriminant model corresponding to each training subset to generate
Figure BDA0003003550390000059
A classifier forming a classifier group, the classifier group outputting
Figure BDA00030035503900000510
Expressed as:
Figure BDA00030035503900000511
the data processing terminal controls the running state of the die and comprises:
step 1, respectively carrying out at least two groups of displacements detected by the vibration sensors to obtain a measured value alpha ij of each vibration sensor, wherein i represents a measured value of a plurality of groups, i is a natural number greater than 1, and j represents the number of the vibration sensors;
step 2, establishing an equation corresponding to the angle deviation kj and the vertical deviation cj for each measured value alpha ij of each vibration sensor:
(α11-c1)×k1＝(α12-c2)×k2＝(α13-c3)×k3＝…＝(α1j-cj)×kj
(α21-c1)×k1＝(α22-c2)×k2＝(α23-c3)×k3＝…＝(α1j-cj)×kj；
step 3, calculating the angle deviation ratio and the vertical deviation of the other vibration sensors by taking any vibration sensor as a reference point;
and 4, judging the vibration condition according to the vertical deviation and the angle deviation ratio.
According to a second aspect of the disclosed embodiments of the present invention, there is provided an intelligent mold control system for an information processing terminal, the intelligent mold control system comprising:
the pressure sensor is used for collecting pressure values at key positions of the die to obtain stamping strength, and feeding the stamping strength back to the data processing terminal to control the thickness of a die finished product;
the data processing terminal is used for obtaining a calculated value of the pressure of the grinding tool in real time according to the stamping strength calculation model to obtain the thickness of the finished product of the die;
the temperature sensor is used for acquiring the temperature of the key part of the mould, feeding the temperature back to the data processing terminal, automatically calling the water cooling device and carrying out constant temperature control on the mould;
and the vibration sensor is used for detecting the working state of the die, feeding back the working state to the data processing terminal and controlling the running state of the die.
According to a third aspect of the disclosed embodiments of the present invention, there is provided an intelligent mold, wherein the intelligent mold is equipped with the intelligent mold control system and implements the control method.
According to a fourth aspect of the disclosed embodiments of the present invention, there is provided a data processing terminal for intelligent mold control, comprising a memory and a processor, the memory storing a computer program which, when executed by the processor, causes the processor to execute the control method.
According to a fifth aspect of the disclosed embodiments of the present invention, there is provided a computer-readable storage medium storing a computer program which, when executed by a processor, causes the processor to execute the control method.
The technical scheme provided by the embodiment of the invention has the following beneficial effects:
according to the invention, the pressure sensor is added in the key part of the die during design, and the stamping strength is obtained through the feedback of the pressure value, so that the consistency of the thickness of the finished product is ensured. And a temperature sensor is added, a water cooling device is automatically called to ensure the constant temperature of the die, and a vibration sensor is added to detect the working state of the die to ensure the healthy operation of the equipment.
According to the invention, the pressure of the key part of the die is preliminarily calculated according to the combination of the pressure sensor and the shape of the key part of the die.
The data processing terminal obtains a calculated value of the pressure of the grinding tool in real time according to the stamping strength calculation model to obtain the thickness of the finished product of the die;
the temperature of key parts of the die is acquired through a temperature sensor and fed back to the data processing terminal, and a water cooling device is automatically called to perform constant temperature control on the die;
detecting the working state of the die by adding a vibration sensor, feeding the working state back to the data processing terminal, and controlling the running state of the die; the vibration sensor detects the working state of the die and comprises the following steps:
placing a vibration sensor in a mold to be processed, and carrying out initialization operation on the vibration sensor;
operating a vibration sensor on the die to be processed, and recording data of the vibration sensor;
the data are processed on line by using built-in data processing software, the position and the operation track of the die to be processed are determined, the range and the quality of the moving data are monitored on line, and the moving data are visualized on a display device;
after part or all of the operation areas are finished, uploading the data to a data processing terminal;
processing the acquired data and determining a position track in the operation process;
generating a spatial moving scanning point cloud, and generating two-dimensional and three-dimensional mold model diagrams of the measured area;
processing the ubiquitous signal data, calibrating the position of the mould data by using the obtained position, and generating a ubiquitous signal positioning feature library or the position of a positioning beacon source for ubiquitous signal positioning calculation;
processing the synchronous visual and physical environment data, extracting relevant information from the data, and calibrating the mould data and the space position by using the obtained position;
outputting a mold model diagram, the position of a ubiquitous positioning signal feature or a positioning beacon source, and multi-dimensional spatial information data of visual and physical environment spatial information for calibrating position service application. Intelligent control of the die is realized.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and together with the description, serve to explain the principles of the disclosure.
Fig. 1 is a flowchart of controlling an intelligent mold according to an embodiment of the present invention.
Fig. 2 is a schematic diagram of an intelligent mold control system according to an embodiment of the present invention.
In fig. 2: 1. a pressure sensor; 2. a data processing terminal; 3. a temperature sensor; 4. a shock sensor.
Fig. 3 is a flow chart of obtaining a calculated value of pressure of a grinding tool in real time by the data processing terminal according to the stamping strength calculation model to obtain a thickness of a finished product of the die according to the embodiment of the invention.
Detailed Description
Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the exemplary embodiments below are not intended to represent all implementations consistent with the present disclosure. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present disclosure, as detailed in the appended claims.
As shown in fig. 1, the present invention discloses an intelligent mold control method applied to a data processing terminal, the intelligent mold control method comprising:
and S101, collecting pressure values at key positions of the die through a pressure sensor to obtain stamping strength, feeding the stamping strength back to the data processing terminal, and controlling the thickness of a die finished product.
In step S101, the pressure sensor is combined with the shape of the key part of the mold to preliminarily calculate the pressure of the key part of the mold according to the formula:
Figure BDA0003003550390000081
and (3) deducing and calculating to obtain a stamping strength calculation model:
Figure BDA0003003550390000091
in the formula plStamping Strength pa(ii) a d-the inner radius of the key part of the mould is mm; dp-the outer radius of the critical part of the mould is mm; d, mold radius mm; rho-die density kg/m3(ii) a L-the length of the die is mm; f-coefficient of friction resistance;
Figure BDA0003003550390000092
-average press speed of the die in the press, m/s;
the data processing terminal obtains a calculated value of the pressure of the grinding tool in real time according to the stamping strength calculation model to obtain the thickness of the finished product of the die;
and S102, acquiring the temperature of the key part of the mold through a temperature sensor, feeding the temperature back to the data processing terminal, automatically calling a water cooling device, and carrying out constant temperature control on the mold.
And S103, detecting the working state of the die by adding a vibration sensor, feeding the working state back to the data processing terminal, and controlling the running state of the die.
In step S103, the step of detecting the working state of the mold by the vibration sensor includes:
placing a vibration sensor in a mold to be processed, and carrying out initialization operation on the vibration sensor;
operating a vibration sensor on the die to be processed, and recording data of the vibration sensor;
the data are processed on line by using built-in data processing software, the position and the operation track of the die to be processed are determined, the range and the quality of the moving data are monitored on line, and the moving data are visualized on a display device;
after part or all of the operation areas are finished, uploading the data to a data processing terminal;
processing the acquired data and determining a position track in the operation process;
generating a spatial moving scanning point cloud, and generating two-dimensional and three-dimensional mold model diagrams of the measured area;
processing the ubiquitous signal data, calibrating the position of the mould data by using the obtained position, and generating a ubiquitous signal positioning feature library or the position of a positioning beacon source for ubiquitous signal positioning calculation;
processing the synchronous visual and physical environment data, extracting relevant information from the data, and calibrating the mould data and the space position by using the obtained position;
outputting a mold model diagram, the position of a ubiquitous positioning signal feature or a positioning beacon source, and multi-dimensional spatial information data of visual and physical environment spatial information for calibrating position service application.
As shown in fig. 2, the present invention provides an intelligent mold control system for an information processing terminal, the intelligent mold control system comprising:
the pressure sensor 1 is used for collecting pressure values at key positions of the die to obtain stamping strength, feeding the stamping strength back to the data processing terminal and controlling the thickness of a die finished product;
the data processing terminal 2 is used for obtaining a calculated value of the pressure of the grinding tool in real time according to the stamping strength calculation model to obtain the thickness of a finished product of the die;
the temperature sensor 3 is used for collecting the temperature of the key part of the mould, feeding the temperature back to the data processing terminal, automatically calling the water cooling device and carrying out constant temperature control on the mould;
and the vibration sensor 4 is used for detecting the working state of the die, feeding back the working state to the data processing terminal and controlling the running state of the die.
The technical solution of the present invention is further described with reference to the following specific examples.
Example 1
As shown in fig. 3, the data processing terminal obtains a calculated value of the pressure of the grinding tool in real time according to the stamping strength calculation model, and the obtaining of the thickness of the finished product of the die comprises:
s201, the data processing terminal obtains real-time measurement data of the die pressure from the pressure sensor;
s202, performing polynomial fitting on a calculated value of the mold pressure obtained through real-time measurement, and correcting a measured real-time calculation mold pressure model;
and S203, utilizing the corrected real-time calculation mold pressure model to track in real time and comparing the model with the mold shape, thereby automatically judging the suitability of the mold pressure along with the measured data.
The automatic judgment of the suitability of the die pressure along with the measurement data comprises the following steps:
the fitting function is
Figure BDA0003003550390000101
The estimated value of the die pressure and the historical measured values are collated, n data are corrected in total, the fitting times k are determined, and the historical data (L) to be correctedi,Pai) Substituting, tabulating and calculating a linear system of equations:
Figure BDA0003003550390000111
the system of linear equations is a positive definite matrix, so that there is a unique solution to solve for a0,a1,a2....ak；
Figure BDA0003003550390000112
In the formula PaFor mold pressure, L is the total mold length.
Example 2
The invention utilizes the built-in data processing software to process data on line and determines the position and the operation track of a die to be processed, and the data preprocessing method of the data processing software comprises the following steps:
step one, training subset selection and generation: each piece of information is called a training sample, and a plurality of training samples form a training set; if the training samples have k types, k is more than or equal to 2; then according to the training sample category, it is composed of two types of samples
Figure BDA0003003550390000113
A training subset, training subset XnExpressed as:
Xn＝{{xi}，{xj}}；
wherein the content of the first and second substances,
Figure BDA0003003550390000114
i, j ∈ {1, 2, …, n } i, j ∈ {1, 2, …, n } with i ≠ j, { x }iAnd { x }jRespectively representing the set of ith and jth samples in the training set;
step two, utilizing the training subset XnGenerating Fisher judgmentOther model yn＝fn(x)：
Step three, the nonlinear continuous function mapping method comprises the following steps:
output to a set of classifiers using a non-linear continuous function
Figure BDA0003003550390000115
Carry out mapping to
Figure BDA0003003550390000116
Non-linear mapping for nth classifier output and:
Figure BDA0003003550390000117
wherein a (a)>0) Is a relaxation variable introduced to enhance the generalization performance of the algorithm; if the classifier group consists of k classifiers, then
Figure BDA0003003550390000121
Is the result of data preprocessing.
The second step specifically comprises:
1) finding XnMean value of two kinds of samples of middle i, j
Figure BDA0003003550390000122
And
Figure BDA0003003550390000123
2) solving an intra-class divergence matrix Swn：
Figure BDA0003003550390000124
Wherein
Figure BDA0003003550390000125
Is that
Figure BDA0003003550390000126
The transposed matrix of (2);
3) solving an inter-class divergence matrix Sbn：
Figure BDA0003003550390000127
4) Calculating the projection direction Wn：
Wn＝Swn -1·Sbn；
5) Calculating a discrimination threshold w0n：
Figure BDA0003003550390000128
Then get the training subset XnThe corresponding discrimination model is as follows: y isn＝fn(x)＝Wn·x-w0n；
6) Determining a discriminant model corresponding to each training subset to generate
Figure BDA0003003550390000129
A classifier forming a classifier group, the classifier group outputting
Figure BDA00030035503900001210
Expressed as:
Figure BDA00030035503900001211
the data processing terminal controls the running state of the die and comprises:
step 1, respectively carrying out at least two groups of displacements detected by the vibration sensors to obtain a measured value alpha ij of each vibration sensor, wherein i represents a measured value of a plurality of groups, i is a natural number greater than 1, and j represents the number of the vibration sensors;
step 2, establishing an equation corresponding to the angle deviation kj and the vertical deviation cj for each measured value alpha ij of each vibration sensor:
(α11-c1)×k1＝(α12-c2)×k2＝(α13-c3)×k3＝…＝(α1j-cj)×kj
(α21-c1)×k1＝(α22-c2)×k2＝(α23-c3)×k3＝…＝(α1j-cj)×kj；
step 3, calculating the angle deviation ratio and the vertical deviation of the other vibration sensors by taking any vibration sensor as a reference point;
and 4, judging the vibration condition according to the vertical deviation and the angle deviation ratio.
Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure disclosed herein. This application is intended to cover any variations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.
It will be understood that the present disclosure is not limited to the precise arrangements described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the present disclosure should be limited only by the attached claims.

Claims (10)

1. An intelligent mold control method is applied to a data processing terminal, and comprises the following steps:
collecting pressure values at key positions of the die through a pressure sensor to obtain stamping strength, feeding the stamping strength back to the data processing terminal, and controlling the thickness of a die finished product;
according to the combination of the pressure sensor and the shape of the key part of the mould, the pressure of the key part of the mould is preliminarily calculated according to a formula:
Figure FDA0003003550380000011
and (3) deducing and calculating to obtain a stamping strength calculation model:
Figure FDA0003003550380000012
in the formula: p is a radical oflStamping Strength pa(ii) a d-the inner radius of the key part of the mould is mm; dp-the outer radius of the critical part of the mould is mm; d, mold radius mm; rho-die density kg/m3(ii) a L-the length of the die is mm; f-coefficient of friction resistance;
Figure FDA0003003550380000013
-average press speed of the die in the press, m/s;
the data processing terminal obtains a calculated value of the pressure of the grinding tool in real time according to the stamping strength calculation model to obtain the thickness of the finished product of the die;
the temperature of key parts of the die is acquired through a temperature sensor and fed back to the data processing terminal, and a water cooling device is automatically called to perform constant temperature control on the die;
detecting the working state of the die by adding a vibration sensor, feeding the working state back to the data processing terminal, and controlling the running state of the die; the vibration sensor detects the working state of the die and comprises the following steps:
placing a vibration sensor in a mold to be processed, and carrying out initialization operation on the vibration sensor;
operating a vibration sensor on the die to be processed, and recording data of the vibration sensor;
the data are processed on line by using built-in data processing software, the position and the operation track of the die to be processed are determined, the range and the quality of the moving data are monitored on line, and the moving data are visualized on a display device;
after part or all of the operation areas are finished, uploading the data to a data processing terminal;
processing the acquired data and determining a position track in the operation process;
generating a spatial moving scanning point cloud, and generating two-dimensional and three-dimensional mold model diagrams of the measured area;
processing the ubiquitous signal data, calibrating the position of the mould data by using the obtained position, and generating a ubiquitous signal positioning feature library or the position of a positioning beacon source for ubiquitous signal positioning calculation;
processing the synchronous visual and physical environment data, extracting relevant information from the data, and calibrating the mould data and the space position by using the obtained position;
outputting a mold model diagram, the position of a ubiquitous positioning signal feature or a positioning beacon source, and multi-dimensional spatial information data of visual and physical environment spatial information for calibrating position service application.
2. The intelligent mold control method according to claim 1, wherein the data processing terminal obtains a calculated value of the pressure of the grinding tool in real time according to the stamping strength calculation model, and obtaining the thickness of the finished mold further comprises:
the method comprises the following steps that firstly, the data processing terminal obtains real-time measurement data of the die pressure from a pressure sensor;
secondly, performing polynomial fitting on a calculated value of the mold pressure obtained by real-time measurement, and correcting a measured real-time calculation mold pressure model;
and (4) utilizing the corrected real-time calculation mold pressure model to perform real-time tracking, and comparing the real-time tracking with the mold shape, thereby automatically judging the suitability of the mold pressure along with the measurement data.
3. The intelligent mold control method according to claim 2, wherein the automatically determining the suitability of the mold pressure with the measurement data comprises:
the fitting function is
Figure FDA0003003550380000021
The estimated value of the die pressure and the historical measured values are collated, n data are corrected in total, the fitting times k are determined, and the historical data (L) to be correctedi,Pai) Substituting, tabulating and calculating a linear system of equations:
Figure FDA0003003550380000022
the system of linear equations is a positive definite matrix, so that there is a unique solution to solve for a0,a1,a2....ak；
Figure FDA0003003550380000023
In the formula PaFor mold pressure, L is the total mold length.
4. The intelligent mold control method according to claim 1, wherein the data is processed online by using built-in data processing software, and the data preprocessing method of the data processing software comprises the following steps:
step one, training subset selection and generation: each piece of information is called a training sample, and a plurality of training samples form a training set; if the training samples have k types, k is more than or equal to 2; then according to the training sample category, it is composed of two types of samples
Figure FDA0003003550380000031
A training subset, training subset XnExpressed as:
Xn＝{{xi}，{xj}}；
wherein the content of the first and second substances,
Figure FDA0003003550380000032
and i ≠ j, { xiAnd { x }jRespectively representing the set of ith and jth samples in the training set;
step two, utilizing the training subset XnGenerating Fisher discriminant model yn＝fn(x)：
Step three, the nonlinear continuous function mapping method comprises the following steps:
output to a set of classifiers using a non-linear continuous function
Figure FDA0003003550380000033
Carry out mapping to
Figure FDA0003003550380000034
Non-linear mapping for nth classifier output and:
Figure FDA0003003550380000035
wherein a (a > 0) is a relaxation variable introduced to enhance the generalization performance of the algorithm; if the classifier group consists of k classifiers, then
Figure FDA0003003550380000036
Is the result of data preprocessing.
5. The intelligent mold control method according to claim 4, wherein the second step specifically comprises:
1) finding XnMean value of two kinds of samples of middle i, j
Figure FDA0003003550380000037
And
Figure FDA0003003550380000038
2) solving an intra-class divergence matrix Swn：
Figure FDA0003003550380000039
Wherein
Figure FDA0003003550380000041
Is that
Figure FDA0003003550380000042
The transposed matrix of (2);
3) solving an inter-class divergence matrix Sbn：
Figure FDA0003003550380000043
4) Calculating the projection direction Wn：
Wn＝Swn -1·Sbn；
5) Calculating a discrimination threshold w0n：
Figure FDA0003003550380000044
Then get the training subset XnThe corresponding discrimination model is as follows: y isn＝fn(x)＝Wn·x-w0n；
6) Determining a discriminant model corresponding to each training subset to generate
Figure FDA0003003550380000045
A classifier forming a classifier group, the classifier group outputting
Figure FDA0003003550380000046
Expressed as:
Figure FDA0003003550380000047
6. the intelligent mold control method according to claim 1, wherein the controlling of the operational state of the mold by the data processing terminal comprises:
step 1, respectively carrying out at least two groups of displacements detected by the vibration sensors to obtain a measured value alpha ij of each vibration sensor, wherein i represents a measured value of a plurality of groups, i is a natural number greater than 1, and j represents the number of the vibration sensors;
step 2, establishing an equation corresponding to the angle deviation kj and the vertical deviation cj for each measured value alpha ij of each vibration sensor:
(α11-c1)×k1＝(α12-c2)×k2＝(α13-c3)×k3＝…＝(α1j-cj)×kj
(α21-c1)×k1＝(α22-c2)×k2＝(α23-c3)×k3＝…＝(α1j-cj)×kj；
step 3, calculating the angle deviation ratio and the vertical deviation of the other vibration sensors by taking any vibration sensor as a reference point;
and 4, judging the vibration condition according to the vertical deviation and the angle deviation ratio.
7. An intelligent mold control system for implementing the intelligent mold control method according to any one of claims 1 to 6, which is used for an information processing terminal, the intelligent mold control system comprising:
the pressure sensor is used for collecting pressure values at key positions of the die to obtain stamping strength, and feeding the stamping strength back to the data processing terminal to control the thickness of a die finished product;
the data processing terminal is used for obtaining a calculated value of the pressure of the grinding tool in real time according to the stamping strength calculation model to obtain the thickness of the finished product of the die;
the temperature sensor is used for acquiring the temperature of the key part of the mould, feeding the temperature back to the data processing terminal, automatically calling the water cooling device and carrying out constant temperature control on the mould;
and the vibration sensor is used for detecting the working state of the die, feeding back the working state to the data processing terminal and controlling the running state of the die.
8. An intelligent mold equipped with the intelligent mold control system according to claim 7 and implementing the intelligent mold control method according to any one of claims 1 to 6.
9. A data processing terminal for intelligent mold control, characterized in that the data processing terminal for intelligent mold control comprises a memory and a processor, the memory storing a computer program which, when executed by the processor, causes the processor to perform the intelligent mold control method according to any one of claims 1-6.
10. A computer-readable storage medium, in which a computer program is stored, which, when executed by a processor, causes the processor to execute the intelligent mold control method according to any one of claims 1 to 6.
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