A stamping operation speed control method and device, electronic equipment and storage medium

Abstract

The application discloses a stamping operation speed control method and device, electronic equipment and a storage medium, and belongs to the technical field of stamping equipment control. The method comprises the following steps: determining a stamping motion influence coefficient according to the striking stroke and striking energy of a stamping device, determining a mechanical arm motion influence coefficient according to the operation time and operation speed of the mechanical arm, and determining a device parameter influence coefficient according to device parameters; constructing a stamping speed fitness function according to the stamping motion influence coefficient, the mechanical arm motion influence coefficient and the device parameter influence coefficient; iteratively training an initial neural network model according to the fitness function and historical operation parameters to obtain an operation speed prediction model; and predicting an optimal stamping operation speed according to the current operation parameters of the stamping device and the mechanical arm based on the operation speed prediction model, and adjusting the current operation speed of the stamping device. The optimal stamping operation speed is obtained through multi-dimensional analysis of stamping operation.

Description

Technical Field

[0001] The present invention relates to the field of stamping equipment control technology, and in particular to a stamping operation speed control method, device, electronic equipment and storage medium. Background Art

[0002] In modern industrial production, stamping equipment is one of the key processing equipment. Its operating efficiency and stability directly affect product quality and production efficiency. At the same time, the loading and unloading robotic arms in the automated production line also play a vital role. Their operating efficiency and the ability of the stamping equipment to work together directly affect the smoothness of the entire production line. In the fast-paced modern industrial production, improving production efficiency is one of the core goals pursued by enterprises. Stamping equipment operating speed prediction technology helps enterprises identify and resolve potential production bottlenecks in advance by accurately predicting the equipment's operating speed, optimize production processes, reduce non-production time, and thus significantly improve production efficiency and production capacity. At the same time, it can also flexibly adjust production plans according to changes in market demand to achieve rapid response to the market.

[0003] In the existing technology, sensors are used to monitor the speed of stamping equipment, and speed data is displayed and recorded in real time through electronic instruments. The speed monitoring data of the stamping equipment is analyzed and evaluated. However, due to the complexity of the stamping equipment structure and the extreme performance requirements, the predicted speed analyzed and evaluated by the existing technology cannot meet the current high requirements for production efficiency and equipment stability.

[0004] Therefore, there is an urgent need for a method for controlling the stamping operation speed, which can obtain the optimal stamping operation speed by performing multi-dimensional analysis on the stamping operation data. Summary of the Invention

[0005] In view of this, it is necessary to provide a stamping operation speed control method, device, electronic device and storage medium, which can obtain the optimal stamping operation speed by performing multi-dimensional analysis on stamping operation data.

[0006] In order to solve the above technical problems, the present invention provides a method for controlling a stamping speed, comprising:

[0007] Determine the impact coefficient of the striking motion according to the striking stroke and striking energy of the stamping equipment, determine the impact coefficient of the robotic arm motion according to the running time and running speed of the robotic arm, and determine the equipment parameter impact coefficient according to the equipment parameters;

[0008] Constructing a fitness function of the punching speed according to the punching motion influence coefficient, the robot arm motion influence coefficient and the equipment parameter influence coefficient;

[0009] Iteratively training the initial neural network model according to the fitness function and historical operating parameters of the stamping equipment and the robotic arm to obtain an operating speed prediction model;

[0010] Based on the running speed prediction model, the optimal stamping running speed is predicted according to the current running parameters of the stamping equipment and the robot arm, and the current running speed of the stamping equipment is adjusted according to the optimal stamping running speed.

[0011] In one possible implementation, the impact coefficient of the striking motion is determined based on the striking stroke and striking energy of the punching device, including:

[0012] Acquiring historical stamping operation data of the stamping equipment, wherein the historical stamping operation data includes striking stroke and striking energy;

[0013] Determine a first correlation between the striking stroke, striking energy, and the expected operating speed of the stamping equipment, wherein the first correlation is:

[0014] ,

[0015] in, is the impact force of the stamping equipment, To combat the trip, is the weight of the hammer of the stamping equipment, is the first expected operating speed of the stamping equipment;

[0016] According to the correlation relationship and the actual operating speed of the stamping equipment, the impact coefficient of the striking motion is determined. The calculation formula of the impact coefficient of the striking motion is:

[0017] ,

[0018] in, is the impact coefficient of the striking motion, is the coefficient constant, is the impact force of the stamping equipment, To combat the trip, is the weight of the hammer of the stamping equipment, is the first expected operating speed of the stamping equipment, is the actual operating speed of the stamping equipment.

[0019] In one possible implementation, the robot arm motion influence coefficient is determined based on the running time and running speed of the robot arm, including:

[0020] Acquire historical operation data of the robotic arm, wherein the historical operation data includes the operation time and operation speed of the robotic arm;

[0021] The feeding and discharging operation efficiency of the mechanical arm is calculated according to the running time of the mechanical arm, and the feeding and discharging operation efficiency is:

[0022] ,

[0023] Wherein, is the feeding and discharging operation efficiency of the mechanical arm, is the time required for the stamping equipment to complete one stamping action, is the total time required for the mechanical arm to complete one feeding and discharging operation;

[0024] The second expected running speed is determined according to the running efficiency of the mechanical arm and the running speed of the mechanical arm;

[0025] The mechanical arm motion influence coefficient is determined according to the second expected running speed and the actual running speed of the stamping equipment.

[0026] In a possible implementation manner, the equipment parameters include physical performance of the stamping equipment, die parameters, motion performance of the mechanical arm, and automation level of the mechanical arm; the equipment parameter influence coefficient is determined according to the equipment parameters, and the determination includes:

[0027] The equipment parameters of the stamping equipment and the equipment parameters of the mechanical arm are obtained;

[0028] A third correlation relationship between the equipment parameters and the running speed of the stamping equipment is determined, and the third correlation relationship is:

[0029] ,

[0030] Wherein, is the third expected running speed determined by the stamping equipment parameters and the mechanical arm equipment parameters, is a constant coefficient, is the physical performance of the stamping equipment, is the die parameter, is the motion performance of the mechanical arm, is the automation level of the mechanical arm;

[0031] The equipment parameter influence coefficient is determined according to the third correlation relationship and the actual running speed of the stamping equipment.

[0032] In a possible implementation manner, the calculation formula of the stamping speed fitness function is:

[0033] ,

[0034] Wherein, is the hitting motion influence coefficient, is the mechanical arm motion influence coefficient, is the equipment parameter influence coefficient, and is a weight coefficient.

[0035] In a possible implementation, the operation parameters include device parameters and operation data, and the initial neural network model is iteratively trained according to the fitness function and historical operation parameters of the stamping device and the mechanical arm to obtain the operation speed prediction model, including:

[0036] The historical operation parameters of the stamping device and the mechanical arm are obtained.

[0037] The historical operation parameters are classified and preprocessed to generate a training data set.

[0038] An initial neural network model is constructed.

[0039] The initial neural network model is iteratively trained according to the training data set to generate an operation speed prediction model.

[0040] In a possible implementation, the initial neural network model is iteratively trained according to the training data set to generate an operation speed prediction model, including:

[0041] The initial neural network model is trained according to the training data set to generate a predicted stamping operation speed.

[0042] Based on a particle swarm optimization algorithm and the fitness function, the fitness value of the predicted stamping operation speed output by each iteration training is calculated in sequence.

[0043] Based on a historical best fitness value and a global best fitness value, a change trend of the fitness value of the predicted stamping operation speed is determined.

[0044] When the change trend is less than a preset threshold, an optimal stamping operation speed is obtained.

[0045] The operation speed prediction model is obtained according to the optimal stamping operation speed.

[0046] In a second aspect, the present application further provides a stamping operation speed control device, including:

[0047] An influence coefficient determination module is configured to determine a stamping motion influence coefficient according to a striking stroke and a striking energy of the stamping device, determine a mechanical arm motion influence coefficient according to an operation time and an operation speed of the mechanical arm, and determine a device parameter influence coefficient according to the device parameters.

[0048] A fitness function construction module is configured to construct a stamping speed fitness function according to the stamping motion influence coefficient, the mechanical arm motion influence coefficient, and the device parameter influence coefficient.

[0049] A model generation module is used to iteratively train the initial neural network model according to the fitness function and the historical operating parameters of the stamping equipment and the robotic arm to obtain an operating speed prediction model;

[0050] The stamping speed control module is used to predict the optimal stamping operation speed based on the operation speed prediction model and the operation parameters of the current stamping equipment and the robot arm, and adjust the current operation speed of the stamping equipment according to the optimal stamping operation speed.

[0051] In a third aspect, the present invention also provides a device comprising a memory and a processor, wherein the memory is used to store programs and data; the processor is coupled to the memory and is used to execute the program stored in the memory to implement the stamping operation speed control method as described above, and / or to implement the stamping operation speed control as described above.

[0052] In a fourth aspect, the present invention further provides a computer storage medium for storing computer-readable programs or instructions, which, when executed by a processor, can implement the stamping speed control method as described above.

[0053] The beneficial effects of the present invention are as follows: first, the striking motion influence coefficient is determined according to the striking stroke and striking energy of the stamping equipment, the robotic arm motion influence coefficient is determined according to the running time and running speed of the robot arm, and the equipment parameter influence coefficient is determined according to the equipment parameters; a fitness function of the stamping speed is constructed according to the stamping motion influence coefficient, the robotic arm motion influence coefficient, and the equipment parameter influence coefficient; then, the initial neural network model is iteratively trained according to the fitness function, the historical operating parameters of the stamping equipment and the robot arm to obtain an operating speed prediction model; finally, based on the operating speed prediction model, the optimal stamping operating speed is predicted according to the current operating parameters of the stamping equipment and the robot arm, and the current operating speed of the stamping equipment is adjusted according to the optimal stamping operating speed. The present invention constructs a fitness function by analyzing the relationship between the striking stroke and striking energy of the stamping equipment and the stamping equipment movement speed, the relationship between the running time and running speed of the robot arm and the stamping equipment movement speed, and the relationship between the equipment parameters and the stamping equipment movement speed, and evaluates and predicts the optimal movement speed of the stamping equipment through these three influence coefficients, and adjusts the current movement speed of the stamping equipment according to the optimal movement speed. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.

[0055] Figure 1 A schematic flow chart of an embodiment of a method for controlling the speed of a stamping operation provided by the present invention;

[0056] Figure 2 For the present invention Figure 1 Flow chart of the first embodiment of step S101;

[0057] Figure 3 For the present invention Figure 2 Flow chart of the second embodiment of step S101;

[0058] Figure 4 For the present invention Figure 1 A flowchart of a third embodiment of step S101;

[0059] Figure 5 For the present invention Figure 1 A flow chart of an embodiment of step S103;

[0060] Figure 6 For the present invention Figure 5 A flow chart of an embodiment of step S504;

[0061] Figure 7 A schematic structural diagram of an embodiment of a stamping speed control device provided by the present invention;

[0062] Figure 8 This is a schematic structural diagram of an embodiment of the punching operation speed control storage medium provided by the present invention. DETAILED DESCRIPTION

[0063] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making any creative efforts shall fall within the scope of protection of the present invention.

[0064] In the description of the embodiments of the present invention, unless otherwise specified, "plurality" means two or more. "And / or" describes the association relationship between associated objects, indicating that three relationships can exist. For example, "A and / or B" can mean: A exists alone, A and B exist simultaneously, or B exists alone.

[0065] The terms "first," "second," and so on, used in the embodiments of the present invention are for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Therefore, a technical feature designated as "first" or "second" may explicitly or implicitly include at least one such feature.

[0066] References herein to "embodiments" mean that a particular feature, structure, or characteristic described in connection with the embodiments may be included in at least one embodiment of the present invention. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor does it constitute a separate or alternative embodiment that is mutually exclusive of other embodiments. It is understood, both explicitly and implicitly, by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0067] The present invention provides a stamping operation speed control method, device, electronic equipment and storage medium, which are respectively described below.

[0068] Figure 1 A schematic flow chart of an embodiment of the method for controlling the stamping speed provided by the present invention is shown in FIG. Figure 1 As shown, the stamping operation speed control method includes:

[0069] S101, determining a striking motion influence coefficient based on the striking stroke and striking energy of the punching device, determining a robotic arm motion influence coefficient based on the operating time and operating speed of the robotic arm, and determining an equipment parameter influence coefficient based on the equipment parameters;

[0070] S102, constructing a fitness function for the punching speed according to the punching motion influence coefficient, the robot arm motion influence coefficient, and the equipment parameter influence coefficient;

[0071] S103, iteratively training the initial neural network model according to the fitness function and the historical operating parameters of the stamping equipment and the robotic arm to obtain an operating speed prediction model;

[0072] S104 : Based on the running speed prediction model, the optimal stamping running speed is predicted according to the current running parameters of the stamping equipment and the robot arm, and the current running speed of the stamping equipment is adjusted according to the optimal stamping running speed.

[0073] It should be noted that the striking stroke of a stamping machine refers to the distance traveled by the punch from the top dead center to the bottom dead center during a single stamping process, usually measured in millimeters. The length of the striking stroke directly affects the shape and dimensional accuracy of the stamped part. The striking energy is the work done by the punch during the striking process, which reflects the processing capability of the stamping machine on the material. The longer the striking stroke, the greater the striking energy that can be generated. However, the longer the stroke is not necessarily better. This is because an excessively long stroke can lead to increased striking energy loss, increased equipment vibration, and other issues, which can reduce overall efficiency. Therefore, it is necessary to analyze the relationship between the striking stroke and striking energy of the stamping machine and the movement speed of the stamping machine to determine the appropriate movement speed.

[0074] It's important to clarify that a robot's operating time refers to the time it takes from startup to complete a specified task. This time includes the robot's movement time, positioning time, and grasping time. The length of the operating time is affected by multiple factors, including the robot's movement speed, acceleration, load capacity, and loading and unloading angle range. The robot's operating speed refers to the speed at which the robot moves while performing a task. This speed is typically limited by the robot's own performance, control system accuracy, and the loading and unloading protection angles required by safety regulations. The robot's operating speed directly impacts production efficiency. However, excessively fast operating speeds can lead to problems such as robot arm jitter and inaccurate positioning. Furthermore, the robot's operating speed must be coordinated with the operating speed of the stamping equipment. When the stamping equipment is operating at a high speed, the robot arm must also be able to respond quickly to promptly remove the stamped part and place it on the next workstation. Therefore, it's necessary to comprehensively consider factors such as production efficiency, safety, and stability to find the optimal operating speed balance.

[0075] It should be further explained that equipment parameters include stamping equipment parameters, robotic arm equipment parameters, and mold parameters. Stamping equipment parameters include the physical properties of the stamping equipment, such as dynamic performance, structural stiffness, memory stability, and dynamic performance, which is represented by motor power and speed. It is a direct factor affecting the speed of the stamping equipment. The greater the motor power, the stronger the driving force provided, and the higher the maximum speed the equipment can achieve. At the same time, the higher the speed, the faster the stamping speed. The stiffness of the stamping equipment determines its stability and vibration resistance during high-speed movement. Equipment with good stiffness can reduce vibration and deformation during high-speed operation, maintain a stable speed, and avoid speed fluctuations caused by vibration. Equipment stability not only affects the speed, but also affects processing accuracy and product quality. Equipment with good stability can maintain stable performance output during long-term operation. Robotic arm equipment parameters include the robot's motion performance and automation level. In terms of motion performance, the robot's acceleration affects its speed. The robot's positioning accuracy is related to the accuracy of its actions such as picking and placing materials. High-precision positioning can reduce errors and scrap rates. The higher the robot's automation level, the stronger its ability to complete tasks autonomously. Highly automated robotic arms can reduce the likelihood of manual intervention and operational errors, thereby improving production efficiency and stability. The above shows that there is a close correlation between the physical properties of the stamping equipment, mold parameters, the kinematic performance of the robotic arm, and the robotic arm's automation level, and the speed of the stamping equipment. In practical applications, comprehensive consideration and optimization adjustments based on production requirements and equipment performance are necessary to achieve optimal production efficiency and processing quality.

[0076] Specifically, based on the above influencing factors closely related to the movement speed of the stamping equipment and combined with the actual movement speed, the neural network model is trained and evaluated to obtain the optimal stamping operation speed. The current stamping operation speed is adjusted according to the optimal stamping operation speed, and then the optimal production rhythm is obtained, that is, the number of stampings of the stamping equipment per minute, so that the stamping equipment is in the optimal production state, thereby improving production efficiency and equipment stability.

[0077] This embodiment determines the impact coefficient of the striking motion according to the striking stroke and striking energy of the stamping equipment, determines the impact coefficient of the robotic arm motion according to the running time and running speed of the manipulator, and determines the equipment parameter impact coefficient according to the equipment parameters; constructs a fitness function of the stamping speed according to the stamping motion impact coefficient, the robotic arm motion impact coefficient, and the equipment parameter impact coefficient; iteratively trains the initial neural network model according to the fitness function, the historical operating parameters of the stamping equipment and the manipulator to obtain an operating speed prediction model; based on the operating speed prediction model, predicts the optimal stamping operating speed according to the current operating parameters of the stamping equipment and the manipulator, and adjusts the current operating speed of the stamping equipment according to the optimal stamping operating speed. The present invention constructs a fitness function by analyzing the relationship between the striking stroke and striking energy of the stamping equipment and the motion speed of the stamping equipment, the relationship between the running time and running speed of the manipulator and the motion speed of the stamping equipment, and the relationship between the equipment parameters and the motion speed of the stamping equipment, evaluates and predicts the optimal motion speed of the stamping equipment through these three impact coefficients, and adjusts the current motion speed of the stamping equipment according to the optimal motion speed.

[0078] This embodiment comprehensively analyzes the motion parameters of the stamping equipment and the robotic arm, and the relationship between the equipment parameters and the motion speed of the stamping equipment, evaluates and predicts the optimal motion speed of the stamping equipment, and adjusts the current motion speed of the stamping equipment according to the optimal motion speed, so that the stamping equipment is in the optimal motion state, thereby improving production efficiency and equipment stability.

[0079] In some embodiments of the present invention, Figure 2 As shown, Figure 2 The present invention provides Figure 1 The flowchart of the first embodiment of step S101 in FIG. 1 includes:

[0080] S201, obtaining historical stamping operation data of the stamping equipment, the historical stamping operation data including striking stroke and striking energy;

[0081] S202: Determine a first correlation between the striking stroke, striking energy, and the expected operating speed of the stamping equipment. The first correlation is:

[0082] ,

[0083] in, a striking force of the stamping device, a striking stroke, a weight of a hammer of the stamping device, a first expected running speed of the stamping device;

[0084] S203, determining a striking motion influence coefficient according to the correlation relationship and an actual running speed of the stamping device, and a calculation formula of the striking motion influence coefficient is:

[0085] ,

[0086] wherein, the striking motion influence coefficient, a coefficient constant, which is adjusted according to actual application needs and experimental data, a striking force of the stamping device, a striking stroke, a weight of a hammer of the stamping device, a first expected running speed of the stamping device, an actual running speed of the stamping device.

[0087] It should be noted that the striking energy is mainly composed of the gravitational potential energy of the hammer (or similar components) controlled by the hydraulic pressure and the kinetic energy at the time of impact. However, in the actual high-speed pressing process, the gravitational potential energy can be ignored compared to the kinetic energy at the time of hammer impact. Therefore, the striking energy mainly depends on the striking force and the striking stroke of the hammer.

[0088] Specifically, the relationship between the striking energy and the striking force and the striking stroke of the stamping device is: ,

[0089] wherein, the striking energy of the stamping device, the striking force of the stamping device, the striking stroke.

[0090] Further, the relationship between the striking energy and the running speed of the stamping device can be understood by the law of conservation of energy, and the relationship is:

[0091] ,

[0092] wherein, the striking energy of the stamping device, the weight of the hammer of the stamping device, a first expected running speed of the stamping device, and the striking motion influence coefficient can be obtained according to the ratio relationship between the first expected running speed and the actual running speed of the stamping device.

[0093] By analyzing the influence of the striking stroke and the striking energy on the movement speed of the stamping equipment, the running speed of the stamping equipment can be set according to specific requirements to achieve the expected striking effect. The performance of the stamping equipment is optimized and improved.

[0094] In some embodiments of the present application, as Figure 3 shown, Figure 3 provided in the present application Figure 1 The flowchart of the second embodiment of step S101 in the present application comprises:

[0095] S301, obtaining historical running data of the mechanical arm, the historical running data comprising running time and running speed of the mechanical arm;

[0096] S302, calculating the loading and unloading running efficiency of the mechanical arm according to the running time of the mechanical arm, wherein the loading and unloading running efficiency is:

[0097] ,

[0098] wherein, is the loading and unloading running efficiency of the mechanical arm, is the time required for the stamping equipment to complete one stamping action, is the total time required for the mechanical arm to complete one loading and unloading operation;

[0099] S303, determining a second expected running speed according to the running efficiency of the mechanical arm, the running speed of the mechanical arm;

[0100] S304, determining the mechanical arm movement influence coefficient according to the second expected running speed and the actual running speed of the stamping equipment.

[0101] It should be noted that the running speed of the mechanical arm directly affects the efficiency of the stamping equipment in obtaining materials and discharging finished products. When the mechanical arm runs at a high speed, it can quickly deliver the materials to be stamped to the working area of the stamping equipment and quickly remove the finished products after stamping, thereby providing more working space and time for the stamping equipment and improving the running speed of the stamping equipment. In order to achieve efficient production, the mechanical arm and the stamping equipment need to be closely coordinated. The running speed of the mechanical arm needs to be matched with the stamping speed of the stamping equipment to ensure the continuity and stability of the production process. If the mechanical arm runs too slowly, it will cause the stamping equipment to wait for materials, reducing the overall running speed; on the contrary, if the mechanical arm runs too fast, it may cause the materials to be unable to be positioned stably during stamping, affecting the product quality and production efficiency.

[0102] Specifically, the loading and unloading efficiency of the robotic arm can be obtained by the time required for the stamping equipment to complete a stamping action and the total time required for the robotic arm to complete a loading and unloading operation. When this efficiency ratio is 1, the efficiency is highest. According to the movement stroke of the stamping equipment and the movement stroke of the robotic arm for loading and unloading, the correlation between the operating efficiency of the robotic arm and the operating speed of the robotic arm and the second expected operating speed of the stamping equipment can be obtained, thereby further obtaining the robotic arm movement influence coefficient.

[0103] This embodiment optimizes the operating speed of the stamping equipment by analyzing the correlation between the operating time and operating speed of the robot arm and the operating speed of the stamping equipment, thereby improving the production efficiency of the stamping equipment.

[0104] In some embodiments of the present invention, Figure 4 As shown, Figure 4 The present invention provides Figure 1 In the flowchart of the third embodiment of step S101, the equipment parameters include the physical properties of the stamping equipment, the mold parameters, the motion performance of the robot arm, and the automation level of the robot arm; determining the equipment parameter influence coefficient based on the equipment parameters includes:

[0105] S401, obtaining equipment parameters of the stamping equipment and equipment parameters of the robotic arm;

[0106] S402: Determine a third correlation between the equipment parameters and the operating speed of the stamping equipment. The third correlation is:

[0107] ,

[0108] in, is the third expected operating speed determined by the stamping equipment parameters and the robot arm equipment parameters, is a coefficient constant, and its size is adjusted according to actual application needs and experimental data. For the physical properties of stamping equipment, are mold parameters, is the motion performance of the robotic arm, is the automation level of the robotic arm;

[0109] S403: Determine the equipment parameter influence coefficient according to the third association relationship and the actual operating speed of the stamping equipment.

[0110] It should be noted that the equipment parameters include stamping equipment parameters, robotic arm equipment parameters and mold parameters. The stamping equipment parameters include the physical properties of the stamping equipment, such as dynamic performance, structural stiffness, memory stability, and robotic arm equipment parameters include the motion performance and automation level of the robotic arm. The physical properties of the stamping equipment, the motion performance of the robotic arm and the automation level of the robotic arm are directly proportional to the operating speed of the stamping equipment and inversely proportional to the mold parameters. The more complex the mold, the slower the movement speed.

[0111] This embodiment analyzes the relationship between the equipment parameters and the movement speed of the stamping equipment, and predicts the third expected operating speed based on the equipment parameters to optimize the operating speed of the stamping equipment, thereby improving production efficiency.

[0112] In some embodiments of the present invention, the calculation formula of the fitness function of the punching speed is:

[0113] ,

[0114] in, is the impact coefficient of the striking motion, is the influence coefficient of the robot motion, is the equipment parameter influence coefficient, and is the weight coefficient.

[0115] Specifically, the impact of these coefficients on speed is not completely linear, and there are complex interactions, in which their weight coefficients are adjusted through experimental data or training process combined with actual applications and other optimization objectives (such as energy consumption, cost, stability, etc.).

[0116] In some embodiments of the present invention, Figure 5 As shown, Figure 5 The present invention provides Figure 1 The flowchart of an embodiment of step S103 includes:

[0117] S501, obtaining historical operating parameters of the stamping equipment and the robotic arm;

[0118] S502, classify and preprocess historical operating parameters to generate a training data set;

[0119] S503, constructing an initial neural network model;

[0120] S504: Iteratively train the initial neural network model according to the training data set to generate a running speed prediction model.

[0121] It should be noted that the historical operating parameters of the stamping equipment and the robotic arm include the striking stroke and striking energy of the stamping equipment, the operating time and operating speed of the robotic arm, the equipment parameters of the stamping equipment and the equipment parameters of the robotic arm, and the historical operating speed of the stamping equipment. These data are obtained through various sensors, machine vision technology, RFID technology and other sensing devices and technologies.

[0122] Specifically, the historical operating parameters of the stamping equipment and the robotic arm are obtained through various sensing devices and technologies, and these data are classified and preprocessed. The preprocessed training data set is used to train the neural network learning model. The neural network learning model predicts the movement speed of the stamping equipment based on the striking stroke and striking energy of the stamping equipment, the operating time and speed of the robotic arm, and the equipment parameters.

[0123] This embodiment trains the neural network model through the historical operating parameters of the stamping equipment and the robotic arm to obtain an operating speed prediction model, which can predict the stamping operating speed in real time through the real-time operating data of the stamping equipment and the robotic arm.

[0124] In some embodiments of the present invention, Figure 6 As shown, Figure 6 The present invention provides Figure 5 The flowchart of an embodiment of step S504 includes:

[0125] S601, training the initial neural network model according to the training data set to generate a predicted stamping operation speed;

[0126] S602, based on the particle swarm optimization algorithm and the adaptability function, sequentially calculating the fitness value of the predicted stamping operation speed output by each iterative training;

[0127] S603: Determine a change trend of the fitness value for predicting the stamping operation speed based on the historical best fitness value and the global best fitness value;

[0128] S604: When the change trend is less than a preset threshold, the optimal stamping operation speed is obtained;

[0129] S605: Obtain an operation speed prediction model according to the optimal stamping operation speed.

[0130] Specifically, in the particle swarm optimization algorithm, each particle represents the output result of a neural network, that is, the predicted stamping running speed. The performance of each particle is evaluated according to the adaptability function. For each particle, if the fitness value of the current position is better than its previously recorded best position, the current position is set as the best position, and the position with the highest fitness value is found among the best positions of all particles and set as the global best position. According to the speed and position update rules of the particle swarm optimization algorithm, as well as the best position and global best position of the particle, the speed and position of each particle are determined. The above steps are repeated until the trend of the fitness value change of the particle is less than the preset threshold or the maximum number of iterations is reached. The predicted stamping running speed corresponding to the particle is the optimal stamping running speed. At this time, the parameters of the model are in the optimal state, and the running speed prediction model is obtained.

[0131] This embodiment uses the particle swarm optimization algorithm to train the neural network model to predict the operating speed of the stamping equipment, which has the advantages of strong global optimization ability, fast convergence speed, simple parameter adjustment, strong adaptability, and improved neural network performance, thereby improving the accuracy of prediction and the efficiency of training.

[0132] In order to better implement the stamping operation speed control method in the embodiment of the present invention, based on the stamping operation speed control method, correspondingly, Figure 7 As shown, the embodiment of the present invention further provides a stamping speed control device 700 including:

[0133] An influence coefficient determination module 701 is used to determine the impact coefficient of the striking motion according to the striking stroke and striking energy of the punching device, determine the influence coefficient of the robot arm motion according to the running time and running speed of the robot arm, and determine the equipment parameter influence coefficient according to the equipment parameters;

[0134] A fitness function construction module 702 is used to construct a fitness function of the punching speed according to the punching motion influence coefficient, the robot arm motion influence coefficient and the equipment parameter influence coefficient;

[0135] The model generation module 703 is used to iteratively train the initial neural network model based on the fitness function and the historical operating parameters of the stamping equipment and the robotic arm to obtain an operating speed prediction model;

[0136] The stamping speed control module 704 is used to predict the optimal stamping operation speed according to the operation speed prediction model and the operation parameters of the current stamping equipment and the robot arm, and adjust the current operation speed of the stamping equipment according to the optimal stamping operation speed.

[0137] The stamping speed control device 700 provided in the above embodiment can implement the technical solution described in the above stamping speed control method embodiment. The specific implementation principles of the above modules or units can refer to the corresponding contents in the above stamping speed control method embodiment, which will not be repeated here.

[0138] In the embodiments of the present invention, the stamping speed control device may be a standalone server, or a server network or server cluster composed of servers. For example, the stamping speed control device described in the embodiments of the present invention includes, but is not limited to, a computer, a network host, a single network server, a set of multiple network servers, or a cloud server composed of multiple servers. The cloud server is composed of a large number of computers or network servers based on cloud computing.

[0139] The present invention also provides a stamping speed control device, such as Figure 8 As shown, Figure 8 This is a block diagram of an embodiment of a press speed control device provided by the present invention. The press speed control device 800 can be a computing device such as a mobile terminal, desktop computer, notebook, PDA, or server. The press speed control device 800 includes a processor 801 and a memory 802 , wherein the memory 802 stores a press speed control program 803 .

[0140] In some embodiments, the memory 802 may be an internal storage unit of a computer device, such as a hard disk or memory of the computer device. In other embodiments, the memory 802 may also be an external storage device of the computer device, such as a plug-in hard disk equipped on the computer device, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. Furthermore, the memory 802 may also include both an internal storage unit of the computer device and an external storage device. The memory 802 is used to store application software and various types of data installed on the computer device, such as program codes installed on the computer device. The memory 802 may also be used to temporarily store data that has been output or is to be output. In one embodiment, the stamping operation speed control program 803 may be executed by the processor 801, thereby realizing the stamping operation speed control method, device, electronic device and storage device of each embodiment of the present invention.

[0141] In some embodiments, the processor 801 may be a central processing unit (CPU), a microprocessor, or other data processing chip, configured to execute program codes or process data stored in the memory 802 , such as executing a stamping speed control program.

[0142] Those skilled in the art will appreciate that all or part of the processes in the above-described method embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the above-described method embodiments. Any reference to memory, storage, database, or other media used in the various embodiments provided herein may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct RAMbus dynamic RAM (DRDRAM), and RAMbus dynamic RAM (RDRAM).

[0143] The above is a detailed introduction to the stamping speed control method, device, electronic device and storage device provided by the present invention. Specific examples are used in this article to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea; at the same time, for technical personnel in this field, based on the ideas of the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on the present invention.

Claims

1. A method for controlling the speed of a stamping operation, characterized in that: include: Determine the impact coefficient of the striking motion according to the striking stroke and striking energy of the stamping equipment, determine the impact coefficient of the robotic arm motion according to the running time and running speed of the robotic arm, and determine the equipment parameter impact coefficient according to the equipment parameters; Constructing a fitness function of the punching speed according to the punching motion influence coefficient, the robot arm motion influence coefficient and the equipment parameter influence coefficient; Iteratively training the initial neural network model according to the fitness function and historical operating parameters of the stamping equipment and the robotic arm to obtain an operating speed prediction model; Based on the operating speed prediction model, the optimal stamping operating speed is predicted according to the current operating parameters of the stamping equipment and the robot arm, and the current operating speed of the stamping equipment is adjusted according to the optimal stamping operating speed; The impact coefficient of the striking motion is determined based on the striking stroke and striking energy of the stamping equipment, including: Acquiring historical stamping operation data of the stamping equipment, wherein the historical stamping operation data includes striking stroke and striking energy; Determine a first correlation between the striking stroke, striking energy, and the expected operating speed of the stamping equipment, wherein the first correlation is: ， in, is the impact force of the stamping equipment, To combat the trip, is the weight of the hammer of the stamping equipment, is the first expected operating speed of the stamping equipment; According to the correlation relationship and the actual operating speed of the stamping equipment, the impact coefficient of the striking motion is determined. The calculation formula of the impact coefficient of the striking motion is: ， in, is the impact coefficient of the striking motion, is the coefficient constant, is the impact force of the stamping equipment, To combat the trip, is the weight of the hammer of the stamping equipment, is the first expected operating speed of the stamping equipment, is the actual operating speed of the stamping equipment; The robot arm motion influence coefficient is determined based on the robot arm's running time and running speed, including: Acquire historical operation data of the robotic arm, wherein the historical operation data includes the operation time and operation speed of the robotic arm; The loading and unloading efficiency of the robotic arm is calculated based on the running time of the robotic arm. The loading and unloading efficiency is: ， in, For the loading and unloading efficiency of the robot arm, It is the time required for the stamping equipment to complete one stamping action. The total time required for the robot arm to complete one loading and unloading operation; Determining a second expected operating speed of the stamping equipment based on the loading and unloading operating efficiency of the robotic arm, the operating speed of the robotic arm, the motion stroke of the stamping equipment, and the motion stroke of the robotic arm; Determining a robot arm motion influence coefficient according to a second expected operating speed of the stamping equipment and an actual operating speed of the stamping equipment; The equipment parameters include the physical properties of the stamping equipment, mold parameters, the motion performance of the robotic arm, and the automation level of the robotic arm; the equipment parameter influence coefficient is determined based on the equipment parameters, including: Obtain the equipment parameters of the stamping equipment and the equipment parameters of the robotic arm; Determine a third correlation between the equipment parameter and the operating speed of the stamping equipment, wherein the third correlation is: ， in, The third expected operating speed of the stamping equipment is determined by the stamping equipment parameters and the robot arm equipment parameters. is the coefficient constant, For the physical properties of stamping equipment, are mold parameters, is the motion performance of the robotic arm, is the automation level of the robotic arm; Determining an equipment parameter influence coefficient according to the third association relationship and an actual operating speed of the stamping equipment; The calculation formula of the fitness function of the punching speed is: ， in, is the impact coefficient of the striking motion, is the influence coefficient of the robot motion, is the equipment parameter influence coefficient, and is the weight coefficient.

2. The method for controlling the stamping speed according to claim 1, wherein: The operating parameters include equipment parameters and operating data. The initial neural network model is iteratively trained according to the fitness function and the historical operating parameters of the stamping equipment and the robotic arm to obtain an operating speed prediction model, including: Obtain historical operating parameters of stamping equipment and robotic arms; Classifying and preprocessing the historical operating parameters to generate a training data set; Build an initial neural network model; The initial neural network model is iteratively trained according to the training data set to generate a running speed prediction model.

3. The method for controlling the stamping speed according to claim 2, wherein: Iteratively training the initial neural network model according to the training data set to generate a running speed prediction model includes: Training the initial neural network model according to the training data set to generate a predicted stamping operation speed; Based on the particle swarm optimization algorithm and the fitness function, sequentially calculating the fitness value of the predicted stamping running speed outputted by each iterative training; Based on the historical best fitness value and the global best fitness value, the changing trend of the fitness value for predicting the stamping operation speed is determined; When the change trend is less than a preset threshold, the optimal stamping operation speed is obtained; A running speed prediction model is obtained according to the optimal stamping running speed.

4. A stamping operation speed control device, used to implement the stamping operation speed control method according to any one of claims 1 to 3, characterized in that: include: An influence coefficient determination module is used to determine the impact coefficient of the striking motion according to the striking stroke and striking energy of the stamping equipment, determine the impact coefficient of the robotic arm motion according to the running time and running speed of the robotic arm, and determine the equipment parameter impact coefficient according to the equipment parameters; A fitness function construction module is used to construct a fitness function of the punching speed according to the punching motion influence coefficient, the robot arm motion influence coefficient and the equipment parameter influence coefficient; A model generation module is used to iteratively train the initial neural network model according to the fitness function and the historical operating parameters of the stamping equipment and the robotic arm to obtain an operating speed prediction model; The stamping speed control module is used to predict the optimal stamping operation speed based on the operation speed prediction model and the operation parameters of the current stamping equipment and the robot arm, and adjust the current operation speed of the stamping equipment according to the optimal stamping operation speed.

5. An electronic device for controlling the speed of a stamping operation, characterized in that: The method comprises a processor and a memory, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, the steps of the stamping speed control method according to any one of claims 1 to 3 are implemented.

6. A storage medium, characterized in that The storage medium stores computer program instructions, and when the computer program instructions are executed by a computer, the computer is caused to execute the stamping operation speed control method according to any one of claims 1 to 3.

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