A method for controlling the clamping force of a clamping device

Abstract

The present application provides a method for controlling the clamping force of a clamping device, which relates to the field of intelligent control technology. The method establishes a damage relationship between clamping force and time and damage to a workpiece; optimizes and solves the relationship in combination with a damage threshold to obtain a first clamping force and a clamping time; adjusts the first clamping force and the clamping time based on process requirements and constraints to generate a second clamping force and a clamping time; obtains a second damage parameter, takes it as a minimization target, and performs adaptive adjustment according to a closed-loop feedback system to obtain a third clamping force and a clamping time; performs predictive control, and generates clamping force control information according to the obtained fourth clamping force and time, thereby solving the technical problem that the clamping device lacks flexibility and precision in adjusting the clamping force, thereby limiting production efficiency and product quality, and achieves real-time and precise control of the clamping force and time, thereby improving the technical effect of improving production efficiency and workpiece quality.

Description

Technical Field

[0001] The present invention relates to the field of intelligent control technology, and in particular to a clamping force control method of a clamping device. Background Art

[0002] In automated production, the clamping device is a key component used to position and fix the processing object to ensure processing accuracy and efficiency. However, traditional clamping devices have certain limitations in controlling the clamping force. These limitations are mainly reflected in the fact that the adjustment of the clamping force is not flexible or precise enough, which often leads to unnecessary damage to the workpiece, such as surface scratches, shape deformation or internal stress accumulation. In the prior art, the setting of the clamping force is often based on experience or preset standards, and there is a lack of dynamic monitoring and real-time adjustment of the actual clamping effect. This method is difficult to adapt to the changing needs of the production line when processing workpieces of special materials or complex shapes, thereby limiting production efficiency and product quality. In addition, traditional control systems usually do not comprehensively consider the relationship between clamping time and clamping force, which may increase the risk of damage to the workpiece in unnecessary situations.

[0003] In summary, the existing clamping devices lack flexibility and precision in adjusting the clamping force, which often leads to damage to the workpiece, thereby limiting the production efficiency and product quality. Summary of the invention

[0004] The present application provides a clamping force control method for a clamping device, which is used to solve the technical problem that existing clamping devices lack flexibility and precision in adjusting the clamping force, often causing damage to the workpiece, thereby limiting production efficiency and product quality.

[0005] The present application provides a method for controlling the clamping force of a clamping device, the method comprising:

[0006] Extract workpiece test data to establish a workpiece damage relationship between clamping force and clamping time and workpiece damage; optimize and solve the workpiece damage relationship in combination with a workpiece damage threshold to obtain a first clamping force and a first clamping time; adjust the first clamping force and the first clamping time based on process requirement constraints to generate a second clamping force and a second clamping time; obtain a second damage parameter of the clamping operation under the second clamping force and the second clamping time, and take the second damage parameter as the goal of minimizing the second damage parameter, adaptively adjust the second clamping force and the second clamping time according to a closed-loop feedback system to obtain a third clamping force and a third clamping time; predictively control the third clamping force and the third clamping time using the third damage parameter obtained based on optimizing the sensor response speed, and generate clamping force control information based on the obtained fourth clamping force and fourth clamping time.

[0007] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0008] The present application provides a clamping force control method for a clamping device, which establishes a workpiece damage relationship between clamping force and clamping time and workpiece damage by extracting workpiece test data; optimizes and solves the workpiece damage relationship in combination with a workpiece damage threshold to obtain a first clamping force and a first clamping time; adjusts the first clamping force and the first clamping time based on process requirement constraints to generate a second clamping force and a second clamping time; obtains a second damage parameter of the clamping operation under the second clamping force and the second clamping time, and takes the second damage parameter as the goal of minimizing the second damage parameter, adaptively adjusts the second clamping force and the second clamping time according to a closed-loop feedback system to obtain a third clamping force and a third clamping time; predictively controls the third clamping force and the third clamping time with the third damage parameter obtained based on the optimized sensor response speed, and generates clamping force control information based on the obtained fourth clamping force and the fourth clamping time, thereby solving the technical problem that the existing clamping device lacks flexibility and precision in clamping force adjustment, often causing workpiece damage, thereby limiting production efficiency and product quality, and achieves real-time and precise control of clamping force and time, thereby improving production efficiency and workpiece quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] Figure 1 A schematic flow chart of a method for controlling the clamping force of a clamping device is provided for the present application.

[0010] Figure 2 The present application provides a flow chart of optimizing the sensor response speed in a clamping force control method of a clamping device. DETAILED DESCRIPTION

[0011] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0012] Examples, such as Figure 1 As shown, the present application provides a method for controlling the clamping force of a clamping device, the method comprising:

[0013] Step S100: extracting workpiece test data to establish a workpiece damage relationship between clamping force and clamping time and workpiece damage.

[0014] Specifically, through precision testing equipment, such as force sensors and displacement sensors, the response data of the workpiece under different clamping forces and clamping time conditions are systematically collected. These data reflect the influence of the clamping operation on the surface integrity, structural stability and functional retention ability of the workpiece, thereby providing an experimental basis for subsequent analysis. The workpiece damage relationship refers to a data model obtained through experiments, which describes how the clamping force and duration affect the integrity of the workpiece. In order to ensure the accuracy and representativeness of the data, a series of standardized test workpieces are used, which represent the common materials and shapes on the target production line. For example, in a specific embodiment, a typical aluminum alloy workpiece can be selected, and different clamping forces (such as 50 Newtons, 100 Newtons, 150 Newtons) and clamping times (such as 10 seconds, 20 seconds, 30 seconds) are set, and the degree of deformation and the probability of damage of the workpiece under each condition are recorded. Furthermore, by statistically analyzing the collected data, a mathematical model between the clamping force and clamping time and the workpiece damage is established. This model can predict the type and degree of damage that may occur to the workpiece under a specific clamping force and time, and provide a scientific basis for the optimization of the clamping force control method. Furthermore, this relationship model will be used in the subsequent optimization solution step to determine the most suitable clamping force and time settings to minimize workpiece damage and ensure improved production efficiency and quality.

[0015] Step S200: optimizing and solving the workpiece damage relationship in combination with the workpiece damage threshold to obtain a first clamping force and a first clamping time.

[0016] Next, after extracting and analyzing the workpiece test data, the established workpiece damage relationship is optimized and solved in combination with the workpiece damage threshold to obtain the optimal first clamping force and first clamping time. The first clamping force and the first clamping time are the clamping force and time finally selected. The main purpose of this step is to determine the clamping conditions that can achieve effective clamping while minimizing damage to the workpiece without exceeding the workpiece damage threshold. Among them, the workpiece damage threshold refers to a quantitative indicator of the maximum degree of damage that the workpiece can withstand without affecting the function and quality of the workpiece. This threshold is set comprehensively based on the properties of the workpiece material, the final use of the product, and safety and quality standards. For example, for a precision aviation component, its damage threshold will be set relatively low to ensure that its high precision and high reliability requirements are met.

[0017] In the present invention, by combining the damage relationship of the workpiece with the damage threshold, an optimization algorithm such as linear programming or genetic algorithm is used to solve the optimal combination of clamping force and time. In a specific embodiment, it is assumed that the damage threshold of the workpiece is that the surface must not have a deformation of more than 0.5 mm and no microcracks occur. Through the optimization software, the workpiece damage relationship model and the damage threshold are input, and the software will calculate a series of possible clamping force and time combinations, and select an optimal solution that meets the clamping requirements and minimizes damage. For example, the optimization results may show that at a clamping force of 100 Newtons and a clamping time of 15 seconds, the risk of surface deformation and microcracks of the workpiece can be effectively controlled below the threshold. This optimization step not only improves the safety and reliability of the production process, but also ensures the economic benefits of the manufacturing process. It reduces material waste and defective rate by precisely controlling the clamping conditions, and is a key link in achieving efficient and precise production.

[0018] Step S300: adjusting the first clamping force and the first clamping time based on process requirement constraints to generate a second clamping force and a second clamping time.

[0019] Optionally, after determining the preliminary optimal clamping force and clamping time, in order to better adapt to the actual production process requirements, the present invention further adjusts these parameters to generate a second clamping force and a second clamping time that adapt to the actual operating conditions. This adjustment step is based on a detailed consideration of the process requirement constraints existing in the production environment to ensure that the clamping scheme is not only effective in theory, but also can achieve optimal performance in actual production. Among them, the process requirement constraints usually involve factors such as the material properties of the workpiece, the processing environment conditions, the equipment capacity, and the production efficiency. For example, for workpieces that are easily deformed or made of sensitive materials, the clamping force may need to be limited to a lower range to avoid damage; and on a high-speed production line, the clamping time may need to be as short as possible to meet the production rhythm.

[0020] In a specific embodiment, it is assumed that the first clamping force obtained by optimization is 100 Newtons and the first clamping time is 15 seconds. However, according to the speed requirements of the actual production line and the operating limits of the machine, it is found that the clamping process needs to be completed in no more than 10 seconds. Therefore, in order to adapt to this more stringent time constraint, the clamping force needs to be re-evaluated and adjusted. By simulating and testing the relevant process parameters, it is finally determined to increase the clamping force to 120 Newtons and shorten the clamping time to 10 seconds to ensure that the workpiece can be kept stable and safe even in the shortened operating time. In this process, special attention is paid to the assessment of the risk of workpiece damage that may be caused by the new settings, and an integrated sensor system is used to monitor the force and workpiece response during the clamping process in real time to ensure that the integrity of the workpiece is not threatened even at higher clamping forces. In addition, the adjusted parameters need to be verified through field tests to verify their effectiveness to confirm that these adjustments can meet the improvement of production efficiency without sacrificing product quality.

[0021] Through this series of adjustments and verifications, the present invention not only improves the adaptability and flexibility of the clamping device, but also ensures that the quality and integrity of the workpiece can be effectively controlled under harsh production conditions, demonstrating its practical application value in modern automated production.

[0022] Step S400: Obtain a second damage parameter of the clamping operation under the second clamping force and the second clamping time, and take the second damage parameter as the minimization target, adaptively adjust the second clamping force and the second clamping time according to the closed-loop feedback system to obtain a third clamping force and a third clamping time.

[0023] Exemplarily, after adjusting the clamping force and time, the closed-loop feedback system is used to further optimize the clamping parameters. This step is mainly based on the clamping results obtained under the second clamping force and the second clamping time, and the third clamping force and the third clamping time are evaluated and adjusted to minimize the damage to the workpiece, and then the third clamping force and the third clamping time are determined. The second damage parameter refers to the workpiece damage data obtained by monitoring and analyzing the sensor after the second clamping, including but not limited to the surface damage of the workpiece, microstructural changes or any signs of functional failure. These parameters directly reflect the actual impact of the current clamping setting on the workpiece and are the key basis for the next adjustment.

[0024] In a specific embodiment, continuing with the above example analysis, assuming that the second clamping force is set to 120 Newtons and the clamping time is 10 seconds, the data in the clamping process is collected in real time through the force sensor and displacement sensor installed on the clamping device, and the damage parameter of the workpiece is calculated through the data analysis software. If it is found that the damage parameter exceeds the preset safety range, it means that the current clamping setting may cause unacceptable impact on the workpiece. Furthermore, in order to minimize the second damage parameter, the present invention adopts an advanced closed-loop feedback system, which dynamically adjusts the clamping force and time based on real-time data, and automatically finds the best clamping conditions to reduce the risk of damage. For example, if preliminary data shows that the workpiece damage is higher under the setting of 120 Newtons and 10 seconds, the system may automatically test a lower clamping force such as 110 Newtons, and increase the clamping time to 12 seconds accordingly to seek a new balance point for minimizing damage. The closed-loop feedback system continuously performs such self-adjustment and optimization until a set of clamping parameters is found so that the second damage parameter reaches the minimum possible value. In this way, a third clamping force and time, such as 110 Newtons and 12 seconds, were obtained, and the settings at this time were verified to be able to effectively reduce damage to the workpiece while maintaining production efficiency.

[0025] Through the above steps, not only the reliability and safety of production are improved, but also the quality of workpieces is guaranteed, and ultimately the production process is made efficient and economical.

[0026] Step S500: predictively control the third clamping force and the third clamping time with a third damage parameter obtained based on optimizing the sensor response speed, and generate clamping force control information according to the obtained fourth clamping force and fourth clamping time.

[0027] Specifically, predictive control of the third clamping force and the third clamping time is performed based on the third damage parameter obtained by optimizing the sensor response speed. This is a key step to ensure that the clamping device achieves optimal performance in a dynamic production environment. The implementation of this step not only relies on high-precision sensor data, but also includes advanced predictive control technology to dynamically generate and adjust the clamping force and time, thereby generating a fourth clamping force and a fourth clamping time, and generating the final clamping force control information accordingly. Among them, the third damage parameter is obtained through real-time monitoring, reflecting the actual damage suffered by the workpiece after the third clamping. These parameters are collected based on sensors with optimized response speeds, such as force sensors and displacement sensors, which can respond faster to changes in the clamping process, thereby providing more accurate data. This optimization ensures that the sensor can accurately measure any force or displacement anomalies that may cause damage during the clamping process in the shortest time.

[0028] In a specific embodiment, assume that the data obtained from the third clamping process shows that the damage parameters of the workpiece have been significantly reduced under the settings of 110 Newton force and 12 seconds, but there is still room for further optimization. Using the predictive control algorithm, the optimal clamping parameters for the next step are predicted based on these real-time data. The algorithm may propose to slightly adjust the clamping force to 105 Newtons and extend the time to 13 seconds to further reduce damage without affecting production efficiency. The implementation of predictive control relies on complex mathematical models and machine learning technologies that can predict future trends based on historical and real-time data and make optimal decisions accordingly. After completing these adjustments, the fourth clamping force and time generated by the system are regarded as the most ideal clamping parameters under the current production conditions. These parameters are then converted into clamping force control information to guide the actual operation of the clamping device on the production line.

[0029] Through the above steps, not only can the clamping force and time be accurately controlled, but workpiece damage can also be minimized through continuous monitoring and adaptive adjustment, thereby optimizing the operating efficiency of the equipment and the overall performance of the production line while ensuring high-quality production.

[0030] Furthermore, the workpiece damage relationship is optimized and solved in combination with the workpiece damage threshold to obtain the first clamping force and the first clamping time. Step S200 of the present application further includes:

[0031] Step S210: Surface deformation, structural damage and functional failure are used as multi-dimensional damage indicators, and the damage threshold of the multi-dimensional damage indicator is used as the workpiece damage threshold.

[0032] Step S220: solving the lower limit of the workpiece damage of the workpiece damage relationship based on the workpiece damage threshold, taking the clamping force and the clamping time as indicators, extracting the workpiece test data according to the damage lower limit, and obtaining the first clamping force and the first clamping time.

[0033] Specifically, in order to optimize the setting of clamping force and time, a method is first used to analyze and solve the damage that the workpiece may suffer during the clamping process. This process involves taking surface deformation, structural damage and functional failure as multidimensional damage indicators, and using the thresholds of these damage indicators to define the maximum acceptable damage level of the workpiece. Among them, the multidimensional damage indicators refer to several key parameters for the comprehensive assessment of the health status of the workpiece, which reflect the different types of damage that may occur to the workpiece during the force process. Surface deformation refers to any undesirable shape change of the workpiece surface due to the clamping force; structural damage involves fractures or cracks in the internal structure of the workpiece, which may affect its structural integrity; functional failure refers to the situation where the workpiece cannot complete the intended function due to clamping.

[0034] After the damage indicators and the corresponding thresholds are determined, an optimization algorithm is used to perform a lower bound solution, aiming to find the minimum clamping force and the shortest clamping time that meet these damage thresholds. In this way, the safety of the workpiece during the clamping process can be ensured while maximizing production efficiency. For example, in the implementation process, if the threshold for surface deformation of the workpiece is set to a deformation of no more than 0.2 mm, the threshold for structural damage is set to no obvious cracks, and the threshold for functional failure is no loss of operability, then the optimization model will operate on these parameters to find the appropriate clamping force and time. Through a detailed analysis of the workpiece test data, such as by performing a series of tests at different clamping force and time settings, collecting data and inputting them into the optimization model, the model will calculate the clamping conditions that will neither cause surface deformation exceeding 0.2 mm nor cause structural damage or functional failure. Assume that the optimal clamping force is 85 Newtons and the clamping time is 8 seconds, which will be used as the recommended settings for the first clamping force and the first clamping time.

[0035] Through this method, the present invention can not only ensure the safety and functional integrity of the workpiece during the production process, but also ensure the optimization of the clamping process through scientific data support and precise optimization algorithms, thereby improving the overall production efficiency and product quality.

[0036] Furthermore, the first clamping force and the first clamping time are adjusted based on the process requirement constraint to generate the second clamping force and the second clamping time. Step S300 of the present application further includes:

[0037] Step S310: Generate a clamping stability evaluation index based on the material properties, shape complexity and production conditions of the workpiece, traverse the first clamping force and the first clamping time to perform stability calculation of the stability evaluation index, and obtain a first stability coefficient.

[0038] Step S320: If the first stability coefficient satisfies a preset first stability coefficient, the second clamping force and the second clamping time are generated.

[0039] Furthermore, the initially determined clamping force and clamping time are adjusted to generate a second clamping force and a second clamping time that are more suitable for the actual production environment. This process is based on an in-depth analysis of the workpiece material properties, shape complexity, and production conditions, and is achieved by generating a clamping stability evaluation index and performing a systematic stability calculation. The clamping stability evaluation index is set based on the physical and chemical properties of the workpiece, the complexity of the shape, and the specific conditions of the production line. For example, for a precision part with a complex geometry, its stability index will take into account factors such as the selection of the clamping point, the distribution of the clamping force, and the internal stress concentration that may be caused by the clamping method.

[0040] In the specific operation, firstly, the stability calculation is performed by analyzing the first clamping force (such as 85 Newtons) and the first clamping time (such as 8 seconds) obtained, which includes simulating the performance of the clamping force and clamping time in the actual production environment, and checking the adaptability of these settings under different production conditions and possible problems. Through calculation, a quantitative first stability coefficient is obtained, which reflects whether the initially set clamping force and time can ensure stable clamping of the workpiece without causing damage under given production conditions.

[0041] When calculating the first stability coefficient, the key factors affecting the clamping stability are first determined, including clamping force, clamping time, workpiece material properties (such as hardness, elastic modulus), workpiece shape complexity (such as geometry, surface area), and production conditions (such as temperature, humidity, mechanical vibration). Using the above factors, data are collected through workpiece testing and simulation experiments. These data should include the displacement, deformation, vibration, etc. of the workpiece under different clamping force and time combinations, as well as whether sliding, falling off or damage occurs. Further, based on the collected test data, various parameters reflecting the clamping stability are calculated, such as: the gripping force coefficient, that is, the minimum displacement of the workpiece under different clamping forces; the friction coefficient, that is, the friction between the workpiece surface and the clamping device; and the vibration influence coefficient, that is, the influence of the vibration frequency and amplitude of the workpiece during the clamping process on its stability. Next, the different stability parameters are normalized so that they are comparable at the same scale. For example, displacement can be in millimeters, friction can be in Newtons, and vibration can be in frequency (Hz) and amplitude (mm). Normalization can be achieved by standardizing or fractionalizing the parameters so that they vary within the range of 0 to 1. Then, weights are assigned to each parameter, which are determined by experiments and actual production experience to reflect the relative importance of each parameter to the overall stability. Then, through weighted averaging or other statistical methods, the various parameters are combined to obtain an overall stability value. The specific calculation formula can be: The first stability coefficient ,in, is the weight of each parameter, is the normalized parameter value.

[0042] Finally, the calculated first stability coefficient is compared with the preset threshold. If the first stability coefficient meets or exceeds the preset standard (for example, the stability coefficient needs to be above 0.8 to be considered acceptable), it is confirmed that the current clamping setting is suitable and the current clamping force and time combination can maintain the stable clamping of the workpiece in actual production; if it is not satisfied, the clamping force and time need to be adjusted according to the actual situation to improve stability. For example, if the simulation shows that under the conditions of 85 Newtons and 8 seconds, the workpiece has slight displacement or vibration in some production batches, it may be necessary to increase the clamping force to 90 Newtons and adjust the time to 9 seconds to improve the overall stability. The first stability coefficient can provide a quantitative evaluation standard to help optimize the clamping parameter settings and ensure that the workpiece can be safely and stably clamped under actual production conditions. In this process, by traversing different clamping force and clamping time combinations and repeatedly calculating their stability coefficients, until an optimal clamping setting that can meet both production efficiency and workpiece safety is found, the final set of clamping force and time (second clamping force and time) will be used in production to ensure an efficient and safe production process.

[0043] Through this systematic approach, the present invention ensures the efficiency and safety of the clamping process, effectively optimizes the operation of the production line, and improves product quality and production stability.

[0044] Furthermore, step S310 of the present application also includes:

[0045] According to the material properties, shape complexity and production conditions of the workpiece, the workpiece test data is used to perform a workpiece simulation test to obtain interval parameters of the workpiece grasping limit and the workpiece friction limit, and the interval parameters are used as the stability evaluation index.

[0046] Exemplarily, the accuracy and adaptability of the clamping device are further improved by conducting simulation tests on the workpiece to determine key stability parameters, including the gripping limit and friction limit of the workpiece. These parameters are designed and calculated based on the material properties, shape complexity and specific production conditions of the workpiece to ensure that the clamping device can operate effectively in different production environments while protecting the workpiece from unnecessary damage. The workpiece gripping limit refers to the maximum value that the clamping force can reach without causing damage to the workpiece, ensuring that the workpiece will not slip or fall off during processing or handling. The workpiece friction limit refers to the minimum friction required to keep the workpiece stable. This indicator ensures that even in fast or complex production operations, the workpiece will not be displaced or damaged due to improper clamping.

[0047] When implementing this method, the required clamping force and friction force are first evaluated based on the material of the workpiece, such as metal, plastic or composite material, and the shape of the workpiece, such as cylinder, block or irregular shape. Then, simulation tests are performed using the workpiece test data, which simulate various conditions in actual production, such as temperature, humidity and mechanical vibration in a controlled environment. For example, for a high-precision aviation component, a series of simulation tests may be required, including a clamping stability test at high-speed rotation and a performance test in an environment with large temperature changes. Through these tests, the optimal clamping force and friction force range required to keep the workpiece stable under different operating conditions can be determined. After data analysis, the obtained gripping and friction limit interval parameters will be set as clamping stability evaluation indicators, which are then used to guide the specific settings of the clamping equipment to ensure that each clamping on the production line can meet the established safety and efficiency standards. Through this method that comprehensively considers material properties, shape complexity and production conditions, the present invention not only improves the reliability and safety of the production process, but also optimizes production efficiency and workpiece quality.

[0048] Furthermore, the step S400 of obtaining the second damage parameter of the clamping operation under the second clamping force and the second clamping time further includes:

[0049] Step S410: Monitor through an integrated sensor generated by a force sensor and a displacement sensor installed on the clamping device to obtain a second clamping deviation.

[0050] Step S420: performing a clamping operation based on the second clamping force and the second clamping time to obtain a second clamping damage.

[0051] Step S430: obtaining the second damage parameter by combining the second clamping deviation and the second clamping damage.

[0052] Optionally, in order to ensure that the effect of the clamping device in actual operation meets the design expectations and reduce potential damage to the workpiece, a further second clamping force and a second clamping time are set and evaluated. This process involves using an integrated force sensor and a displacement sensor to monitor the clamping operation, so as to accurately obtain the deviation and damage in the clamping process, and calculate the second damage parameter.

[0053] First, define the second clamping deviation and the second clamping damage as key monitoring indicators, where the second clamping deviation refers to the deviation between the expected clamping force or position and the actual clamping result caused by uncertainty factors in the equipment, operation or the workpiece itself during the clamping process. For example, if the clamping force is set to 120 Newtons, and the actual monitored force is 118 Newtons, the difference of 2 Newtons is the clamping deviation. The displacement sensor is used to detect the actual displacement of the workpiece during the clamping process to determine the accuracy and stability of the clamping position. The second clamping damage refers to any form of damage that the workpiece may suffer when the second clamping force and time are set, including but not limited to surface scratches, deformation or internal structure damage. These damages are evaluated through analysis software using the data from the force sensor and displacement sensor, combined with the material properties and geometric structure of the workpiece.

[0054] In the specific implementation, the clamping force and displacement are monitored in real time by the integrated sensor system installed on the clamping device. The data is transmitted to the control system in real time. The control system uses this data to calculate the clamping deviation and evaluate the potential damage during the clamping process. By combining the clamping deviation and the observed clamping damage, a comprehensive second damage parameter is calculated, which reflects the overall safety and stability of the workpiece under the current clamping conditions. This series of monitoring and calculation processes not only improves the accuracy and reliability of the clamping operation, but also enables the clamping parameters to be adjusted according to the actual situation through a real-time feedback mechanism, thereby minimizing damage to the workpiece. This method effectively combines advanced sensing technology and intelligent analysis to ensure the optimization of the clamping operation, while protecting the workpiece from unnecessary damage, improving production efficiency and product quality.

[0055] Furthermore, taking the second damage parameter as the minimization target, the second clamping force and the second clamping time are adaptively adjusted according to the closed-loop feedback system to obtain the third clamping force and the third clamping time. Step S400 of the present application also includes:

[0056] Step S440: using the adaptive optimization function to construct the closed-loop feedback system.

[0057] Step S450: Taking minimization of the second damage parameter as an optimization goal, dynamically predicting the optimization parameters of the clamping process through the closed-loop feedback system.

[0058] Step S460: adjusting the second clamping force and the second clamping time according to the clamping process optimization parameters to obtain the third clamping force and the third clamping time.

[0059] Furthermore, in order to further improve the accuracy of the clamping operation and reduce workpiece damage, the present invention adopts an advanced closed-loop feedback system to achieve dynamic adjustment of clamping force and time through adaptive optimization function. The core of this system is to minimize the second damage parameter to ensure the best clamping effect in actual operation, thereby obtaining a more accurate and safe third clamping force and third clamping time.

[0060] First, the closed-loop feedback system is defined as an integrated control system that can receive data collected from the sensors of the clamping device (including force sensors and displacement sensors) in real time and automatically adjust the clamping parameters based on these data. The design of this system is based on the adaptive optimization function, which can dynamically adjust the operating parameters to achieve the predetermined performance goals based on the clamping effect and workpiece response monitored in real time. During the implementation process, the system first analyzes the damage parameters (i.e., the second damage parameters) obtained by the second clamping operation to evaluate the effect of the current clamping force and time settings. If it is found that the workpiece damage caused by these parameters exceeds the acceptable range, the optimization algorithm will be started to find new clamping force and time settings to reduce the damage.

[0061] During the optimization process, the closed-loop feedback system uses an algorithm model to simulate different combinations of clamping force and clamping time to predict the possible damage results under each combination. These simulations are based on historical data and real-time feedback to ensure the accuracy and reliability of the prediction model. Once the model finds the clamping force and time combination that can minimize the second damage parameter, the system automatically adjusts to these new parameters to achieve the third clamping. For example, if the preliminary simulation shows that adjusting the clamping force from 120 Newtons to 115 Newtons and increasing the clamping time from 10 seconds to 12 seconds can significantly reduce the surface damage and structural deformation of the workpiece, the system will automatically apply these new parameters for the next round of clamping.

[0062] Through this closed-loop feedback and adaptive optimization method, not only the accuracy and safety of the clamping operation are significantly improved, but also the automation and intelligence level of the production process is enhanced, thereby effectively improving the overall production efficiency and product quality. The application of this system ensures that in a dynamic and changing production environment, the clamping operation is always kept in the best state, minimizing damage to the workpiece.

[0063] Furthermore, the closed-loop feedback system dynamically predicts the optimized parameters of the clamping process, and step S450 of the present application further includes:

[0064] Step S451: In dynamic prediction, dynamically adjust the iteration step size and parameter weights.

[0065] Step S452: For each iteration result of the dynamic optimization algorithm, the clamping risk is evaluated in real time using an artificial intelligence-driven prediction model, and the evaluation result is fed back to the dynamic optimization algorithm for parameter adjustment.

[0066] Step S453: improving the dynamic optimization algorithm by adjusting parameters, and matching and verifying the final iteration result with the actual clamping parameters.

[0067] Step S454: If the verification is qualified, the clamping process optimization parameters are generated.

[0068] The closed-loop feedback system of the present invention adopts an advanced dynamic prediction method to optimize the operating parameters in the clamping process. This process involves dynamically adjusting the iteration step size and parameter weights, using an artificial intelligence-driven prediction model to conduct a real-time assessment of the clamping risk, and adjusting the parameters based on the assessment results. Ultimately, matching verification is used to ensure the optimality and practicality of the selected parameters.

[0069] First of all, dynamically adjusting the iteration step and parameter weights is the core of the optimization algorithm, ensuring that the algorithm can flexibly respond to various production changes and needs. The iteration step determines the moving distance of the optimization algorithm when searching for the best parameters. A smaller step may increase the calculation accuracy but slow down the convergence speed, while a larger step may quickly approach the optimal solution but easily miss the optimal point. The adjustment of parameter weights is to balance between multiple optimization goals, such as finding the most appropriate balance between clamping force and clamping time, ensuring that neither excessive clamping causes damage nor insufficient clamping causes the workpiece to slip.

[0070] Next, the system uses an artificial intelligence-driven predictive model to evaluate the clamping risk after each iteration in real time. The model is built based on a large amount of historical data and machine learning technology, and can predict the damage or failure of the workpiece under specific clamping force and time settings. The results of each iteration are fed back to the optimization algorithm in real time, and the algorithm continues to adjust the parameters based on these feedbacks, and continuously optimizes until the ideal clamping parameters are found.

[0071] In addition, after the dynamic optimization algorithm makes parameter adjustments, it will match and verify the final iteration results with the actual clamping parameters. This step is a key step to confirm that the selected parameters can be effectively implemented in actual production. Through simulation or actual production testing, the consistency of the actual clamping results and the predicted results is compared to verify the applicability and accuracy of the parameter settings. If the parameters are proven to be effective and appropriate after this series of verification steps, the system will finally determine these parameters as the optimized parameters for the clamping process. In this way, the closed-loop feedback system ensures that every step of the clamping operation is based on precise calculations and real-time feedback, greatly improving the safety, efficiency and quality of the production process. Through this highly automated and intelligent method, the clamping risk in production is effectively reduced, ensuring the integrity of the workpiece and the continuity of production.

[0072] Furthermore, if Figure 2 As shown, to optimize the sensor response speed, step S500 of the present application also includes:

[0073] Step S510: Taking the continuous working time, temperature change, and humidity change as aging factors, the aging degree of the sensor is obtained.

[0074] Step S520: Evaluate the sensor state according to the aging influence model established based on the sensor aging degree and the sensor response speed to obtain a sensor aging coefficient.

[0075] Step S530: setting the sampling frequency according to the sensor aging coefficient, extracting the sampling frequency that meets the sampling frequency threshold and adding redundant sensors to optimize the sensor response speed.

[0076] Specifically, in order to ensure the accuracy and reliability of the sensor in the clamping device, the present invention also takes a series of measures to optimize the response speed of the sensor, especially for the aging problem that may occur during the long-term use of the sensor. This process includes evaluating the aging degree of the sensor, establishing an aging impact model, and adjusting the sampling frequency and adding redundant sensors accordingly, so as to maintain the sensor performance in the best state.

[0077] The sensor aging degree refers to the degree of performance degradation of the sensor due to long-term influence of factors such as continuous working time, temperature changes, and humidity changes. The performance degradation of the sensor may cause its response speed to slow down and its accuracy to decrease, thus affecting the effect of the entire clamping system. In order to systematically evaluate this problem, the present invention adopts a special monitoring technology to record and analyze the performance of the sensor under different working conditions, and collect necessary data for subsequent analysis. Based on the collected aging data, an aging impact model is established. This model is based on statistical analysis and machine learning technology, and can predict the performance changes of the sensor under the influence of specific aging factors, thereby generating an aging coefficient for each sensor. This coefficient is a quantitative indicator that represents the degree of performance degradation of the sensor due to aging, and is then used to adjust the sensor usage strategy.

[0078] Furthermore, according to the aging coefficient of each sensor, the system will automatically adjust the sampling frequency of the sensor. The key to setting the sampling frequency is to ensure the real-time and accuracy of the data without exceeding the processing capacity of the sensor. If the aging coefficient indicates that the performance of a sensor has significantly declined, its sampling frequency will be appropriately reduced to avoid overload and further damage to the sensor. At the same time, in order to ensure the redundancy and reliability of the system, when the sampling frequency of a sensor needs to be reduced to below the threshold that cannot meet the actual monitoring needs, the system will add redundant sensors. These redundant sensors can provide backup when the performance of the original sensors is insufficient, ensuring the integrity of the monitoring data and the stable operation of the system.

[0079] Through the above steps, the service life of the sensor can be effectively extended, its performance under high-intensity working conditions can be improved, and the overall performance of the clamping device can be ensured to be always in the best condition, thereby improving production efficiency and ensuring workpiece quality.

[0080] Through the technical solutions of the above-mentioned embodiments, a method for controlling the clamping force of a clamping device provided in the present application solves the technical problems that the existing clamping devices lack flexibility and precision in adjusting the clamping force, which often leads to damage to the workpiece and thus limits production efficiency and product quality. It achieves real-time and precise control of the clamping force and time, thereby improving the technical effect of production efficiency and workpiece quality.

[0081] The technical features of the above embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0082] The above-mentioned embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application.

Claims

1. A method for controlling the clamping force of a clamping device, characterized in that: include: Extract workpiece test data to establish the workpiece damage relationship between clamping force and clamping time on workpiece damage; The workpiece damage relationship is optimized and solved in combination with the workpiece damage threshold to obtain a first clamping force and a first clamping time; Adjusting the first clamping force and the first clamping time based on process requirement constraints to generate a second clamping force and a second clamping time; Obtaining a second damage parameter of the clamping operation under the second clamping force and the second clamping time, taking the second damage parameter as the goal of minimizing, adaptively adjusting the second clamping force and the second clamping time according to the closed-loop feedback system, and obtaining a third clamping force and a third clamping time; Predictively controlling the third clamping force and the third clamping time with a third damage parameter obtained based on the optimized sensor response speed, and generating clamping force control information according to the obtained fourth clamping force and fourth clamping time; The step of optimizing and solving the workpiece damage relationship in combination with the workpiece damage threshold to obtain the first clamping force and the first clamping time includes: Taking surface deformation, structural damage and functional failure as multi-dimensional damage indicators, and taking the damage threshold of the multi-dimensional damage indicators as the damage threshold of the workpiece; The lower limit of the workpiece damage of the workpiece damage relationship is solved based on the workpiece damage threshold, and the workpiece test data is extracted according to the damage lower limit with the clamping force and the clamping time as indicators to obtain the first clamping force and the first clamping time.

2. A method for controlling the clamping force of a clamping device according to claim 1, characterized in that: The step of adjusting the first clamping force and the first clamping time based on the process requirement constraint to generate the second clamping force and the second clamping time comprises: Generate a clamping stability evaluation index based on the material properties, shape complexity and production conditions of the workpiece, traverse the first clamping force and the first clamping time to perform stability calculation on the stability evaluation index, and obtain a first stability coefficient; If the first stability coefficient satisfies a preset first stability coefficient, the second clamping force and the second clamping time are generated.

3. A method for controlling the clamping force of a clamping device as claimed in claim 2, characterized in that: According to the material properties, shape complexity and production conditions of the workpiece, the workpiece test data is used to perform a workpiece simulation test to obtain interval parameters of the workpiece grasping limit and the workpiece friction limit, and the interval parameters are used as the stability evaluation index.

4. A method for controlling the clamping force of a clamping device as claimed in claim 1, characterized in that: The obtaining of the second damage parameter of the clamping operation under the second clamping force and the second clamping time comprises: The second clamping deviation is obtained by monitoring the integrated sensor generated by the force sensor and the displacement sensor installed on the clamping device; Perform a clamping operation based on the second clamping force and the second clamping time to obtain a second clamping damage; The second damage parameter is obtained by combining the second clamping deviation and the second clamping damage.

5. A method for controlling the clamping force of a clamping device as claimed in claim 1, characterized in that: The method of taking the second damage parameter as the minimization target and adaptively adjusting the second clamping force and the second clamping time according to the closed-loop feedback system to obtain the third clamping force and the third clamping time includes: Using adaptive optimization function to construct the closed-loop feedback system; Taking minimization of the second damage parameter as an optimization goal, dynamically predicting the optimization parameters of the clamping process through the closed-loop feedback system; The second clamping force and the second clamping time are adjusted according to the clamping process optimization parameters to obtain the third clamping force and the third clamping time.

6. A method for controlling the clamping force of a clamping device as claimed in claim 5, characterized in that: The method of dynamically predicting the optimized parameters of the clamping process through the closed-loop feedback system includes: In dynamic prediction, the iteration step size and parameter weights are dynamically adjusted; For each iteration of the dynamic optimization algorithm, an artificial intelligence-driven prediction model is used to evaluate the clamping risk in real time, and the evaluation results are fed back to the dynamic optimization algorithm for parameter adjustment; The dynamic optimization algorithm is improved by adjusting parameters, and the final iteration result is matched and verified with the actual clamping parameters; If the verification is qualified, the optimized parameters of the clamping process are generated.

7. A method for controlling the clamping force of a clamping device as claimed in claim 1, characterized in that: Optimize sensor response speed, including: The aging degree of the sensor is obtained by taking the continuous working time, temperature change and humidity change as aging factors; Evaluate the sensor state according to the aging influence model established based on the sensor aging degree and the sensor response speed to obtain the sensor aging coefficient; The sampling frequency is set according to the sensor aging coefficient, and the sampling frequency that meets the sampling frequency threshold is extracted to add redundant sensors to optimize the sensor response speed.

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