Reversing control method and system for rapid forging and pressing

Abstract

The invention provides a reversing control method and system for rapid forging and pressing, and relates to the technical field of forging and pressing control, and the method comprises the steps: obtaining a historical forging and pressing data set of rapid forging and pressing equipment; acquiring a commutation control parameter space, and randomly generating a first commutation control parameter; according to historical forging data, the first reversing control parameter is optimized and evaluated so as to shorten reversing time, reduce the influence of reversing impact force on the rapid forging equipment and improve reversing stability, and the second reversing control parameter is optimized and evaluated according to the historical forging data; and continuously optimizing the reversing control parameters until convergence, obtaining the optimal reversing control parameter with the maximum reversing fitness, and performing reversing control on the rapid forging and pressing equipment. The technical problems that in the prior art, under the conditions of high-speed movement and frequent reversing, reversing control can not be accurately in place instantly, so that reversing time is long, and impact force is large can be solved, reversing control parameters are continuously optimized and evaluated, the reversing speed is increased, and reversing vibration is reduced.

Description

Technical Field

[0001] The present application relates to the field of forging control technology, and in particular to a switching control method and system for rapid forging. Background Art

[0002] Rapid forging is a metal forming technology that applies high-intensity, short-term impact force to metal materials to cause them to undergo plastic deformation to achieve the desired shape and size. During the rapid forging process, the material undergoes rapid plastic deformation due to the high-intensity impact force, so the control requirements for the forging equipment are extremely strict, especially in terms of reversing control. Any inaccurate or delayed control may affect the forming quality and production efficiency. Reversing 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] At present, the existing commutation control method realizes the rapid commutation of forging tools through high-frequency impact force adjustment. However, rapid movement and frequent commutation will cause the control system to respond untimely, thus affecting the forging quality. In the case of high-speed movement and frequent commutation, the existing commutation control often cannot accurately complete the commutation in an instant, resulting in a long commutation time. Due to the delay or inaccuracy of the commutation control, the peak value of the impact force may be too large, resulting in a large mechanical load on the equipment, aggravating the wear of the equipment and reducing its service life. At the same time, excessive impact force may cause local damage to the material, resulting in unstable forging quality.

[0004] In summary, the prior art has a technical problem that, in the case of high-speed movement and frequent switching, the switching control may not be able to be accurately controlled in an instant, resulting in a long switching time and a large impact force, thereby causing 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, so as to solve the technical problem in the prior art that the commutation control may not be able to be accurately positioned in an instant under high-speed movement and frequent commutation, resulting in long commutation time and large impact force, thereby generating vibration and loss.

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

[0007] In the first aspect, the present application provides a commutation control method for rapid forging, which is implemented by a commutation control system for rapid forging, wherein 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, wherein the historical forging data includes multiple historical commutation data; obtaining a commutation control parameter space for commutation control of the rapid forging device, and randomly generating a first commutation control parameter in the commutation control parameter space; in order to reduce the commutation time, reduce the influence of the commutation impact force on the rapid forging device and improve the commutation stability, the first commutation control parameter is optimized and evaluated according to the historical forging data to obtain a first commutation fitness; the commutation control parameter optimization is continued until convergence, and the optimal commutation control parameter with the largest commutation fitness is obtained to perform commutation control on the rapid forging device.

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

[0009] One or more technical solutions provided in this application have at least the following technical effects or advantages: By obtaining 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 commutation data; obtaining a commutation control parameter space for commutation control of the rapid forging device, and randomly generating a first commutation control parameter in the commutation control parameter space; optimizing and evaluating the first commutation control parameter according to the historical forging data to reduce the commutation time, reduce the influence of the commutation impact force on the rapid forging device, and improve the commutation stability, and obtaining a first commutation fitness; continuing to optimize the commutation control parameter until convergence, obtaining the optimal commutation control parameter with the maximum commutation fitness, and performing commutation control on the rapid forging device. In other words, by analyzing the historical forging data, dynamically adjusting the commutation control parameters, and accurately adjusting the strength and timing of the impact, more accurate commutation control can be achieved, thereby reducing the commutation time and impact force, avoiding unnecessary vibration, and improving the commutation speed.

[0010] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented according to the contents of the specification, and in order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are specifically cited below. It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present application, nor is it intended to limit the scope of the present application. Other features of the present application will become easy to understand through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] In order to more clearly illustrate the technical solutions in the present application or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only exemplary, and for ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0012] Figure 1 This is a flow chart of a reversing control method for rapid forging in the present application.

[0013] Figure 2 This is a schematic diagram of the structure of a reversing control system for rapid forging in the present application.

[0014] Explanation of the reference numerals: historical data acquisition module 11 , parameter space acquisition module 12 , optimization evaluation module 13 , and switching control module 14 . DETAILED DESCRIPTION

[0015] This application provides a commutation control method and system for rapid forging, which solves the technical problem in the prior art that the commutation control may not be accurate in an instant under high-speed movement and frequent commutation, resulting in long commutation time and large impact force, thereby generating vibration and loss. By analyzing historical forging data, dynamically adjusting the commutation control parameters, and accurately adjusting the impact force and timing, more accurate commutation control can be achieved, thereby reducing the commutation time and impact force, avoiding unnecessary vibration, and improving the commutation speed.

[0016] Below, the technical solutions in the present application will be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments of the present application. It should be understood that the present application is not limited to the example embodiments described herein. Based on the embodiments of the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present application. It should also be noted that, for the convenience of description, only the parts related to the present application are shown in the accompanying drawings, rather than all of them.

[0017] For example, please refer to the attached Figure 1 The present application provides a commutation control method for rapid forging, wherein the commutation control method for rapid forging is applied to a commutation control system for rapid forging, and the commutation control method for rapid forging specifically includes the following steps: Step 1: Obtain 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 commutation data.

[0018] Specifically, rapid forging equipment refers to equipment used for rapid metal forming, which plastically deforms metal through impact force or rapid pressing, such as forging machines, presses, hydraulic hammers and other equipment. Rapid forging equipment often requires reversing control during the forging process to change the direction of tool movement to adapt to different forging tasks. The preset historical time range is a pre-defined time range based on the operating cycle of the equipment, such as forging data for the past month, three months or one year.

[0019] The historical forging data set refers to the collection 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 foundation can be provided for subsequent parameter optimization, thereby improving the efficiency and stability of commutation control.

[0020] Step 2: Obtain 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.

[0021] Specifically, the commutation control parameter space refers to the set of possible value ranges of all control parameters during the commutation process of the equipment, including hydraulic control parameters (such as pressure, flow, hydraulic cylinder displacement, etc.), and other control parameters (such as commutation speed, torque, etc.). Different combinations of control parameters will affect the efficiency, impact and stability of the commutation. The first commutation control parameter is randomly generated in the commutation control parameter space as the initial setting of the commutation control. Although it is not necessarily the optimal solution, it provides a starting point for the subsequent optimization process. By randomly generating control parameters, different areas of the commutation control parameter space can be explored and a variety of initial solutions can be provided for subsequent optimization algorithms. 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, and stability. By randomly generating commutation control parameters, it is helpful to explore a wide area of ​​the commutation control parameter space and avoid relying only on certain specific control parameter settings.

[0022] Step three: 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 parameters are optimized and evaluated according to the historical forging data to obtain the first commutation adaptability.

[0023] Specifically, the evaluation indicators include reducing the impact of the reversing time on the rapid forging equipment, reducing the impact of the reversing impact on the rapid forging equipment, and improving the reversing stability. Among them, the reversing time refers to the time required for the equipment to complete the reversing operation, which is usually affected by the adjustment speed, pressure, flow and other parameters of the hydraulic control system. The shorter the reversing time, the higher the working efficiency of the equipment. The reversing impact force is the force generated by the sudden change of the movement direction of the equipment during the reversing process, which usually causes vibration or impact. The impact of the impact force on the rapid forging equipment is usually manifested as vibration, wear and other problems. In order to reduce the impact force, the control parameters need to be optimized to reduce the unstable operation during the reversing process. The reversing stability reflects the smoothness of the reversing operation. The larger the stability parameter, the more similar the current reversing operation is to the historical operation, and the less likely it is to change dramatically. Reversing operations with higher stability can usually reduce equipment failures and extend the service life of the equipment. The calculation of the first reversing fitness combines the three factors of reversing control time, impact force and stability.

[0024] According to the historical forging data set of the rapid forging equipment, multiple historical commutation times are obtained. According to the commutation control time table, the first commutation control time corresponding to the first commutation control parameter is obtained. The commutation control time table includes a mapping relationship between the sample commutation control parameter and the corresponding sample commutation control time. According to the first commutation control parameter, the vibration amplitude during the commutation process is predicted by the model, so as to obtain the first commutation impact force parameter.

[0025] By comparing the first commutation control time with multiple commutation times in the historical forging data set, the stability parameter is calculated. If the difference between the first commutation control time and the multiple historical commutation times in the historical forging data set 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. According to the calculated first commutation control time, the first commutation impact force parameter and the first commutation stability parameter, the first commutation adaptability is calculated. By optimizing the commutation control parameters, the commutation time is shortened, which improves production efficiency. By reducing the impact force during commutation, 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.

[0026] Step 4: Continue to optimize the commutation control parameters until convergence, obtain the optimal commutation control parameters with the maximum commutation adaptability, and perform commutation control on the rapid forging equipment.

[0027] Specifically, continue to optimize the switching control parameters, continuously adjust the switching control parameters, and use iterative optimization algorithms (such as genetic algorithms, particle swarm optimization algorithms, gradient descent methods, etc.) to find the optimal switching control parameters, including hydraulic pressure, flow, valve opening, etc. According to the first switching control parameters, the initial first switching control time, the first switching impact force parameter and the first switching stability parameter, as well as the first switching fitness are obtained. By optimizing the switching control parameters, the switching time is minimized, the switching impact force is minimized, and the switching stability is maximized.

[0028] Taking the particle swarm optimization algorithm as an example, the size of the particle swarm is first defined, and each particle represents a combination of control parameters. The control parameters of each particle can be hydraulic pressure, flow, and other parameters that may affect the commutation process. Each particle has a position (a collection of control parameters) and a speed. The position of the particle represents the combination of the commutation control parameters; the speed of the particle represents the amplitude of the adjustment of the commutation control parameters. For each particle, the commutation time, impact force, and stability are calculated according to its control parameters, and then its fitness is calculated. The particle adjusts its speed and position according to its current fitness and the historical optimal solution, and moves towards the optimal solution in the control parameter space.

[0029] The optimization process will continue to iterate, and the speed and position of the particles will be updated each time. The commutation fitness is calculated based on the optimized commutation control parameters. If the new fitness is greater than the current fitness, the current parameters are 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, the optimization is considered to have 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 control parameter combination (optimal commutation control parameters) in the particle swarm is output, that is, the optimal commutation control parameters with the largest fitness, which are used for actual commutation control.

[0030] Through the optimized control parameters, the commutation time is significantly shortened, which improves the working efficiency of the rapid forging equipment; the commutation impact force and vibration amplitude are reduced, which reduces the loss and wear of the equipment, thereby extending the service life of the equipment; the commutation fitness is continuously calculated, and the control parameters are iteratively optimized, and finally the goal of maximizing the commutation fitness is achieved, thereby improving 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 needs and environmental changes, thereby improving the adaptability of the production line.

[0031] Furthermore, step one of this application includes: Acquire multiple historical reversing times of the rapid forging equipment within a preset historical time range; use the multiple historical reversing times as multiple historical reversing data to construct the historical forging data set.

[0032] Specifically, rapid forging equipment refers to equipment used for rapid metal forming, which usually plastically deforms the metal 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 pre-defined according to the operating cycle of the equipment, such as forging data for 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 record of the reversing operation, usually the time consumed by the equipment to complete a reversing operation during the reversing operation. The historical reversing time refers to the time required for the rapid forging equipment to switch from one direction to another during the past forging process. During the forging process, the reversing time affects the response speed of the equipment, the transmission of the impact force and the final forming effect. The historical reversing time usually records the operation history of the equipment and reflects the stability and efficiency of the reversing process.

[0033] The multiple historical commutation times obtained are used as multiple historical commutation data and integrated into a historical forging data set, which contains a collection of forging equipment operation data in the past period of time, mainly including commutation time data. Through the historical data set, a data set containing multiple historical commutation times is constructed, which can analyze the commutation time distribution of the equipment and evaluate the efficiency of commutation control.

[0034] Further, step 2 of this application includes: The hydraulic control parameter space for the reversing control of the rapid forging equipment is obtained, and the reversing control parameter space is constructed; and the first reversing control parameter is randomly generated in the reversing control parameter space.

[0035] Specifically, when the rapid forging equipment performs reversing control, the effect and accuracy of the reversing control are closely related to the hydraulic parameters. The hydraulic control parameters include pressure, flow, flow rate, hydraulic cylinder displacement, etc., which together determine the speed, force and stability of the reversing of the forging tool (such as a punch or a forging hammer). Different combinations of these parameters will affect the efficiency, accuracy and stability of the reversing. By obtaining the control range of the 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 reversing control of rapid forging equipment, the hydraulic system is usually responsible for driving the forging tools (such as punches and dies) for reversing operations. The control parameter space is all possible combinations of these parameters.

[0036] 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.), including all control variables affecting the commutation process and their range of variation. The hydraulic control parameter space is fully analyzed, and other parameters affecting the commutation process are added according to the actual needs of the commutation process to form a more comprehensive control parameter space.

[0037] A first commutation control parameter is randomly generated in the commutation control parameter space, and a set of parameters is selected as a preliminary commutation control setting, i.e., a starting point for optimization. The method for determining the randomly selected parameter value may be a random number generator, a lottery, etc. A value is randomly selected within the range of each parameter to form the first commutation control parameter. By randomly generating a set of hydraulic control parameters as a preliminary setting, it is helpful to explore the entire control parameter space, avoid being limited to a certain part of the parameter range, ensure the diversity of the selected parameters, and increase the probability of finding the optimal parameter combination.

[0038] Further, step three of this application includes: Obtain a first commutation control time for commutation control according to the first commutation control parameter, wherein the first commutation control parameter is input into a commutation control timetable to map and obtain the first commutation control time, and the commutation control timetable includes a 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 a first commutation vibration amplitude as a first commutation impact force parameter; according to the historical forging data set and the first commutation control time, analyze and obtain a 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 a first commutation adaptability of the first commutation control parameter.

[0039] Specifically, according to the first commutation control parameter, commutation control is performed, and the first commutation control parameter is input into the commutation control timetable to find the corresponding first commutation control time. The commutation control timetable is a pre-built 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 timetable includes the mapping relationship between the sample commutation control parameters and the corresponding sample commutation control time.

[0040] The commutation impact prediction channel is trained according to historical data to predict the vibration amplitude or impact force of the equipment according to the input control parameters. The first commutation control parameter is input into the commutation impact prediction channel to obtain the first commutation vibration amplitude, and the first commutation vibration amplitude is used as the first commutation impact force parameter. The first commutation vibration amplitude refers to the vibration amplitude of the rapid forging equipment when commutation is performed according to the first commutation control parameter, which is used as the commutation impact force parameter.

[0041] Randomly select two forging data from the historical forging data set as a group of historical forging data, thereby obtaining 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, thereby obtaining 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 above operation in the same way to obtain the updated average historical forging data difference of the updated historical forging data set. According to the analysis of the average historical forging data difference and the updated average historical forging data difference, if the first commutation control time is significantly different from the previous multiple historical commutation times, the deviation between the updated average historical forging data difference after adding the first commutation time and the average historical forging data difference will be larger, and the first commutation stability parameter will be smaller.

[0042] According to the first commutation control time, the first commutation impact force parameter, the first commutation stability parameter, and the corresponding weights, the first commutation fitness is calculated to evaluate the current commutation operation effect. If the fitness value is low, it means 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 production needs. Through the analysis of historical data and the optimization of control parameters, the overall impact of the first commutation control parameters on the commutation process is accurately evaluated, and the commutation control time, impact force and stability are accurately predicted, thereby optimizing the commutation process and improving the stability and work efficiency of the equipment.

[0043] Furthermore, the present application also includes the following steps: According to the commutation data records of the rapid forging equipment in historical time, a sample commutation control parameter set and a sample commutation vibration amplitude set are collected; the sample commutation control parameter set and the sample commutation vibration amplitude set are used as supervised training data to train the commutation impact prediction channel; the first commutation control parameter is input into the trained commutation impact prediction channel, and the first commutation vibration amplitude is obtained by prediction output as the first commutation impact force parameter.

[0044] Specifically, the commutation data records of the rapid forging equipment in the historical time are obtained, including the commutation control parameters used in the commutation process and the corresponding commutation vibration amplitude data. The sample commutation control parameter set is a combination of all parameters used for commutation control, and the sample commutation vibration amplitude set is the vibration amplitude generated by the equipment when corresponding to each set of control parameters. Commutation data recording refers to recording relevant data in each commutation process of the equipment within a certain time range, such as commutation control parameters (hydraulic pressure, flow, commutation speed, etc.) and vibration amplitude, impact force and other data generated by the equipment during the commutation process. The sample commutation control parameter set includes different control parameters used in the commutation process, and each set of control parameters (such as hydraulic pressure, flow, 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 a manifestation of the force applied to the equipment during the commutation process, which is usually related to factors such as impact force and stability.

[0045] Use the collected sample commutation control parameter set and sample commutation vibration amplitude set 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 sample commutation control parameter set is used as the input data (features), and the sample commutation vibration amplitude set is used as the output data (labels). The goal of the training model is to learn to predict the output of future input data through 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, etc.) and vibration amplitude, the vibration amplitude can be predicted based on the new control parameters. The training data (sample commutation control parameters and sample commutation vibration amplitude) are input into the support vector machine regression model for training. By minimizing the loss function, the model continuously adjusts the parameters so that the vibration amplitude can be accurately predicted under the given input (control parameters). The loss function usually measures the gap between the model prediction value and the true value through the mean square error. Cross-validation is used during training to select the best model hyperparameters and avoid overfitting. Cross-validation divides the dataset into multiple subsets, uses one part as the validation set each time and the rest as the training set, and repeats the training and validation process multiple times to more comprehensively evaluate the generalization ability of the model.

[0046] The commutation impact prediction channel is a machine learning model or algorithm used to predict the vibration amplitude or impact force of the equipment based on the input control parameters. After the commutation impact prediction channel is trained, the first commutation control parameter is input 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 equipment under different control parameters can be accurately predicted, the control parameters can be adjusted, unnecessary vibration and impact force can be reduced, and fatigue damage and maintenance costs of the equipment can be reduced.

[0047] Furthermore, the present application also includes the following steps: A plurality of groups of historical forging data are randomly selected from the historical forging data set, wherein each group of historical forging data includes two historical forging data; the difference of each group of historical forging data is calculated to obtain a plurality of historical forging data differences, and the average is calculated to obtain an average historical forging data difference; the first commutation control time is added to the historical forging data set to calculate 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.

[0048] Specifically, multiple groups of historical forging data are randomly selected from the historical forging data set, and each group of historical forging data includes two historical forging data, usually forging data of two different switching operations, including the switching time of the two historical switching data. For each group of historical forging data, the difference is calculated, usually the difference between the two historical switching times, indicating the time difference between the two switching operations. By calculating the difference of multiple groups of historical forging data, multiple historical forging data differences are obtained. The average of all historical forging data differences is obtained to obtain the average historical forging data difference.

[0049] The first commutation control time obtained according to the first commutation control parameter is added to the historical forging data set to obtain a new historical forging data set. Multiple groups of historical forging data are randomly selected and the difference is calculated. The average value of all the differences is also calculated to obtain the updated average historical forging data difference. The updated average historical forging data difference is the average difference after the new first commutation control time is added. The new commutation control time affects the mean of the historical data, resulting in a new difference calculation.

[0050] The deviation ratio between the updated average historical forging data difference and the average historical forging data difference after calculation reflects the influence of the first switching control time on the historical data set. For example, the difference of multiple historical forging data is calculated to be 4 milliseconds, 5 milliseconds, 3 milliseconds, and 6 milliseconds, and the average historical forging data difference is 4.5 milliseconds. After adding the first switching control time, the difference of multiple updated historical forging data is calculated to be 4 milliseconds, 5 milliseconds, 3 milliseconds, 6 milliseconds, and 4 milliseconds, and the difference of the updated average historical forging data is 4.4 milliseconds; then the deviation ratio = |4.4-4.5| / 4.5 = 0.02 milliseconds.

[0051] According to the deviation ratio, the first commutation stability parameter is calculated, which is calculated by subtracting the deviation ratio from 1, reflecting the stability of the commutation operation. If the first commutation control time is significantly different from the previous multiple historical commutation times, 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 larger, and the first commutation stability parameter is smaller. On the contrary, the first commutation stability parameter is larger. By calculating the first commutation stability parameter, the stability difference between the current commutation operation and the historical operation is quantified. If the new commutation control time is significantly different from the historical commutation time, the stability parameter is smaller, indicating that the current operation is significantly different from the historical operation, and the commutation process may be unstable.

[0052] Furthermore, the present application also includes the following steps: 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.

[0053] Specifically, the expression for calculating the first commutation adaptability of the first commutation control parameter is: ; Among them, HXF is the commutation fitness, which evaluates the overall effect of the current commutation control parameters. Through the commutation fitness, the pros and cons of the commutation process are quantified, thereby helping to optimize the commutation control strategy; w1, w2 and w3 are weights, which determine the relative importance of commutation control time, impact force and stability in the commutation fitness formula. The weight coefficient is adjustable, and these values ​​can be adjusted according to different application scenarios and needs; 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 indicates the stability of the commutation process and reflects the difference between the current commutation operation and the historical data. The higher the stability, the better the fitness.

[0054] Commutation adaptability evaluates the overall effect of the commutation operation by combining multiple factors (commutation time, impact force, stability), so as to comprehensively optimize the commutation process of the equipment. By calculating the commutation adaptability, the pros and cons of the current commutation operation are reflected in real time, the impact force is reduced, the commutation time is shortened, and the stability is improved, which can effectively reduce the wear and damage of the equipment and improve the stability and service life of the equipment.

[0055] In summary, the commutation control method for rapid forging provided in this application has the following technical effects: By obtaining 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 commutation data; obtaining a commutation control parameter space for commutation control of the rapid forging device, and randomly generating a first commutation control parameter in the commutation control parameter space; optimizing and evaluating the first commutation control parameter according to the historical forging data to reduce the commutation time, reduce the influence of the commutation impact force on the rapid forging device, and improve the commutation stability, and obtaining a first commutation fitness; continuing to optimize the commutation control parameter until convergence, obtaining the optimal commutation control parameter with the maximum commutation fitness, and performing commutation control on the rapid forging device. In other words, by analyzing the historical forging data, dynamically adjusting the commutation control parameters, and accurately adjusting the strength and timing of the impact, more accurate commutation control can be achieved, thereby reducing the commutation time and impact force, avoiding unnecessary vibration, and improving the commutation speed.

[0056] Embodiment 2: Based on the same inventive concept as the reversing control method for rapid forging in the aforementioned embodiment 1, the present application also provides a reversing control system for rapid forging, see the attached Figure 2 , the reversing control system for rapid forging comprises: A historical data acquisition module 11, the historical data acquisition module 11 is used to obtain 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 commutation data; a parameter space acquisition module 12, the parameter space acquisition module 12 is used to obtain a commutation control parameter space for commutation control of the rapid forging device, and randomly generate a first commutation control parameter in the commutation control parameter space; an optimization evaluation module 13, the optimization evaluation module 13 is used to reduce the commutation time, reduce the influence of the commutation impact force on the rapid 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; a commutation control module 14, the commutation control module 14 is used to continue to optimize the commutation control parameters until convergence, obtain the optimal commutation control parameters with the largest commutation fitness, and perform commutation control on the rapid forging device.

[0057] Furthermore, the historical data acquisition module 11 in the reversing control system for rapid forging is also used for: Acquire multiple historical reversing times of the rapid forging equipment within a preset historical time range; use the multiple historical reversing times as multiple historical reversing data to construct the historical forging data set.

[0058] Furthermore, the parameter space acquisition module 12 in the commutation control system for rapid forging is also used for: The hydraulic control parameter space for the reversing control of the rapid forging equipment is obtained, and the reversing control parameter space is constructed; and the first reversing control parameter is randomly generated in the reversing control parameter space.

[0059] Furthermore, the optimization evaluation module 13 in the reversing control system for rapid forging is also used for: Obtain a first commutation control time for commutation control according to the first commutation control parameter, wherein the first commutation control parameter is input into a commutation control timetable to map and obtain the first commutation control time, and the commutation control timetable includes a 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 a first commutation vibration amplitude as a first commutation impact force parameter; according to the historical forging data set and the first commutation control time, analyze and obtain a 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 a first commutation adaptability of the first commutation control parameter.

[0060] Furthermore, the optimization evaluation module 13 in the reversing control system for rapid forging is also used for: According to the commutation data records of the rapid forging equipment in historical time, a sample commutation control parameter set and a sample commutation vibration amplitude set are collected; the sample commutation control parameter set and the sample commutation vibration amplitude set are used as supervised training data to train the commutation impact prediction channel; the first commutation control parameter is input into the trained commutation impact prediction channel, and the first commutation vibration amplitude is obtained by prediction output as the first commutation impact force parameter.

[0061] Furthermore, the optimization evaluation module 13 in the reversing control system for rapid forging is also used for: A plurality of groups of historical forging data are randomly selected from the historical forging data set, wherein each group of historical forging data includes two historical forging data; the difference of each group of historical forging data is calculated to obtain a plurality of historical forging data differences, and the average is calculated to obtain an average historical forging data difference; the first commutation control time is added to the historical forging data set to calculate 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.

[0062] Furthermore, the optimization evaluation module 13 in the reversing control system for rapid forging is also used for: 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.

[0063] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. Figure 1 The commutation control method and specific examples for rapid forging in the first embodiment are also applicable to the commutation control system for rapid forging in the present embodiment. Through the above detailed description of the commutation control method for rapid forging, those skilled in the art can clearly know the commutation control system for rapid forging in the present embodiment, so for the sake of brevity of the specification, it will not be described in detail here. For the system disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method description.

[0064] 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 apparent to those skilled in the art, and the general principles defined herein may 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 the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

[0065] 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 belong to the scope of the present application and its equivalent technology, the present application is also intended to include these modifications and variations.

Claims

1. A commutation 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; 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; In order to reduce the commutation time, reduce the influence of the commutation impact force on the rapid forging equipment and improve the commutation stability, the first commutation control parameter is optimized and evaluated according to the historical forging data to obtain a first commutation adaptability; The optimization of the commutation control parameters is continued until convergence, and the optimal commutation control parameters with the maximum commutation adaptability are obtained, and the commutation control of the rapid forging equipment is performed.

2. A method for controlling the direction change of 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 multiple historical commutation times are used as multiple historical commutation data to construct the historical forging data set.

3. A method for controlling the direction change of rapid forging according to claim 1, characterized in that: Acquiring 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 the hydraulic control parameter space of the rapid forging equipment for reversing control, and construct the reversing control parameter space; A first commutation control parameter is randomly generated in the commutation control parameter space.

4. A method for controlling the direction change of rapid forging according to claim 2, characterized in that: In order to reduce the commutation time, reduce the influence of the commutation impact force on the rapid forging equipment and improve the commutation stability, the first commutation control parameter is optimized and evaluated according to the historical forging data, including: Acquire a first commutation control time for 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, and the commutation control timetable includes a mapping relationship between sample commutation control parameters and sample commutation control times; According to the first commutation control parameter, the vibration amplitude of the rapid forging device during commutation is predicted to obtain a first commutation vibration amplitude as a first commutation 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 according to the first commutation control time, the first commutation impact force parameter, and the first commutation stability parameter.

5. A method for controlling the direction change of rapid forging according to claim 4, characterized in that: According to the first commutation control parameter, the vibration amplitude of the rapid forging device during commutation is predicted to obtain the first commutation vibration amplitude as the first commutation impact force parameter, including: According to the commutation data records of the rapid forging equipment in the historical time, 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 the first commutation vibration amplitude as the first commutation impact force parameter.

6. A method for controlling the direction change of rapid forging according to claim 4, 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; Calculate the difference of each group of historical forging data to obtain multiple historical forging data differences, and calculate the average to obtain the average historical forging data difference; Adding the first switching control time to the historical forging data set, and calculating and obtaining 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.

7. A method for controlling the direction change of rapid forging according to claim 4, 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.

8. A reversing control system for rapid forging, characterized in that: Steps for implementing a method for rapid forging control as described in any one of claims 1 to 7, wherein the method for rapid forging control comprises: A historical data acquisition module, the historical data acquisition module is used to acquire 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 commutation data; A parameter space acquisition module, the parameter space acquisition module is 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 evaluation module, the optimization evaluation module is used to optimize and evaluate the first commutation control parameter according to the historical forging data in order to reduce the commutation time, reduce the influence of the commutation impact force on the rapid forging equipment and improve the commutation stability, and obtain a first commutation adaptability; A commutation control module is used to continue optimizing the commutation control parameters until convergence, obtain the optimal commutation control parameters with the maximum commutation adaptability, and perform commutation control on the rapid forging equipment.

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