Method for predicting bending springback radius of metal plate

Abstract

The invention discloses a metal plate bending springback radius prediction method, and relates to the technical field of metal material processing and manufacturing. According to the method, the theoretical rebound radius of the to-be-predicted metal plate is determined according to the first relational expression representing the relationship among the thickness of the metal plate, the bending core radius, the stress-strain curve and the rebound radius, the elastic-plastic thickness anisotropy of the material is considered in the first relational expression, and the rebound radius of the elastic-plastic thickness anisotropy material metal plate can be accurately predicted.

Description

Technical Field

[0001] The invention relates to the technical field of metal material processing and manufacturing, and in particular to a method for predicting the bending springback radius of a metal plate. Background Art

[0002] Metal sheet bending is the most common metal sheet processing method, but there is a springback problem after metal sheet bending. The springback needs to be predicted in production to control the metal sheet shape. The current metal sheet bending springback prediction model can only predict the springback radius of pure plastic thick isotropic material metal sheet, and cannot accurately predict the springback radius of elastic-plastic thick anisotropic material metal sheet. Summary of the invention

[0003] The invention solves the technical problem of how to accurately predict the springback radius of a metal plate made of thick elastic-plastic anisotropic material by providing a metal plate bending springback radius prediction method.

[0004] On the one hand, the present invention provides the following technical solutions:

[0005] A method for predicting the springback radius of a metal plate bending, comprising:

[0006] Acquire the actual thickness of the metal plate to be predicted, the actual radius of the bending core for bending the metal plate to be predicted, and the actual stress-strain curve of the material of the metal plate to be predicted;

[0007] Substituting the actual thickness, the actual radius and the actual stress-strain curve into a pre-established first relationship to determine a theoretical springback radius of the metal plate to be predicted;

[0008] The first relationship is used to characterize the relationship between the thickness of the metal plate, the bending core radius, the stress-strain curve and the springback radius.

[0009] Optionally, the first relational expression is:

[0010] Wherein, t is the thickness of the metal plate, R is the radius of the bending core, E is the elastic modulus of the metal plate material, a, b, c are all constants, and f(ε y ) is the stress-strain curve, y is the coordinate of the sampling point on the cross section of the metal plate in the thickness direction of the metal plate, and R′ is the springback radius.

[0011] Optionally, before obtaining the actual thickness of the metal plate to be predicted, the actual radius of the bending core of the metal plate to be predicted, and the actual stress-strain curve of the material of the metal plate to be predicted, the method further includes:

[0012] Establishing the first relationship;

[0013] Solve the first relationship for a, b, and c.

[0014] Optionally, solving a, b, and c in the first relational equation includes:

[0015] Controlling three bending cores with different radii to perform bending forming experiments on metal plate samples respectively, and obtaining three groups of experimental data, each group of the experimental data includes the coordinates of the sampling points, the thickness of the metal plate, the bending core radius, the stress-strain curve, the total number of the sampling points, and the springback radius;

[0016] Substituting the three groups of experimental data into the pre-established second relational expression respectively, three three-variable linear equations with a, b, and c as unknowns are obtained; wherein the second relational expression is established based on the first relational expression, and the second relational expression is used to characterize the relationship between a, b, c, the coordinates of the sampling points, the thickness of the metal plate, the bending core radius, the stress-strain curve, the total number of the sampling points, and the springback radius;

[0017] Solve the three linear equations of three variables to obtain a, b, and c.

[0018] Optionally, the second relational expression is:

[0019]

[0020] Wherein, i is the serial number of the sampling point, yi is the coordinate of the i-th sampling point, and n is the total number of the sampling points.

[0021] Optionally, a=-15636.538, b=6340.902, c=-562.725.

[0022] Optionally, after solving a, b, and c in the first relational equation, the method further includes:

[0023] Obtaining a target springback radius, the actual thickness, and the actual stress-strain curve;

[0024] The target springback radius, the actual thickness and the actual stress-strain curve are substituted into the first relationship to determine the bending core radius corresponding to the target springback radius.

[0025] On the other hand, the present invention also provides the following technical solutions:

[0026] A computer program product includes a computer program, wherein when the computer program is executed by a processor, the steps of any of the above-mentioned methods for predicting the bending springback radius of a metal plate are implemented.

[0027] On the other hand, the present invention also provides the following technical solutions:

[0028] A computer device comprises a memory, a processor and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of any method for predicting the springback radius of metal plate bending.

[0029] On the other hand, the present invention also provides the following technical solutions:

[0030] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of any method for predicting the springback radius of a metal plate bending.

[0031] One or more technical solutions provided by the present invention have at least the following technical effects or advantages:

[0032] The present invention determines the theoretical springback radius of the metal plate to be predicted according to a first relationship formula that characterizes the relationship between the metal plate thickness, the bending core radius, the stress-strain curve and the springback radius. The first relationship formula takes into account the elastic-plastic thickness anisotropy of the material and can accurately predict the springback radius of the metal plate of the elastic-plastic thickness anisotropy material. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings in the following description are some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0034] Figure 1 Flow chart of a method for predicting the springback radius of metal plate bending in an embodiment of the present invention;

[0035] Figure 2 Schematic diagram of stress distribution in a cross section of a metal plate during pure bending in an embodiment of the present invention;

[0036] Figure 3 Schematic diagram of stress-strain curve in an embodiment of the present invention;

[0037] Figure 4 Schematic diagram of the bent core and metal plate pattern in an embodiment of the present invention;

[0038] Figure 5 Schematic diagram of a device for predicting the springback radius of metal plate bending in an embodiment of the present invention. DETAILED DESCRIPTION

[0039] The embodiments of the present invention solve the technical problem of how to accurately predict the springback radius of a thick elastic-plastic anisotropic metal plate by providing a method and device for predicting the springback radius of a metal plate bending.

[0040] In order to better understand the technical solution of the present invention, the technical solution of the present invention will be described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0041] like Figure 1 As shown, the method for predicting the springback radius of metal plate bending according to an embodiment of the present invention includes:

[0042] Step S1, obtaining the actual thickness of the metal plate to be predicted, the actual radius of the bending core of the metal plate to be predicted, and the actual stress-strain curve of the material of the metal plate to be predicted;

[0043] Step S2, substituting the actual thickness of the metal plate to be predicted, the actual radius of the bending core of the metal plate to be predicted, and the actual stress-strain curve of the material of the metal plate to be predicted into a pre-established first relationship to determine a theoretical springback radius of the metal plate to be predicted;

[0044] The first relationship is used to characterize the relationship between the thickness of the metal plate, the bending core radius, the stress-strain curve and the springback radius.

[0045] The first relation can be: t is the thickness of the metal plate, R is the radius of the bending core, E is the elastic modulus of the metal plate material, a, b, c are all constants, f(ε y ) is the stress-strain curve, y is the coordinate of the sampling point on the metal plate cross section in the thickness direction of the metal plate, and R′ is the springback radius.

[0046] The embodiment of the present invention needs to establish a first relational expression before predicting the bending springback radius of the metal plate for the first time, and it is not necessary to establish the first relational expression when predicting the bending springback radius of the metal plate subsequently. Therefore, before step S1, the metal plate bending springback radius prediction method may also include: establishing a first relational expression; solving a, b and c in the first relational expression.

[0047] Taking the pure bending deformation of a rectangular cross-section metal plate as an example, first, the bending moment will produce elastic deformation on the cross section of the metal plate (such as Figure 2 As shown in the left figure), when the bending moment exceeds a certain limit bending moment, plastic yield will occur on the upper and lower surfaces of the metal plate (such as Figure 2 As the bending moment increases further, the plastic yield on the metal plate section will develop toward the neutral plane (assuming that the stress neutral plane and the geometric neutral plane coincide and do not deviate), until the entire section undergoes plastic yield (as shown in the middle figure). Figure 2 The bending moment at this time is the maximum load-bearing bending moment.

[0048] The process of establishing the first relationship is described below.

[0049] Assume that the thickness of the metal plate sample with a rectangular cross section is t, the width is b, and the radius of the bending core of the metal plate sample is R;

[0050] The maximum strain ε on the upper and lower surfaces of the metal plate sample is max for:

[0051]

[0052] Assume that the stress-strain curve of the metal plate sample material is: σ=f(ε y ), σ is stress, y is the coordinate of the sampling point on the cross section of the metal plate sample in the thickness direction of the metal plate sample, and the coordinate of the midpoint of the metal plate sample in the thickness direction is 0; ε y is the tangential strain (the direction perpendicular to the cross section of the metal plate sample and parallel to the plate surface of the metal plate sample) of the metal plate sample at the y coordinate point;

[0053] Under ideal conditions (the neutral plane of the metal plate pattern does not deviate and the thickness of the metal plate pattern does not change), the tangential strain distribution on the cross section of the metal plate pattern is:

[0054]

[0055] Assume that the tangential stress distribution on the cross section of the metal plate sample considering the thickness anisotropy is:

[0056] σ y =g(ε y ,y);

[0057] The bending moment M caused by the tangential stress is:

[0058]

[0059] Assume that the strain ε generated by the elastic recovery of the metal plate is ey Linear distribution in the thickness direction of the metal plate sample section:

[0060] ε emax is the maximum elastic recovery strain of the metal plate sample;

[0061] The bending moment M generated by the elastic recovery of the metal plate is e for:

[0062] σ ey is ey The corresponding recovery stress;

[0063] After elastic recovery, the bending moment of the metal plate sample loading is 0, then M e =M, therefore:

[0064]

[0065] Right now:

[0066]

[0067] The radius R' of the metal plate after elastic recovery satisfies the following formula:

[0068]

[0069] Right now:

[0070]

[0071] For elastic-plastic thick anisotropic materials, the stress-strain curve of the material is corrected by a quadratic curve:

[0072] g(ε y ,y)=(ay 2 +by+c)f(ε y )………………Formula (2);

[0073] Substituting formula (2) into formula (1), we get the first relation:

[0074]

[0075] At this point, the first relationship is established, and a, b, and c in the first relationship are unknown quantities.

[0076] Solving a, b and c in the first relationship may specifically include: controlling three bending cores with different radii to perform bending forming experiments on the metal plate sample respectively, and obtaining three sets of experimental data, each set of experimental data including the coordinates of the sampling points on the cross section of the metal plate sample, the thickness of the metal plate sample, the bending core radius, the stress-strain curve of the metal plate sample material, the total number of sampling points, and the springback radius of the metal plate sample; substituting the three sets of experimental data into the pre-established second relationship respectively, and obtaining three three-variable linear equations with a, b, and c as unknowns; wherein the second relationship is established based on the first relationship, and the second relationship is used to characterize the relationship between a, b, c, the coordinates of the sampling points, the thickness of the metal plate, the bending core radius, the stress-strain curve, the total number of sampling points, and the springback radius; solving the three three-variable linear equations to obtain a, b, and c.

[0077] Here, the integral in formula (3) is transformed into a difference summation and the undetermined coefficients are separated to obtain:

[0078]

[0079] That is, the second relationship is established:

[0080] Among them, i is the serial number of the sampling point, yi is the coordinate of the i-th sampling point, and n is the total number of sampling points.

[0081] Taking a metal plate sample with a thickness of 0.6 mm as an example, the stress-strain curve of the metal plate sample material is as follows: Figure 3 The bending core radius was controlled to be 3mm, 5mm, and 8mm respectively to carry out bending forming experiments on the metal plate sample. The experimental results are shown in Table 1:

[0082] Table 1

[0083]

[0084] The bending core radius, pattern inner radius, and pattern outer radius in Table 1 are as follows: Figure 4 As shown. By processing the three rows of data in Table 1, three sets of experimental data can be obtained. Each set of experimental data includes the coordinates of n sampling points, the thickness of the metal plate sample, the bending core radius, the stress-strain curve of the metal plate sample material, the total number of sampling points n (60), and the springback radius of the metal plate sample; the three sets of experimental data are substituted into the second relationship respectively, and three three-variable linear equations with a, b, and c as unknowns are obtained:

[0085]

[0086] By solving the three linear equations with three variables, we can get the values ​​of a, b, and c, such as a=-15636.538, b=6340.902, and c=-562.725.

[0087] Substituting a=-15636.538, b=6340.902, c=-562.725 into the established first relational expression, the complete first relational expression can be obtained, and the theoretical springback radius of the metal plate to be predicted can be calculated by the first relational expression.

[0088] In step S1, the actual thickness of the metal plate to be predicted and the actual radius of the bending core of the metal plate to be predicted can be obtained by direct measurement; the strain of the metal plate to be predicted can be continuously changed by experiment and the stress of the metal plate to be predicted under each strain can be detected to obtain the actual stress-strain curve of the metal plate material to be predicted. The theoretical springback radius of the metal plate material to be predicted can be predicted through step S2. Table 2 shows the theoretical springback radius of the metal plate predicted under certain bending core radii:

[0089] Table 2

[0090] R / mm 2 4 7 11 R′ / mm 2.48 5.15 8.72 17.25

[0091] From the above, it can be seen that the metal plate bending springback radius prediction method of the embodiment of the present invention can determine the theoretical springback radius of the metal plate to be predicted based on the first relationship that characterizes the relationship between the metal plate thickness, the bending core radius, the stress-strain curve and the springback radius. The first relationship takes into account the elastic-plastic thickness anisotropy of the material and can accurately predict the springback radius of the metal plate of the elastic-plastic thickness anisotropy material.

[0092] It can be understood that after obtaining the first relationship, the embodiment of the present invention can determine the theoretical springback radius based on the known bending core radius, and can also determine the required bending core radius based on the desired springback radius, that is, after solving a, b and c in the first relationship, the metal plate bending springback radius prediction method can also include: obtaining the target springback radius, the actual thickness of the metal plate to be predicted and the actual stress-strain curve of the metal plate material to be predicted; substituting the target springback radius, the actual thickness of the metal plate to be predicted and the actual stress-strain curve of the metal plate material to be predicted into the first relationship to determine the bending core radius corresponding to the target springback radius. Among them, the target springback radius is the springback radius that the metal plate needs to reach after the metal plate is bent and deformed, which is a known value; the bending core radius corresponding to the target springback radius is the bending core radius required to make the springback radius of the metal plate reach the target springback radius.

[0093] Based on the same inventive concept as the above-mentioned method for predicting the springback radius of metal plate bending, the embodiment of the present invention also provides the following Figure 5 The metal plate bending springback radius prediction device shown comprises:

[0094] An acquisition module, used to acquire the actual thickness of the metal plate to be predicted, the actual radius of the bending core of the metal plate to be bent and deformed, and the actual stress-strain curve of the metal plate material to be predicted;

[0095] A determination module, used for substituting the actual thickness of the metal plate to be predicted, the actual radius of the bending core of the metal plate to be predicted, and the actual stress-strain curve of the material of the metal plate to be predicted into a pre-established first relationship to determine the theoretical springback radius of the metal plate to be predicted;

[0096] The first relationship is used to characterize the relationship between the thickness of the metal plate, the bending core radius, the stress-strain curve and the springback radius.

[0097] Furthermore, the first relational expression can be:

[0098] Among them, t is the thickness of the metal plate, R is the radius of the bending core, E is the elastic modulus of the metal plate material, a, b, c are all constants, and f(ε y ) is the stress-strain curve, y is the coordinate of the sampling point on the metal plate cross section in the thickness direction of the metal plate, and R′ is the springback radius.

[0099] Furthermore, the metal plate bending springback radius prediction device may also include:

[0100] Establishing a module, for establishing a first relational expression;

[0101] The solution module is used to solve a, b, and c in the first relationship.

[0102] Furthermore, the solution module can be specifically used for:

[0103] The bending cores with three different radii were used to perform bending forming experiments on the metal plate samples, and three sets of experimental data were obtained. Each set of experimental data included the coordinates of the sampling points, the thickness of the metal plate, the radius of the bending core, the stress-strain curve, the total number of sampling points, and the springback radius.

[0104] Substitute the three sets of experimental data into the pre-established second relational expression respectively, and obtain three three-variable linear equations with a, b, and c as unknowns; wherein the second relational expression is established based on the first relational expression, and the second relational expression is used to characterize the relationship between a, b, c, the coordinates of the sampling points, the thickness of the metal plate, the bending core radius, the stress-strain curve, the total number of sampling points, and the springback radius;

[0105] Solve three linear equations with three variables and get a, b, and c.

[0106] Furthermore, the second relational expression can be:

[0107] Among them, i is the serial number of the sampling point, yi is the coordinate of the i-th sampling point, and n is the total number of sampling points.

[0108] Furthermore, the values ​​of abc may be a=-15636.538, b=6340.902, and c=-562.725.

[0109] Furthermore, the acquisition module can also be used to obtain the target springback radius, actual thickness and actual stress-strain curve;

[0110] The determination module can also be used to substitute the target springback radius, the actual thickness and the actual stress-strain curve into the first relationship to determine the target bending core radius.

[0111] Based on the same inventive concept as the above-mentioned method for predicting the bending springback radius of a metal plate, an embodiment of the present invention further provides a computer program product, including a computer program, which, when executed by a processor, implements the steps of any of the above-mentioned methods for predicting the bending springback radius of a metal plate.

[0112] Based on the same inventive concept as the above-mentioned method for predicting the bending springback radius of a metal plate, an embodiment of the present invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory, and the processor executes the computer program to implement the steps of any of the above-mentioned methods for predicting the bending springback radius of a metal plate.

[0113] Since the computer device introduced in the embodiment of the present invention is a computer device used to implement the metal plate bending springback radius prediction method in the embodiment of the present invention, based on the metal plate bending springback radius prediction method introduced in the embodiment of the present invention, the technical personnel in the field can understand the specific implementation of the computer device in the embodiment of the present invention and its various variations, so how the computer device implements the method in the embodiment of the present invention is not described in detail here. As long as the computer device used by the technical personnel in the field to implement the metal plate bending springback radius prediction method in the embodiment of the present invention, it belongs to the scope of protection of the present invention.

[0114] Based on the same inventive concept as the above-mentioned metal plate bending springback radius prediction method, the present invention also provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps of any of the above-mentioned metal plate bending springback radius prediction methods are implemented.

[0115] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, computer program products, computer equipment or computer-readable storage media. Therefore, the present invention may take the form of complete hardware embodiments, complete software embodiments, or embodiments in combination with software and hardware. Moreover, the present invention may take the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0116] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1These computer program instructions can also be stored in a computer-readable memory that can guide a computer or other programmable data processing device to work in a specific way, so that the instructions stored in the computer-readable memory produce a product including an instruction device, which implements the functions specified in the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0117] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0118] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0119] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A method for predicting the springback radius of metal plate bending, characterized in that: include: Acquire the actual thickness of the metal plate to be predicted, the actual radius of the bending core for bending the metal plate to be predicted, and the actual stress-strain curve of the material of the metal plate to be predicted; Substituting the actual thickness, the actual radius and the actual stress-strain curve into a pre-established first relationship to determine a theoretical springback radius of the metal plate to be predicted; The first relationship is used to characterize the relationship between the thickness of the metal plate, the bending core radius, the stress-strain curve and the springback radius.

2. The method for predicting the springback radius of metal plate bending according to claim 1, characterized in that: The first relation is: Wherein, t is the thickness of the metal plate, R is the radius of the bending core, E is the elastic modulus of the metal plate material, a, b, c are all constants, and f(ε y ) is the stress-strain curve, y is the coordinate of the sampling point on the cross section of the metal plate in the thickness direction of the metal plate, and R′ is the springback radius.

3. The method for predicting the springback radius of metal plate bending according to claim 2, characterized in that: Before obtaining the actual thickness of the metal plate to be predicted, the actual radius of the bending core of the metal plate to be predicted, and the actual stress-strain curve of the metal plate material to be predicted, the method further includes: Establishing the first relationship; Solve the first relationship for a, b, and c.

4. The method for predicting the springback radius of metal plate bending according to claim 3, characterized in that: The solving a, b, and c in the first relational equation includes: Controlling three bending cores with different radii to perform bending forming experiments on metal plate samples respectively, and obtaining three groups of experimental data, each group of the experimental data includes the coordinates of the sampling points, the thickness of the metal plate, the bending core radius, the stress-strain curve, the total number of the sampling points, and the springback radius; Substituting the three groups of experimental data into the pre-established second relational expression respectively, three three-variable linear equations with a, b, and c as unknowns are obtained; wherein the second relational expression is established based on the first relational expression, and the second relational expression is used to characterize the relationship between a, b, c, the coordinates of the sampling points, the thickness of the metal plate, the bending core radius, the stress-strain curve, the total number of the sampling points, and the springback radius; Solve the three linear equations of three variables to obtain a, b, and c.

5. The method for predicting the springback radius of metal plate bending according to claim 4, characterized in that: The second relation is: Wherein, i is the serial number of the sampling point, yi is the coordinate of the i-th sampling point, and n is the total number of the sampling points.

6. The method for predicting the springback radius of metal plate bending according to any one of claims 2 to 5, characterized in that: a=-15636.538, b=6340.902, c=-562.

725.

7. The method for predicting the springback radius of metal plate bending according to claim 3, characterized in that: After solving a, b, and c in the first relational equation, the method further includes: Obtaining a target springback radius, the actual thickness, and the actual stress-strain curve; The target springback radius, the actual thickness and the actual stress-strain curve are substituted into the first relationship to determine the bending core radius corresponding to the target springback radius.

8. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method described in any one of claims 1 to 7 are implemented.

9. A computer device, characterized in that: The invention comprises a memory, a processor and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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