A dynamic compensation method and system based on a bending machine

Abstract

This invention relates to a dynamic compensation method and system based on a bending machine, belonging to the field of sheet metal bending compensation technology. It includes: acquiring deflection data, stress data, sheet metal springback data, and bending machine parameters during bending operations; performing finite element analysis on the bending process based on the acquired data; calculating the error between the actual deflection and springback data of the bending machine and the finite element analysis data; establishing a deflection compensation model based on the error data; optimizing the model using an improved locust optimization algorithm; and using the bending compensation model to control the bending machine for deflection compensation based on real-time deflection data, and controlling the bending robot for springback compensation. This invention acquires deflection and springback data during the bending process, performs finite element analysis on the bending process, and establishes a model to control the bending compensation value, enabling real-time monitoring and compensation of bending deflection, thus improving the accuracy of bending compensation during the bending process.

Description

Technical Field

[0001] This invention relates to the field of sheet metal bending compensation technology, and in particular to a dynamic compensation method and system based on a bending machine. Background Technology

[0002] During the bending process of sheet metal, the stress on the bending machine's structure inevitably causes elastic deformation of the slider. Although the deformation is extremely small, for high-precision bending workpieces, the processed workpiece is still prone to deflection deformation, making it impossible to guarantee the consistency of the angle along the entire length. For traditional bending machines that use hydraulics as the force source, the uneven force applied by the cylinder will cause the workpiece to deflect along its length during the bending process. In addition, extra-long bending dies are often used to bend shorter workpieces, which will cause the upper die to apply force only in the middle part of the lower die. After long-term use, the middle part of the worktable is prone to sinking, which also seriously affects the straightness of the workpiece.

[0003] In existing technologies, the deflection value during the bending process is often detected and compensated. For example, the invention patent with application number CN202311135587.2 discloses a method for detecting and compensating the accuracy of sheet metal bending based on machine vision. However, when using machine vision for detection, it is limited by environmental factors, deflection scale and position. If the detection environment is poor, the deflection scale is small, or the deflection of a three-dimensional structure is measured, the error is often large and the deflection compensation accuracy is low. Summary of the Invention

[0004] To address the problem of low accuracy in compensating bending deflection of sheet metal parts, this invention provides a dynamic compensation method and system based on a bending machine.

[0005] The present invention provides a dynamic compensation method and system based on a bending machine, which adopts the following technical solution:

[0006] On one hand, the present invention provides a dynamic compensation method based on a bending machine, comprising the following steps:

[0007] Acquire deflection data, stress data, sheet metal springback data, and bending machine parameters during bending operations;

[0008] Finite element analysis was performed on the bending process based on the deflection data, stress data, sheet metal springback data, and bending machine parameters.

[0009] The errors between the actual deflection data and the finite element analysis deflection data of the bending machine, and the errors between the actual springback data and the finite element analysis springback data are calculated. A deflection compensation model is established based on the error data, and the improved locust optimization algorithm is used to optimize the model.

[0010] The bending compensation model uses real-time deflection data to control the hydraulic cylinders of the bending machine for deflection compensation, and controls the bending robot that grips the sheet metal parts for springback compensation.

[0011] Preferably, the deflection and stress data obtained during the bending operation of the bending machine include:

[0012] Strain rosettes are attached to the designated measurement points to obtain the strain at those points.

[0013] Calculate the stress values ​​in each direction at the measurement point based on the strain;

[0014] Calculate the principal stress value of the measuring point based on the stress values ​​in each direction.

[0015] Preferably, the stress values ​​in each direction at the measurement point are calculated based on the strain, using the following formula:

[0016]

[0017]

[0018]

[0019] in, , , These represent the strain values ​​at the measurement point in the horizontal, vertical, and angular directions, respectively, while E represents the Young's modulus of the slider and the worktable. Let be the Poisson's ratio of the slider and the worktable.

[0020] Preferably, the principal stress value of the measuring point is calculated based on the stress values ​​in each direction, using the following formula:

[0021]

[0022] in, The principal stress at the measurement point, , These represent the normal stresses at the measurement points in the horizontal and vertical directions, respectively. The shear stress at the measurement point is given.

[0023] Preferably, the bending process is analyzed using finite element analysis based on the deflection data, stress data, sheet metal springback data, and bending machine parameters, including:

[0024] Establish a finite element model of the bending process, and set the geometry, material properties and boundary conditions of the finite element model;

[0025] The finite element model is divided into discrete finite element meshes;

[0026] The stress distribution, deflection changes, and springback force of sheet metal parts during the bending process of a bending machine were simulated using the finite element analysis software ANSYS.

[0027] Preferably, a bending compensation model is established based on error data, including:

[0028] A BP neural network model is established as the bending compensation model. The input layer is the bending force of the bending machine, the deflection value of each point of the bending machine under the corresponding bending force, and the sheet metal data. The output layer outputs the deflection compensation applied by each hydraulic cylinder of the bending machine and the springback force of the sheet metal.

[0029] Preferably, the model is optimized using an improved locust optimization algorithm, including:

[0030] Initialize the parameters of the improved locust optimization algorithm and the initial position of each individual, and calculate the optimal individual position and its optimal fitness value;

[0031] Update the position of each individual and perform Levy flight adjustments to generate new individual positions;

[0032] Calculate the fitness value of the new individual, and update the position and fitness value of the best individual;

[0033] Perform iterative updates until the maximum number of iterations is reached, and output the parameters of the trained neural network.

[0034] Preferably, a bending compensation model is used to control the hydraulic cylinders of the bending machine for deflection compensation based on real-time deflection data, and the bending robot that grips the sheet metal part is controlled for springback compensation, including:

[0035] During the compensation process, deflection data is continuously collected and monitored, and the deflection data and sheet metal data are input into the bending compensation model. The model predicts the deflection compensation to be applied at each point and the springback compensation of the bending robot based on the input deflection data. Based on the deflection value predicted by the model, the hydraulic cylinders of the bending machine are controlled to apply the corresponding force for deflection compensation. Based on the springback force predicted by the model, the bending robot is controlled to apply the corresponding force, and the predicted compensation value is adjusted in real time until the compensation is completed.

[0036] Preferably, the springback compensation force applied by the bending robot is:

[0037]

[0038] Where F is the springback force of the sheet metal part, and θ is the angle between the bending robot arm and the sheet metal part.

[0039] On the other hand, an embodiment of the present invention provides a dynamic compensation system based on a bending machine, comprising:

[0040] The data acquisition module acquires deflection data, stress data, sheet metal springback data, and bending machine parameters during the bending operation.

[0041] The finite element analysis module performs finite element analysis on the bending process based on the deflection data, stress data, sheet metal springback data, and bending machine parameters.

[0042] The model building module calculates the error between the actual deflection data of the bending machine and the deflection data of the finite element analysis, as well as the error between the actual springback data and the springback data of the finite element analysis. Based on the error data, a deflection compensation model is established, and the improved locust optimization algorithm is used to optimize the model.

[0043] The deflection compensation module uses a bending compensation model to control the hydraulic cylinders of the bending machine to perform deflection compensation based on real-time deflection data, and controls the bending robot that grips the sheet metal parts to perform springback compensation.

[0044] In summary, the present invention has the following beneficial technical effects:

[0045] 1. By acquiring strain and stress data during the bending process, finite element analysis is performed on the bending process. A neural network model is used for bending compensation control, and an improved locust optimization algorithm is combined for optimization. This allows for real-time prediction and generation of bending compensation values, achieving accurate, efficient, adaptive, and scalable bending compensation control, thereby improving the precision and quality of the bending process.

[0046] 2. Inputting strain and stress data simultaneously during finite element analysis can better verify the accuracy of the finite element analysis and is helpful for the analysis of bending compensation;

[0047] 3. Simultaneously, it compensates for deflection changes during the bending process and springback after bending, thereby improving the quality of sheet metal bending. Attached Figure Description

[0048] Figure 1 This is a flowchart illustrating a dynamic compensation method based on a bending machine according to an embodiment of the present invention;

[0049] Figure 2 This is a schematic diagram of a dynamic compensation system based on a bending machine, as shown in an embodiment of the present invention. Detailed Implementation

[0050] The following is in conjunction with the appendix Figure 1-2 The present invention will be described in further detail below.

[0051] like Figure 1 As shown, this embodiment of the invention discloses a dynamic compensation method based on a bending machine, including the following steps:

[0052] Acquire deflection data, stress data, sheet metal springback data, and bending machine parameters during bending operations;

[0053] Finite element analysis was performed on the bending process based on the deflection data, stress data, sheet metal springback data, and bending machine parameters.

[0054] The errors between the actual deflection data and the finite element analysis deflection data of the bending machine, and the errors between the actual springback data and the finite element analysis springback data are calculated. A deflection compensation model is established based on the error data, and the improved locust optimization algorithm is used to optimize the model.

[0055] The bending compensation model uses real-time deflection data to control the hydraulic cylinders of the bending machine for deflection compensation, and controls the bending robot that grips the sheet metal parts for springback compensation.

[0056] As one possible implementation of this embodiment, obtaining deflection data and stress data during the bending operation of the bending machine includes:

[0057] Strain gauges are attached to the set measurement points to obtain the strain at the set measurement points. In this embodiment, the strain gauges are set in the vertical direction, horizontal direction and 45-degree angle direction of the measurement points to obtain the plane stress state of the measurement points.

[0058] Calculate the stress values ​​in each direction at the measurement point based on the strain:

[0059]

[0060]

[0061]

[0062] in, , , These represent the strain values ​​at the measurement point in the horizontal, vertical, and angular directions, respectively, while E represents the Young's modulus of the slider and the worktable. Let be the Poisson's ratio of the slider and the worktable.

[0063] Calculate the principal stress value at the measurement point based on the stress values ​​in each direction:

[0064]

[0065] in, The principal stress at the measurement point, , These represent the normal stresses at the measurement points in the horizontal and vertical directions, respectively. The shear stress at the measurement point is given.

[0066] Using strain gauges to measure strain and stress data during the bending process of a bending machine allows for flexible arrangement based on structural characteristics and needs, adapting to structures of different shapes and sizes. This enables more accurate measurement of strain in different areas of the structure, obtaining more detailed strain distribution information, while reducing measurement errors caused by strain gauge errors themselves or insecure fixing.

[0067] As one possible implementation of this embodiment, finite element analysis is performed on the bending process based on the deflection data, stress data, sheet metal springback data, and bending machine parameters, including:

[0068] The sheet metal springback data includes the upper die pressing amount, thickness, actual forming angle, material elastic modulus, etc.

[0069] A finite element model of the bending process is established, and the geometry, material properties, and boundary conditions of the finite element model are set. The model is then divided into discrete finite element meshes. When establishing the finite element model, it is necessary to simplify the model, treat the frame as an ideal welded structure, and model it as a single part. Parts that will not damage the frame structure are simplified or merged. Structures such as threads, process holes, and fillets that have little impact on strength and stiffness are ignored. This avoids generating a large number of finite element elements when small features and small structural parts are meshed, thereby improving computational efficiency.

[0070] Simultaneous input of strain and stress data allows for a more comprehensive analysis of the material's mechanical behavior. Strain describes the material's deformation, while stress describes its mechanical response. Analyzing the bending process of a bending machine based on the stress state allows for direct observation and analysis of the stress distribution and changes of the structure under different load conditions, thus providing a more intuitive understanding of the structure's stress state. In some cases, strain may not be easy to measure or determine accurately, especially in material nonlinearity, large deformation, or contact problems. Simultaneous stress analysis can avoid these problems, improve the accuracy and reliability of the model, and thus more accurately analyze the bending deflection value.

[0071] The finite element model is divided into discrete finite element meshes. When meshing, tetrahedral elements suitable for spatial solution are selected, and automatic meshing is performed using ANSYS software.

[0072] The stress distribution and deflection changes during the bending process of a bending machine were simulated using the finite element analysis software ANSYS.

[0073] As one possible implementation of this embodiment, a bending compensation model is established based on error data, including:

[0074] A BP neural network model is established as the bending compensation model. The input layer is the bending force of the bending machine, the deflection value of each point of the bending machine under the corresponding bending force, and the sheet metal data. The output layer outputs the deflection compensation applied by each hydraulic cylinder of the bending machine and the springback force of the sheet metal. The sheet metal data includes the length, width, thickness, and set forming angle of the sheet metal.

[0075] As one possible implementation of this embodiment, the model is optimized using an improved locust optimization algorithm, including:

[0076] Initialize the parameters of the improved locust optimization algorithm and the initial position of each individual, and calculate the optimal individual position and its optimal fitness value.

[0077] Update the position of each individual and perform Levy flight adjustments to generate new individual positions, which are:

[0078]

[0079]

[0080]

[0081] in, This represents the original location of the locust individual. This refers to the stride length of Levy's flight. For dot product, Let t be the current iteration number and T be the maximum iteration number. Let u and v follow a Gaussian distribution and β be in the range (0,2).

[0082]

[0083]

[0084] Where г is the gamma function and β = 1.5.

[0085] Calculate the fitness value of the new individual, and update the position and fitness value of the best individual.

[0086] Perform iterative updates until the maximum number of iterations is reached, and output the parameters of the trained neural network.

[0087] As one possible implementation of this embodiment, a bending compensation model is used to control the hydraulic cylinders of the bending machine to perform deflection compensation based on real-time deflection data, and the bending robot that grips the sheet metal part is controlled to perform springback compensation, including:

[0088] During the compensation process, deflection data is continuously collected and monitored, and the deflection data and sheet metal data are input into the bending compensation model. The model predicts the deflection compensation to be applied at each point and the springback compensation of the bending robot based on the input deflection data. Based on the deflection value predicted by the model, the hydraulic cylinders of the bending machine are controlled to apply the corresponding force for deflection compensation. Based on the springback force predicted by the model, the bending robot is controlled to apply the corresponding force, and the predicted compensation value is adjusted in real time until the compensation is completed.

[0089] As one possible implementation of this embodiment, the springback compensation force applied by the bending robot is:

[0090]

[0091] Where F is the springback force of the sheet metal part, and θ is the angle between the bending robot arm and the sheet metal part.

[0092] like Figure 2 As shown in the figure, an embodiment of the present invention provides a dynamic compensation system based on a bending machine, comprising:

[0093] The data acquisition module acquires deflection data, stress data, sheet metal springback data, and bending machine parameters during the bending operation.

[0094] The finite element analysis module performs finite element analysis on the bending process based on the deflection data, stress data, sheet metal springback data, and bending machine parameters.

[0095] The model building module calculates the error between the actual deflection data of the bending machine and the deflection data of the finite element analysis, as well as the error between the actual springback data and the springback data of the finite element analysis. Based on the error data, a deflection compensation model is established, and the improved locust optimization algorithm is used to optimize the model.

[0096] The deflection compensation module uses a bending compensation model to control the hydraulic cylinders of the bending machine to perform deflection compensation based on real-time deflection data, and controls the bending robot that grips the sheet metal parts to perform springback compensation.

[0097] The above are all preferred embodiments of the present invention and are not intended to limit the scope of protection of the present invention. Therefore, all equivalent changes made in accordance with the structure, shape and principle of the present invention should be covered within the scope of protection of the present invention.

Claims

1. A dynamic compensation method based on a bending machine, characterized in that, include: Acquire deflection data, stress data, sheet metal springback data, and bending machine parameters during bending operations; Finite element analysis was performed on the bending process based on the deflection data, stress data, sheet metal springback data, and bending machine parameters. The errors between the actual deflection data and the finite element analysis deflection data of the bending machine, and the errors between the actual springback data and the finite element analysis springback data are calculated. A deflection compensation model is established based on the error data, and the improved locust optimization algorithm is used to optimize the model. The bending compensation model is used to control the hydraulic cylinder of the bending machine to perform deflection compensation based on real-time deflection data, and to control the bending robot that grips the sheet metal parts to perform springback compensation. A bending compensation model is established based on the error data, including: A BP neural network model is established as the bending compensation model. The input layer is the bending force of the bending machine, the deflection value of each point of the bending machine under the corresponding bending force, and the sheet metal data. The output layer outputs the deflection compensation applied by each hydraulic cylinder of the bending machine and the springback force of the sheet metal. The model was optimized using an improved locust optimization algorithm, including: Initialize the parameters of the improved locust optimization algorithm and the initial position of each individual, and calculate the optimal individual position and its optimal fitness value; Update the position of each individual and perform Levy flight adjustments to generate new individual positions; Calculate the fitness value of the new individual, and update the position and fitness value of the best individual; Perform iterative updates until the maximum number of iterations is reached, and output the parameters of the trained neural network; The method of using a bending compensation model to control the hydraulic cylinders of the bending machine for deflection compensation based on real-time deflection data, and controlling the bending robot that grips the sheet metal part for springback compensation, includes: During the compensation process, deflection data is continuously collected and monitored, and the deflection data and sheet metal data are input into the bending compensation model. The model predicts the deflection compensation to be applied at each point and the springback compensation of the bending robot based on the input deflection data. Based on the deflection value predicted by the model, the hydraulic cylinders of the bending machine are controlled to apply the corresponding force for deflection compensation. Based on the springback force predicted by the model, the bending robot is controlled to apply the corresponding force, and the predicted compensation value is adjusted in real time until the compensation is completed.

2. The dynamic compensation method based on a bending machine according to claim 1, characterized in that, Obtain deflection and stress data during bending operations using a bending machine, including: Strain rosettes are attached to the designated measurement points to obtain the strain at those points. Calculate the stress values ​​in each direction at the measurement point based on the strain; Calculate the principal stress value of the measuring point based on the stress values ​​in each direction.

3. The dynamic compensation method based on a bending machine according to claim 2, characterized in that, The stress values ​​in each direction at the measurement point are calculated based on the strain, using the following formula: in, , , These represent the strain values ​​at the measurement point in the horizontal, vertical, and angular directions, respectively, while E represents the Young's modulus of the slider and the worktable. Let Poisson's ratio be the ratio of the slider and the worktable. , These represent the normal stresses at the measurement points in the horizontal and vertical directions, respectively. The shear stress at the measurement point is given.

4. The dynamic compensation method based on a bending machine according to claim 3, characterized in that, The principal stress value at the measurement point is calculated based on the stress values ​​in each direction. The formula is as follows: in, The principal stress at the measurement point, , These represent the normal stresses at the measurement points in the horizontal and vertical directions, respectively. The shear stress at the measurement point is given.

5. The dynamic compensation method based on a bending machine according to claim 1, characterized in that, Based on the deflection data, stress data, sheet metal springback data, and bending machine parameters, a finite element analysis of the bending process was performed, including: Establish a finite element model of the bending process, and set the geometry, material properties and boundary conditions of the finite element model; The finite element model is divided into discrete finite element meshes; The stress distribution, deflection changes, and springback force of sheet metal parts during the bending process of a bending machine were simulated using the finite element analysis software ANSYS.

6. The dynamic compensation method based on a bending machine according to claim 1, characterized in that, The springback compensation force applied by the bending robot is: Where F is the springback force of the sheet metal part, and θ is the angle between the bending robot arm and the sheet metal part.

7. A dynamic compensation system based on a bending machine, characterized in that, The method of claim 1 includes: The data acquisition module acquires deflection data, stress data, sheet metal springback data, and bending machine parameters during the bending operation. The finite element analysis module performs finite element analysis on the bending process based on the deflection data, stress data, sheet metal springback data, and bending machine parameters. The model building module calculates the error between the actual deflection data of the bending machine and the deflection data of the finite element analysis, as well as the error between the actual springback data and the springback data of the finite element analysis. Based on the error data, a deflection compensation model is established, and the improved locust optimization algorithm is used to optimize the model. The deflection compensation module uses a bending compensation model to control the hydraulic cylinders of the bending machine to perform deflection compensation based on real-time deflection data, and controls the bending robot that grips the sheet metal parts to perform springback compensation. A bending compensation model is established based on the error data, including: A BP neural network model is established as the bending compensation model. The input layer is the bending force of the bending machine, the deflection value of each point of the bending machine under the corresponding bending force, and the sheet metal data. The output layer outputs the deflection compensation applied by each hydraulic cylinder of the bending machine and the springback force of the sheet metal. The model was optimized using an improved locust optimization algorithm, including: Initialize the parameters of the improved locust optimization algorithm and the initial position of each individual, and calculate the optimal individual position and its optimal fitness value; Update the position of each individual and perform Levy flight adjustments to generate new individual positions; Calculate the fitness value of the new individual, and update the position and fitness value of the best individual; Perform iterative updates until the maximum number of iterations is reached, and output the parameters of the trained neural network.

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