A commutation control method and system for rapid forging

Abstract

The present application provides a commutation control method and system for rapid forging, which relates to the technical field of forging control and includes: obtaining a historical forging data set of rapid forging equipment; obtaining a commutation control parameter space and randomly generating a first commutation control parameter; in order to reduce the commutation time, reduce the impact of commutation impact force on the rapid forging equipment and improve commutation stability, optimizing and evaluating the first commutation control parameter according to the historical forging data; continuing to optimize the commutation control parameter until convergence, obtaining an optimal commutation control parameter with the maximum commutation fitness, and performing commutation control on the rapid forging equipment. Through the present application, the technical problem in the prior art that commutation control may not be accurately in place instantly under the conditions of high-speed movement and frequent commutation, resulting in long commutation time and large impact force, can be solved. By continuously optimizing and evaluating the commutation control parameter, the commutation speed is improved and the commutation vibration is reduced.

Description

Technical Field

[0001] This application relates to the field of forging control technology, and particularly to a commutation control method and system for rapid forging. Background Art

[0002] Rapid forging is a metal forming technology that applies high-intensity, short-time impact forces to metal materials to cause plastic deformation and achieve the required shape and size. During rapid forging, due to the action of high-intensity impact forces, the material undergoes rapid plastic deformation. Therefore, the control requirements for forging equipment are extremely strict, especially in terms of commutation control. Any inaccurate or delayed control may affect the forming quality and production efficiency. Commutation control refers to adjusting the movement direction of the punch or forging hammer in the forging equipment to achieve appropriate mechanical effects and deformation control during the forging process.

[0003] Currently, existing commutation control methods achieve rapid commutation of forging tools through high-frequency impact force adjustment. However, rapid movement and frequent commutation can cause the control system to respond untimely, thus affecting the forging quality. In the case of high-speed movement and frequent commutation, existing commutation control often fails to complete commutation accurately in an instant, resulting in too long commutation time. Due to the delay or inaccuracy of commutation control, the peak value of the impact force may be too large, imposing a large mechanical load on the equipment, exacerbating equipment wear, and reducing its service life. At the same time, excessive impact forces may cause local damage to the material, resulting in unstable forging quality.

[0004] In summary, there is a technical problem in the prior art that, in the case of high-speed movement and frequent commutation, commutation control may not be accurately in place instantly, resulting in a long commutation time and large impact force, thereby generating vibration and loss. Summary of the Invention

[0005] The purpose of this application is to provide a commutation control method and system for rapid forging to solve the technical problem in the prior art that, in the case of high-speed movement and frequent commutation, commutation control may not be accurately in place instantly, resulting in a long commutation time and large impact force, thereby generating vibration and loss.

[0006] In view of the above problems, this application provides a commutation control method and system for rapid forging.

[0007] In a first aspect, the present application provides a commutation control method for rapid forging, and the commutation control method for rapid forging is implemented through a commutation control system for rapid forging. Among them, the commutation control method for rapid forging includes: obtaining a historical forging data set of a rapid forging device within a preset historical time range, where the historical forging data includes multiple historical commutation data; obtaining a commutation control parameter space for the rapid forging device to perform commutation control, and randomly generating a first commutation control parameter within the commutation control parameter space; in order to reduce the commutation time, reduce the impact of the commutation impact force on the rapid forging device, and improve the commutation stability, optimize and evaluate the first commutation control parameter according to the historical forging data to obtain a first commutation fitness; continue to optimize the commutation control parameter until convergence, obtain an optimal commutation control parameter with the maximum commutation fitness, and perform commutation control on the rapid forging device.

[0008] In a second aspect, the present application also provides a commutation control system for rapid forging, which is used to execute the commutation control method for rapid forging described in the first aspect. Among them, the commutation control system for rapid forging includes: a historical data acquisition module, which is used to obtain a historical forging data set of a rapid forging device within a preset historical time range, where the historical forging data includes multiple historical commutation data; a parameter space acquisition module, which is used to obtain a commutation control parameter space for the rapid forging device to perform commutation control, and randomly generate a first commutation control parameter within the commutation control parameter space; an optimization evaluation module, which is used to reduce the commutation time, reduce the impact of the commutation impact force on the rapid forging device, and improve the commutation stability, optimize and evaluate the first commutation control parameter according to the historical forging data to obtain a first commutation fitness; a commutation control module, which is used to continue to optimize the commutation control parameter until convergence, obtain an optimal commutation control parameter with the maximum commutation fitness, and perform commutation control on the rapid forging device.

[0009] One or more technical solutions provided in the present application have at least the following technical effects or advantages:

[0010] By obtaining a historical forging data set of a quick forging device within a preset historical time range, where the historical forging data includes multiple historical commutation data; obtaining a commutation control parameter space for the commutation control of the quick forging device, and randomly generating a first commutation control parameter within the commutation control parameter space; in order to reduce the commutation time, reduce the impact of the commutation impact force on the quick forging device, and improve the commutation stability, according to the historical forging data, optimize and evaluate the first commutation control parameter to obtain a first commutation fitness; continue to optimize the commutation control parameter until convergence, obtain an optimal commutation control parameter with the maximum commutation fitness, and perform commutation control on the quick forging device. That is to say, by analyzing the historical forging data, dynamically adjust the commutation control parameter, accurately adjust the intensity and timing of the impact, and achieve more precise commutation control, thereby reducing the commutation time and impact force, avoiding unnecessary vibrations, and improving the commutation speed.

[0011] The above description is only an overview of the technical solution of the present application. In order to be able to understand the technical means of the present application more clearly, it can be implemented according to the content of the specification. And in order to make the above and other purposes, features and advantages of the present application more obvious and understandable, the following specifically gives the specific embodiments of the present application. It should be understood that the content described in this part is not intended to identify the key or important features of the embodiments of the present application, nor is it used to limit the scope of the present application. Other features of the present application will become easily understood through the following description. Brief Description of the Drawings

[0012] In order to more clearly illustrate the technical solutions in the present application or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only exemplary. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained according to the provided drawings.

[0013] Figure 1 It is a schematic flow chart of a commutation control method for a quick forging in the present application.

[0014] Figure 2 It is a schematic structural diagram of a commutation control system for a quick forging in the present application.

[0015] Description of the reference numerals: Historical data acquisition module 11, parameter space acquisition module 12, optimization evaluation module 13, commutation control module 14. Detailed Description of the Embodiments

[0016] By providing a commutation control method and system for rapid forging, this application solves the technical problem in the prior art that in the case of high-speed movement and frequent commutation, the commutation control may not be accurately in place instantly, resulting in a long commutation time and a large impact force, thereby generating vibration and loss. By analyzing historical forging data, dynamically adjusting the commutation control parameters, precisely regulating the intensity and timing of the impact, and achieving more precise commutation control, thereby reducing the commutation time and impact force, avoiding unnecessary vibration, and improving the commutation speed.

[0017] Next, the technical solutions in this application will be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, rather than all the embodiments of this application. It should be understood that this application is not limited by the example embodiments described here. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of this application. Additionally, it should be noted that for the sake of description, only the parts related to this application are shown in the drawings rather than all of them.

[0018] Embodiment 1, please refer to the attached Figure 1 , this application provides a commutation control method for rapid forging. Among them, the commutation control method for rapid forging is applied to a commutation control system for rapid forging. The commutation control method for rapid forging specifically includes the following steps:

[0019] Step 1: Obtain a historical forging data set of the rapid forging equipment within a preset historical time range. Among them, the historical forging data includes multiple historical commutation data.

[0020] Specifically, the rapid forging equipment refers to the equipment used for rapid metal forming, which plastically deformes metals through impact force or rapid pressing, such as forging machines, presses, hydraulic hammers and other equipment. The rapid forging equipment often needs to perform commutation control during the forging process to change the movement direction of the tool to adapt to different forging tasks. The preset historical time range is a time range predefined according to the operation cycle of the equipment, such as the forging data of the past month, three months or one year.

[0021] The historical forging data set refers to the set of all relevant data collected from the equipment operation process within the preset historical time range, usually including the commutation time, etc. The historical forging data includes multiple historical commutation data. The historical commutation data refers to the specific data recorded during the commutation process of the equipment, such as the time required for commutation, impact force and other information. By constructing a high-quality historical forging data set, a reliable basis can be provided for subsequent parameter optimization, thereby improving the efficiency and stability of commutation control.

[0022] Step 2: Obtain the commutation control parameter space for the quick forging equipment to perform commutation control, and randomly generate the first commutation control parameter within the commutation control parameter space.

[0023] Specifically, the commutation control parameter space refers to the set of possible value ranges of all control parameters during the equipment commutation process, including hydraulic control parameters (such as pressure, flow rate, hydraulic cylinder displacement, etc.), and other control parameters (such as commutation speed, torque, etc.). Different combinations of control parameters will affect the commutation efficiency, impact force, and stability. Randomly generating the first commutation control parameter within the commutation control parameter space, as the initial setting for commutation control, although not necessarily the optimal solution, provides a starting point for the subsequent optimization process. By randomly generating control parameters, different regions of the commutation control parameter space can be explored, and diverse initial solutions can be provided for the subsequent optimization algorithm. The randomly generated first commutation control parameter will be used to test its performance on the equipment and evaluate whether it meets the target requirements such as commutation time, impact force, stability, etc. By randomly generating commutation control parameters, it helps to explore a wide range of regions in the commutation control parameter space and avoid relying solely on certain specific control parameter settings.

[0024] Step 3: To reduce the impact of commutation time and commutation impact force on the quick forging equipment and improve commutation stability, optimize and evaluate the first commutation control parameter according to the historical forging data to obtain the first commutation fitness.

[0025] Specifically, the evaluation indicators include reducing the impact of commutation time on the quick forging equipment, reducing the commutation impact on the quick forging equipment, and improving commutation stability. Among them, the commutation time refers to the time required for the equipment to complete the commutation operation, which is usually affected by parameters such as the adjustment speed, pressure, and flow rate of the hydraulic control system. The shorter the commutation time, the higher the working efficiency of the equipment. The commutation impact force is the force generated due to the sudden change in the movement direction of the equipment during the commutation process, which usually causes vibration or impact. The impact of the impact force on the quick forging equipment is usually manifested as problems such as vibration and wear. To reduce the impact force, the control parameters need to be optimized to reduce the unsteady operation during the commutation process. The commutation stability reflects the smoothness of the commutation operation. The larger the stability parameter, the more similar the current commutation operation is to the historical operation, and the less likely it is to produce drastic changes. A commutation operation with higher stability can usually reduce equipment failures and extend the service life of the equipment. The calculation of the first commutation fitness combines these three factors: commutation control time, impact force, and stability.

[0026] Based on the historical forging dataset of the quick forging equipment, multiple historical commutation times are obtained. According to the commutation control schedule, the first commutation control time corresponding to the first commutation control parameter is obtained. The commutation control schedule includes the mapping relationship between the sample commutation control parameters and the corresponding sample commutation control times. According to the first commutation control parameter, the vibration amplitude during the commutation process is predicted through a model, so as to obtain the first commutation impact force parameter.

[0027] By comparing the first commutation control time with the multiple commutation times in the historical forging dataset, the stability parameter is calculated. If the difference between the first commutation control time and the multiple historical commutation times in the historical forging dataset is large, then the deviation between the updated average historical forging data difference after adding the first commutation time and the average historical forging data difference is large, and the first commutation stability parameter is small. According to the calculated first commutation control time, first commutation impact force parameter and first commutation stability parameter, the first commutation fitness is calculated. By optimizing the commutation control parameters, the commutation time is shortened, the production efficiency is improved, the impact force during commutation is reduced, the vibration and wear of the equipment are reduced, thereby extending the service life of the equipment. The optimized control parameters can make the commutation process more stable, reduce the failure rate, and improve the reliability of the equipment.

[0028] Step 4: Continue to optimize the commutation control parameters until convergence, obtain the optimal commutation control parameters with the maximum commutation fitness, and perform commutation control on the quick forging equipment.

[0029] Specifically, continue to optimize the commutation control parameters, continuously adjust the commutation control parameters, and use iterative optimization algorithms (such as genetic algorithm, particle swarm optimization algorithm, gradient descent method, etc.) to find the optimal commutation control parameters, including hydraulic pressure, flow rate, valve opening, etc. According to the first commutation control parameter, the initial first commutation control time, first commutation impact force parameter, first commutation stability parameter, and first commutation fitness are obtained. By optimizing the commutation control parameters, the commutation time is the shortest, the commutation impact force is the smallest, and the commutation stability is the largest.

[0030] Taking the particle swarm optimization algorithm as an example, first define the size of the particle swarm. Each particle represents a combination of control parameters. The control parameters of each particle can be hydraulic pressure, flow rate, and other parameters that may affect the commutation process. Each particle has a position (a set of control parameters) and a velocity. The position of the particle represents the combination of commutation control parameters; the velocity of the particle represents the adjustment amplitude of the commutation control parameters. For each particle, calculate the commutation time, impact force, and stability according to its control parameters, and then calculate its fitness. The particle adjusts its velocity and position according to its current fitness and historical optimal solution, and moves in the control parameter space towards the direction of the optimal solution.

[0031] The optimization process will iterate continuously. In each iteration, the velocity and position of the particles will be updated. The commutation fitness is calculated based on the commutation control parameters obtained from the optimized experience. If the new fitness is greater than the current fitness, the current parameters will be updated. The optimization process continues until the convergence condition is met. The convergence condition may be that the fitness is stable (if the change in fitness is very small after several consecutive iterations and reaches the set threshold, it is considered that the optimization has converged) or the maximum number of iterations is reached (after reaching the preset maximum number of iterations, the optimization stops automatically). When the particle swarm optimization converges, the optimal combination of control parameters in the particle swarm (the optimal commutation control parameters) is output, that is, the optimal commutation control parameters with the maximum fitness, which are used for actual commutation control.

[0032] With the optimized control parameters, the commutation time is significantly shortened, improving the working efficiency of the rapid forging equipment; the commutation impact force and vibration amplitude are reduced, reducing the loss and wear of the equipment, thereby extending the service life of the equipment; continuously calculating the commutation fitness and iteratively optimizing the control parameters, ultimately achieving the goal of maximizing the commutation fitness, thus enhancing the overall performance and production efficiency of the equipment. Through the adaptive adjustment of the optimization algorithm, the commutation control parameters can be flexibly optimized and adjusted according to production requirements and environmental changes, thereby improving the adaptability of the production line.

[0033] Furthermore, step one of this application includes:

[0034] Obtain multiple historical commutation times of the rapid forging equipment within a preset historical time range; use the multiple historical commutation times as multiple historical commutation data to construct the historical forging data set.

[0035] Specifically, the rapid forging equipment refers to the equipment used for rapid metal forming, usually plastically deforming metals through impact force or rapid pressing, such as forging machines, presses, hydraulic hammers and other equipment. The preset historical time range is a time range predefined according to the operation cycle of the equipment, such as the forging data in the past month, three months or one year. Relevant data is extracted from the control unit of the equipment within the preset historical time range, mainly the time records of the commutation operation, usually the time consumed for the equipment to complete one commutation during the commutation operation. The historical commutation time refers to the time required for the rapid forging equipment to switch from one direction to another during past forging processes. During the forging process, the commutation time affects the response speed of the equipment, the transmission of the impact force and the final forming effect. The historical commutation time usually records the operation history of the equipment, reflecting the stability and efficiency of the commutation process.

[0036] The multiple historical commutation times obtained are used as multiple historical commutation data, which are integrated into a historical forging dataset, including a set of operation data of the forging equipment in the past period, mainly including commutation time data. Through the historical dataset, a dataset containing multiple historical commutation times is constructed, and the commutation time distribution of the equipment can be analyzed to evaluate the efficiency of commutation control.

[0037] Further, step two of this application includes:

[0038] Obtain the hydraulic control parameter space for commutation control of the quick forging equipment, and construct a commutation control parameter space; randomly generate a first commutation control parameter within the commutation control parameter space.

[0039] Specifically, when the quick forging equipment conducts commutation control, the effect and accuracy of commutation control are closely related to hydraulic parameters. Hydraulic control parameters include pressure, flow rate, flow velocity, hydraulic cylinder displacement, etc., which jointly determine the speed, force, and stability of the commutation of forging tools (such as punches or forging hammers). Different combinations of these parameters will affect the efficiency, accuracy, and stability of commutation. By obtaining the control range of hydraulic control parameters, a hydraulic control parameter space is constructed. The hydraulic control parameter space refers to the set of all possible control parameters in the hydraulic system. In the commutation control of the quick forging equipment, the hydraulic system is usually responsible for driving the forging tools (such as punches, dies) to conduct commutation operations. The control parameter space is all possible combinations of these parameters.

[0040] The commutation control parameter space is further expanded on the basis of the hydraulic control parameter space. The commutation control parameter space refers to the set of all parameters used for commutation control, including hydraulic control parameters and possible other related control parameters (such as commutation speed, position, torque, etc.), and includes all control variables affecting the commutation process and their variation ranges. The hydraulic control parameter space is fully analyzed, and according to the actual requirements of the commutation process, other parameters affecting the commutation process are added to form a more comprehensive control parameter space.

[0041] Randomly generate a first commutation control parameter within the commutation control parameter space, select a set of parameters as the initial commutation control setting, that is, the starting point of optimization. The method for determining the randomly selected parameter values can be a random number generator, lottery, etc. Randomly select a value within the range of each parameter to form the first commutation control parameter. By randomly generating a set of hydraulic control parameters as the initial setting, it helps to explore the entire control parameter space, avoid being limited to only a certain part of the parameter range, ensure the diversity of the selected parameters, and increase the probability of finding the optimal parameter combination.

[0042] Further, step three of this application includes:

[0043] Obtain the first commutation control time for commutation control according to the first commutation control parameter, where the first commutation control time is obtained by mapping the first commutation control parameter into a commutation control time table, and the commutation control time table includes the mapping relationship between sample commutation control parameters and sample commutation control times; predict the vibration amplitude of the quick forging equipment during commutation according to the first commutation control parameter, and obtain the first commutation vibration amplitude as the first commutation impact force parameter; analyze the first commutation stability parameter of the first commutation control parameter according to the historical forging data set and the first commutation control time; calculate the first commutation fitness of the first commutation control parameter according to the first commutation control time, the first commutation impact force parameter, and the first commutation stability parameter.

[0044] Specifically, according to the first commutation control parameter, perform commutation control, and input the first commutation control parameter into the commutation control time table to find the corresponding first commutation control time. The commutation control time table is a pre-constructed mapping table that records the mapping relationship between different commutation control parameters and corresponding commutation control times, and each commutation control parameter has a corresponding commutation control time. The commutation control time table includes the mapping relationship between sample commutation control parameters and corresponding sample commutation control times.

[0045] Train a commutation impact prediction channel according to historical data to predict the vibration amplitude or impact force of the equipment according to the input control parameter. Input the first commutation control parameter into the commutation impact prediction channel to obtain the first commutation vibration amplitude, and use the first commutation vibration amplitude as the first commutation impact force parameter. The first commutation vibration amplitude refers to the vibration amplitude of the quick forging equipment during commutation according to the first commutation control parameter, and is used as the commutation impact force parameter.

[0046] Randomly select two forging data from the historical forging data set as a group of historical forging data, so as to obtain multiple groups of historical forging data. Calculate the difference of each group of historical forging data, and statistically obtain the average value of all differences to obtain the average historical forging data difference. Then add the first commutation control time to the historical forging data set to obtain an updated historical forging data set, and perform the foregoing operations again to obtain the updated average historical forging data difference of the updated historical forging data set. Analyze according to the average historical forging data difference and the updated average historical forging data difference. If the difference between the first commutation control time and multiple previous historical commutation times is large, the deviation between the updated average historical forging data difference after adding the first commutation time and the average historical forging data difference is large, and the first commutation stability parameter is small.

[0047] Based on the first commutation control time, the first commutation impact force parameter, and the first commutation stability parameter, combined with the corresponding weights, calculate the first commutation fitness to evaluate the current commutation operation effect. If the fitness value is low, it indicates that the commutation time is long, the impact force is large, or the stability is poor, and the control parameters need to be optimized. If the fitness value is high, it means that the current commutation operation is ideal and meets the production requirements. Through the analysis of historical data and the optimization of control parameters, accurately evaluate the overall impact of the first commutation control parameters on the commutation process, accurately predict the commutation control time, impact force, and stability, and then optimize the commutation process to improve the stability and working efficiency of the equipment.

[0048] Furthermore, the present application further includes the following steps:

[0049] According to the commutation data records of the fast forging equipment within the historical time, collect the sample commutation control parameter set and the sample commutation vibration amplitude set; use the sample commutation control parameter set and the sample commutation vibration amplitude set as supervised training data to train the commutation impact prediction channel; input the first commutation control parameter into the trained commutation impact prediction channel, and predict and output to obtain the first commutation vibration amplitude as the first commutation impact force parameter.

[0050] Specifically, obtain the commutation data records of the fast forging equipment within the historical time, including the commutation control parameters used during the commutation process and the corresponding commutation vibration amplitude data. The sample commutation control parameter set is all the parameter combinations used for commutation control, and the sample commutation vibration amplitude set is the vibration amplitude generated by the equipment corresponding to each set of control parameters. The commutation data record refers to the relevant data recorded during each commutation process of the equipment within a certain time range, such as commutation control parameters (hydraulic pressure, flow rate, commutation speed, etc.) and the vibration amplitude, impact force, etc. data generated by the equipment during the commutation process. The sample commutation control parameter set includes different control parameters used during the commutation process, and each set of control parameters (such as hydraulic pressure, flow rate, commutation speed, etc.) represents a specific commutation control operation. The sample commutation vibration amplitude set includes the vibration amplitude data generated by the equipment during the commutation process under different control parameters. The vibration amplitude is the manifestation of the force on the equipment during the commutation process and is usually related to factors such as impact force and stability.

[0051] Use the collected set of sample commutation control parameters and the set of sample commutation vibration amplitudes to train a commutation impact prediction channel. In supervised learning, the training data is paired, and each pair of data includes input data (features) and output data (labels). The set of sample commutation control parameters serves as the input data (features), while the set of sample commutation vibration amplitudes serves as the output data (labels). The goal of training the model is to learn to predict the output for future input data based on these known input and output relationships. Select an appropriate machine learning model, such as support vector machine regression, linear regression, etc. Taking support vector machine regression as an example, by learning the relationship between control parameters (such as hydraulic pressure, flow rate, etc.) and vibration amplitude, the vibration amplitude can be predicted based on new control parameters. Input the training data (sample commutation control parameters and sample commutation vibration amplitudes) into the support vector machine regression model for training. By minimizing the loss function, the model continuously adjusts its parameters so that, given the input (control parameters), it can accurately predict the vibration amplitude. The loss function usually measures the gap between the model's predicted value and the true value through the mean squared error. Cross-validation is used during the training process to select the best model hyperparameters and avoid overfitting. Cross-validation divides the dataset into multiple subsets, uses a part of them as the validation set each time, and the rest as the training set, repeating the training and validation processes multiple times to more comprehensively evaluate the generalization ability of the model.

[0052] The commutation impact prediction channel is a machine learning model or algorithm used to predict the vibration amplitude or impact force of a device based on the input control parameters. After the commutation impact prediction channel is trained, input the first commutation control parameter into the trained commutation impact prediction channel, and the predicted first commutation vibration amplitude is output as the first commutation impact force parameter. By training the model, the vibration amplitude of the device under different control parameters can be accurately predicted, the control parameters can be adjusted to reduce unnecessary vibrations and impact forces, and the fatigue damage and maintenance costs of the device can be reduced.

[0053] Furthermore, this application also includes the following steps:

[0054] Randomly select multiple groups of historical forging data from the historical forging dataset. Each group of historical forging data includes two historical forging data. Calculate the difference between each group of historical forging data to obtain multiple historical forging data differences, and calculate the mean to obtain the average historical forging data difference. Add the first commutation control time to the historical forging dataset and calculate the updated average historical forging data difference. Calculate the deviation ratio between the updated average historical forging data difference and the average historical forging data difference, and subtract this deviation ratio from 1 to obtain the first commutation stability parameter.

[0055] Specifically, multiple groups of historical forging data are randomly selected from the historical forging data set. Each group of historical forging data includes two historical forging data, usually the forging data of two different commutation operations, including the commutation times of two historical commutation data. For each group of historical forging data, calculate its difference, usually the difference between two historical commutation times, representing the time difference between two commutation operations. By calculating the differences of multiple groups of historical forging data, multiple historical forging data differences are obtained. Calculate the average of all historical forging data differences to obtain the average historical forging data difference.

[0056] Add the first commutation control time obtained according to the first commutation control parameter to the historical forging data set to obtain a new historical forging data set. Randomly select multiple groups of historical forging data from it, calculate the differences, and also calculate the average of all differences to obtain the updated average historical forging data difference. The updated average historical forging data difference is the average difference after adding the new first commutation control time. The new commutation control time affects the mean of the historical data, resulting in new difference calculations.

[0057] Calculate the deviation ratio between the updated average historical forging data difference and the average historical forging data difference, which reflects the influence degree of the first commutation control time on the historical data set. For example, if the calculated multiple historical forging data differences are 4 milliseconds, 5 milliseconds, 3 milliseconds, and 6 milliseconds, then the average historical forging data difference is 4.5 milliseconds. After adding the first commutation control time, the calculated multiple updated historical forging data differences are 4 milliseconds, 5 milliseconds, 3 milliseconds, 6 milliseconds, and 4 milliseconds, then the updated average historical forging data difference is 4.4 milliseconds; then the deviation ratio = |4.4 - 4.5| / 4.5 = 0.02 milliseconds.

[0058] According to the deviation ratio, calculate the first commutation stability parameter, which is calculated by subtracting the deviation ratio from 1, reflecting the stability of the commutation operation. If the first commutation control time has a large gap with multiple previous historical commutation times, then the deviation ratio between the updated average historical forging data difference after adding the first commutation time and the average historical forging data difference is large, and the first commutation stability parameter is small. On the contrary, the first commutation stability parameter is large. By calculating the first commutation stability parameter, quantify the stability difference between the current commutation operation and the historical operation. If the new commutation control time has a large gap with the historical commutation time and the stability parameter is small, it means that the current operation has a large difference from the historical operation and the commutation process may be unstable.

[0059] Furthermore, the present application further includes the following steps:

[0060] According to the first commutation control time, the first commutation impact force parameter, and the first commutation stability parameter, calculate the first commutation fitness of the first commutation control parameter, as shown in the following formula: ; where, HXF is the commutation fitness, w1, w2, and w3 are weights, T is the commutation control time, Z is the commutation impact force parameter, and P is the commutation stability parameter.

[0061] Specifically, the expression for calculating the first commutation fitness of the first commutation control parameter is: ; where, HXF is the commutation fitness, which evaluates the overall effect of the current commutation control parameter. Through the commutation fitness, the advantages and disadvantages of the commutation process are quantified, thus helping to optimize the commutation control strategy; w1, w2, and w3 are weights, which determine the relative importance of the commutation control time, impact force, and stability in the commutation fitness formula. The weight coefficients are adjustable and can be adjusted according to different application scenarios and requirements; T is the commutation control time, which is the time required for the commutation operation. The shorter the commutation control time, the more efficient the commutation process; Z is the commutation impact force parameter, which is the vibration amplitude or impact force during the commutation process. The smaller the impact force, the less damage to the equipment; P is the commutation stability parameter, which represents the stability of the commutation process and reflects the difference between the current commutation operation and historical data. The higher the stability, the better the fitness.

[0062] The commutation fitness evaluates the overall effect of the commutation operation by combining multiple factors (commutation time, impact force, stability), thereby enabling a comprehensive optimization of the commutation process of the equipment. By calculating the commutation fitness, the advantages and disadvantages of the current commutation operation are reflected in real time. Reducing the impact force, shortening the commutation time, and improving the stability can effectively reduce the wear and damage of the equipment and improve the stability and service life of the equipment.

[0063] In summary, a commutation control method for rapid forging provided by this application has the following technical effects:

[0064] By obtaining a historical forging data set of the rapid forging equipment within a preset historical time range, where the historical forging data includes multiple historical commutation data; obtaining a commutation control parameter space for the rapid forging equipment to perform commutation control, and randomly generating a first commutation control parameter within the commutation control parameter space; to reduce the commutation time, reduce the impact of the commutation impact force on the rapid forging equipment, and improve the commutation stability. According to the historical forging data, the first commutation control parameter is optimized and evaluated to obtain the first commutation fitness; continue to optimize the commutation control parameter until convergence, obtain the optimal commutation control parameter with the maximum commutation fitness, and perform commutation control on the rapid forging equipment. That is, by analyzing the historical forging data, the commutation control parameter is dynamically adjusted, the intensity and timing of the impact are precisely adjusted, and more precise commutation control is achieved, thereby reducing the commutation time and impact force, avoiding unnecessary vibrations, and improving the commutation speed.

[0065] Embodiment 2. Based on the same inventive concept as the commutation control method for rapid forging in the foregoing Embodiment 1, the present application further provides a commutation control system for rapid forging. Please refer to the appendix Figure 2 , the commutation control system for rapid forging includes:

[0066] A historical data acquisition module 11, which is configured to acquire a historical forging data set of a rapid forging device within a preset historical time range. Among them, the historical forging data includes a plurality of historical commutation data; a parameter space acquisition module 12, which is configured to acquire a commutation control parameter space for commutation control of the rapid forging device, and randomly generate a first commutation control parameter within the commutation control parameter space; an optimization evaluation module 13, which is configured to reduce the commutation time, reduce the impact of commutation impact force on the rapid forging device, and improve commutation stability. According to the historical forging data, optimize and evaluate the first commutation control parameter to obtain a first commutation fitness; a commutation control module 14, which is configured to continue to optimize the commutation control parameter until convergence, obtain an optimal commutation control parameter with the maximum commutation fitness, and perform commutation control on the rapid forging device.

[0067] Further, the historical data acquisition module 11 in the commutation control system for rapid forging is further configured to:

[0068] Acquire a plurality of historical commutation times of the rapid forging device within a preset historical time range; use the plurality of historical commutation times as a plurality of historical commutation data to construct the historical forging data set.

[0069] Further, the parameter space acquisition module 12 in the commutation control system for rapid forging is further configured to:

[0070] Acquire a hydraulic control parameter space for commutation control of the rapid forging device, construct a commutation control parameter space; randomly generate a first commutation control parameter within the commutation control parameter space.

[0071] Further, the optimization evaluation module 13 in the commutation control system for rapid forging is further configured to:

[0072] Obtain the first commutation control time for commutation control according to the first commutation control parameter. Among them, input the first commutation control parameter into the commutation control time table for mapping to obtain the first commutation control time. The commutation control time table includes the mapping relationship between sample commutation control parameters and sample commutation control times; according to the first commutation control parameter, predict the vibration amplitude of the rapid forging equipment during commutation to obtain the first commutation vibration amplitude as the first commutation impact force parameter; according to the historical forging data set and the first commutation control time, analyze and obtain the first commutation stability parameter of the first commutation control parameter; according to the first commutation control time, the first commutation impact force parameter, and the first commutation stability parameter, calculate and obtain the first commutation fitness of the first commutation control parameter.

[0073] Further, the optimization evaluation module 13 in the commutation control system for rapid forging is further configured to:

[0074] According to the commutation data records of the rapid forging equipment within the historical time, collect the sample commutation control parameter set and the sample commutation vibration amplitude set; use the sample commutation control parameter set and the sample commutation vibration amplitude set as supervised training data to train the commutation impact prediction channel; input the first commutation control parameter into the trained commutation impact prediction channel, and predict and output to obtain the first commutation vibration amplitude as the first commutation impact force parameter.

[0075] Further, the optimization evaluation module 13 in the commutation control system for rapid forging is further configured to:

[0076] Randomly select multiple groups of historical forging data from the historical forging data set. Among them, each group of historical forging data includes two historical forging data; calculate the difference of each group of historical forging data to obtain multiple historical forging data differences, and calculate the mean value to obtain the average historical forging data difference; add the first commutation control time to the historical forging data set, and calculate to obtain the updated average historical forging data difference; calculate the deviation ratio of the updated average historical forging data difference to the average historical forging data difference, and use 1 minus the deviation ratio to obtain the first commutation stability parameter.

[0077] Further, the optimization evaluation module 13 in the commutation control system for rapid forging is further configured to:

[0078] According to the first commutation control time, the first commutation impact force parameter, and the first commutation stability parameter, calculate and obtain the first commutation fitness of the first commutation control parameter as follows: ; where HXF is the commutation fitness, w1, w2, and w3 are weights, T is the commutation control time, Z is the commutation impact force parameter, and P is the commutation stability parameter.

[0079] In this specification, each embodiment is described in a progressive manner. The key point of each embodiment is to illustrate the differences from other embodiments. The Figure 1 A commutation control method and specific example for rapid forging in Embodiment 1 are equally applicable to a commutation control system for rapid forging in this embodiment. Through the detailed description of the commutation control method for rapid forging above, those skilled in the art can clearly understand the commutation control system for rapid forging in this embodiment. Therefore, for the sake of brevity of the specification, it will not be elaborated here. For the system disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple. For relevant parts, refer to the description in the method section.

[0080] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be obvious to those skilled in the art. The general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to these embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

[0081] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalent technologies, the present application also intends to include these changes and variations.

Claims

1. A reversing control method for rapid forging, characterized in that: include: Acquire a historical forging data set of a rapid forging device within a preset historical time range, wherein the historical forging data includes a plurality of historical reversing data; Obtaining a commutation control parameter space for commutation control of the rapid forging equipment, and randomly generating a first commutation control parameter in the commutation control parameter space; In order to reduce the commutation time, reduce the impact of the commutation impact force on the rapid forging equipment and improve the commutation stability, the first commutation control parameter is optimized and evaluated based on the historical forging data to obtain a first commutation adaptability; Continuing to optimize the commutation control parameters until convergence, obtaining the optimal commutation control parameters with the greatest commutation adaptability, and performing commutation control on the rapid forging equipment; Obtaining a commutation control parameter space for commutation control of the rapid forging equipment, and randomly generating a first commutation control parameter in the commutation control parameter space, including: Acquire a hydraulic control parameter space for reversing control of the rapid forging equipment and construct a reversing control parameter space; randomly generating a first commutation control parameter in the commutation control parameter space; In order to reduce the commutation time, reduce the impact of the commutation impact force on the rapid forging equipment and improve the commutation stability, the first commutation control parameter is optimized and evaluated based on the historical forging data, including: Obtaining a first commutation control time for performing commutation control according to the first commutation control parameter, wherein the first commutation control parameter is input into a commutation control timetable for mapping to obtain the first commutation control time, the commutation control timetable including a mapping relationship between sample commutation control parameters and sample commutation control times; Predicting the vibration amplitude of the rapid forging device during reversing according to the first reversing control parameter to obtain a first reversing vibration amplitude as a first reversing impact force parameter; Analyze and obtain a first commutation stability parameter of the first commutation control parameter according to the historical forging data set and the first commutation control time; A first commutation adaptability of the first commutation control parameter is calculated based on the first commutation control time, the first commutation impact force parameter, and the first commutation stability parameter.

2. A reversing control method for rapid forging according to claim 1, characterized in that: Obtain historical forging data sets of rapid forging equipment within a preset historical time range, including: Obtain multiple historical reversing times of the rapid forging equipment within a preset historical time range; The historical forging data set is constructed by using the multiple historical commutation times as multiple historical commutation data.

3. A reversing control method for rapid forging according to claim 1, characterized in that: Predicting the vibration amplitude of the rapid forging device during reversing according to the first reversing control parameter to obtain a first reversing vibration amplitude as a first reversing impact force parameter includes: According to the commutation data records of the rapid forging equipment in the historical period, a sample commutation control parameter set and a sample commutation vibration amplitude set are collected; Using the sample commutation control parameter set and the sample commutation vibration amplitude set as supervised training data to train a commutation impact prediction channel; The first commutation control parameter is input into the trained commutation impact prediction channel, and the prediction output obtains a first commutation vibration amplitude as a first commutation impact force parameter.

4. A reversing control method for rapid forging according to claim 1, characterized in that: Analyzing and obtaining a first commutation stability parameter of the first commutation control parameter according to the historical forging data set and the first commutation control time includes: Randomly selecting multiple groups of historical forging data from the historical forging data set, wherein each group of historical forging data includes two historical forging data; Calculating the difference of each set of historical forging data to obtain multiple historical forging data differences, and calculating the average to obtain the average historical forging data difference; Adding the first reversing control time to the historical forging data set to calculate and obtain an updated average historical forging data difference; The deviation ratio between the updated average historical forging data difference and the average historical forging data difference is calculated, and the first commutation stability parameter is obtained by subtracting the deviation ratio from 1.

5. A reversing control method for rapid forging according to claim 1, characterized in that: According to the first commutation control time, the first commutation impact force parameter, and the first commutation stability parameter, a first commutation adaptability of the first commutation control parameter is calculated and obtained as follows: ； Among them, HXF is the commutation adaptability, w1, w2 and w3 are weights, T is the commutation control time, Z is the commutation impact force parameter, and P is the commutation stability parameter.

6. A reversing control system for rapid forging, characterized in that: Steps for implementing a reversing control method for rapid forging as claimed in any one of claims 1 to 5, wherein the reversing control system for rapid forging comprises: A historical data acquisition module, the historical data acquisition module is used to obtain a historical forging data set of the rapid forging equipment within a preset historical time range, wherein the historical forging data includes a plurality of historical reversing data; a parameter space acquisition module, the parameter space acquisition module being used to acquire a commutation control parameter space for commutation control of the rapid forging equipment, and randomly generate a first commutation control parameter in the commutation control parameter space; an optimization and evaluation module, configured to optimize and evaluate the first commutation control parameter based on the historical forging data to obtain a first commutation adaptability in order to reduce commutation time, reduce the impact of commutation impact force on the rapid forging equipment, and improve commutation stability; A reversing control module is used to continue optimizing the reversing control parameters until convergence, obtain the optimal reversing control parameters with the maximum reversing adaptability, and perform reversing control on the rapid forging equipment.

Images