CN112275985A - Improved compensation method for stroke of die forging press - Google Patents
Improved compensation method for stroke of die forging press Download PDFInfo
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- CN112275985A CN112275985A CN202011190468.3A CN202011190468A CN112275985A CN 112275985 A CN112275985 A CN 112275985A CN 202011190468 A CN202011190468 A CN 202011190468A CN 112275985 A CN112275985 A CN 112275985A
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B21—MECHANICAL METAL-WORKING WITHOUT ESSENTIALLY REMOVING MATERIAL; PUNCHING METAL
- B21J—FORGING; HAMMERING; PRESSING METAL; RIVETING; FORGE FURNACES
- B21J9/00—Forging presses
- B21J9/10—Drives for forging presses
- B21J9/20—Control devices specially adapted to forging presses not restricted to one of the preceding subgroups
Abstract
The invention discloses a die forging press stroke improved compensation method, and belongs to the technical field of die forging. The invention comprises the following steps: s1, in the normal operation process of the die forging press on site, acquiring image video information of the die forging press on site operation in real time by using the somatosensory 3D binocular vision sensor module; s2, reconstructing the die forging press and the virtual three-dimensional environment of the operation site thereof by using the three-dimensional reconstruction module based on image processing; and S3, under the virtual three-dimensional environment, acquiring and processing data by using the data processing module, building a BP artificial neural network model, calculating a corresponding compensation value, calculating a new stroke deviation value, detecting, judging whether the stroke deviation value meets an error range, and S4, displaying each item of data by using the display module. According to the invention, the actual operation state of the die forging press is comprehensively and accurately reflected by utilizing 3D modeling, the precision damage of the die forging caused by the inaccurate compensation value is effectively avoided, and the loss of manpower and material resources is greatly reduced.
Description
Technical Field
The invention relates to the technical field of die forging, in particular to an improved compensation method for the stroke of a die forging press.
Background
In large mechanical equipment and important equipment, such as steel rolling, power stations (hydroelectric power, thermal power and nuclear power), petroleum, chemical engineering, shipbuilding, aviation, aerospace, heavy weapons and the like, large free forgings and large die forgings are adopted, and the large forgings are forged by a large free forging hydraulic press and a large die forging hydraulic press. Therefore, the production of large forgings plays a very important role in advanced industrial countries, and even the industrial level and the defense strength of the large free forging hydraulic machine and the large die forging hydraulic machine can be measured from the variety, the number and the grade of the large free forging hydraulic machine and the large die forging hydraulic machine owned by one country.
For large-scale precision die forgings, such as aviation die forgings, the overall development trend is large-scale, precise and integrated, the precision degree of the forgings seriously affects the use condition in the later period, and the die forging press is suitable for the die forging press of the large-scale die forgings, the size of the die forging press is very large and can reach more than 30 meters, and in the die forging process of the press, the vertical column of the press bears huge pressure to generate axial deformation, so that the deviation between the actual displacement and the predicted displacement of the press is caused, the precision of the die forgings is further caused to be insufficient, and the repair cost is greatly improved.
Disclosure of Invention
The invention aims to provide an improved compensation method for the stroke of a die forging press, which aims to solve the problems in the background technology.
In order to solve the technical problems, the invention provides the following technical scheme: the improved compensation method for the stroke of the die forging press is characterized by comprising the following steps: the method comprises the following steps:
s1, in the normal operation process of the die forging press on site, acquiring image video information of the die forging press on site operation in real time by using the somatosensory 3D binocular vision sensor module;
s2, extracting three-dimensional information of the die forging press and the operation site thereof according to the image video information of the die forging press field operation acquired in the step S1, and reconstructing a virtual three-dimensional environment of the die forging press and the operation site thereof by using a three-dimensional reconstruction module based on image processing;
s3, acquiring, analyzing and processing data by using the data processing module according to the real-time virtual three-dimensional environment obtained in the step S2 to obtain a corresponding compensation value, substituting the compensation value for operation, calculating a new stroke deviation value, detecting the new stroke deviation value, and judging whether an error range meets the requirement, if so, directly outputting the error range; if not, performing secondary compensation treatment;
and S4, displaying each item of data by using a display module.
According to the technical scheme, in the steps S1-S2, the somatosensory 3D binocular vision sensor module is electrically connected with the three-dimensional reconstruction module based on image processing; in steps S3-S4, the data processing module is electrically connected to the display module.
According to the technical scheme, in step S1, the somatosensory 3D binocular vision sensor module adopts Kinect as an image sensor, which is a binocular vision sensor, and can collect image video information of all objects in the current working area of the swaging press, and transmit the image video information back to the control computer of the routine through the serial unshielded twisted pair.
According to the above technical solution, in step S2, the image processing-based three-dimensional reconstruction module includes an OpenCV machine vision processing library, an OpenGL library, and an OpenGL module;
the OpenCV machine vision processing library is used for filtering the image to eliminate noise in the image, and extracting characteristic points and matching corresponding points of the image to obtain matching point coordinates of an object in the working area of the die forging press;
the OpenGL library is used for identifying information obtained by the OpenCV machine vision processing library and used for converting the coordinates of the matching points of the object;
and the OpenGL module is used for carrying out three-dimensional reconstruction on the obtained information to obtain a virtual three-dimensional representation model of the object in the working range of the die forging press.
According to the above technical solution, in step S3, the data processing module includes a data acquisition unit, a processor, a database, a data retrieval unit, a data update unit, a compensation unit, and a detection unit;
the output end of the data acquisition unit is electrically connected with the input end of the processor, the output end of the processor is electrically connected with the input ends of the database, the compensation unit and the display module, the output end of the database is electrically connected with the input end of the data retrieval unit, the output end of the data retrieval unit is electrically connected with the input end of the data updating unit, the output end of the data updating unit is electrically connected with the input end of the processor, the output end of the compensation unit is electrically connected with the input end of the detection unit, and the output end of the detection unit is electrically connected with the input end of the database;
the data acquisition unit is used for acquiring various key data during the operation of the die forging press, and the key data comprises position data of the movable cross beam, the extrusion degree of a cushion plate of the workbench and the deformation degree of a strut of the press;
the processor is used for classifying and processing various data acquired by the data acquisition unit, the database is used for storing various data, the data retrieval unit is used for retrieving related data from the database, supplying the relevant data to the processor for processing and reference, the compensation unit compensates according to a compensation value obtained by the processor, the detection unit is used for judging the compensated error and judging whether secondary compensation is needed, and the data updating unit is used for updating the compensated data and feeding the data back to the processor for secondary analysis processing.
According to the above technical solution, in step S4, the display module is configured to display the data processed by the processor, the data updated by the data updating unit, and the compensated improvement result.
According to the technical scheme, the establishment of the BP artificial neural network model in the data processing module comprises the following steps:
1.1, defining key data of a die forging press;
1.2, collecting the key data in the step 1.1, and setting an error level classification standard;
1.3, determining an optimal error range of the stroke deviation value;
1.4, determining a mapping relation between the key data and a compensation value of a die forging press;
1.5, establishing an initial BP artificial neural network model, and inputting the key data into the model to obtain a compensation value of a die forging press;
and 1.6, adding the compensation value into the forging stroke parameter of the press, operating, calculating the stroke deviation value again, and comparing with the optimal error range.
The key data of the die forging press comprise movable cross beam position data, workbench backing plate extrusion degree and press strut deformation degree, the movable cross beam position data is recorded as H, and the workbench backing plate extrusion degree proportionality coefficient is k1The proportional coefficient of the deformation degree of the press pillar is k2At present, the die closing pressure of the die forging press is gradually increased, and an aggregate H ═ H is formed in each preset die closing pressure value and pressure maintaining preset time1，H2，H3，…，Hn},
And recording the compensation value h of the die forging press, wherein the mapping relation between the key data and the compensation value h is as follows:
h＝k1k2H；
recording the optimal error range of the stroke deviation value as A, inputting H into the initial BP artificial neural network model, and calculating the stroke deviation value delta H of the new die forging pressxWhen Δ HxAnd when the value is less than or equal to A, ending the simulation.
According to the technical scheme, in the step 1.2, the error grade classification standard comprises the grade classification of the extrusion degree of the base plate of the workbench and the grade classification of the deformation degree of the strut of the press;
the extrusion degree grade of the working table backing plate is divided into 0-3 grades, and the extrusion degree grade specifically comprises the following steps:
level 0: no extrusion at all;
level 1: no more than a is extruded;
and 2, stage: b is not more than extruded;
and 3, level: the extrusion is greater than b;
wherein a, b are constant values and 0< a < b in mm;
the deformation degree grade of the press pillar is divided into 0-3 grades, and the method specifically comprises the following steps:
level 0: no deformation at all;
level 1: the deformation angle is not more than c;
and 2, stage: the deformation angle is not more than d;
and 3, level: the deformation angle is larger than d;
wherein c, d are constant values, and 0< c < d,
according to the technical scheme, the relation between the extrusion degree level and the proportionality coefficient of the working table cushion plate is as follows:
when the extrusion degree of the working table backing plate is in the grade of 0-3, the proportionality coefficient k of the working table backing plate is1The values are respectively 1.0, m, n and p, wherein m, n and p are constant values, and 0<m<n<p<1；
The deformation degree grade and the proportionality coefficient relation of the press strut are as follows:
when the deformation degree of the press pillar is in the grade of 0-3, the proportionality coefficient k thereof2The values are respectively 1.0, x, y and z, wherein x, y and z are constant values, and 0<x<y<z<1。
Compared with the prior art, the invention has the following beneficial effects:
1. the method comprises the steps of collecting image video information of all objects in the current working area of the die forging press by using a somatosensory 3D binocular vision sensor, reconstructing a virtual three-dimensional environment of the die forging press and a working site thereof by using a three-dimensional reconstruction module based on image processing, accurately, effectively and conveniently simulating the working state of the die forging press in the virtual three-dimensional environment, generating any view, keeping correct projection relation between the views, generating a perspective view and an axis side view, enabling data to be clearer and more accurate, obtaining a more accurate compensation value and improving the precision of die forging pieces.
2. Under virtual three-dimensional environment, be provided with data processing module, utilize the treater to carry out analysis processes to the data of gathering, utilize the database to save, utilize detecting element, the data retrieval unit, data updating unit carries out the detection of data, build BP artificial neural network model, this model has stronger non-linear mapping ability and high self-learning, self-adaptation ability, clearly accurate data have been expressed, carry out the secondary simultaneously and detect and provide accurate compensation value, prevent to lead to the forging precision impaired because of the compensation value is accurate inadequately, reduce and restore the number of times.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention. In the drawings:
FIG. 1 is a schematic diagram of a die forging press stroke modification compensation method of the present invention;
FIG. 2 is a schematic block diagram showing the die forging press stroke modification compensation method of the present invention;
FIG. 3 is a schematic representation of the steps of a method of the present invention for improved compensation of die forging press stroke;
FIG. 4 is a schematic flow diagram of a method of the present invention for compensating for die forging press stroke modifications;
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1-4, the present invention provides the following technical solutions:
a method of die forging press stroke improvement compensation, the method comprising the steps of:
s1, in the normal operation process of the die forging press on site, acquiring image video information of the die forging press on site operation in real time by using the somatosensory 3D binocular vision sensor module;
s2, extracting three-dimensional information of the die forging press and the operation site thereof according to the image video information of the die forging press field operation acquired in the step S1, and reconstructing a virtual three-dimensional environment of the die forging press and the operation site thereof by using a three-dimensional reconstruction module based on image processing;
s3, acquiring, analyzing and processing data by using the data processing module according to the real-time virtual three-dimensional environment obtained in the step S2 to obtain a corresponding compensation value, substituting the compensation value for operation, calculating a new stroke deviation value, detecting the new stroke deviation value, and judging whether an error range meets the requirement, if so, directly outputting the error range; if not, performing secondary compensation treatment;
and S4, displaying each item of data by using a display module.
In steps S1-S2, the somatosensory 3D binocular vision sensor module is electrically connected to a three-dimensional reconstruction module based on image processing; in steps S3-S4, the data processing module is electrically connected to the display module.
In step S1, the somatosensory 3D binocular vision sensor module uses Kinect as an image sensor, which is a binocular vision sensor, and can collect image video information of all objects in the current working area of the die forging press, and transmit the image video information back to the control computer of the routine through the serial unshielded twisted pair.
In step S2, the image processing-based three-dimensional reconstruction module includes an OpenCV machine vision processing library, an OpenGL library, and an OpenGL module;
the OpenCV machine vision processing library is used for filtering the image to eliminate noise in the image, and extracting characteristic points and matching corresponding points of the image to obtain matching point coordinates of an object in the working area of the die forging press;
the OpenGL library is used for identifying information obtained by the OpenCV machine vision processing library and used for converting the coordinates of the matching points of the object;
and the OpenGL module is used for carrying out three-dimensional reconstruction on the obtained information to obtain a virtual three-dimensional representation model of the object in the working range of the die forging press.
In step S3, the data processing module includes a data acquisition unit, a processor, a database, a data retrieval unit, a data update unit, a compensation unit, and a detection unit;
the output end of the data acquisition unit is electrically connected with the input end of the processor, the output end of the processor is electrically connected with the input ends of the database, the compensation unit and the display module, the output end of the database is electrically connected with the input end of the data retrieval unit, the output end of the data retrieval unit is electrically connected with the input end of the data updating unit, the output end of the data updating unit is electrically connected with the input end of the processor, the output end of the compensation unit is electrically connected with the input end of the detection unit, and the output end of the detection unit is electrically connected with the input end of the database;
the data acquisition unit is used for acquiring various key data during the operation of the die forging press, and the key data comprises position data of the movable cross beam, the extrusion degree of a cushion plate of the workbench and the deformation degree of a strut of the press;
the processor is used for classifying and processing various data acquired by the data acquisition unit, the database is used for storing various data, the data retrieval unit is used for retrieving related data from the database, supplying the relevant data to the processor for processing and reference, the compensation unit compensates according to a compensation value obtained by the processor, the detection unit is used for judging the compensated error and judging whether secondary compensation is needed, and the data updating unit is used for updating the compensated data and feeding the data back to the processor for secondary analysis processing.
In step S4, the display module is configured to display the data after being processed by the processor, the data after being updated by the data updating unit, and the compensated improvement result.
In the data processing module, establishing a BP artificial neural network model, comprising the following steps:
1.1, defining key data of a die forging press;
1.2, collecting the key data in the step 1.1, and setting an error level classification standard;
1.3, determining an optimal error range of the stroke deviation value;
1.4, determining a mapping relation between the key data and a compensation value of a die forging press;
1.5, establishing an initial BP artificial neural network model, and inputting the key data into the model to obtain a compensation value of a die forging press;
and 1.6, adding the compensation value into the forging stroke parameter of the press, operating, calculating the stroke deviation value again, and comparing with the optimal error range.
The key data of the die forging press comprise movable cross beam position data, workbench backing plate extrusion degree and press strut deformation degree, the movable cross beam position data is recorded as H, and the workbench backing plate extrusion degree proportionality coefficient is k1The proportional coefficient of the deformation degree of the press pillar is k2At present, the die closing pressure of the die forging press is gradually increased, and an aggregate H ═ H is formed in each preset die closing pressure value and pressure maintaining preset time1，H2，H3，…，Hn},
And recording the compensation value h of the die forging press, wherein the mapping relation between the key data and the compensation value h is as follows:
h＝k1k2H；
recording the optimal error range of the stroke deviation value as A, inputting H into the initial BP artificial neural network model, and calculating the stroke deviation value delta H of the new die forging pressxWhen Δ HxAnd when the value is less than or equal to A, ending the simulation.
In the step 1.2, the error grade classification standard comprises the grade classification of the extrusion degree of a base plate of the workbench and the grade classification of the deformation degree of a strut of the press;
the extrusion degree grade of the working table backing plate is divided into 0-3 grades, and the extrusion degree grade specifically comprises the following steps:
level 0: no extrusion at all;
level 1: no more than a is extruded;
and 2, stage: b is not more than extruded;
and 3, level: the extrusion is greater than b;
wherein a, b are constant values and 0< a < b in mm;
the deformation degree grade of the press pillar is divided into 0-3 grades, and the method specifically comprises the following steps:
level 0: no deformation at all;
level 1: the deformation angle is not more than c;
and 2, stage: the deformation angle is not more than d:
and 3, level: the deformation angle is larger than d;
wherein c, d are constant values, and 0< c < d;
according to the technical scheme, the relation between the extrusion degree level and the proportionality coefficient of the working table cushion plate is as follows:
when the extrusion degree of the working table backing plate is in the grade of 0-3, the proportionality coefficient k of the working table backing plate is1The values are respectively 1.0, m, n and p; wherein m, n, p are constant values, and 0<m<n<p<1；
The deformation degree grade and the proportionality coefficient relation of the press strut are as follows:
when the deformation degree of the press pillar is in the grade of 0-3, the proportionality coefficient k thereof2The values are respectively 1.0, x, y and z, wherein x, y and z are constant values, and 0<x<y<z<1。
The first embodiment is as follows:
the method comprises the steps of reconstructing a virtual three-dimensional environment by using a somatosensory 3D binocular vision sensor and a three-dimensional reconstruction module based on image processing, taking a working table as a center, establishing an x axis in the left-right parallel direction of the working table, establishing a y axis in the front-back direction of the working table, establishing a z axis in the up-down direction of the working table, acquiring position data H of a movable beam as {0.1,0.2 and 0.5} by using a data acquisition unit under different mold clamping pressures by using a data acquisition unit,
the level of the extrusion degree of the cushion plate of the workbench is defined to be 0-3, and the method specifically comprises the following steps:
level 0: no extrusion at all;
level 1: extruding to be not more than 0.1 mm;
and 2, stage: extruding to be not more than 0.5 mm;
and 3, level: extruding for more than 0.5 mm;
defining the deformation degree grade of the press pillar into 0-3 grades, which specifically comprises the following steps:
level 0: no deformation at all;
level 1: the deformation angle is not more than 0.5 degrees;
and 2, stage: angle of deformation no greater than 1 °:
and 3, level: the deformation angle is greater than 1 deg..
Defining the relation between the extrusion degree grade and the proportionality coefficient of the cushion plate of the workbench as follows:
when the extrusion degree of the working table backing plate is in the grade of 0-3Coefficient of proportionality k thereof1The values are respectively 1.0,0.2,0.4 and 0.8;
defining the relationship between the deformation degree grade of the press pillar and the proportionality coefficient thereof as follows:
when the deformation degree of the press pillar is in the grade of 0-3, the proportionality coefficient k thereof2The values are 1.0,0.2,0.4 and 0.8 respectively.
The data acquisition unit is utilized to obtain that the extrusion degree of the working table backing plate is 1 grade, the deformation degree of the press strut is 2 grades, and therefore the proportionality coefficient k10.2, coefficient of proportionality k2＝0.4；
According to the formula:
h1＝k1k2H1＝0.2*0.4*0.1＝0.008
h2＝k1k2H2＝0.2*0.4*0.2＝0.016
h3＝k1k2H3＝0.2*0.4*0.5＝0.04
adding the compensation value H into the forging stroke parameter of the press, and acquiring position data delta H of the movable cross beam by using a data acquisition modulexThe optimum error range a is defined as 0.01 because of 0.005,0.007,0.012, and {0.005,0.007 }<0.01；0.007<0.01, the simulation ends.
Cause 0.012>0.01, data acquisition is carried out again to obtain that the position data H of the movable cross beam is 0.53, the extrusion degree of the cushion plate of the workbench is 2 grades, the deformation degree of the strut of the press is 2 grades, and therefore the proportionality coefficient k is10.4, coefficient of proportionality k2＝0.4；
According to the formula:
h＝k1k2H＝0.4*0.4*0.53＝0.0848
adding the compensation value H into the forging stroke parameter of the press, and acquiring position data delta H of the movable cross beam by using a data acquisition modulex0.0092 due to 0.0092<0.01, the simulation ends.
The working principle of the invention is as follows: utilize body to feel 3D binocular vision sensor and reconstruct out virtual three-dimensional environment with the three-dimensional reconstruction module based on image processing, in virtual three-dimensional environment, build BP artificial neural network model, utilize the treater to carry out data processing analysis, calculate the offset value, and substitute the offset value into BP artificial neural network model, calculate new stroke deviation value, contrast optimum error range, carry out the secondary and detect, effectively avoid leading to the forging precision impaired because of the offset value is accurate inadequately, promote the precision of forging, reduce manpower and materials loss.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
Finally, it should be noted that: although the present invention has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that changes may be made in the embodiments and/or equivalents thereof without departing from the spirit and scope of the invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. The improved compensation method for the stroke of the die forging press is characterized by comprising the following steps: the method comprises the following steps:
s1, in the normal operation process of the die forging press on site, acquiring image video information of the die forging press on site operation in real time by using the somatosensory 3D binocular vision sensor module;
s2, extracting three-dimensional information of the die forging press and the operation site thereof according to the image video information of the die forging press field operation acquired in the step S1, and reconstructing a virtual three-dimensional environment of the die forging press and the operation site thereof by using a three-dimensional reconstruction module based on image processing;
s3, acquiring, analyzing and processing data by using the data processing module according to the real-time virtual three-dimensional environment obtained in the step S2 to obtain a corresponding compensation value, substituting the compensation value for operation, calculating a new stroke deviation value, detecting the new stroke deviation value, and judging whether an error range meets the requirement, if so, directly outputting the error range; if not, performing secondary compensation treatment;
and S4, displaying each item of data by using a display module.
2. The die forging press stroke improvement compensation method of claim 1, wherein: in steps S1-S2, the somatosensory 3D binocular vision sensor module is electrically connected to a three-dimensional reconstruction module based on image processing; in steps S3-S4, the data processing module is electrically connected to the display module.
3. The die forging press stroke improvement compensation method of claim 1, wherein: in step S1, the somatosensory 3D binocular vision sensor module uses Kinect as an image sensor, which is a binocular vision sensor, and can collect image video information of all objects in the current working area of the die forging press, and transmit the image video information back to the control computer of the routine through the serial unshielded twisted pair.
4. The die forging press stroke improvement compensation method of claim 1, wherein: in step S2, the image processing-based three-dimensional reconstruction module includes an OpenCV machine vision processing library, an OpenGL library, and an OpenGL module;
the OpenCV machine vision processing library is used for filtering the image to eliminate noise in the image, and extracting characteristic points and matching corresponding points of the image to obtain matching point coordinates of an object in the working area of the die forging press;
the OpenGL library is used for identifying information obtained by the OpenCV machine vision processing library and used for converting the coordinates of the matching points of the object;
and the OpenGL module is used for carrying out three-dimensional reconstruction on the obtained information to obtain a virtual three-dimensional representation model of the object in the working range of the die forging press.
5. The die forging press stroke improvement compensation method according to claim 1, wherein: in step S3, the data processing module includes a data acquisition unit, a processor, a database, a data retrieval unit, a data update unit, a compensation unit, and a detection unit;
the output end of the data acquisition unit is electrically connected with the input end of the processor, the output end of the processor is electrically connected with the input ends of the database, the compensation unit and the display module, the output end of the database is electrically connected with the input end of the data retrieval unit, the output end of the data retrieval unit is electrically connected with the input end of the data updating unit, the output end of the data updating unit is electrically connected with the input end of the processor, the output end of the compensation unit is electrically connected with the input end of the detection unit, and the output end of the detection unit is electrically connected with the input end of the database;
the data acquisition unit is used for acquiring various key data during the operation of the die forging press, and the key data comprises position data of the movable cross beam, the extrusion degree of a cushion plate of the workbench and the deformation degree of a strut of the press;
the processor is used for classifying and processing various data acquired by the data acquisition unit, the database is used for storing various data, the data retrieval unit is used for retrieving related data from the database, supplying the relevant data to the processor for processing and reference, the compensation unit compensates according to a compensation value obtained by the processor, the detection unit is used for judging the compensated error and judging whether secondary compensation is needed, and the data updating unit is used for updating the compensated data and feeding the data back to the processor for secondary analysis processing.
6. The die forging press stroke improvement compensation method of claim 1, wherein: in step S4, the display module is configured to display the data after being processed by the processor, the data after being updated by the data updating unit, and the compensated improvement result.
7. The swaging press stroke improvement compensation method of claim 5, wherein: in the data processing module, establishing a BP artificial neural network model, comprising the following steps:
1.1, defining key data of a die forging press;
1.2, collecting the key data in the step 1.1, and setting an error level classification standard;
1.3, determining an optimal error range of the stroke deviation value;
1.4, determining a mapping relation between the key data and a compensation value of a die forging press;
1.5, establishing an initial BP artificial neural network model, and inputting the key data into the model to obtain a compensation value of a die forging press;
and 1.6, adding the compensation value into the forging stroke parameter of the press, operating, calculating the stroke deviation value again, and comparing with the optimal error range.
8. The swaging press stroke improvement compensation method of claim 7, wherein: the key data of the die forging press comprise movable cross beam position data, workbench backing plate extrusion degree and press strut deformation degree, the movable cross beam position data is recorded as H, and the workbench backing plate extrusion degree proportionality coefficient is k1The proportional coefficient of the deformation degree of the press pillar is k2At present, the die closing pressure of the die forging press is gradually increased, and an aggregate H ═ H is formed in each preset die closing pressure value and pressure maintaining preset time1，H2，H3，…，Hn},
And recording the compensation value h of the die forging press, wherein the mapping relation between the key data and the compensation value h is as follows:
h＝k1k2H；
recording the optimal error range of the travel deviation value as A, and inputting h into an initial BP personCalculating the stroke deviation value delta H of a new die forging press by using an industrial neural network modelxWhen Δ HxAnd when the value is less than or equal to A, ending the simulation.
9. The swaging press stroke improvement compensation method of claim 7, wherein: in the step 1.2, the error grade classification standard comprises a workbench base plate extrusion degree grade classification and a press strut deformation degree grade classification;
the extrusion degree grade of the working table backing plate is divided into 0-3 grades, and the extrusion degree grade specifically comprises the following steps:
level 0: no extrusion at all;
level 1: no more than a is extruded;
and 2, stage: b is not more than extruded;
and 3, level: the extrusion is greater than b;
wherein a, b are constant values and 0< a < b in mm;
the deformation degree grade of the press pillar is divided into 0-3 grades, and the method specifically comprises the following steps:
level 0: no deformation at all;
level 1: the deformation angle is not more than c;
and 2, stage: the deformation angle is not more than d;
and 3, level: the deformation angle is larger than d;
where c, d are constant values, and 0< c < d.
10. The swaging press stroke improvement compensation method of claim 9, wherein: the extrusion degree grade and the proportional coefficient relation of the working table backing plate are as follows:
when the extrusion degree of the working table backing plate is in the grade of 0-3, the proportionality coefficient k of the working table backing plate is1The values are respectively 1.0, m, n and p, wherein m, n and p are constant values, and 0<m<n<p<1；
The deformation degree grade and the proportionality coefficient relation of the press strut are as follows:
when the deformation degree of the press pillar is in the grade of 0-3, the proportionality coefficient k thereof2The values are respectively 1.0, x, y and z, wherein x, y and z are constant values, and 0<x<y<z<1。
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