A method for controlling the bending of a sheet metal

Abstract

The application relates to a metal plate bending forming control method, plate parameters in each sample data are used to classify each sample data, so that a plurality of sample data corresponding to each plate parameter are stored in the same first database, then all sample data in each first database are classified based on plate bending data, so that a plurality of sample data corresponding to each plate bending data in the first database are stored in the same second database, then the plate bending rebound angle of the sample data in each second database is calculated based on the plate bending data, then the plate bending rebound angles of the sample data based on different plate parameters and different plate bending data are counted to construct and train a plate bending rebound prediction model, so that the plate bending rebound prediction model has the function of predicting the plate bending rebound angle.

Description

Technical Field

[0001] The present application relates to the technical field of metal sheet bending, and in particular to a metal sheet bending forming control method. Background Art

[0002] Sheet metal bending is a process that uses mechanical force to bend sheet metal into a desired shape. The main steps include designing the mold, preparing the workpiece, setting parameters, adjusting the equipment, and performing the bending. Throughout the bending process, the operator must select the appropriate mold and set appropriate parameters based on factors such as the workpiece material, thickness, and shape to ensure the quality and accuracy of the bend. The main equipment used in sheet metal bending is the bending machine and the mold. A bending machine is a device used to apply mechanical force to bend sheet metal and is typically categorized as a manual bending machine, a hydraulic bending machine, or a CNC bending machine.

[0003] Currently, most existing metal sheet bending methods calculate the springback angle of the sheet through material properties, such as elastic deformation, plastic deformation, yield strength, and elastic modulus, to predict the amount of springback and further predict the required bending angle. However, this method is not accurate in the actual use environment due to the presence of too many interference factors, which leads to a large error in the final metal sheet bending angle. Summary of the Invention

[0004] Based on this, it is necessary to provide a metal sheet bending forming control method to address the problem that the traditional method of predicting the springback angle of metal sheets through the springback angle formula is not accurate in the actual use environment due to too many interference factors, which in turn leads to a large error in the final bending angle of the metal sheet.

[0005] The present application provides a metal sheet bending forming control method, comprising:

[0006] Acquire a plurality of sample data, wherein the sample data includes plate parameters and plate bending data;

[0007] creating a plurality of first databases based on different plate parameters;

[0008] Performing a first classification on all sample data based on different plate parameters, so that each sample data is classified into a corresponding first database;

[0009] creating a plurality of second databases based on different plate bending data;

[0010] Performing a second classification on all sample data in each first database based on different plate bending data, so that all sample data in each first database are classified into the corresponding second database;

[0011] Calculating the plate bending springback angle of each sample data in each second database based on the plate bending data of all sample data in each second database;

[0012] Build and train a plate bending springback prediction model based on each sample data, each plate parameter, each plate bending data, and each plate bending springback angle;

[0013] Get a sample data to be tested;

[0014] Analyzing the sample data to be tested to obtain parameters of the plate to be tested and bending data of the plate to be tested in the sample data to be tested;

[0015] The obtained parameters of the plate to be tested and the bending data of the plate to be tested are input into the plate bending springback prediction model, the plate bending springback prediction model is started, and the plate bending strategy output by the plate bending springback prediction model is obtained.

[0016] Furthermore, the step of creating multiple first databases based on different plate parameters includes:

[0017] Counting the plate parameters in all sample data to obtain the number of types of plate parameters in all sample data;

[0018] Building a corresponding number of first databases based on the number of types of plate parameters in all sample data;

[0019] Each plate parameter corresponds to a first database.

[0020] Furthermore, the plate parameters include: plate composition data, plate thickness data and plate width data.

[0021] Furthermore, the first classification of all sample data based on different plate parameters so that each sample data is classified into a corresponding first database includes:

[0022] Count the plate composition data in all sample data to obtain the number of types of plate composition data in all sample data;

[0023] Creating a corresponding number of first supplementary databases based on the number of types of plate composition data in all the obtained sample data;

[0024] Each type of plate component data corresponds to a first supplementary database;

[0025] storing each sample data in a corresponding first supplementary database based on the plate composition data;

[0026] Select a first supplemental database;

[0027] Counting the plate thickness data of all sample data in the first supplementary database to obtain the number of types of plate thickness data of all sample data in the first supplementary database;

[0028] Creating a corresponding number of second supplementary databases based on the number of plate thickness data types in all the obtained sample data;

[0029] Each type of plate thickness data corresponds to a second supplementary database;

[0030] storing each sample data in a corresponding second supplementary database based on the plate thickness data;

[0031] Returning to selecting a first supplementary database until each first supplementary database has been selected once;

[0032] selecting a second supplementary database;

[0033] Counting the plate width data of all the sample data in the second supplementary database to obtain the number of types of plate width data of all the sample data in the second supplementary database;

[0034] Creating a corresponding number of third supplementary databases based on the number of plate width data types in all the obtained sample data;

[0035] Each type of plate width data corresponds to a third supplementary database;

[0036] storing each sample data in a corresponding third supplementary database based on the plate width data;

[0037] Return to the step of selecting a second supplementary database until each second supplementary database has been selected once.

[0038] Furthermore, the creation of multiple second databases based on different plate bending data includes:

[0039] selecting a first database;

[0040] Counting the plate bending data in all the sample data in the first database to obtain the number of types of plate bending data in all the sample data in the first database;

[0041] Creating a corresponding number of second databases based on the number of types of plate bending data in all sample data in the first database;

[0042] Each type of plate bending data corresponds to a second database;

[0043] Return to the step of selecting a first database until each first database has been selected once.

[0044] Furthermore, the plate bending data includes: a preset bending angle of the plate, an actual bending angle of the plate, and a temperature at a bending position of the plate.

[0045] Furthermore, the second classification of all sample data in each first database based on different plate bending data so that all sample data in each first database are classified into the corresponding second database includes:

[0046] Select a third supplementary database;

[0047] Counting the preset bending angles of the plates of all the sample data in the third supplementary database to obtain the number of types of the preset bending angles of the plates of all the sample data in the third supplementary database;

[0048] Creating a corresponding number of fourth supplementary databases based on the number of preset bending angle types of the plate in all the obtained sample data;

[0049] Corresponding the preset bending angle of each type of plate to a fourth supplementary database;

[0050] storing each sample data in a corresponding fourth supplementary database based on a preset bending angle;

[0051] Return to the step of selecting a third supplementary database until each third supplementary database has been selected once.

[0052] Furthermore, the second classification of all sample data in each first database based on different plate bending data so that all sample data in each first database are classified into the corresponding second database also includes:

[0053] Select a fourth supplementary database;

[0054] Counting the temperatures of the plate bending positions of all sample data in the third supplementary database to obtain the number of temperature types of the plate bending positions of all sample data in the third supplementary database;

[0055] Creating a corresponding number of fifth supplementary databases based on the number of temperature types at the plate bending positions in all the obtained sample data;

[0056] The temperature of each plate bending position corresponds to a fifth supplementary database;

[0057] storing each sample data in a corresponding fifth supplementary database based on the temperature at the bending position of the plate;

[0058] Return to the step of selecting a fourth supplementary database until each fourth supplementary database has been selected once.

[0059] Furthermore, the step of calculating the plate bending springback angle of each sample data in each second database based on the plate bending data of all sample data in each second database includes:

[0060] Selecting a sample data of a fifth supplementary database;

[0061] Obtaining the preset bending angle and the actual bending angle of the plate in the sample data;

[0062] Subtract the obtained preset bending angle of the plate from the actual bending angle of the plate to obtain a difference, which is the rebound angle of the sample data;

[0063] The step of selecting a sample data from a fifth supplementary database is returned until each sample data from each fifth supplementary database is selected once.

[0064] Furthermore, the step of calculating the plate bending springback angle of each sample data in each second database based on the plate bending data of all sample data in each second database further includes:

[0065] Select a fifth supplementary database;

[0066] Counting the rebound angle of each sample data in the fifth supplementary database to obtain the median of all rebound angles in the fifth supplementary database;

[0067] Create target screening ranges;

[0068] Obtaining a first preset value;

[0069] The sum of the median and the first preset value is used as the maximum boundary value of the target screening range;

[0070] Obtaining a second preset value;

[0071] The difference between the median and the second preset value is used as the minimum boundary value of the target screening range;

[0072] Filtering all the rebound angles in the fifth supplementary database based on the target screening range to obtain all the rebound angles included in the target screening range;

[0073] Calculating an average of all rebound angles included in the target screening range, and defining the obtained average value as the predicted rebound angle of the fifth supplementary database;

[0074] Return to the step of selecting a fifth supplementary database until each fifth supplementary database has been selected once.

[0075] The present application relates to a metal sheet bending forming control method, which classifies each sample data according to the plate parameters in each sample data, so that multiple sample data corresponding to each plate parameter are stored in the same first database, and then classifies all sample data in each first database based on the plate bending data, so that multiple sample data corresponding to each plate bending data in the first database are stored in the same second database, and then calculates the plate bending rebound angle of each sample data in the second database based on the plate bending data, and then constructs and trains a plate bending rebound prediction model by statistically analyzing the plate bending rebound angles of sample data based on different plate parameters and different plate bending data, so that the plate bending rebound prediction model has the function of predicting the plate bending rebound angle. BRIEF DESCRIPTION OF THE DRAWINGS

[0076] Figure 1 A schematic flow chart of a metal sheet bending and forming control method provided in one embodiment of the present application. DETAILED DESCRIPTION

[0077] In order to make the purpose, technical solutions and advantages of this application more clearly understood, the present application is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0078] like Figure 1 As shown, in one embodiment of the present application, the metal sheet bending forming control method includes the following S001 to S010:

[0079] S001, obtaining a plurality of sample data, wherein the sample data includes plate parameters and plate bending data.

[0080] S002: Create multiple first databases based on different plate parameters.

[0081] Specifically, different plate parameters correspond to a first database, and the name of the database is the full name of the plate parameter.

[0082] S003: Perform a first classification on all sample data based on different plate parameters, so that each sample data is classified into a corresponding first database.

[0083] Specifically, based on the difference in plate parameters in each sample data, sample data with the same plate parameters are stored in the same first database.

[0084] S004: Create multiple second databases based on different plate bending data.

[0085] Specifically, different plate bending data corresponds to a second database, and the name of the database is the full name of the plate bending data.

[0086] S005 , performing a second classification on all sample data in each first database based on different plate bending data, so that all sample data in each first database are classified into the corresponding second database.

[0087] Specifically, based on the first database, all sample data in each first database are classified based on different plate bending data of all sample data in each first database, and sample data with the same plate bending data are stored in the same second database.

[0088] S006: Calculate the plate bending springback angle of each sample data in each second database based on the plate bending data of all sample data in each second database.

[0089] S007, constructing and training a plate bending springback prediction model based on each sample data, each plate parameter, each plate bending data, and each plate bending springback angle.

[0090] S008, obtaining a sample data to be tested.

[0091] S009: Analyze the sample data to obtain parameters of the plate to be tested and bending data of the plate to be tested in the sample data.

[0092] S010, inputting the obtained parameters of the plate to be tested and the bending data of the plate to be tested into a plate bending springback prediction model, starting the plate bending springback prediction model, and obtaining a plate bending strategy output by the plate bending springback prediction model.

[0093] In this embodiment, each sample data is classified according to the plate parameters in each sample data, so that multiple sample data corresponding to each plate parameter are stored in the same first database, and then all sample data in each first database are classified based on the plate bending data, so that multiple sample data corresponding to each plate bending data in the first database are stored in the same second database, and then the plate bending rebound angle of each sample data in the second database is calculated based on the plate bending data, and then the plate bending rebound prediction model is constructed and trained by statistically analyzing the plate bending rebound angles of sample data based on different plate parameters and different plate bending data, so that the plate bending rebound prediction model has the function of predicting the plate bending rebound angle.

[0094] Furthermore, the material bending springback prediction model has the function of predicting the bending springback angle of the plate to predict the expected bending of different plates when bending, so that the springback of the plate after bending reaches the final desired bending angle.

[0095] In one embodiment of the present application, the creation of multiple first databases based on different plate parameters includes the following S002a to S002c:

[0096] S002a: Count the plate parameters in all sample data to obtain the number of types of plate parameters in all sample data.

[0097] Specifically, the plate parameters include: plate composition data, plate thickness data and plate width data.

[0098] S002b: Construct a corresponding number of first databases based on the number of types of plate parameters in all sample data.

[0099] S002c, corresponding each plate parameter to a first database.

[0100] Specifically, associations are established between different plate parameters and different first databases, so that corresponding sample data can be stored in corresponding databases.

[0101] The first classification of all sample data based on different plate parameters so that each sample data is classified into the corresponding first database includes the following S003a to S003p:

[0102] S003a, counting the plate composition data in all sample data to obtain the number of types of plate composition data in all sample data.

[0103] S003b: Create a corresponding number of first supplementary databases based on the number of types of plate composition data in all the obtained sample data.

[0104] S003c, corresponding each type of plate material component data to a first supplementary database.

[0105] Specifically, associations are established between different plate composition data and different first supplementary databases, so that corresponding sample data can be stored in the corresponding first supplementary database.

[0106] S003d, storing each sample data into a corresponding first supplementary database based on the plate composition data.

[0107] Specifically, steps S003a to S003d are storing all corresponding sample data into the corresponding first supplementary database based on different plate composition data.

[0108] S003e, selecting a first supplementary database.

[0109] S003f, counting the plate thickness data of all sample data in the first supplementary database to obtain the number of types of plate thickness data of all sample data in the first supplementary database.

[0110] S003g: Create a corresponding number of second supplementary databases based on the number of types of plate thickness data in all the obtained sample data.

[0111] S003h, each type of plate thickness data corresponds to a second supplementary database.

[0112] Specifically, associations are established between different plate thickness data and different second supplementary databases, so that corresponding sample data can be stored in the corresponding second supplementary database.

[0113] S003i. Store each sample data into a corresponding second supplementary database based on the plate thickness data.

[0114] S003j, returning to the process of selecting a first supplementary database until each first supplementary database has been selected once.

[0115] Specifically, steps S003e to S003j are storing all corresponding sample data into the corresponding second supplementary database based on different plate thickness data.

[0116] S003k, select a second supplementary database.

[0117] S0031 , counting the plate width data of all the sample data in the second supplementary database to obtain the number of types of plate width data of all the sample data in the second supplementary database.

[0118] S003m: Create a corresponding number of third supplementary databases based on the number of types of plate width data in all the obtained sample data.

[0119] S003n, each type of plate width data corresponds to a third supplementary database.

[0120] Specifically, associations are established between different plate width data and different second supplementary databases, so that corresponding sample data can be stored in the corresponding third supplementary database.

[0121] S003o, storing each sample data into a corresponding third supplementary database based on the plate width data.

[0122] S003p, returning to the process of selecting a second supplementary database until each second supplementary database has been selected once.

[0123] Specifically, steps S003k to S003p are to store all corresponding sample data into the corresponding third supplementary database based on different plate width data.

[0124] In this embodiment, the first supplementary database, the second supplementary database and the third supplementary database are established step by step through the plate composition data, plate thickness data and plate width data included in the plate parameters, and the first supplementary database contains at least one second supplementary database, and the second supplementary database contains at least one third supplementary database.

[0125] By gradually establishing the first supplementary database, the second supplementary database and the third supplementary database, each sample data is subdivided into the corresponding database, and the sample data contained in each third supplementary database has basically the same plate composition data, plate thickness data and plate width, so that all sample data in the third supplementary database have similar characteristics.

[0126] In one embodiment of the present application, the creation of multiple second databases based on different plate bending data includes the following S004a to S004e:

[0127] S004a: Select a first database.

[0128] S004b: Count the plate bending data in all the sample data in the first database to obtain the number of types of plate bending data in all the sample data in the first database.

[0129] Specifically, the plate bending data includes: a preset bending angle of the plate, an actual bending angle of the plate, and a temperature at the bending position of the plate.

[0130] S004c: Create a corresponding number of second databases based on the number of types of plate bending data in all sample data in the first database.

[0131] S004d, corresponding each type of plate bending data to a second database.

[0132] Specifically, associations are established between different plate bending data and different second databases, so that corresponding sample data can be stored in the corresponding second database.

[0133] S004e, returning to the process of selecting a first database until each first database has been selected once.

[0134] The second classification of all sample data in each first database based on different plate bending data, so that all sample data in each first database are classified into the corresponding second database, includes the following S005a to S005f:

[0135] S005a, selecting a third supplementary database.

[0136] S005b, counting the preset bending angles of the plates of all the sample data in the third supplementary database to obtain the number of types of preset bending angles of the plates of all the sample data in the third supplementary database.

[0137] S005c: creating a corresponding number of fourth supplementary databases based on the number of preset bending angle types of the plate in all the obtained sample data.

[0138] S005d, corresponding the preset bending angle of each type of plate to a fourth supplementary database.

[0139] Specifically, associations are established between preset bending angles of different plates and different fourth supplementary databases, so that corresponding sample data can be stored in the corresponding fourth supplementary database.

[0140] S005e: storing each sample data into a corresponding fourth supplementary database based on the preset bending angle.

[0141] S005f, returning to the process of selecting a third supplementary database, until each third supplementary database has been selected once.

[0142] Specifically, steps S005a to S005f are storing all corresponding sample data into the corresponding fourth supplementary database based on the preset bending angles of different plates.

[0143] The second classification of all sample data in each first database based on different plate bending data so that all sample data in each first database are classified into the corresponding second database also includes the following S005g to S0051:

[0144] S005g, select a fourth supplementary database.

[0145] S005h: Count the temperatures of the plate bending positions of all sample data in the third supplementary database to obtain the number of temperature types of the plate bending positions of all sample data in the third supplementary database.

[0146] S005i: Create a corresponding number of fifth supplementary databases based on the number of temperature types at the plate bending positions in all the obtained sample data.

[0147] S005j, correspond the temperature of each plate bending position to a fifth supplementary database.

[0148] Specifically, associations are established between the temperatures at different bending positions of the plate and different fifth supplementary databases, so that corresponding sample data can be stored in the corresponding fifth supplementary database.

[0149] S005k, storing each sample data into a corresponding fifth supplementary database based on the temperature at the bending position of the plate.

[0150] S0051, returning to the process of selecting a fourth supplementary database, until each fourth supplementary database has been selected once.

[0151] Specifically, steps S005g to S0051 are to store all corresponding sample data into the corresponding fifth supplementary database based on the temperatures at different bending positions of the plate.

[0152] In this embodiment, the fourth supplementary database and the fifth supplementary database are established step by step through the plate parameters including the preset bending angle of the plate and the temperature of the bending position of the plate, and the fourth supplementary database includes at least one fifth supplementary database.

[0153] By gradually establishing the fourth supplementary database and the fifth supplementary database, each sample data is subdivided into the corresponding database, and the sample data contained in each fifth supplementary database have basically the same preset bending angle of the plate and the temperature of the plate bending position, so that all sample data in the fifth supplementary database have similar characteristics.

[0154] In one embodiment of the present application, the step of calculating the plate bending springback angle of each sample data in each second database based on the plate bending data of all sample data in each second database includes the following steps S006a to S006d:

[0155] S006a, selecting a sample data from a fifth supplementary database.

[0156] S006b: Obtain the preset bending angle and the actual bending angle of the plate in the sample data.

[0157] S006c: Subtract the obtained preset bending angle of the plate from the actual bending angle of the plate to obtain a difference, which is the rebound angle of the sample data.

[0158] S006d, returning to the step of selecting a sample data from a fifth supplementary database, until each sample data from each fifth supplementary database is selected once.

[0159] In this embodiment, the preset bending angle and the actual bending angle of the plate in each sample data in each fifth supplementary database are obtained to calculate the difference between the two, and the obtained difference is defined as the rebound angle of the sample data.

[0160] In one embodiment of the present application, the step of calculating the plate bending springback angle of each sample data in each second database based on the plate bending data of all sample data in each second database further includes the following steps S001 to S006n:

[0161] S006e, selecting a fifth supplementary database.

[0162] S006f, counting the rebound angle of each sample data in the fifth supplementary database to obtain the median of all rebound angles in the fifth supplementary database.

[0163] S006g, create target screening range.

[0164] S006h, obtaining a first preset value.

[0165] S006i, taking the sum of the median and the first preset value as the maximum boundary value of the target screening range, performing a first update on the target screening range to obtain the target screening range after the first update.

[0166] S006j, obtain a second preset value.

[0167] Specifically, the first preset value may be equal to the second preset value. The first preset value may also not be equal to the second preset value, specifically, the first preset value is greater than the second preset value, or the first preset value is less than the second preset value.

[0168] The first preset value and the second preset value can be set manually.

[0169] S006k, taking the difference between the median and the second preset value as the minimum boundary value of the target screening range, and performing a second update on the target screening range to obtain a second updated target screening range.

[0170] Specifically, the median is greater than a second preset value.

[0171] S0061: Filter all the rebound angles in the fifth supplementary database based on the second updated target screening range to obtain all the rebound angles included in the second updated target screening range.

[0172] S006m, calculating the average value of all rebound angles included in the target screening range after the second update, and defining the obtained average value as the predicted rebound angle of the fifth supplementary database.

[0173] S006n, returning to the process of selecting a fifth supplementary database, until each fifth supplementary database has been selected once.

[0174] In this embodiment, taking a fifth supplementary database as an example, the rebound angles of all sample data in the fifth supplementary database are counted to obtain the median of all rebound angles. The sum of the median and the first preset value is used as the maximum boundary value of the target screening range, and the target screening range is updated for the first time. The difference between the median and the second preset value is used as the minimum boundary value of the target screening range, and the target screening range is updated for the second time.

[0175] Based on the target screening range obtained after the second update, all rebound angles are screened, and all rebound angles within the target screening range after the second update are screened out. The average value of all the screened rebound angles is calculated, and the average value is used as the predicted rebound angle of the fifth supplementary database.

[0176] Furthermore, all the sample data in the fifth supplementary database can be further classified through more constraints, so that the sample data after fine classification can be more robust in predicting the rebound angle of the plate bending.

[0177] The various technical features of the above-described embodiments can be combined arbitrarily, and the execution order of the method steps is not restricted. In order to make the description concise, not all possible combinations of the various technical features in the above-described embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0178] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present application. It should be noted that a person of ordinary skill in the art may make various modifications and improvements without departing from the spirit of the present application, and these modifications and improvements fall within the scope of protection of the present application. Therefore, the scope of protection of the present application shall be determined by the appended claims.

Claims

1. A metal sheet bending forming control method, characterized in that: The metal sheet bending forming control method comprises: Acquire a plurality of sample data, wherein the sample data includes plate parameters and plate bending data; creating a plurality of first databases based on different plate parameters; Performing a first classification on all sample data based on different plate parameters, so that each sample data is classified into a corresponding first database; The plate parameters include: plate composition data, plate thickness data and plate width data; The first classification of all sample data based on different plate parameters so that each sample data is classified into a corresponding first database includes: Count the plate composition data in all sample data to obtain the number of types of plate composition data in all sample data; Creating a corresponding number of first supplementary databases based on the number of types of plate composition data in all the obtained sample data; Each type of plate component data corresponds to a first supplementary database; storing each sample data in a corresponding first supplementary database based on the plate composition data; Select a first supplemental database; Counting the plate thickness data of all sample data in the first supplementary database to obtain the number of types of plate thickness data of all sample data in the first supplementary database; Creating a corresponding number of second supplementary databases based on the number of plate thickness data types in all the obtained sample data; Each type of plate thickness data corresponds to a second supplementary database; storing each sample data in a corresponding second supplementary database based on the plate thickness data; Returning to selecting a first supplementary database until each first supplementary database has been selected once; selecting a second supplementary database; Counting the plate width data of all the sample data in the second supplementary database to obtain the number of types of plate width data of all the sample data in the second supplementary database; Creating a corresponding number of third supplementary databases based on the number of plate width data types in all the obtained sample data; Each type of plate width data corresponds to a third supplementary database; storing each sample data in a corresponding third supplementary database based on the plate width data; Returning to the step of selecting a second supplementary database until each second supplementary database has been selected once; creating a plurality of second databases based on different plate bending data; Performing a second classification on all sample data in each first database based on different plate bending data, so that all sample data in each first database are classified into the corresponding second database; Calculating the plate bending springback angle of each sample data in each second database based on the plate bending data of all sample data in each second database; Build and train a plate bending springback prediction model based on each sample data, each plate parameter, each plate bending data, and each plate bending springback angle; Get a sample data to be tested; Analyzing the sample data to be tested to obtain parameters of the plate to be tested and bending data of the plate to be tested in the sample data to be tested; The obtained parameters of the plate to be tested and the bending data of the plate to be tested are input into the plate bending springback prediction model, the plate bending springback prediction model is started, and the plate bending strategy output by the plate bending springback prediction model is obtained.

2. The metal sheet bending forming control method according to claim 1, characterized in that: The step of creating multiple first databases based on different plate parameters includes: Counting the plate parameters in all sample data to obtain the number of types of plate parameters in all sample data; Building a corresponding number of first databases based on the number of types of plate parameters in all sample data; Each plate parameter corresponds to a first database.

3. The metal sheet bending forming control method according to claim 1, characterized in that: The step of creating multiple second databases based on different plate bending data includes: selecting a first database; Counting the plate bending data in all the sample data in the first database to obtain the number of types of plate bending data in all the sample data in the first database; Creating a corresponding number of second databases based on the number of types of plate bending data in all sample data in the first database; Each type of plate bending data corresponds to a second database; Return to the step of selecting a first database until each first database has been selected once.

4. The metal sheet bending forming control method according to claim 3, characterized in that: The plate bending data includes: a preset bending angle of the plate, an actual bending angle of the plate, and a temperature at a bending position of the plate.

5. The metal sheet bending forming control method according to claim 4, characterized in that: The second classification of all sample data in each first database based on different plate bending data so that all sample data in each first database are classified into the corresponding second database includes: Select a third supplementary database; Counting the preset bending angles of the plates of all the sample data in the third supplementary database to obtain the number of types of the preset bending angles of the plates of all the sample data in the third supplementary database; Creating a corresponding number of fourth supplementary databases based on the number of preset bending angle types of the plate in all the obtained sample data; Corresponding the preset bending angle of each type of plate to a fourth supplementary database; storing each sample data in a corresponding fourth supplementary database based on a preset bending angle; Return to the step of selecting a third supplementary database until each third supplementary database has been selected once.

6. The metal sheet bending forming control method according to claim 5, characterized in that: The second classification of all sample data in each first database based on different plate bending data so that all sample data in each first database are classified into the corresponding second database further includes: Select a fourth supplementary database; Counting the temperatures of the plate bending positions of all sample data in the third supplementary database to obtain the number of temperature types of the plate bending positions of all sample data in the third supplementary database; Creating a corresponding number of fifth supplementary databases based on the number of temperature types at the plate bending positions in all the obtained sample data; The temperature of each plate bending position corresponds to a fifth supplementary database; storing each sample data in a corresponding fifth supplementary database based on the temperature at the bending position of the plate; Return to the step of selecting a fourth supplementary database until each fourth supplementary database has been selected once.

7. The metal sheet bending forming control method according to claim 6, characterized in that: The calculating the plate bending springback angle of each sample data in each second database based on the plate bending data of all sample data in each second database includes: Selecting a sample data of a fifth supplementary database; Obtaining the preset bending angle and the actual bending angle of the plate in the sample data; Subtract the obtained preset bending angle of the plate from the actual bending angle of the plate to obtain a difference, which is the rebound angle of the sample data; The step of selecting a sample data from a fifth supplementary database is returned until each sample data from each fifth supplementary database is selected once.

8. The metal sheet bending forming control method according to claim 7, characterized in that: The step of calculating the plate bending springback angle of each sample data in each second database based on the plate bending data of all sample data in each second database further includes: Select a fifth supplementary database; Counting the rebound angle of each sample data in the fifth supplementary database to obtain the median of all rebound angles in the fifth supplementary database; Create target screening ranges; Obtaining a first preset value; The sum of the median and the first preset value is used as the maximum boundary value of the target screening range; Obtaining a second preset value; The difference between the median and the second preset value is used as the minimum boundary value of the target screening range; Filtering all the rebound angles in the fifth supplementary database based on the target screening range to obtain all the rebound angles included in the target screening range; Calculating an average of all rebound angles included in the target screening range, and defining the obtained average value as the predicted rebound angle of the fifth supplementary database; Return to the step of selecting a fifth supplementary database until each fifth supplementary database has been selected once.

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