A press balancer simplified modeling method, system and computer equipment

Abstract

The application discloses a press balancer simplified modeling method and system and computer equipment, relates to the technical field of presses, and solves the problems that the press balancer modeling needs joint simulation in the prior art, and the workload is large and the efficiency is low, has the beneficial effect of improving the calculation efficiency, and has the following scheme: a press balancer simplified modeling method, which comprises the following steps: setting a balancer between a slide and a frame of a press, and equivalent the effect of the balancer on the slide in the direction of motion of the slide to a spring-damping-preload force model; collecting the displacement of the slide in the working stroke, obtaining the force applied to the slide by the balancer, identifying the equivalent stiffness coefficient, the equivalent damping coefficient and the equivalent preload force parameter in the parameter identification model by using the recursive least square method, and obtaining the equivalent force model of the balancer according to the parameter results output after identification; and introducing the equivalent force model into a press system dynamics simulation environment as a simplified model of the balancer.

Description

Technical Field

[0001] This invention relates to the field of mechanical system dynamics modeling and simulation technology, and in particular to a simplified modeling method, system and computer equipment for a pressure machine balancer. Background Technology

[0002] The statements in this section are merely background information related to the present invention and do not necessarily constitute prior art.

[0003] Large-scale presses are core equipment in modern manufacturing, especially in the stamping production of automotive body panels. Their performance directly affects the forming quality and production efficiency of the products. (Reference) Figure 1 As shown, due to the large mass of the press slide 1 and upper die assembly, the system inertial effect is significant, which can easily generate a large load on the drive system under high-speed and heavy-load conditions, affecting the smoothness of motion. In order to reduce the load on the drive system, improve the smoothness of slide motion and reduce the thermal load on the brake, balancers are usually arranged on both sides or above the slide 1 to compensate for the weight of the slide in real time, thereby ensuring the working accuracy of the press. The balancer includes a balancer piston cylinder 3, and balancer piston rods 2 are respectively arranged on both sides of the balancer piston cylinder 3. The balancer piston rods 2 are connected to the slide 1, and the slide 1 is connected to the air tank 7. The air tank 7 is equipped with a pressure sensor 4, a lock-up valve 5 and a pressure gauge 8, and also has a safety valve 6.

[0004] With the development of computer technology and numerical simulation methods, dynamic simulation has become an important tool for the design and performance evaluation of press systems. When conducting whole-system dynamic simulation analysis of large presses including balancers, existing technologies typically employ a multi-software co-simulation approach to obtain high-fidelity simulation results: the press shaft system, transmission system, actuator linkage mechanism, and slider assembly are modeled as rigid-flexible coupled models in multibody dynamics software, while the hydraulic or pneumatic balancer is modeled in specialized hydraulic / pneumatic simulation software. The two systems interact in real-time through a joint interface, thereby completing the integrated mechanical-electrical-pneumatic co-simulation of the press.

[0005] The inventors found that although the above-mentioned co-simulation method has high analytical accuracy, it still has problems such as complex modeling process, cumbersome interface configuration, and long simulation calculation time. It is difficult to meet the needs of rapid iteration in engineering design. Moreover, the existing simulation method is highly dependent on the software environment and interface stability. The simulation process is easily affected by the coupling interface, and the debugging and maintenance costs are high, which has certain limitations in practical engineering applications. Summary of the Invention

[0006] To address the shortcomings of existing technologies, the purpose of this invention is to provide a simplified modeling method for press balancers. The balancer is equivalent to a simplified model composed of springs, damping, and preload, and this simplified model is input into the press multibody dynamics model in the form of translational forces, thereby avoiding joint simulation and significantly improving computational efficiency.

[0007] To achieve the above objectives, the present invention is implemented through the following technical solution: A simplified modeling method for a press balancer includes the following: A balancer is installed between the press slide and the frame, and the effect of the balancer in the direction of slide movement is equivalent to a spring-damping-preload model. A displacement sensor and a force sensor are installed at the slider. The displacement sensor collects the slider displacement within the working stroke, and the force sensor or the force sensor inside the balancer collects the force applied to the slider by the balancer. The displacement sensor and the force sensor send the collected data to the control unit. The control unit performs time differentiation on the acquired slider displacement to obtain the slider velocity. The spring-damping-preload model is used as the parameter identification model. Based on the acquired slider displacement, slider velocity and force, the recursive least squares method is used to identify the equivalent stiffness coefficient, equivalent damping coefficient and equivalent preload parameters in the parameter identification model. The equivalent force model of the balancer is obtained based on the parameter results output after identification. The equivalent force model is introduced into the dynamic simulation environment of the press system as a simplified model of the balancer.

[0008] As described above, in a simplified modeling method for a press balancer, the equivalent force provided by the spring-damping-preload model satisfies the following relationship: in, For equivalent forces, This is the equivalent stiffness coefficient. This refers to the displacement data of the slider within its working stroke. For slider speed, This is the equivalent damping coefficient. This is the equivalent preload.

[0009] In the simplified modeling method for a press balancer described above, the equivalent stiffness coefficient, the equivalent damping coefficient, and the equivalent preload in the equivalent force expression are related to the structural parameters and operating state parameters of the balancer. The equivalent force is calculated by the number of balancers, the working pressure collected by the pressure sensor inside the balancer, and the effective working area of ​​the balancer.

[0010] The simplified modeling method for a press balancer, as described above, involves identifying the equivalent stiffness coefficient, equivalent damping coefficient, and equivalent preload parameters in the parameter identification model using the recursive least squares method based on the collected slider displacement, slider velocity, and applied force. The equivalent force model of the balancer is then obtained based on the identified parameter results, including the following: The equivalent force provided by the spring-damping-preload model is rewritten from the linear parameter form to the form of a standard linear regression model. Set the initial conditions for the recursive least squares method, including initializing the parameter vector to be identified and the covariance matrix, and obtain the first... The equivalent force at each sampling time; By using the recursive least squares method to obtain each set of regression vectors and equivalent forces, the previous set of regression vectors is updated and corrected to obtain a new parameter vector to be identified. Substitute the finally identified parameter vector into the first... The equivalent simplified model of the balancer is obtained from the equivalent forces at each sampling time, which is the equivalent force model of the balancer.

[0011] As described above, a simplified modeling method for a press balancer involves rewriting the equivalent force provided by the spring-damping-preload model from a linear parametric form to a standard linear regression model (to transform the physical model into a parameter vector form that can be recognized by the MATLAB matrix lab). The formula is as follows:

[0012] in, F eq ( t () is the equivalent force. For the regression vector, Given the parameter vector to be identified, the corresponding formula is shown below:

[0013]

[0014] in, For the regression vector, K This is the equivalent stiffness coefficient. C This is the equivalent damping coefficient. F 0 represents the equivalent preload.

[0015] Let the sampling period be , No. The regression vector corresponding to each sampling time point as follows:

[0016] Therefore, we can obtain the first Equivalent force at each sampling time as follows:

[0017] The simplified modeling method for a pressure machine balancer, as described above, involves setting initial conditions for the recursive least squares method, including initializing the parameter vector to be identified and the covariance matrix, and obtaining the first... The equivalent force at each sampling time includes the following: Initial estimation of the parameter vector to be identified: To ensure generality and reproducibility of parameter identification, the parameter vector to be identified is set. initial vector It is a zero vector; Covariance matrix initialization: covariance matrix The formula representing the uncertainty of the current parameter estimate is as follows:

[0018] in, The coefficients of the covariance matrix and , It is the identity matrix; Each set of regression vectors is obtained by recursive least squares method. and equivalent force For the previous set of regression vectors The parameters are updated and corrected to obtain a new parameter vector to be identified. .

[0019] As described above, a simplified modeling method for a pressure machine balancer is used, wherein a set of regression vectors is obtained by recursive least squares method. and equivalent force For the previous set of regression vectors The parameters are updated and corrected to obtain a new parameter vector to be identified. It includes the following: Calculate the gain matrix, the first... Gain matrix at each sampling time The formula is as follows:

[0020] in, Forgetting factor and ; Obtain the prediction error of the parameter vector to be identified: Prediction Error It is the difference between the force at the current sampling moment and the predicted value based on the previous sampling moment. The following formula is used to obtain the force at the current sampling moment. Prediction error at each sampling time :

[0021] Iterative update of the parameter vector to be identified: The predicted values ​​of the parameters to be identified are updated based on the gain matrix and the prediction error, as shown in the following formula:

[0022] Update the covariance matrix:

[0023] For each sampling time Execute the above steps sequentially. After the recursive process is completed, the parameter vector... This is the final identification result.

[0024] Secondly, the present invention also provides a simplified modeling system for a press balancer, comprising: The data acquisition module, which is configured in the press, is used to acquire data on the displacement of the slider and the balancing force provided to the slider by the balancer. The parameter identification module is connected to the data acquisition module to receive the acquired data and run a parameter identification algorithm based on the recursive least squares method to solve for the equivalent stiffness coefficient, equivalent damping coefficient and equivalent preload of the equivalent simplified model of the balancer. The equivalent model construction module is used to construct the equivalent force model of the balancer based on the parameter results output by the parameter identification module. The equivalent force application module is used to apply the equivalent force model generated by the equivalent model building module to the multibody dynamics model of the press.

[0025] Thirdly, the present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps in the simplified modeling method for a pressure machine balancer.

[0026] Fourthly, the present invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps in the simplified modeling method for a pressure machine balancer as described above.

[0027] The beneficial effects of the present invention are as follows: This invention applies a simplified model of the balancing force provided by the balancer as an equivalent of a spring-damping-preload force, directly to the slider in the press. By using pressure and displacement sensors to acquire data on slider displacement and balancing force during actual press operation, it accurately reflects the compensation characteristics of the balancer under real-world conditions, simplifying the model while maintaining simulation accuracy. The recursive least squares method is used to identify the equivalent stiffness coefficient, equivalent damping coefficient, and equivalent preload force parameters in the parameter identification model. Based on the identified parameter results, the equivalent force model of the balancer is obtained, transforming the modeling process into one based on parameter identification. The equivalent modeling process is clearer and the operation steps are simplified, facilitating rapid model building, parameter adjustment, and multi-condition simulation analysis, effectively improving the efficiency of press system design and optimization. It avoids the joint simulation process between multibody dynamics software and hydraulic / pneumatic simulation software, effectively reducing model coupling, interface configuration, and computational burden during the solution process, significantly improving simulation efficiency. The entire method is independent of specific balancer structures or working media, and can be widely applied to presses with different structural forms as well as pneumatic or hydraulic balancers, demonstrating good versatility and engineering application value. Attached Figure Description

[0028] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0029] Figure 1 This is a schematic diagram of the balancer of a pressure machine.

[0030] Figure 2 This is a flowchart of a simplified modeling method for a press balancer provided in an embodiment of the present invention; Figure 3 This is the iterative process of updating the equivalent stiffness coefficient, equivalent damping coefficient, and equivalent preload force with sampling points under the 20 SPM condition provided in the embodiments of the present invention. Figure 4 This is a comparison chart of the identification results of the equivalent model of the balancer under the 20 SPM working condition and the measured data provided in the embodiment of the present invention; Figure 5 This is a comparison chart of the relative errors of different identification methods under the 20 SPM condition provided in the embodiments of the present invention; Figure 6 This is the iterative process of updating the equivalent stiffness coefficient, equivalent damping coefficient, and equivalent preload force with sampling points under the 40 SPM condition provided in the embodiments of the present invention. Figure 7 This is a comparison chart of the identification results of the equivalent model of the balancer under the 40 SPM working condition and the measured data provided in the embodiment of the present invention; Figure 8 This is a comparison chart of the relative errors of different identification methods under the 40 SPM condition provided in the embodiments of the present invention.

[0031] The diagram exaggerates the spacing or dimensions between parts to show their positions; the diagram is for illustrative purposes only.

[0032] The components include: 1. slider; 2. balancer piston rod; 3. balancer piston cylinder; 4. pressure sensor; 5. lock-up valve; 6. safety valve; 7. air tank; and 8. pressure gauge. Detailed Implementation

[0033] It should be noted that the following detailed description is illustrative and intended to provide further explanation of the invention. Unless otherwise specified, all technical and scientific terms used in this invention have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0034] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the scope of exemplary embodiments according to the invention. As used herein, unless otherwise expressly indicated by the invention, the singular form is intended to include the plural form as well. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0035] As introduced in the background section, the modeling of press balancers in the prior art requires co-simulation, which results in low workload and efficiency. In order to solve the above technical problems, this invention proposes a simplified modeling method for press balancers.

[0036] Example 1 In a typical embodiment of the present invention, reference is made to Figure 2 As shown, a simplified modeling method for a press balancer includes the following: Step 1: Install a displacement sensor at the slider of the press prototype, and collect displacement data of the slider during its working stroke using the displacement sensor. Simultaneously, a force sensor is installed at the balancer to directly collect data on the force applied by the balancer to the slider. .

[0037] In another alternative implementation, when the force of the balancer... When direct measurement is not possible, the pressure data of the working medium inside the balancer is collected using a pressure sensor. The pressure data is then converted into the corresponding force data based on the effective working area of ​​the balancer, as shown in the following formula: (1) (2) in, The number of balancers, The working pressure is collected by the pressure sensor inside the balancer. The effective working area of ​​the balancer The inner diameter of the balancer cylinder. This refers to the diameter of the balancer piston rod.

[0038] Step 2: Analyze the collected slider displacement By performing the time derivative, the slider velocity is obtained. The formula is as follows: (3) Step 3: Based on the actual operating characteristics of the press, the effect of the balancer in the direction of slider movement is equivalent to a spring-damping-preload model.

[0039] The equivalent forces provided by the spring-damped-preload model satisfy the following relationship: (4) in, For equivalent forces, This is the equivalent stiffness coefficient. This is the equivalent damping coefficient. This is the equivalent preload.

[0040] Step 4: Using the spring-damping-preload model as the parameter identification model, based on the collected displacement, velocity and force data, the equivalent stiffness coefficient, equivalent damping coefficient and equivalent preload parameters in the parameter identification model are identified by the recursive least squares method.

[0041] Step 4.1: Rewrite the equivalent force provided by the spring-damped-preload model from the linear parametric form to the form of a standard linear regression model, as shown in the following formula: (5) in, For the regression vector, Given the parameter vector to be identified, the corresponding formula is shown below: (6) (7) The signals acquired by actual sensors are obtained in discrete-time format. Let the sampling period be... , No. The regression vectors corresponding to each sampling time point are as follows: (8) Therefore, we can obtain the first The equivalent forces at each sampling time are as follows: (9) Step 4.2: Set the initial conditions for the recursive least squares method, including the initialization of the parameter vector to be identified and the initialization of the covariance matrix.

[0042] Step 4.2.1: Initial estimation of the parameter vector to be identified. To ensure generality and reproducibility of parameter identification, the parameter vector to be identified is set. initial vector It is the zero vector, that is .

[0043] Step 4.2.2: Initialize the covariance matrix. Covariance matrix The formula representing the uncertainty of the current parameter estimate is as follows: (10) in, The coefficients of the covariance matrix and , It is an identity matrix.

[0044] For the initial vector of the parameters to be identified The initial estimate has significant uncertainty, therefore a coefficient is set. .

[0045] Step 4.3: Recursive least squares method for each set of regression vectors obtained and equivalent force For the previous set of regression vectors The parameters are updated and corrected to obtain a new parameter vector to be identified. .

[0046] Step 4.3.1: Calculate the gain matrix. Gain matrix Decision No. The regression vector corresponding to each sampling time point The parameter vector to be identified The degree of correction. (The first...) Gain matrix at each sampling time The formula is as follows: (11) in, Forgetting factor and Its function is to balance the weights of data from previous sampling times and data from the current sampling time. (Settings) To increase the weight of the data from the latest sampling time.

[0047] Step 4.3.2: Obtain the prediction error of the parameter vector to be identified. The prediction error is the difference between the force at the current sampling time and the predicted value based on the previous sampling time. It is obtained using the following formula: Prediction error at each sampling time : (12) Step 4.3.3: Iteratively update the vector of parameters to be identified. The predicted values ​​of the parameters to be identified are updated based on the gain matrix and prediction error, using the following formula: (13) Step 4.3.4: Update the covariance matrix. The formula is as follows: (14) Step 4.3.5: For each sampling time... The calculations in steps 4.3.1 to 4.3.4 are executed sequentially. After the recursive process is completed, the parameter vector... This is the final identification result.

[0048] It is easy to understand that, in order to reduce the influence of the initial convergence transient, the average value of the parameter estimates in the later stable stage of the recursion is taken as the final values ​​of the equivalent stiffness coefficient, equivalent damping coefficient and equivalent preload.

[0049] Step 4.4: The final identified parameter vector Substituting into formula (9), we can obtain the equivalent simplified model of the press balancer, with relative error. The calculation accuracy of the equivalent simplified model of the balancer obtained is used as an evaluation index to verify its accuracy. The formula is as follows: (15) Step 5: After obtaining the equivalent force model of the balancer, the equivalent force model is introduced into the dynamic simulation environment of the press system to replace the traditional hydraulic or pneumatic balancer simulation model.

[0050] Step 5.1: Equivalent force as translational force A translational force is applied to the slider in the form of a force that is always opposite to the direction of gravity. The formula for the translational force is as follows: (16) To avoid introducing additional rotational effects, the equivalent force is applied to the center of mass of the slider. By applying the equivalent force to the center of mass, the force only produces a translational compensation effect on the slider without introducing additional torque, thus ensuring the accuracy and physical plausibility of the simulation results.

[0051] Step 5.2: During the simulation time progression, the multibody dynamics simulation software acquires the displacement and velocity information of the slider in real time at each time step, and substitutes it into the equivalent force model to calculate the equivalent force value at the current moment. The equivalent force value changes in real time with the slider's motion state, thus dynamically reflecting the compensation characteristics of the balancer on the slider.

[0052] Using the above method, in the multibody dynamics simulation process, there is no need to establish a hydraulic or pneumatic simulation model of the balancer, nor is it necessary to perform joint simulation of multibody dynamics and hydraulic / pneumatic systems. This allows for accurate reflection of the balancer's gravity compensation and dynamic influence on the slider. The system dynamics simulation environment can be a multibody dynamics simulation environment or other equivalent dynamics simulation environment; the specific modeling method does not constitute a technical feature of this invention.

[0053] To verify the accuracy of the simplified modeling method for the press balancer in this embodiment, the force obtained from the equivalent simplified model of the balancer is compared with the force collected by the sensors of the press prototype.

[0054] Example 1: Press production cycle time 20 SPM Production cycle time (SPM, Strokes Per Minute) refers to the number of strokes a press completes per unit time, and it characterizes the press's production efficiency. Example 1: Table 1 shows partial data on slider displacement and force collected by sensors under a 20 SPM operating condition. Table 1 Sensor Data Acquired under 20 SPM Conditions

[0055] To visually demonstrate the convergence characteristics of the simplified model parameters for identifying the balancer based on the recursive least squares method, Figure 3 The iterative process of updating the equivalent stiffness coefficient, equivalent damping coefficient, and equivalent preload as the sampling points are shown under the 20 SPM condition. Figure 2 This shows that the recursive least squares method can approximate the true parameters with fewer iterations and the identification results can eventually converge to a stable value.

[0056] Figure 4 The results show the comparison between the output balance force of the equivalent model identified by the recursive least squares method and the least squares method and the actual force measured by the sensor under the 20 SPM condition. Figure 5 This shows the change of the relative error over time, calculated based on formula (15). Figure 4 It can be seen that, compared to the least squares method, the model identified by the recursive least squares method has higher consistency with the measured data in terms of amplitude and phase, while the least squares method has a certain lag in phase. Combined with... Figure 5It can be seen that the relative error of the model identified by the recursive least squares method does not exceed 0.25%, while the maximum relative error of the least squares method exceeds 1.65%, indicating that the former has a significant advantage in terms of accuracy and dynamic response.

[0057] Example 2: Press production cycle 40 SPM In this embodiment, the 20 SPM condition corresponds to the low-speed operation of the press, while 40 SPM corresponds to the high-speed operation of the press. Under the 40 SPM condition, the slider motion frequency increases, the velocity and acceleration amplitudes increase, and the dynamic components in the balancer compensation force become more pronounced. This places higher demands on the accuracy of the equivalent simplified model and is more conducive to verifying the effectiveness of the equivalent model. Example 2: Partial data on slider displacement and force collected by the sensor are shown in Table 2. Table 2. Sensor Data Acquired under 40 SPM Conditions

[0058] Depend on Figure 6 It can be seen that under the 40 SPM condition, the equivalent stiffness coefficient, equivalent damping coefficient, and equivalent preload can still quickly approximate the true parameters and converge stably within a relatively small number of iterations, indicating that the recursive least squares method still has high stability and convergence under the high-speed operation of the press. (Reference) Figure 7 As shown, under high-speed operation of the press, the force output by the model identified using the recursive least squares method still maintains a high degree of consistency with the measured force, while the model identified using the least squares method exhibits a large deviation and significant phase lag. Combined with... Figure 8 It can be seen that the relative error of the model identified by the recursive least squares method does not exceed 0.35%, while the maximum relative error of the least squares method exceeds 3.43%, further indicating that the former has better accuracy and adaptability under high dynamic conditions.

[0059] Although the dynamic effects of the system are more complex and model errors are more likely to accumulate and amplify under high-speed operation of the press, the equivalent simplified model identified by the recursive least squares method can still maintain a low error level. The relative error is slightly increased compared to the 20 SPM condition, indicating that the simplified modeling method for the press balancer provided in this embodiment has high accuracy not only under medium and low speed conditions, but also under high speed conditions. This verifies the versatility and engineering applicability of the method within different production cycle ranges of the press.

[0060] Example 2 This embodiment provides a simplified modeling system for a press balancer, including: The data acquisition module, which is configured on the press prototype, is used to collect the displacement of the slider and the force exerted on the slider by the weighing instrument. The parameter identification module communicates with the data acquisition module to receive the acquired data and runs a parameter identification algorithm based on recursive least squares to solve for the equivalent stiffness coefficients of the equivalent simplified model of the pressure machine balancer. Equivalent damping coefficient and equivalent preload ; The equivalent model construction module is used to construct the equivalent force model of the balancer based on the parameter results output by the parameter identification module. In an optional implementation, the equivalent model construction module can also verify the calculation accuracy of the equivalent model to ensure that the constructed equivalent model meets the requirements of press dynamics simulation. The equivalent force application module is used to apply the equivalent force model generated by the equivalent model building module to the multibody dynamics model of the press.

[0061] The parameter identification module, the equivalent model construction module, and the equivalent force application module are all integrated in the control unit, which is a PLC controller or other types of controller.

[0062] It should be noted that the specific implementation of the simplified modeling system for the pressure machine balancer in this embodiment of the invention is similar to the specific implementation of the simplified modeling method for the pressure machine balancer in Embodiment 1. For details, please refer to the description in the method section. To reduce redundancy, it will not be repeated here.

[0063] Example 3: This embodiment provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps in the simplified modeling method for pressure machine balancers described above. The processor is an existing processor.

[0064] Example 4: This example provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the steps in the simplified modeling method for pressure machine balancers described above.

[0065] The memory is existing memory.

[0066] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A simplified modeling method for a press balancer, characterized in that, Includes the following: A balancer is installed between the press slide and the frame, and the effect of the balancer in the direction of slide movement is equivalent to a spring-damping-preload model. A displacement sensor and a force sensor are installed at the slider. The displacement sensor collects the slider displacement within the working stroke, and the force sensor or the force sensor inside the balancer collects the force applied to the slider by the balancer. The displacement sensor and the force sensor send the collected data to the control unit. The control unit performs time differentiation on the acquired slider displacement to obtain the slider velocity. The spring-damping-preload model is used as the parameter identification model. Based on the acquired slider displacement, slider velocity and force, the recursive least squares method is used to identify the equivalent stiffness coefficient, equivalent damping coefficient and equivalent preload parameters in the parameter identification model. The equivalent force model of the balancer is obtained based on the parameter results output after identification. An equivalent force model is introduced into the dynamic simulation environment of the press system as a simplified model of the balancer. The equivalent force provided by the spring-damping-preload model satisfies the following relationship: in, For equivalent forces, This is the equivalent stiffness coefficient. This refers to the displacement data of the slider within its working stroke. For slider speed, This is the equivalent damping coefficient. This is the equivalent preload; The equivalent stiffness coefficient, the equivalent damping coefficient, and the equivalent preload in the equivalent force expression are related to the structural parameters and operating state parameters of the balancer. The equivalent force is calculated by the number of balancers, the working pressure collected by the pressure sensor inside the balancer, and the effective working area of ​​the balancer.

2. The simplified modeling method for a press balancer according to claim 1, characterized in that, Based on the collected slider displacement, slider velocity, and applied force, the equivalent stiffness coefficient, equivalent damping coefficient, and equivalent preload parameters in the parameter identification model are identified using the recursive least squares method. The equivalent force model of the balancer is obtained based on the identified parameter results, including the following: The equivalent force provided by the spring-damping-preload model is rewritten from the linear parameter form to the form of a standard linear regression model. Set the initial conditions for the recursive least squares method, including initializing the parameter vector to be identified and the covariance matrix, and obtain the first... The equivalent force at each sampling time; By using the recursive least squares method to obtain each set of regression vectors and equivalent forces, the previous set of regression vectors is updated and corrected to obtain a new parameter vector to be identified. Substitute the finally identified parameter vector into the first... The equivalent simplified model of the balancer is obtained from the equivalent forces at each sampling time, which is the equivalent force model of the balancer.

3. The simplified modeling method for a press balancer according to claim 1, characterized in that, The equivalent force provided by the spring-damping-preload model is rewritten from a linear parametric form to a standard linear regression model, as shown in the formula: in, F eq ( t () is the equivalent force. For the regression vector, Given the parameter vector to be identified, the corresponding formula is shown below: in, For the regression vector, This is the equivalent stiffness coefficient. This is the equivalent damping coefficient. This is the equivalent preload; Let the sampling period be , No. The regression vector corresponding to each sampling time point as follows: Therefore, we can obtain the first Equivalent force at each sampling time as follows: 。 4. The simplified modeling method for a press balancer according to claim 2, characterized in that, The initial conditions for setting the recursive least squares method include initializing the parameter vector to be identified and the covariance matrix, and obtaining the first... The equivalent force at each sampling time includes the following: Initial estimation of the parameter vector to be identified: To ensure generality and reproducibility of parameter identification, the parameter vector to be identified is set. initial vector It is a zero vector; Covariance matrix initialization: covariance matrix The formula representing the uncertainty of the current parameter estimate is as follows: in, The coefficients of the covariance matrix and , It is the identity matrix; Each set of regression vectors is obtained by recursive least squares method. and equivalent force For the previous set of regression vectors The parameters are updated and corrected to obtain a new parameter vector to be identified. .

5. The simplified modeling method for a press balancer according to claim 4, characterized in that, The recursive least squares method is used to obtain each set of regression vectors. and equivalent force For the previous set of regression vectors The parameters are updated and corrected to obtain a new parameter vector to be identified. It includes the following: Calculate the gain matrix, the first... Gain matrix at each sampling time The formula is as follows: in, Forgetting factor and ; Obtain the prediction error of the parameter vector to be identified: The prediction error is the difference between the force at the current sampling time and the predicted value based on the previous sampling time. It is obtained using the following formula: Prediction error at each sampling time : Iterative update of the parameter vector to be identified: The predicted values ​​of the parameters to be identified are updated based on the gain matrix and the prediction error, as shown in the following formula: Update the covariance matrix: For each sampling time Execute the above steps sequentially. After the recursive process is completed, the parameter vector... This is the final identification result.

6. A simplified modeling system for a press balancer, characterized in that, The steps in the simplified modeling method for a press balancer according to any one of claims 1-5 include: The data acquisition module, which is configured in the press, is used to acquire data on the displacement of the slider and the balancing force provided to the slider by the balancer. The parameter identification module is connected to the data acquisition module to receive the acquired data and run a parameter identification algorithm based on the recursive least squares method to solve for the equivalent stiffness coefficient, equivalent damping coefficient and equivalent preload of the equivalent simplified model of the balancer. The equivalent model construction module is used to construct the equivalent force model of the balancer based on the parameter results output by the parameter identification module. The equivalent force application module is used to apply the equivalent force model generated by the equivalent model building module to the multibody dynamics model of the press.

7. A computer-readable storage medium, characterized in that, It stores a computer program that, when executed by a processor, implements the steps in a simplified modeling method for a pressure machine balancer as described in any one of claims 1-5.

8. A computer device, characterized in that, It includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps in a simplified modeling method for a pressure machine balancer as described in any one of claims 1-5.

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