Automated cylinder clamping system and method

Abstract

The present invention discloses an automated cylinder clamping system and method, which belongs to the field of cylinder clamping technology, wherein the system includes: a map drawing module for drawing up a three-dimensional clamping force distribution map; a risk prediction module for synchronously predicting the deformation offset risk value during the clamping process; a model calculation module for generating a dynamic attenuation coefficient; and a cylinder adjustment module for dynamically adjusting the multi-stage buffer valve opening and servo boost rate of the automated cylinder group through an adaptive control strategy until the clamping force error stabilizes within a preset threshold. This application solves the technical problems of unstable clamping force and low workpiece clamping accuracy due to environmental interference and system errors during automated cylinder clamping, and achieves the technical effects of improving clamping force stability, reducing deformation risk, and improving clamping accuracy through dynamic compensation and adaptive adjustment.

Description

Technical Field

[0001] The present invention relates to the technical field of cylinder clamping, and in particular to an automated cylinder clamping system and method. Background Art

[0002] The in-depth application of automation technology in industrial manufacturing has placed higher demands on workpiece clamping systems. This is particularly true in precision manufacturing and assembly processes, where stability and accuracy in clamping are crucial. Traditional pneumatic cylinder clamping devices generally employ a uniform pressure control strategy, which struggles to adapt to the force distribution requirements of complex clamping objects such as irregularly shaped workpieces and composite materials. Existing fixed-parameter control methods cannot effectively address multiple interference sources, such as mechanical vibration, air pressure fluctuations, and temperature changes. This leads to insufficient clamping force stability and significantly increases the risk of workpiece deformation. Particularly in fields such as aerospace and precision electronics, where workpieces exhibit significant variations in material hardness and complex structural features, conventional clamping schemes are prone to localized stress concentrations, resulting in irreversible workpiece damage. Current clamping systems generally lack the ability to predict risks in the clamping process in real time, resulting in delayed responses to sudden changes in operating conditions, severely impacting machining quality and efficiency. The strong coupling between environmental interference factors and the characteristics of the clamped object makes parameter tuning for traditional control algorithms a significant challenge. Summary of the Invention

[0003] This application provides an automated cylinder clamping system and method, aiming to solve the technical problems of unstable clamping force and low workpiece clamping accuracy caused by environmental interference and system errors during the automated cylinder clamping process, and to achieve the technical effects of improving clamping force stability, reducing deformation risk and improving clamping accuracy through dynamic compensation and adaptive adjustment.

[0004] In view of the above problems, the present application provides an automated cylinder clamping system and method.

[0005] The first aspect disclosed in the present application provides an automated cylinder clamping system, which includes: a map drawing module for obtaining structural basic features including surface morphology features and workpiece size features based on the workpiece to be clamped, and drawing up a three-dimensional clamping force distribution map with material hardness data as a constraint condition; a risk prediction module for matching the target pressure sequence of each execution unit in the automated cylinder group based on the three-dimensional clamping force distribution map, and synchronously predicting the deformation offset risk value during the clamping process; a model calculation module for introducing environmental interference factors and inputting them into a pre-trained interference compensation model to generate a dynamic attenuation coefficient, wherein the environmental interference factors include the vibration spectrum of the positioning plate, the fluctuation rate of the air pressure pipeline and the temperature gradient field; a cylinder adjustment module for dynamically adjusting the multi-stage buffer valve opening and the servo boost rate of the automated cylinder group through an adaptive control strategy according to the target pressure sequence, the deformation offset risk value and the dynamic attenuation coefficient until the clamping force error stabilizes within a preset threshold.

[0006] Another aspect disclosed in the present application provides an automated cylinder clamping method, which includes: based on the workpiece to be clamped, obtaining structural basic features including surface morphology features and workpiece size features, and formulating a three-dimensional clamping force distribution map with material hardness data as a constraint condition; based on the three-dimensional clamping force distribution map, matching the target pressure sequence of each execution unit in the automated cylinder group, and synchronously predicting the deformation offset risk value during the clamping process; introducing environmental interference factors and inputting them into a pre-trained interference compensation model to generate a dynamic attenuation coefficient, wherein the environmental interference factors include the vibration spectrum of the positioning plate, the fluctuation rate of the air pressure pipeline, and the temperature gradient field; according to the target pressure sequence, the deformation offset risk value and the dynamic attenuation coefficient, the multi-stage buffer valve opening and the servo boost rate of the automated cylinder group are dynamically adjusted through an adaptive control strategy until the clamping force error stabilizes within a preset threshold.

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

[0008] The map generation module obtains structural basic features, including surface topography and workpiece dimensional features, based on the workpiece to be clamped. Using material hardness data as a constraint, a three-dimensional clamping force distribution map is generated. The risk prediction module matches the target pressure sequence of each actuator in the automated cylinder group based on the three-dimensional clamping force distribution map and simultaneously predicts the deformation offset risk value during the clamping process. The model calculation module introduces environmental interference factors, including the positioning plate vibration spectrum, air pressure line fluctuation rate, and temperature gradient field, and inputs them into a pre-trained interference compensation model to generate a dynamic attenuation coefficient. The cylinder adjustment module dynamically adjusts the opening of the automated cylinder group's multi-stage buffer valves and the servo boost rate through an adaptive control strategy based on the target pressure sequence, deformation offset risk value, and dynamic attenuation coefficient until the clamping force error stabilizes within a preset threshold. This solves the technical problems of unstable clamping force and low workpiece clamping accuracy caused by environmental interference and system errors during the automated cylinder clamping process, achieving the technical effect of improving clamping force stability, reducing deformation risk, and enhancing clamping accuracy through dynamic compensation and adaptive adjustment.

[0009] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Figure 1 A structural schematic diagram of an automated cylinder clamping system is provided for an embodiment of the present application.

[0011] Figure 2 A flow chart of an automated cylinder clamping method is provided for an embodiment of the present application.

[0012] Figure 3 A schematic diagram of the positioning plate structure of an automated cylinder clamping system is provided for an embodiment of the present application.

[0013] Explanation of the accompanying symbols: map drawing module 11, risk prediction module 12, model calculation module 13, cylinder adjustment module 14, bottom surface of positioning plate 21, plane height of positioning plate 22, circular positioning hole of positioning plate 23, distance between two positioning holes of positioning plate 24, waist-shaped positioning hole of positioning plate 25. DETAILED DESCRIPTION

[0014] This application solves the technical problems of unstable clamping force and low workpiece clamping accuracy caused by environmental interference and system errors during the automated cylinder clamping process by providing an automated cylinder clamping system and method, and achieves the technical effect of improving clamping force stability, reducing deformation risk and improving clamping accuracy through dynamic compensation and adaptive adjustment.

[0015] Below, the technical solutions in this application will be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of this application, rather than all the embodiments of this application. It should be understood that this application is not limited to the example embodiments described herein. Based on the embodiments of this application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application. It should also be noted that, for the convenience of description, only the parts related to this application, rather than all of them, are shown in the accompanying drawings.

[0016] Example 1, as Figure 1 、 Figure 3 As shown, an embodiment of the present application provides an automated cylinder clamping system, the system comprising:

[0017] The map drawing module 11 is used to obtain basic structural features including surface morphology features and workpiece size features based on the workpiece to be clamped, and draw up a three-dimensional clamping force distribution map with material hardness data as a constraint condition.

[0018] Specifically, the clamping system is a precision-designed mechanical device, which is mainly used to fix and position components to ensure the stability and accuracy of the components during processing or testing. The core component of the clamping system is the positioning plate. The flatness requirement of the bottom surface of the positioning plate is 0.02, and the plane height of the positioning plate is 19.15±0.015. These precise designs ensure the close contact and assembly accuracy between the positioning plate and the component to be fixed. Multiple circular holes and waist-shaped holes are designed on the positioning plate for positioning and fixing components. The positioning plate circular hole positioning aperture is 10.001, and the positioning plate waist-shaped positioning aperture is 8.00. These aperture sizes match the corresponding sizes on the component to be fixed, achieving precise positioning. In addition, the distance between the two positioning holes of the positioning plate is 209.75±0.01. This precise distance design further ensures the stability and accuracy of the component during assembly. The clamping system achieves a stable connection through specific aperture and distance design. Figure 3 The figure shows multiple clamping elements and connecting parts that work together to clamp and secure the components. To ensure the quality and performance of the clamping system, all numbered dimensions are tested to ensure assembly accuracy and product quality.

[0019] Specifically, in the map preparation module 11, the basic structural features of the workpiece to be clamped are first obtained from the workpiece database, including the surface morphology and dimensional features of the workpiece, such as the shape, texture, surface roughness, length, width, height, etc. of the workpiece. These parameters are the basis for determining the clamping force distribution. Subsequently, because the material of the workpiece to be clamped will directly affect its deformation characteristics and the distribution of the clamping force, the material hardness data of the workpiece to be clamped will be measured and used as a constraint to further calculate and design the clamping force distribution. Afterwards, the obtained basic structural features and material hardness data will be mechanically integrated to formulate a three-dimensional clamping force distribution map. This map can reflect the clamping force strength and changes in different areas, and can ensure that the workpiece will not be deformed or damaged due to uneven force distribution during the clamping process, thereby optimizing the clamping effect and ensuring clamping accuracy and workpiece safety.

[0020] Furthermore, a three-dimensional clamping force distribution map is developed based on the material hardness data as a constraint, including:

[0021] The basic structural features of the workpiece to be clamped are obtained, and the material hardness data is detected using an ultrasonic flaw detector; the basic structural features and the material hardness data are integrated to set a three-dimensional clamping force distribution map, wherein the three-dimensional clamping force distribution map includes at least three clamping force gradient partitions.

[0022] In a preferred embodiment, after obtaining the basic structural features of the workpiece to be clamped using a workpiece database, 3D scanning technology, or measurement tools, the workpiece's material hardness is tested using testing equipment such as an ultrasonic flaw detector. The ultrasonic flaw detector can assess the material's hardness by emitting ultrasonic waves and measuring changes in the echo signal. This process is based on the fact that ultrasonic waves propagate faster in hard materials. Specifically, the ultrasonic flaw detector transmits high-frequency ultrasonic pulses into the material through a probe and then calculates the ultrasonic wave's propagation speed by measuring the time from emission to reception. Typically, ultrasonic waves propagate faster in hard materials. The propagation speed is then divided by a standard propagation speed, and the result is multiplied by the standard hardness value corresponding to the standard propagation speed to obtain the material's hardness data. This method is applicable to different types of materials, such as metals, plastics, and ceramics. After obtaining the basic structural features and material hardness data, a clamping force distribution model is constructed using characteristic data such as the surface topography, workpiece dimensions, and material hardness of the workpiece to be clamped. These characteristic data determine the clamping force at each location during the clamping process. In order to clearly represent the distribution of clamping force on the workpiece surface, a three-dimensional coordinate system is established, with the coordinate axes typically determined based on the workpiece's geometry and dimensions. Subsequently, the clamping force is divided into gradient zones based on the workpiece's morphological characteristics, resulting in at least three gradient zones representing regions of clamping force of varying strength (e.g., high, medium, and low strength). These zones are based on data such as the workpiece's surface morphology and material hardness, specifically determining the clamping force strength within each zone. Mechanical models (such as finite element analysis and mechanical analysis) are then used to calculate the clamping force distribution in different regions. This calculation process takes into account the structural characteristics and material hardness of different locations, and calculates the clamping force magnitude at different locations based on the workpiece's geometric characteristics and hardness data. Based on the calculation results, a clamping force distribution diagram is drawn in a three-dimensional coordinate system to obtain a three-dimensional clamping force distribution map containing at least three clamping force gradient partitions. This three-dimensional clamping force distribution map usually uses different colors or heights to represent the changes in clamping force. It can clearly show the clamping force strength in different areas (that is, each gradient partition) to adapt to the needs of workpieces in different parts, ensure the stability and accuracy of the clamping process, and provide basic data for subsequent risk prediction.

[0023] The risk prediction module 12 is used to match the target pressure sequence of each execution unit in the automated cylinder group based on the three-dimensional clamping force distribution map, and simultaneously predict the deformation offset risk value during the clamping process.

[0024] Specifically, in the risk prediction module 12, based on the three-dimensional clamping force distribution map, the automated cylinder group will be accurately pressure-configured. First, the map contains the clamping force gradient distribution in different areas. According to these gradient areas, the system divides the automated cylinder group into multiple execution units, and each execution unit corresponds to a specific clamping force area. Subsequently, according to the clamping force gradient area where each execution unit is located, a corresponding target pressure value sequence is matched. These target pressure values ensure that each execution unit generates appropriate pressure, thereby achieving precise clamping of the workpiece. At the same time, the system will also perform risk prediction simultaneously. During this process, finite element analysis software will be used to simulate the effect of clamping force on the workpiece, calculate the stress and strain of various parts of the workpiece, and display the possible deformation of the workpiece under the action of the clamping force. The deformation offset risk value is quantified based on this. This deformation offset risk value can help adjust the pressure distribution in real time to avoid clamping failure caused by uneven clamping force or workpiece deformation, and ensure the stability and accuracy of the entire clamping process.

[0025] Furthermore, based on the three-dimensional clamping force distribution map, the target pressure sequence of each execution unit in the automated cylinder group is matched, including:

[0026] According to the clamping force gradient partitioning of the three-dimensional clamping force distribution map, the automated cylinder group is divided into a corresponding number of multiple execution units, each execution unit corresponding to a clamping force gradient partition; for the multiple execution units in the automated cylinder group, the target pressure value of each execution unit is configured in combination with the spatial position and the force density requirements of the gradient partition to obtain a target pressure sequence.

[0027] In one possible implementation, based on the clamping force gradient partitioning information in the three-dimensional clamping force distribution map, the system divides the automated cylinder group into multiple actuators. Each actuator corresponds to a specific clamping force gradient partition in the clamping force gradient map. These partitions have different clamping force intensities, and each actuator is tasked with applying appropriate pressure based on the clamping force gradient in its region. Furthermore, each clamping force gradient partition represents a different force density requirement. Force density refers to the amount of pressure required per unit area, which typically varies depending on the workpiece shape, material, and clamping method. After determining the gradient partitions, the system assigns a target pressure value to each actuator based on its spatial position and the force density requirement of the corresponding clamping force gradient partition. This target pressure value is determined based on the workpiece clamping requirements and the force density requirements of the gradient partition, combined with prior experience, to ensure uniform pressure distribution during clamping and meet the workpiece clamping accuracy requirements. Once a target pressure value is assigned to each actuator, the target pressure values of all actuators are sequentially combined into a target pressure sequence. This target pressure sequence represents the pressure adjustment sequence of each actuator in the automated cylinder group, which is used to guide subsequent clamping operations to ensure that each actuator can apply appropriate pressure according to its gradient partition to achieve a stable and uniform clamping effect.

[0028] Furthermore, with the uniformity of clamping force, load balance of execution units and energy efficiency as optimization goals, the local curvature of the surface morphology features is introduced as a weight correction factor to optimize the configuration of the target pressure value; at the same time, based on the historical clamping data, a finite element analysis model is constructed by comparing the three-dimensional clamping force distribution map; the finite element analysis model is used to simulate the stress-strain distribution under the clamping state, quantify the deformation offset risk value, and determine whether to trigger the pressure compensation mechanism, which is used to dynamically correct the target pressure sequence.

[0029] In a feasible implementation, after obtaining the target pressure sequence, this target pressure sequence may have a certain error due to deformation offset. Therefore, the uniformity of clamping force, load balance of execution unit and energy efficiency will be used as optimization goals, and the local curvature of the workpiece surface morphology characteristics will be introduced. The local curvature is a parameter that describes the degree of curvature of the surface shape. Different curvature values will affect the force distribution of the workpiece during the clamping process. By using the local curvature as a weight correction factor, the configuration of the target pressure value can be adjusted so that the surface shape changes of the workpiece can be fully considered during the clamping process, thereby optimizing the distribution of the clamping force, making the clamping force more uniform, and avoiding local over-clamping or under-clamping. Subsequently, the historical clamping data is compared with the three-dimensional clamping force distribution map. These historical data contain the actual pressure values and clamping effects during the previous workpiece clamping process. By analyzing these data, it is possible to evaluate which areas may have uneven clamping force or excessive force density. Afterwards, a finite element analysis (FEA) model was constructed based on historical clamping data and three-dimensional clamping force distribution maps. This model simulates the stress-strain distribution of the workpiece in the clamped state through numerical methods. Finite element analysis can accurately predict the deformation of the workpiece during the clamping process, identify high-stress areas, and calculate the clamping force uniformity index (by extracting the pressure distribution in the workpiece clamping area and calculating the standard deviation of the pressure), the execution unit load balance index (by summarizing the total clamping force in the action area of each execution unit and calculating the deviation ratio between the maximum and minimum values), and the energy efficiency index (based on the overall stress change caused by the unit clamping force applied by each unit, and calculating the ratio of the work or energy parameters input in the simulation). These indicators are then subtracted from 1 and the resulting difference is weighted to obtain a deformation offset risk value that describes the quality of the current target pressure value. This risk value reflects the degree of workpiece deformation that may be caused by uneven clamping force or unreasonable force density distribution. If the deformation offset risk value is greater than or equal to the preset deformation offset risk value, the pressure compensation mechanism will be triggered. The goal of this mechanism is to dynamically correct the target pressure sequence to offset the impact of the deformation offset. Specifically, the pressure compensation mechanism will calculate the pressure amplitude that needs to be adjusted according to the size of the deformation risk value. The larger the deformation risk value, the larger the pressure adjustment amplitude. For workpiece areas with large curvature, appropriate adjustments will be made according to their surface shape to avoid pressure concentration or unevenness due to curvature differences. After the adjustment is completed, the adjusted target pressure sequence will be subjected to finite element analysis again, and the deformation offset risk value will be recalculated. This process is iterative until the deformation offset risk value is less than the preset deformation offset risk value. At this time, the current target pressure sequence will be output. In summary, based on the above steps, the system dynamically corrects the target pressure sequence to ensure that during the clamping process, the pressure value can be adjusted in real time according to the actual deformation of the workpiece.In this way, through real-time pressure adjustment, deformation offset can be effectively prevented, and uniformity of clamping force, load balance and energy efficiency optimization can be ensured. Deformation or damage caused by uneven clamping can also be effectively avoided.

[0030] The model calculation module 13 is used to introduce environmental interference factors and input them into the pre-trained interference compensation model to generate a dynamic attenuation coefficient, wherein the environmental interference factors include the positioning plate vibration spectrum, the air pressure pipeline fluctuation rate and the temperature gradient field.

[0031] Specifically, in the model calculation module 13, first, the system identifies and collects environmental interference factors that may affect the clamping process. These interference factors include but are not limited to positioning plate vibration, air pressure fluctuations, temperature, etc. By collecting these interference factors, interference data such as the positioning plate vibration spectrum, air pressure pipeline fluctuation rate, and temperature gradient field can be obtained. Among them, the positioning plate vibration spectrum reflects the vibration frequency and amplitude characteristics of the positioning plate caused by the mechanical structure or external disturbance during the clamping process. This data can be collected in real time by an accelerometer or laser vibrometer installed on the positioning plate; the air pressure pipeline fluctuation rate is used to characterize the stability of the air pressure change of the air source system in different time periods. Continuous air pressure readings can be obtained by a pressure sensor installed on the cylinder intake pipe, and the fluctuation rate can be obtained by statistical analysis. For example, it can be quantified by calculating the standard deviation of the air pressure change; the temperature gradient field describes the difference in temperature distribution between the fixture and the workpiece in space. This data can be measured by a multi-point arranged thermocouple or infrared temperature sensor array. After obtaining this environmental interference data, it is input into a pre-trained interference compensation model. This model has been trained based on historical data and experimental results. It can effectively identify the impact of these interference factors on the clamping process and calculate a dynamic attenuation coefficient. This dynamic attenuation coefficient is used to correct the clamping force, automatically adjusting it based on the current environmental conditions to ensure the stability and accuracy of the clamping force. In this way, the clamping force compensation can be dynamically adjusted in real time based on environmental interference factors, reducing the adverse effects of environmental factors and thus improving the reliability and accuracy of the automated cylinder clamping system.

[0032] Furthermore, environmental interference factors are introduced and input into the pre-trained interference compensation model to generate dynamic attenuation coefficients, including:

[0033] Data noise reduction preprocessing is performed on the positioning plate vibration spectrum, air pressure pipeline fluctuation rate and temperature gradient field in the environmental interference factors to obtain preprocessed data; based on the preprocessed data, the interference compensation model under the Transformer architecture is used to generate a dynamic attenuation coefficient.

[0034] In one feasible implementation, after collecting the positioning plate vibration spectrum, air pressure line fluctuation rate, and temperature gradient field, algorithms such as wavelet transform, Kalman filtering, or SG filtering are used to filter out high-frequency or random noise, retaining the main characteristic trends. Sliding window statistics or Z-score methods are then used to identify and eliminate mutation points or invalid data. Subsequently, the positioning plate vibration spectrum, air pressure line fluctuation rate, and temperature gradient field, which have been stripped of high-frequency noise, random noise, mutation points, and invalid data, are normalized to obtain preprocessed data that adapts to the input format of subsequent models. Normalization is performed using maximum-minimum normalization. This preprocessed data is then input into an interference compensation model built on a Transformer architecture. The model utilizes its multi-head attention mechanism to extract features from interference information of different types and at different times, identifying correlations between environmental disturbances. Through the model's forward propagation process, it outputs a dynamic attenuation coefficient for clamping force correction. This dynamic attenuation coefficient numerically reflects the potential impact of various interference factors on clamping accuracy under current environmental conditions, serving as an important basis for the subsequent dynamic adjustment of clamping force by the cylinder adjustment module.

[0035] For the interference compensation model, the sample positioning plate vibration spectrum, sample air pressure line fluctuation rate, and sample temperature gradient field are divided into training and validation sets, and then annotated with the corresponding sample dynamic attenuation coefficients. After data preparation is completed, a interference compensation model structure is constructed using Transformer, which includes an input layer, a multi-head self-attention mechanism, a feedforward network, and an output layer. The weights of the Transformer model are initialized using a random initialization method. The training set is input into the initialized interference compensation model for forward propagation. The data is passed layer by layer through the input layer, the multi-head self-attention mechanism, the feedforward network, and the output layer to generate the predicted dynamic attenuation coefficient. Subsequently, the mean squared error (MSE) loss function is used to calculate the loss between the predicted results and the sample dynamic attenuation coefficients. The gradient of the loss with respect to each layer weight is calculated layer by layer through the backpropagation algorithm. The model parameters are then optimized using the Adam optimizer, adjusting the weights to minimize the loss function. This process is repeated through batch training until the maximum number of iterations is reached or the loss function converges. After training, the model performance is verified using the validation set to evaluate the accuracy of the model in the dynamic attenuation coefficient prediction task. If the prediction error meets the expected level, the current interference compensation model is output as the final model. Otherwise, hyperparameters such as learning rate, batch size, and training rounds are adjusted to further optimize the model's prediction accuracy and improve its interference compensation capabilities.

[0036] The cylinder adjustment module 14 is used to dynamically adjust the multi-stage buffer valve opening and servo boost rate of the automated cylinder group through an adaptive control strategy according to the target pressure sequence, deformation offset risk value and dynamic attenuation coefficient until the clamping force error is stabilized within a preset threshold.

[0037] Specifically, after obtaining the target pressure sequence, the deformation excursion risk value, and the dynamic attenuation coefficient, the cylinder adjustment module 14 acquires an adaptive control strategy. This strategy describes how to dynamically adjust the valve opening based on the target pressure sequence and how to plan the servo boost rate based on the deformation excursion risk value and the dynamic attenuation coefficient. Using this adaptive control strategy, the system dynamically adjusts the cylinder group's control parameters, including the multi-stage cushion valve opening and the servo boost rate. Among them, the buffer valve is used to adjust the flow and pressure of the air flow inside the cylinder, and controls the reaction speed and smoothness of the cylinder by adjusting its opening to avoid too fast or too slow clamping process. The opening adjustment amount of the buffer valve is calculated by the difference between the target pressure value and the actual pressure value, and is used to guide the fuzzy control algorithm to perform real-time dynamic adjustment of the valve port, so as to balance the relationship between the airflow response speed and the clamping stability in the process of the clamping force gradually approaching the target value; the servo boost rate controls the rate of change of the cylinder pressure, and ensures that the cylinder group reaches the preset target pressure within a stable time by adjusting the boost rate. Specifically, according to the adjustment amount, the deformation offset risk value and the dynamic attenuation coefficient, a gradient path of pressure change is planned and formed. This path is manifested as a step-by-step decrease in the amplitude of the boost rate change to cope with the clamping force fluctuations caused by environmental interference or workpiece stress changes, ensuring that the clamping process has continuity and flexible response capabilities. After adjustment begins, the system continuously monitors the clamping force and adjusts the valve opening and boost rate through real-time feedback until the clamping force error stabilizes within a preset threshold. This ensures accurate and uniform clamping force during the clamping process, minimizing the risk of deformation. In summary, the buffer valve opening and servo boost rate act as two coupled control variables: the former controls instantaneous gas flow, and the latter controls cumulative pressure changes. Driven by the adaptive control strategy, these two variables are adjusted in tandem to gradually achieve the target clamping state.

[0038] Furthermore, based on the difference between the target pressure sequence and the actual pressure value, the opening adjustment amount of the multi-stage buffer valve is determined. , the fuzzy control algorithm is used to dynamically adjust the valve opening; according to the opening adjustment amount of the multi-stage buffer valve, combined with the deformation offset risk value and the dynamic attenuation coefficient, the gradient change path of the servo boost rate is planned.

[0039] In an optional embodiment, the difference between the target pressure values and the actual pressure values in the target pressure sequence is calculated. The target pressure sequence is determined based on the workpiece and clamping requirements, while the actual pressure values are measured in real time by a pressure sensor. The difference between the two is the pressure error, which reflects the deviation between the current clamping force and the target clamping force. The calculated pressure error is then input into the opening adjustment calculation formula to obtain the opening adjustment of the multi-stage buffer valve. Subsequently, the calculated opening adjustment of the multi-stage buffer valve is used as the input signal of a fuzzy control algorithm. The fuzzy control algorithm first converts the opening adjustment into a fuzzy set, typically including fuzzy terms such as "increase," "decrease," or "remain unchanged." The fuzzy control rule base determines the valve opening adjustment method based on the size and change trend of the opening adjustment. For example, when the opening adjustment is large and changes rapidly, the fuzzy control algorithm may choose to quickly increase the valve opening, while if the opening adjustment is small or changes slowly, it may choose to adjust gradually. During the fuzzy inference process, the fuzzy control algorithm combines empirical rules from the control rule library and comprehensively considers factors such as the change rate of the opening adjustment and the valve response characteristics to derive an appropriate valve opening adjustment. This adjustment is defuzzified and converted into a specific control signal, which adjusts the valve opening so that the cylinder group pressure steadily approaches the target value. While determining the cushion valve opening adjustment, the servo boost rate is also planned based on the deformation offset risk value and the dynamic attenuation coefficient. Specifically, a preliminary servo boost rate is first determined based on the cushion valve opening adjustment and the deformation offset risk value. If the deformation offset risk value is high, the boost rate is slowed to reduce the risk of workpiece deformation and avoid over-clamping. Conversely, if the deformation risk is low, a higher boost rate is selected to accelerate the clamping process. The boost rate change path is then adjusted based on the dynamic attenuation coefficient. The dynamic attenuation coefficient reflects the influence of environmental factors, and the system dynamically adjusts the boost rate based on current factors such as air pressure fluctuations and temperature gradients. If the environmental interference is strong (for example, the air pressure fluctuates greatly), the amplitude of the increase in the pressure rate will be reduced to ensure the stability of the clamping process. If the environment is relatively stable, the pressure rate can be allowed to increase rapidly. Then, based on these factors, a gradient change path of the pressure rate is calculated. This path defines how the pressure rate changes throughout the clamping process. The change path of the pressure rate can be gradually increased, gradually decreased, or adjusted as needed to ensure that the clamping force gradually stabilizes to the target value and remains within the preset error range. Through the above steps, adaptive pressure regulation and precise clamping force control are achieved, which can cope with complex environmental changes and ensure the stability, accuracy and efficiency of the clamping process.

[0040] Furthermore, the opening adjustment amount of the multi-stage buffer valve The calculation formula is: ;in, is the proportionality coefficient, is the integration coefficient, is the target pressure value, is the actual pressure value.

[0041] In a feasible implementation manner, the calculation formula for the opening adjustment amount is as follows: ;in, is the opening adjustment amount; is the proportional coefficient, which controls the proportional relationship between the opening adjustment and the pressure error; is the integral coefficient, responsible for processing the accumulated error and further optimizing the adjustment process; is the target pressure value; The current actual pressure value. The opening adjustment calculation formula dynamically calculates the opening of the buffer valve to ensure that the pressure during the clamping process gradually approaches the target value.

[0042] Furthermore, the positioning aperture of the circular hole of the positioning plate is collected, and the positioning aperture of the circular hole of the positioning plate is used for correlation mapping to extract the temperature gradient field distribution characteristics; based on the temperature gradient field distribution characteristics, the response threshold of the automated cylinder group is dynamically adjusted with stability as the goal, and a unidirectional decreasing correction is performed on the gradient change path of the servo boost rate.

[0043] In a feasible embodiment, the positioning aperture data of the circular hole of the positioning plate is collected. This aperture data is generally 10.00mm to 10.005mm, which is used to determine the precise position and stability of the positioning plate in the cylinder. The size and distribution of the positioning holes will affect the clamping process of the cylinder. Therefore, according to the size of these positioning holes, they are associated with the distribution characteristics of the temperature gradient field through correlation mapping to obtain the distribution characteristics of the temperature gradient field. The purpose of this step is to understand how temperature changes affect the clamping state of the positioning holes and the workpiece, because changes in the temperature gradient field will cause the material to expand or contract, thereby affecting the clamping force. Specifically, a larger positioning aperture will cause the gap between the workpiece and the fixture to increase, the system stiffness to decrease, resulting in response lag and weakened force feedback during the application of the clamping force; while a smaller aperture enhances positioning accuracy, it is more likely to form local stress concentration when the temperature rises, resulting in clamping deformation. These changes in mechanical properties caused by aperture changes will be transmitted to the servo control module in the form of clamping errors or unstable clamping responses. To this end, the positioning aperture, as an indicator of structural rigidity and thermal deformation sensitivity, is embedded in the servo boost control decision logic. This is used to adjust the correlation function between the response threshold and the boost rate, thereby establishing a closed-loop regulation path between structural dimensions, environmental disturbances, and pressure control. Subsequently, the temperature gradient field distribution characteristics are used to assess the stability of the overall clamping state. Temperature changes affect the clamping force, especially in high-temperature environments, where they may cause excessive expansion or stress concentration, affecting clamping accuracy. By analyzing the temperature variation characteristics, the system dynamically adjusts the response threshold of the automated cylinder group based on the actual temperature gradient field to address changes in clamping force under different temperature conditions. The response threshold refers to the cylinder's tolerance to pressure changes under specific temperature conditions. When the temperature gradient is large, the response threshold is lowered to avoid unstable clamping force caused by temperature fluctuations. Finally, based on these temperature characteristics and response adjustments, a unidirectional decrement correction is implemented for the servo boost rate gradient change path. The servo boost rate determines the rate of pressure change within the cylinder. If the boost rate is too fast, it may cause system overreaction or workpiece deformation, especially in environments with large temperature fluctuations. To avoid this, a one-way decrement correction is used to smooth the change in the boost rate. This means that when the temperature fluctuates significantly, the system will slow down the boost rate to prevent excessive pressure increase from causing workpiece deformation or unstable clamping force. This ensures that the pressure gradually approaches the target value, effectively controlling the cylinder group's response while maintaining clamping stability, ensuring a smooth transition and precise control of the clamping process.

[0044] Table 1: Positioning hole-temperature gradient table

[0045] Positioning hole diameter (mm) Temperature (°C) Temperature gradient (°C) Response threshold adjustment (bar / °C) Servo boost rate (bar / s) Final clamping force (N) 10.002 30 2 3 1.2 9.8 10.003 35 3 2.8 1.1 10.2 10.001 40 4 3.2 0.9 9.6 10.004 45 5 3 0.8 10 10.005 50 6 3.5 0.7 9.9

[0046] The table shows data such as positioning aperture, temperature, temperature gradient, response threshold, boost rate, and clamping force. These data are used to evaluate the impact of temperature on the clamping state and dynamically adjust the response of the cylinder system based on these factors to ensure the stability of the clamping force.

[0047] Furthermore, the diameter of the waist-shaped positioning hole of the positioning plate is collected, and the major axis length, minor axis length and major axis direction of the waist-shaped positioning hole parameters are parametrically decoupled from the angle of the force direction of the workpiece to be clamped, and a clamping force distribution mapping relationship is established; according to the clamping force distribution mapping relationship, the influence coefficient of the waist-shaped positioning hole parameters on the pressure distribution is fitted to obtain the reference pressure correction value of each execution unit in the automated cylinder group.

[0048] In a feasible embodiment, data of the waist-shaped positioning hole diameter is collected, including the major axis length, minor axis length and major axis direction of the waist-shaped positioning hole, wherein the major axis length refers to the maximum diameter of the waist-shaped hole, indicating the longest part of the hole; the minor axis length refers to the shortest diameter of the waist-shaped hole, indicating the shortest part of the hole; the major axis direction refers to the direction pointed by the maximum diameter of the waist-shaped hole, usually expressed by an angle or a direction vector in a coordinate system. The system parametrically decouples the geometric parameters of these waist-shaped positioning holes from the angle between the force direction of the workpiece to be clamped, that is, the major axis length, minor axis length and major axis direction are converted into several independent variables (for example, the angle between the major axis direction and the force direction), and the relationship between them is expressed by a mathematical formula. The purpose of this step is to convert complex geometric and mechanical relationships into parameters that can be quantified and optimized, so that the system can control the clamping force more accurately. The decoupling process can usually be solved by mathematical methods such as matrix transformation or linear regression, so as to derive the influence of the waist-shaped positioning hole parameters on the force of the workpiece, so as to further optimize the clamping force distribution. For example, a linear regression equation is constructed, ,in, 、 、 、 is the coefficient to be fitted, which indicates the influence of each parameter on the clamping force. is a constant term (intercept), which is used to correct the deviation. In order to perform linear regression, a set of experimental data needs to be collected. These data include the major axis length (a), minor axis length (b), major axis direction (θ), force direction ( ), the corresponding clamping force (F), these data can be obtained through experimental measurement, record each parameter and the corresponding clamping force, and then use the least squares method (Least Squares Method) to perform linear regression fitting to obtain the coefficients in the above equation 、 、 、 and constant term , the goal of the least squares method is to minimize the difference between the actual value and the predicted value of the clamping force. Subsequently, based on the results of the above decoupling, a clamping force distribution mapping relationship will be established. This mapping relationship reflects how the geometric parameters of the waist-shaped locating hole affect the clamping force of various parts of the workpiece. Different waist-shaped locating hole parameters (such as the length and direction of the major axis and minor axis) will lead to different mechanical distributions, which in turn affect the uniformity of the clamping force and the stability of the workpiece. Afterwards, based on the clamping force distribution mapping relationship, the influence coefficient of the waist-shaped locating hole parameters on the pressure distribution will be further fitted. This fitting process uses a regression model to determine the linear or nonlinear relationship between the major axis direction, major axis length, minor axis length and pressure. Furthermore, to establish a closed-loop connection between structural characteristics and control behavior, the aforementioned fitting coefficients are introduced as influencing factors in the servo boost control logic, forming a mapping function of "waist-shaped hole parameters—clamping error—control compensation." When a discrepancy is detected between the structural characteristics of the waist-shaped positioning hole (such as the longitudinal direction or eccentricity) and the current clamping target, the servo boost rate curve is automatically adjusted. For example, if the longitudinal direction deviates significantly from the force direction, it is identified as a risk of asymmetry in the clamping area. In this case, the initial boost rate is reduced and the pressure stabilization time is extended to avoid workpiece rotation or deformation caused by asymmetric clamping. This servo control adjustment strategy, based on real-time feedback execution of structural parameters, serves as the core bridge between the pressure correction value and the actual control curve, thereby enhancing the dynamic response accuracy and environmental adaptability of the clamping system. Finally, the target clamping force value is calculated by taking the difference between the current clamping force in the clamping force distribution mapping relationship, and the calculated result is divided by the influence coefficient to calculate the baseline pressure correction value for each execution unit. The baseline pressure correction is used to adjust the target pressure of each actuator in the automated cylinder group, ensuring even pressure distribution across the cylinder group during the clamping process, thereby improving clamping accuracy and stability. This method allows precise adjustment of the clamping force based on the parameters of the waist-shaped positioning hole, ensuring workpiece stability and accuracy during the clamping process.

[0049] In summary, the automated cylinder clamping system provided by the embodiments of the present application has the following technical effects:

[0050] A map drawing module 11 is used to obtain basic structural features including surface morphology features and workpiece size features based on the workpiece to be clamped, and to draw up a three-dimensional clamping force distribution map with material hardness data as a constraint condition; a risk prediction module 12 is used to match the target pressure sequence of each execution unit in the automated cylinder group based on the three-dimensional clamping force distribution map, and simultaneously predict the deformation offset risk value during the clamping process; a model calculation module 13 is used to introduce environmental interference factors and input them into a pre-trained interference compensation model to generate a dynamic attenuation coefficient, wherein the environmental interference factors include the vibration spectrum of the positioning plate, the fluctuation rate of the air pressure pipeline and the temperature gradient field; a cylinder adjustment module 14 is used to dynamically adjust the multi-stage buffer valve opening and the servo boost rate of the automated cylinder group through an adaptive control strategy according to the target pressure sequence, the deformation offset risk value and the dynamic attenuation coefficient until the clamping force error stabilizes within a preset threshold. Through the above steps, the technical problems of unstable clamping force and low workpiece clamping accuracy caused by environmental interference and system errors in the automated cylinder clamping process are solved, and the technical effects of improving clamping force stability, reducing deformation risk and improving clamping accuracy through dynamic compensation and adaptive adjustment are achieved.

[0051] Embodiment 2 is based on the same inventive concept as the automated cylinder clamping system in the previous embodiment. Figure 2 As shown, an embodiment of the present application provides an automated cylinder clamping method, the method comprising:

[0052] Based on the workpiece to be clamped, the basic structural features including surface morphology features and workpiece size features are obtained, and with the material hardness data as a constraint condition, a three-dimensional clamping force distribution map is drawn up; based on the three-dimensional clamping force distribution map, the target pressure sequence of each execution unit in the automated cylinder group is matched, and the deformation offset risk value during the clamping process is simultaneously predicted; environmental interference factors are introduced and input into the pre-trained interference compensation model to generate a dynamic attenuation coefficient, wherein the environmental interference factors include the vibration spectrum of the positioning plate, the fluctuation rate of the air pressure pipeline and the temperature gradient field; according to the target pressure sequence, the deformation offset risk value and the dynamic attenuation coefficient, the multi-stage buffer valve opening and the servo boost rate of the automated cylinder group are dynamically adjusted through an adaptive control strategy until the clamping force error stabilizes within the preset threshold.

[0053] Furthermore, the method includes:

[0054] The basic structural features of the workpiece to be clamped are obtained, and the material hardness data is detected using an ultrasonic flaw detector; the basic structural features and the material hardness data are integrated to set a three-dimensional clamping force distribution map, wherein the three-dimensional clamping force distribution map includes at least three clamping force gradient partitions.

[0055] Furthermore, the method includes:

[0056] According to the clamping force gradient partitioning of the three-dimensional clamping force distribution map, the automated cylinder group is divided into a corresponding number of multiple execution units, each execution unit corresponding to a clamping force gradient partition; for the multiple execution units in the automated cylinder group, the target pressure value of each execution unit is configured in combination with the spatial position and the force density requirements of the gradient partition to obtain a target pressure sequence.

[0057] Furthermore, the method includes:

[0058] With clamping force uniformity, execution unit load balance and energy efficiency as optimization goals, the local curvature of the surface morphology features is introduced as a weight correction factor to optimize the configuration of the target pressure value; at the same time, based on historical clamping data and compared with the three-dimensional clamping force distribution map, a finite element analysis model is constructed; the finite element analysis model is used to simulate the stress-strain distribution under the clamping state, quantify the deformation offset risk value, and determine whether to trigger the pressure compensation mechanism, which is used to dynamically correct the target pressure sequence.

[0059] Furthermore, the method includes:

[0060] Data noise reduction preprocessing is performed on the positioning plate vibration spectrum, air pressure pipeline fluctuation rate and temperature gradient field in the environmental interference factors to obtain preprocessed data; based on the preprocessed data, the interference compensation model under the Transformer architecture is used to generate a dynamic attenuation coefficient.

[0061] Furthermore, the method includes:

[0062] Based on the target pressure sequence and the difference between the actual pressure value, the opening adjustment amount of the multi-stage buffer valve is determined, and the valve opening is dynamically adjusted using a fuzzy control algorithm; according to the opening adjustment amount of the multi-stage buffer valve, combined with the deformation offset risk value and the dynamic attenuation coefficient, the gradient change path of the servo boost rate is planned.

[0063] Furthermore, the method includes:

[0064] The opening adjustment amount of the multi-stage buffer valve The calculation formula is: ;in, is the proportionality coefficient, is the integration coefficient, is the target pressure value, is the actual pressure value.

[0065] Furthermore, the method includes:

[0066] The positioning aperture of the circular hole in the positioning plate is collected, and correlation mapping is performed with the positioning aperture of the circular hole in the positioning plate to extract the temperature gradient field distribution characteristics. Based on the temperature gradient field distribution characteristics, the response threshold of the automated cylinder group is dynamically adjusted with stability as the goal, and a unidirectional decreasing correction is performed on the gradient change path of the servo boost rate.

[0067] Furthermore, the method includes:

[0068] The diameter of the waist-shaped positioning hole of the positioning plate is collected, and the major axis length, minor axis length and major axis direction of the waist-shaped positioning hole parameters are parametrically decoupled from the angle between the force direction of the workpiece to be clamped, and a clamping force distribution mapping relationship is established; according to the clamping force distribution mapping relationship, the influence coefficient of the waist-shaped positioning hole parameters on the pressure distribution is fitted, and the reference pressure correction value of each execution unit in the automated cylinder group is obtained.

[0069] Any step of the method described above can be stored as a computer instruction or program in an unlimited computer memory, and can be called and recognized by an unlimited computer processor to implement any method in the embodiments of the present application, without any unnecessary restrictions.

[0070] Furthermore, the terms "first" or "second" as described above may not only represent an order relationship but may also represent a specific concept and / or refer to the selectability of multiple elements, either individually or in combination. Obviously, those skilled in the art may make various modifications and variations to this application without departing from the scope of this application. Thus, if such modifications and variations fall within the scope of this application and its equivalents, this application is intended to include such modifications and variations.

Claims

1. Automated cylinder clamping system, characterized by: include: The map drawing module is used to obtain the basic structural features of the workpiece to be clamped, including surface topography and workpiece size features, and draw up a three-dimensional clamping force distribution map based on the material hardness data as a constraint condition; A risk prediction module is used to match the target pressure sequence of each actuator in the automated cylinder group based on the three-dimensional clamping force distribution map, and simultaneously predict the deformation offset risk value during the clamping process; A model calculation module is used to introduce environmental interference factors and input them into a pre-trained interference compensation model to generate a dynamic attenuation coefficient, wherein the environmental interference factors include the vibration spectrum of the positioning plate, the fluctuation rate of the air pressure pipeline, and the temperature gradient field; The cylinder adjustment module is used to dynamically adjust the multi-stage buffer valve opening and servo boost rate of the automated cylinder group through an adaptive control strategy according to the target pressure sequence, deformation offset risk value and dynamic attenuation coefficient until the clamping force error is stabilized within a preset threshold.

2. The automated cylinder clamping system according to claim 1, characterized in that: Using material hardness data as a constraint, a three-dimensional clamping force distribution map is developed, including: Obtain the basic structural features of the workpiece to be clamped and use an ultrasonic flaw detector to detect the material hardness data; The structural basic features are integrated with the material hardness data to formulate a three-dimensional clamping force distribution map, wherein the three-dimensional clamping force distribution map includes at least three clamping force gradient partitions.

3. The automated cylinder clamping system according to claim 2, wherein: Based on the three-dimensional clamping force distribution map, the target pressure sequence of each actuator in the automated cylinder group is matched, including: According to the clamping force gradient partition of the three-dimensional clamping force distribution map, the automated cylinder group is divided into a corresponding number of execution units, each execution unit corresponding to a clamping force gradient partition; For multiple execution units in the automated cylinder group, the target pressure value of each execution unit is configured in combination with the force density requirements of spatial position and gradient partitioning to obtain the target pressure sequence.

4. The automated cylinder clamping system according to claim 3, wherein: Taking the uniformity of clamping force, load balance of actuators and energy efficiency as optimization objectives, the local curvature of surface topography is introduced as a weight correction factor to optimize the configuration of target pressure value. At the same time, based on the historical clamping data and in comparison with the three-dimensional clamping force distribution map, a finite element analysis model is constructed; The finite element analysis model is used to simulate the stress-strain distribution in the clamping state, quantify the deformation offset risk value, and determine whether to trigger a pressure compensation mechanism, which is used to dynamically correct the target pressure sequence.

5. The automated cylinder clamping system according to claim 4, wherein: Environmental interference factors are introduced and input into the pre-trained interference compensation model to generate dynamic attenuation coefficients, including: Performing data noise reduction preprocessing on the positioning plate vibration spectrum, air pressure pipeline fluctuation rate and temperature gradient field in the environmental interference factors to obtain preprocessed data; Based on the preprocessed data, a dynamic attenuation coefficient is generated using an interference compensation model under a Transformer architecture.

6. The automated cylinder clamping system according to claim 5, wherein: include: Based on the difference between the target pressure sequence and the actual pressure value, the opening adjustment amount of the multi-stage buffer valve is determined , the fuzzy control algorithm is used to dynamically adjust the valve opening; According to the opening adjustment of the multi-stage buffer valve , combined with the deformation offset risk value and the dynamic attenuation coefficient, the gradient change path of the servo boost rate is planned.

7. The automated cylinder clamping system according to claim 6, wherein: The opening adjustment amount of the multi-stage buffer valve The calculation formula is: ； in, is the proportionality coefficient, is the integration coefficient, is the target pressure value, is the actual pressure value.

8. The automated cylinder clamping system according to claim 6, wherein: Collecting the positioning aperture of the circular hole of the positioning plate, performing correlation mapping with the positioning aperture of the circular hole of the positioning plate, and extracting the temperature gradient field distribution characteristics; Based on the temperature gradient field distribution characteristics, the response threshold of the automated cylinder group is dynamically adjusted with stability as the goal, and a one-way decreasing correction is performed on the gradient change path of the servo boost rate.

9. The automated cylinder clamping system according to claim 8, wherein: The diameter of the waist-shaped positioning hole of the positioning plate is collected, and the major axis length, minor axis length and major axis direction of the waist-shaped positioning hole parameters are parametrically decoupled from the angle of the force direction of the workpiece to be clamped, and a clamping force distribution mapping relationship is established; According to the mapping relationship of the clamping force distribution, the influence coefficient of the waist-shaped positioning hole parameters on the pressure distribution is fitted to obtain the reference pressure correction value of each execution unit in the automated cylinder group.

10. Automated cylinder clamping method, characterized in that, The method is performed by the automated cylinder clamping system according to any one of claims 1 to 9, comprising: Based on the workpiece to be clamped, the basic structural features including surface topography and workpiece size are obtained, and the three-dimensional clamping force distribution map is drawn up with the material hardness data as the constraint condition; Based on the three-dimensional clamping force distribution map, the target pressure sequence of each actuator in the automated cylinder group is matched, and the deformation offset risk value during the clamping process is simultaneously predicted; Introducing environmental interference factors and inputting them into the pre-trained interference compensation model to generate a dynamic attenuation coefficient, wherein the environmental interference factors include the vibration spectrum of the positioning plate, the fluctuation rate of the air pressure pipeline, and the temperature gradient field; According to the target pressure sequence, deformation offset risk value and dynamic attenuation coefficient, the multi-stage buffer valve opening and servo boost rate of the automated cylinder group are dynamically adjusted through an adaptive control strategy until the clamping force error is stabilized within a preset threshold.

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