Carrying robot clamping force control method and system

Abstract

The embodiment of the invention provides a transfer robot clamping force control method and system, and relates to the technical field of transfer robot clamping force control, and the method comprises the steps: obtaining a clamping force signal of a first clamping force sensing element, a driving parameter of a clamping device driving mechanism, and displacement information of a clamping device jaw; obtaining a reference clamping force according to the driving parameters and the displacement information; detecting a deviation trend between the clamping force signal and the reference clamping force to obtain a target deviation; compensating the clamping force signal according to the target deviation and a preset online self-adaptive compensation strategy to obtain a compensated clamping force signal; and based on the compensated clamping force signal, the clamping force of the clamping device driving mechanism on the high-temperature metal blank is controlled. The forming quality and the production efficiency of the forge piece can be improved.

Description

Technical Field

[0001] This application relates to the field of clamping force control technology for handling robots, and in particular to a method and system for clamping force control of handling robots. Background Technology

[0002] In related technologies, the harsh environment of forging workshops, with high temperatures, high vibrations, and the splashing of metal oxide scale and debris, leads to problems with robotic grippers after long-term operation. On one hand, the outer protective layer of the force sensor ages and wears down due to high-temperature baking, mechanical wear, and impacts from foreign objects. Tiny foreign objects infiltrate the minute gap between the sensor and the gripper body and gradually accumulate and compact. These compacted foreign objects alter the mechanical support structure of the force sensor, disturbing the force transmission mechanism of the sensor's internal sensitive elements. This causes the sensor's output electrical signal to no longer accurately reflect the actual gripping force, resulting in a systematic downward deviation. This deviation is gradual and initially difficult to detect through conventional system self-checks. The control system processes the signal with the deviation, mistakenly believing the gripping force is normal. On the other hand, the robotic gripper's jaws constantly rub against the high-temperature, high-hardness metal billet, causing jaw wear and a gradual decrease in the coefficient of friction. To prevent the billet from slipping during handling, the control system needs to instruct the drive mechanism to execute a larger jaw closure to maintain sufficient gripping stability. Summary of the Invention

[0003] This application aims to address at least one of the technical problems existing in the prior art. To this end, this application proposes a method and system for controlling the clamping force of a handling robot, aiming to improve the forming quality and production efficiency of forgings.

[0004] In a first aspect, embodiments of this application provide a method for controlling the gripping force of a handling robot, including: Acquire the clamping force signal from the first clamping force sensing element, the driving parameters of the clamping drive mechanism, and the displacement information of the clamping jaws; The reference clamping force is obtained based on the driving parameters and the displacement information; The deviation trend between the clamping force signal and the reference clamping force is detected to obtain the target deviation; The clamping force signal is compensated according to the target deviation and the preset online adaptive compensation strategy to obtain the compensated clamping force signal; Based on the compensated clamping force signal, the clamping force of the clamping device drive mechanism on the high-temperature metal billet is controlled.

[0005] According to some embodiments of this application, the step of compensating the clamping force signal based on the target deviation and a preset online adaptive compensation strategy to obtain a compensated clamping force signal includes: Obtain quality feedback data for forgings; The quality feedback data is correlated with the compensated clamping force signal to obtain quality feedback data; The effectiveness of the online adaptive compensation strategy is evaluated based on the quality feedback data to obtain the evaluation result; Based on the evaluation results, adjust the parameters of the online adaptive compensation strategy; The clamping force signal is compensated based on the adjusted parameters of the online adaptive compensation strategy and the target deviation to obtain the compensated clamping force signal, and the clamping force of the clamping device drive mechanism on the high-temperature metal billet is controlled. According to some embodiments of this application, adjusting the parameters of the online adaptive compensation strategy based on the evaluation results includes: Obtain the rate and direction of change of the mass feedback data of the forging; Based on the rate of change and the direction, the update step size of the parameters of the online adaptive compensation strategy is dynamically adjusted to obtain the target step size; Obtain the transient deviation between the clamping force signal and the reference clamping force; The parameters of the online adaptive compensation strategy are adjusted based on the transient deviation, the evaluation result, and the target step size.

[0006] According to some embodiments of this application, adjusting the parameters of the online adaptive compensation strategy based on the transient deviation, the evaluation result, and the target step size includes: After acquiring the transient deviation, the transient deviation signal is subjected to multi-band filtering to separate the vibration component corresponding to the preset mechanical resonance frequency and the noise component corresponding to the preset electromagnetic interference frequency. Based on the vibration components, the component of the transient deviation signal caused by mechanical resonance is suppressed; Based on the noise component, the electromagnetic interference-induced component in the transient deviation signal is eliminated to obtain a pure transient deviation signal; The parameters of the online adaptive compensation strategy are adjusted based on the purified transient deviation signal, the evaluation results, and the target step size.

[0007] According to some embodiments of this application, adjusting the parameters of the online adaptive compensation strategy based on the transient deviation, the evaluation result, and the target step size includes: After acquiring the pure transient deviation signal, the batch information and furnace temperature information of the currently clamped billet are acquired; based on the batch information, the typical force-displacement curve and elastic modulus range of the corresponding batch of billet at the current furnace temperature are queried from the preset billet material property database. Based on the typical force-displacement curve, the elastic modulus range, and the displacement information, the theoretical transient deviation range caused by changes in billet material properties is calculated. The pure transient deviation signal is compared with the theoretical transient deviation range to distinguish transient deviations caused by system degradation. The parameters of the online adaptive compensation strategy are pre-adjusted based on the transient deviation caused by system degradation, the evaluation results, and the target step size.

[0008] According to some embodiments of this application, the step of pre-adjusting the parameters of the online adaptive compensation strategy based on the transient deviation caused by system degradation, the evaluation result, and the target step size includes: After obtaining the transient deviations caused by system degradation that are distinguished, the billet temperature, clamping speed and environmental vibration intensity under the current clamping conditions are obtained. Based on the billet temperature, the clamping speed, and the environmental vibration intensity, the dominant and secondary degradation modes of the system under the current working condition are identified from the preset working condition-degradation mode association rule library. Based on the dominant mode and the secondary mode, as well as the amplitude and trend of the transient deviation caused by system degradation, determine the set of parameters of the compensation strategy that need to be adjusted and the initial adjustment direction of each parameter. Based on the set of compensation strategy parameters, the initial adjustment direction, and the preset parameter interaction suppression rules, the online adaptive compensation strategy parameters are coordinated and adjusted to obtain the coordinated and adjusted compensation strategy parameters. The parameters of the online adaptive compensation strategy are adjusted based on the collaboratively adjusted compensation strategy parameters, the evaluation results, and the target step size.

[0009] According to some embodiments of this application, the step of coordinating the online adaptive compensation strategy parameters based on the set of compensation strategy parameters, the initial adjustment direction, and a preset parameter interaction suppression rule to obtain the coordinatingly adjusted compensation strategy parameters includes: The residual deviation between the clamping force signal and the reference clamping force, as well as the changing trend of the mass feedback data of the forging, are obtained. Based on the residual deviation and the rate of change of the trend, the step size and convergence factor of the preset iterative optimization algorithm are dynamically adjusted. Based on the step size and convergence factor of the dynamically adjusted preset iterative optimization algorithm, the set of compensation strategy parameters, the initial adjustment direction, and the preset parameter interaction suppression rules, the online adaptive compensation strategy parameters are coordinated and adjusted to obtain the coordinated adjusted compensation strategy parameters.

[0010] According to some embodiments of this application, after obtaining the residual deviation between the clamping force signal and the reference clamping force, and the changing trend of the quality feedback data of the forging, the process includes: The residual deviation signal between the clamping force signal and the reference clamping force is subjected to high-pass filtering to eliminate the low-frequency vibration component caused by mechanical resonance. The residual deviation signal after high-pass filtering is subjected to notch filtering to eliminate specific frequency noise components caused by electromagnetic interference, thereby obtaining a clean residual deviation signal. The data on the changing trend are processed by a moving average to smooth out instantaneous fluctuations; Outlier detection is performed on the trend data after the moving average processing to identify and remove abnormal quality data points affected by environmental interference, so as to obtain stable quality trend data. The adjustment effect of each parameter of the online adaptive compensation strategy is evaluated based on the purified residual deviation signal and the stable quality trend data. According to some embodiments of this application, the step of performing outlier detection on the trend data after the moving average processing to identify and remove abnormal quality data points affected by environmental interference, and obtaining stable quality trend data, includes: Obtain the clamping pressure under the current clamping condition; Based on the billet temperature and the clamping pressure, identify the range of quality data fluctuations caused by changes in the microstructure of the billet material or local stress concentration under the current working conditions. When the data of the change trend after the moving average processing exceeds the fluctuation range of the quality data, the environmental vibration intensity and electromagnetic interference intensity corresponding to the data point are obtained. Based on the environmental vibration intensity and the electromagnetic interference intensity, when the data point is an outlier caused by environmental interference, the outlier caused by environmental interference is removed.

[0011] Secondly, embodiments of this application provide a gripping force control system for a handling robot, comprising: The first acquisition module is used to acquire the clamping force signal of the first clamping force sensing element, the driving parameters of the clamping drive mechanism, and the displacement information of the clamping jaws. The second acquisition module is used to obtain the reference clamping force based on the driving parameters and displacement information; The detection module is used to detect the deviation between the clamping force signal and the reference clamping force to obtain the target deviation; The compensation module is used to compensate the clamping force signal according to the target deviation and the preset online adaptive compensation strategy to obtain the compensated clamping force signal. The control module is used to control the clamping force of the clamping device drive mechanism on the high-temperature metal billet based on the compensated clamping force signal.

[0012] According to the technical solution of the embodiments of this application, at least the following beneficial effects are achieved: The embodiments of this invention first acquire the clamping force signal of the first clamping force sensing element, the driving parameters of the clamping drive mechanism, and the displacement information of the clamping jaws; a reference clamping force is obtained based on the driving parameters and the displacement information; the deviation trend between the clamping force signal and the reference clamping force is detected to obtain a target deviation; the clamping force signal is compensated based on the target deviation and a preset online adaptive compensation strategy to obtain a compensated clamping force signal; based on the compensated clamping force signal, the clamping force of the clamping drive mechanism on the high-temperature metal billet is controlled. The embodiments of this application can improve the forming quality and production efficiency of forgings.

[0013] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0014] The accompanying drawings are used to provide a further understanding of the technical solutions of this application and constitute a part of the specification. They are used together with the embodiments of this application to explain the technical solutions of this application and do not constitute a limitation on the technical solutions of this application.

[0015] Figure 1 This is a flowchart illustrating a method for controlling the clamping force of a handling robot according to an embodiment of this application. Figure 2 This is a schematic diagram of a clamping force control system for a handling robot provided in one embodiment of this application. Detailed Implementation

[0016] To make the objectives, technical methods, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0017] It should be noted that the meaning of "multiple" (or "more than") in the description of the embodiments of this application refers to two or more, and "greater than," "less than," "exceeding," etc. are understood to exclude the number itself, while "above," "below," "within," etc. are understood to include the number itself. If "first," "second," etc. are used in the description, they are only for the purpose of distinguishing technical features and should not be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated or the order of the technical features indicated.

[0018] In this application embodiment, "at least one" refers to one or more, and "more than one" refers to two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent the existence of A alone, the simultaneous existence of A and B, or the existence of B alone. A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one of the following" and similar expressions refer to any combination of these items, including any combination of singular or plural items. For example, at least one of a, b, and c can represent: the existence of a alone, the existence of b alone, the existence of c alone, the simultaneous existence of a and b, the simultaneous existence of a and c, the simultaneous existence of b and c, or the simultaneous existence of a, b, and c, where a, b, and c can be single or multiple.

[0019] In the description of this application, unless otherwise expressly defined, terms such as "setup," "installation," and "connection" should be interpreted broadly, and those skilled in the art can reasonably determine the specific meaning of the above terms in this application in conjunction with the specific content of the technical solution.

[0020] Based on the above, this application proposes a method and system for controlling the clamping force of a handling robot, aiming to improve the forming quality and production efficiency of forgings.

[0021] The gripping force control method for a handling robot provided in this application can be applied to a terminal, a server, or software running on either a terminal or a server. In some embodiments, the terminal can be a smartphone, tablet, laptop, desktop computer, etc.; the server can be configured as an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks (CDN), and big data and artificial intelligence platforms; the software can be an application that implements the gripping force control method for the handling robot, but is not limited to the above forms.

[0022] This application can be applied to numerous general-purpose or special-purpose computer system environments or configurations. Examples include: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics devices, network PCs, minicomputers, mainframe computers, and distributed computing environments including any of the above systems or devices. This application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform specific tasks or implement specific abstract data types. This application can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via communication networks. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices. It should be noted that in various specific embodiments of this invention, when processing is required based on data related to the characteristics of an object (e.g., user attributes or sets of attribute information), permission or consent from the corresponding object is obtained first, and the collection, use, and processing of this data comply with relevant laws and standards. Furthermore, when the embodiments of the present invention need to obtain the attribute information of an object, they will obtain the separate permission or separate consent of the corresponding object through pop-up windows or redirection to a confirmation page. After obtaining the separate permission or separate consent of the corresponding object, they will then obtain the relevant data of the object necessary for the embodiments of the present invention to operate normally.

[0023] See Figure 1 , Figure 1 This is a flowchart illustrating a method for controlling the gripping force of a handling robot according to an embodiment of this application. The method for controlling the gripping force of a handling robot provided in this embodiment includes, but is not limited to, steps S110 to S170, which will be described in detail below.

[0024] Step S110: Obtain the clamping force signal of the first clamping force sensing element, the driving parameters of the clamping drive mechanism, and the displacement information of the clamping jaws; Step S120: Obtain the reference clamping force based on the driving parameters and displacement information; Step S130: Detect the deviation trend between the clamping force signal and the reference clamping force to obtain the target deviation; Step S140: Compensate the clamping force signal according to the target deviation and the preset online adaptive compensation strategy to obtain the compensated clamping force signal; Step S150: Based on the compensated clamping force signal, control the clamping force of the clamping device drive mechanism on the high-temperature metal billet.

[0025] It should be noted that the first clamping force sensing element refers to the force sensor integrated into the gripper of the handling robot. Its function is to monitor the force applied by the gripper to the high-temperature metal billet in real time and convert it into an electrical signal output. This can be a resistance strain gauge sensor, piezoelectric sensor, or fiber optic sensor, etc., converting mechanical force into a measurable electrical signal. The gripper drive mechanism refers to the actuator responsible for driving the opening and closing of the gripper jaws and applying clamping force. This can be a hydraulic cylinder, pneumatic cylinder, servo motor with lead screw transmission mechanism, etc. The gripper jaws are the part that directly contacts the high-temperature metal billet, usually made of high-temperature and wear-resistant materials. Displacement information reflects the degree of jaw opening or clamping depth. Drive parameters can include the current, voltage, speed, and pressure of the drive mechanism, and these parameters have a certain physical relationship with the clamping force. Displacement information refers to the real-time position data of the gripper jaws relative to a certain reference point, which can be obtained through displacement sensors (such as encoders or linear displacement sensors). The reference clamping force refers to the expected clamping force calculated based on the theoretical output of the drive mechanism and the jaw displacement; it serves as a reference for the actual clamping force signal. Online adaptive compensation strategy is an algorithm or model that can adjust compensation parameters in real time based on system operating status and feedback information, aiming to eliminate or reduce deviations in clamping force signals.

[0026] In one embodiment, the clamping force signal can be directly measured by a first clamping force sensing element. When the clamp holds the workpiece, the first clamping force sensing element generates an electrical signal proportional to the clamping force. The driving parameters of the clamp drive mechanism can be read from the controller of the drive mechanism, and can be the current or voltage value of the drive motor. The displacement information of the clamp jaws can be obtained by a displacement sensor installed on the jaws, and can be a digital signal output by a linear encoder. A clamping force model based on the driving parameters and displacement information can be pre-established. This model can be a physical model, an empirical model, or a machine learning-based model. After obtaining the current driving parameters and displacement information, they are input into the model to calculate the current reference clamping force. A lookup table method can be used to pre-store the reference clamping force values ​​under different combinations of driving parameters and displacement information, and directly query them during runtime. Furthermore, the deviation trend between the clamping force signal and the reference clamping force is detected to obtain the target deviation. The real-time acquired clamping force signal can be compared point by point with the calculated reference clamping force to obtain the instantaneous deviation. Then, by processing these instantaneous deviations using moving averages, exponential smoothing, or Kalman filtering, random noise and instantaneous fluctuations are eliminated, thereby extracting the long-term trend of the deviation, i.e., the target deviation. Next, the clamping force signal is compensated according to the target deviation and a preset online adaptive compensation strategy to obtain the compensated clamping force signal. The preset online adaptive compensation strategy can be a simple proportional compensator that adjusts the clamping force signal proportionally according to the magnitude of the target deviation. It can also be a more complex adaptive filter, such as the least mean square (LMS) algorithm or the recursive least squares (RLS) algorithm. These algorithms can adjust the compensation parameters online according to changes in the target deviation to make the compensated clamping force signal closer to the true value. Finally, based on the compensated clamping force signal, the clamping force of the clamping device drive mechanism on the high-temperature metal billet is controlled. The compensated clamping force signal can be used as feedback input to the closed-loop control system of the clamping device drive mechanism. The control system adjusts the output of the drive mechanism and the current of the motor or the opening of the hydraulic valve based on the difference between the compensated clamping force signal and the target clamping force, so as to precisely control the clamping force of the clamp on the high-temperature metal billet.

[0027] It should be noted that obtaining quality feedback data for forgings refers to inspecting the final product quality after the high-temperature metal billet has been clamped and processed (forging, stamping), and using the inspection results as feedback data. Quality feedback data can include, but is not limited to, indicators such as dimensional accuracy, surface roughness, internal defects, and mechanical properties of the forgings. Its purpose is to obtain information on the direct impact of clamping force control on product quality during actual production. Correlating the quality feedback data with the compensated clamping force signal means establishing a correspondence between the quality feedback data and the clamping force control process, matching the clamping force signal within a specific batch or time period with the forging quality data of the corresponding batch or time period. Its purpose is to provide a data foundation for subsequent strategy evaluation. Evaluating the effectiveness of the online adaptive compensation strategy based on the quality feedback data and obtaining the evaluation results involves analyzing the difference between the quality feedback data and the expected quality standard using a preset evaluation model or algorithm, thereby determining the performance of the current online adaptive compensation strategy. If the quality feedback data shows a decrease in the product pass rate or an increase in the defect rate, it indicates that the effectiveness of the compensation strategy may have decreased. Adjusting the parameters of the online adaptive compensation strategy based on the evaluation results refers to modifying the internal parameters of the compensation strategy (such as gain coefficient, time constant, nonlinear function parameters, etc.) according to the performance deviation indicated by the evaluation results. The aim is to enable the compensation strategy to better adapt to the current working conditions and improve compensation accuracy. Compensating the clamping force signal based on the adjusted parameters of the online adaptive compensation strategy and the target deviation, obtaining the compensated clamping force signal, and controlling the clamping force of the clamping device drive mechanism on the high-temperature metal billet refers to applying the adjusted compensation strategy to subsequent clamping force signal compensation processes to achieve more precise clamping force control.

[0028] In one embodiment, assuming that after the handling robot clamps the high-temperature metal billet, subsequent processes inspect the dimensional accuracy, surface quality, or internal defects of the forging, and use these inspection results as quality feedback data. For example, if multiple batches of forgings are found to be smaller than expected, this may indicate that the clamping force is too large, leading to excessive deformation of the billet. In this case, the system will evaluate the effectiveness of the current online adaptive compensation strategy based on this quality feedback data and identify potential overcompensation issues. Based on this evaluation, the system will automatically adjust relevant parameters in the compensation strategy, such as reducing the compensation gain or adjusting the compensation curve, to reduce the actual clamping force. In this way, the compensation strategy can learn and optimize online, ensuring that the clamping force control system can continuously provide the best clamping effect, thereby guaranteeing the quality of the forging.

[0029] It should be noted that acquiring the rate and direction of change in the forging's quality feedback data refers to analyzing historical quality feedback data to calculate the speed (rate) and trend (direction, such as rising, falling, or remaining stable) of change over time. The purpose is to capture the dynamic characteristics of forging quality changes, providing a more precise basis for subsequent parameter adjustments. Specifically, when the quality feedback data changes rapidly or the direction of change indicates a need for a quick response, the parameter update step size can be increased accordingly to accelerate the convergence of the compensation strategy; conversely, when the change is gradual, the step size is decreased to improve the stability of the adjustment. The aim is to make the parameter adjustment process more flexible and efficient. Acquiring the transient deviation between the clamping force signal and the reference clamping force refers to the instantaneous difference between the clamping force signal measured by the clamping force sensing element and the reference clamping force obtained based on the driving parameters and displacement information within a short period. This transient deviation may be caused by external disturbances, instantaneous nonlinearity within the system, or sensor noise. The purpose is to capture the instantaneous errors of the system during dynamic operation, providing a basis for more precise compensation. Furthermore, adjusting the parameters of the online adaptive compensation strategy based on transient deviations, evaluation results, and target step size refers to comprehensively considering the overall effectiveness evaluation results of the strategy, instantaneous errors, and the dynamically adjusted step size to correct the internal parameters of the compensation strategy. The aim is to ensure that parameter adjustments can respond to both long-term trends and adapt to short-term fluctuations, thereby improving the robustness and accuracy of the compensation strategy.

[0030] In one embodiment, assuming that when a handling robot is clamping a batch of high-temperature metal billets, the quality feedback data of the forgings exhibits a trend of first slowly decreasing and then rapidly increasing due to furnace temperature fluctuations or batch differences. Traditional adjustment methods may require a long time to identify and respond to this change. However, the solution of this application first obtains the rate and direction of change of the quality feedback data. During the slow decreasing phase, it is identified as a low rate and decreasing direction; during the rapid increasing phase, it is identified as a high rate and increasing direction. Based on this, during the rapid increasing phase, the parameter update step size of the online adaptive compensation strategy is dynamically adjusted and increased to accelerate the adjustment of the compensation strategy parameters and quickly adapt to the new quality characteristics. Simultaneously, the system continuously acquires the transient deviation between the clamping force signal and the reference clamping force. When a momentary impact or vibration occurs during the clamping process, causing the clamping force signal to deviate briefly from the reference clamping force, this transient deviation is captured. Combining the evaluation results at this time and the increased target step size, the parameters of the compensation strategy are immediately adjusted to quickly eliminate the transient deviation and ensure the stability of the clamping force. In this way, even under complex dynamic working conditions, the clamping force control system can maintain high precision and high stability.

[0031] It should be noted that multi-band filtering refers to using one or more filters to process interference components in different frequency ranges of a signal. Its purpose is to effectively separate the vibration component caused by mechanical resonance and the noise component caused by electromagnetic interference in the transient deviation signal. The preset mechanical resonance frequency refers to the frequency point or range that the handling robot or its gripper is prone to resonate under specific working conditions, determined through theoretical analysis, experimental testing, or experience accumulation during system design or operation. The vibration component corresponding to the preset mechanical resonance frequency refers to the periodic or quasi-periodic fluctuation component in the transient deviation signal related to these resonance frequencies. The preset electromagnetic interference frequency refers to the specific frequency generated by power supplies, motors, high-frequency equipment, etc., in an industrial environment that interferes with the sensor signal. The noise component corresponding to the preset electromagnetic interference frequency refers to the random or periodic fluctuation component in the transient deviation signal related to these interference frequencies. After separating the vibration component, the component caused by mechanical resonance in the transient deviation signal is suppressed based on this vibration component. Suppression refers to reducing or eliminating the amplitude of the vibration component through filtering and other means to reduce its impact on subsequent parameter adjustments. For example, a notch filter or band-stop filter can be used to attenuate signal components near a preset mechanical resonance frequency. After separating the noise component, the electromagnetic interference-induced components in the transient deviation signal are eliminated based on this noise component. Elimination refers to completely removing the noise component through filtering or other means to ensure signal purity. A low-pass filter can be used to filter out high-frequency electromagnetic noise, or an adaptive filter can be used to dynamically eliminate electromagnetic interference of known or unknown frequencies. After the above processing, a pure transient deviation signal is obtained, which contains no or significantly reduces the effects of mechanical resonance and electromagnetic interference. Finally, based on this pure transient deviation signal, the evaluation results, and the target step size, the parameters of the online adaptive compensation strategy are adjusted to ensure the accuracy and effectiveness of the parameter adjustment.

[0032] In one embodiment, digital signal processing techniques can be employed when performing multi-band filtering on the transient deviation signal. First, the spectrum of the transient deviation signal can be analyzed using Fast Fourier Transform (FFT) to identify the main mechanical resonance frequencies and electromagnetic interference frequencies. Assuming the mechanical resonance frequencies are mainly concentrated at 50Hz ± 5Hz, and the electromagnetic interference frequencies are mainly concentrated above 1kHz, a digital notch filter with a center frequency of 50Hz and a bandwidth of 10Hz can be designed to suppress the vibration components caused by mechanical resonance. Simultaneously, a digital low-pass filter with a cutoff frequency of 500Hz can be designed to eliminate high-frequency electromagnetic noise components above 1kHz. These filters can be implemented using IIR (Infinite Impulse Response) or FIR (Finite Impulse Response) structures, and their parameters can be optimized based on the actual system response. In this way, a highly purified transient deviation signal can be obtained for subsequent online adaptive compensation strategy parameter adjustment.

[0033] It should be noted that batch information can be understood as a unique code identifying billets produced in the same batch. Its purpose is to trace the origin and production process of the billets, as different batches of billets may have slight differences in composition, microstructure, etc. Furnace temperature information refers to the actual temperature of the billet in the heating furnace before it is clamped. Its purpose is to reflect the actual temperature state of the billet at the moment of clamping, because the mechanical properties of high-temperature metal billets (such as elastic modulus and yield strength) change significantly with temperature. Based on the acquired batch information, the system can query the typical force-displacement curve and elastic modulus range of the corresponding batch of billets at the current furnace temperature from a pre-set billet material property database. The database pre-stores mechanical property data of various metal billets from different batches at different temperatures, which can be obtained through experimental testing or numerical simulation. The typical force-displacement curve describes the force-displacement relationship of the billet during the deformation process under stress at a specific temperature; the elastic modulus range gives the range of elastic deformation capacity of the material at the current temperature. Furthermore, this theoretical transient deviation range represents the possible deviation range between the clamping force signal and the reference clamping force under ideal conditions, considering only changes in the billet material's own properties (rather than system degradation). The theoretical deviation can be obtained by substituting displacement information into a typical force-displacement curve, combined with the elastic modulus range, to predict the clamping force that the billet should generate at a given displacement, and comparing it with the reference clamping force. Subsequently, if the pure transient deviation signal falls within the theoretical transient deviation range, the deviation is considered to be mainly caused by changes in the billet material properties; if it exceeds this range, a transient deviation caused by system degradation is considered to exist. Through this comparison, transient deviations caused by system degradation can be effectively distinguished. Finally, based on the distinguished transient deviations caused by system degradation, the evaluation results, and the target step size, the parameters of the online adaptive compensation strategy are pre-adjusted.

[0034] In one embodiment, assume the handling robot is clamping a high-temperature steel billet with batch number "A001" at a furnace temperature of 1200°C. After acquiring a clean transient deviation signal, the system first obtains the batch information "A001" and the furnace temperature of 1200°C. Then, the system queries a preset billet material property database for the typical force-displacement curve and elastic modulus range of batch "A001" billets at 1200°C. The database shows that this batch of billets has a specific nonlinear force-displacement relationship and an elastic modulus range of 100 GPa to 120 GPa at this temperature. Based on the current displacement information of the gripper jaws, the system uses this material property data to calculate that, without system degradation, the theoretical transient deviation range caused by changes in material properties should be within ±50N. If the clean transient deviation signal at this time shows +80N, exceeding the theoretical range of ±50N, the system will distinguish that the +30N deviation is caused by system degradation. Based on this +30N transient deviation caused by system degradation, the current evaluation results, and the target step size, the parameters of the online adaptive compensation strategy will be pre-adjusted to more accurately compensate for the impact of system degradation.

[0035] It should be noted that the working condition-degradation mode association rule base is a pre-built knowledge base that stores association rules between different combinations of working condition parameters (high temperature, high speed, high vibration) and various system degradation modes (gripper jaw wear, drive motor aging, sensor drift, hydraulic system leakage, etc.). The rule base can be built and optimized through historical data analysis or machine learning methods. By querying the rule base, based on the current billet temperature, clamping speed, and environmental vibration intensity, the most likely dominant degradation mode and potentially coexisting secondary modes can be intelligently determined, thus achieving refined identification of degradation types. When the dominant and secondary degradation modes are identified, with gripper jaw wear as the dominant mode and sensor drift as the secondary mode, compensation parameters directly related to these degradation modes are selected from the online adaptive compensation strategy, forming a set of parameters that need adjustment. For jaw wear, the friction compensation coefficient may need to be adjusted; for sensor drift, the sensor zero-point or gain compensation parameters may need to be adjusted. At the same time, by combining the amplitude (i.e. the size of the deviation) and the trend (continuous increase, periodic fluctuation) of the transient deviation caused by system degradation, we can preliminarily determine the adjustment direction (increase or decrease) and approximate adjustment range of these parameters.

[0036] In one embodiment, it is assumed that after the handling robot has been holding a high-temperature metal billet for an extended period, a continuous transient deviation occurs between the output signal of its first clamping force sensing element and the reference clamping force. Using the method described above, this deviation has been identified as primarily caused by system degradation. First, the billet temperature is measured to be 1000°C using an infrared thermometer, the clamping speed is calculated to be 0.5 m / s using an encoder, and the environmental vibration intensity is measured to be at a moderate level using an accelerometer. Next, the system inputs these operating parameters into a preset operating condition-degradation mode association rule base. This rule base contains the following rule: "If the billet temperature is greater than 900°C and the clamping speed is greater than 0.4 m / s, the dominant degradation mode is clamp jaw wear, and the secondary degradation mode is increased drive mechanism clearance." Based on this, the system identifies the dominant degradation mode under the current operating condition as clamp jaw wear, and the secondary mode as increased drive mechanism clearance. Subsequently, for jaw wear, the friction compensation parameters need to be adjusted; for increased drive mechanism clearance, the clearance compensation parameters need to be adjusted. Simultaneously, the initial adjustment direction for the friction compensation parameter is determined to be increasing, while the initial adjustment direction for the clearance compensation parameter is decreasing. The system inputs these parameter sets, the initial adjustment directions, and preset parameter interaction suppression rules (the adjustment of the friction compensation parameter should not excessively affect the stability of the clearance compensation parameter) into the collaborative adjustment module. This module, through an iterative optimization algorithm, collaboratively adjusts the friction compensation parameter and the clearance compensation parameter while satisfying the suppression rules, obtaining a set of optimized compensation strategy parameters. Finally, the system combines the collaboratively adjusted compensation strategy parameters with the aforementioned evaluation results (which show that the current compensation strategy has moderate effectiveness) and the target step size (the dynamically adjusted step size is 0.01) to perform a final update of the online adaptive compensation strategy parameters. Through this series of fine adjustments, the clamping force control system of the handling robot can more accurately compensate for clamping force deviations caused by jaw wear and increased clearance in the drive mechanism, ensuring stable and precise clamping of high-temperature metal billets.

[0037] It should be noted that residual deviation refers to the error that still exists between the clamping force signal and the reference clamping force after initial compensation, and which has not been completely eliminated. The trend of the forging quality feedback data refers to the change pattern of forging quality indicators (such as dimensional accuracy, surface damage, etc.) over a period of time or batch, with the rate of change indicating the speed of quality deterioration or the degree of improvement. The step size refers to the distance the parameters move along the optimization direction in each iteration. A larger step size helps with rapid convergence but may lead to oscillations or overshoot; a smaller step size ensures stability but results in slower convergence. The convergence factor is used to control the convergence behavior of the algorithm. In some algorithms, the convergence factor can affect the search range or the balance between exploration and utilization. In practical applications, dynamically adjusting the step size and convergence factor means intelligently adjusting the key parameters of the iterative optimization algorithm based on the real-time residual deviation and the rate of change of the quality feedback data. When the residual deviation is large and the rate of change of the quality feedback data is rapid, the step size can be appropriately increased to accelerate convergence; when the residual deviation is small and the rate of change of the quality feedback data tends to be stable, the step size can be decreased to improve the adjustment accuracy, and the convergence factor can be adjusted to avoid overfitting or local optima. The purpose is to enable the iterative optimization algorithm to better adapt to the current working conditions and system state, thereby improving the efficiency and accuracy of parameter adjustment.

[0038] In one embodiment, it is assumed that during the handling of a high-temperature metal billet, temperature fluctuations in the billet or wear of the clamping device cause a continuous residual deviation between the clamping force signal and the reference clamping force. Simultaneously, quality inspection of subsequent forgings reveals an increasing surface damage rate, indicating a deterioration in the current clamping force control. At this point, the system will acquire a large residual deviation signal and a rapidly deteriorating trend in the quality feedback data. Based on this information, the control system determines that a rapid and significant adjustment to the online adaptive compensation strategy parameters is necessary. Specifically, the step size of the preset iterative optimization algorithm (an improved adaptive gradient descent algorithm) is dynamically increased, and the convergence factor is adjusted to allow for faster exploration. The step size can be adjusted from an initial 0.01 to 0.05, and the convergence factor from 0.9 to 0.7. In this way, the set of compensation strategy parameters will be coordinated and adjusted towards a new optimal value at a faster pace. As adjustments are made, the residual deviation gradually decreases, and the deterioration trend of the quality feedback data also tends to stabilize. At this point, the system will again dynamically reduce the step size (from 0.05 to 0.02, and then to 0.005) and adjust the convergence factor (for example, from 0.7 to 0.95) based on real-time data to ensure that the parameters are finely adjusted near the optimal value, avoid overshoot and oscillation, and ultimately achieve precise and stable clamping force control.

[0039] It should be noted that high-pass filtering of the residual deviation signal between the clamping force signal and the reference clamping force is performed to effectively filter out low-frequency interference caused by vibrations of internal mechanical components of the handling robot or gripper. These low-frequency vibrations may mask the true deviation information and affect the accuracy of subsequent analysis. The cutoff frequency of the high-pass filter can be set according to the actual mechanical resonant frequency range. Furthermore, notch filtering is performed on the residual deviation signal after high-pass filtering to accurately eliminate specific frequency noise components generated by external electromagnetic fields or internal electronic components. Electromagnetic interference usually manifests as periodic noise at a fixed frequency; notch filters can specifically suppress these frequencies, resulting in a cleaner residual deviation signal. Moving average processing of the trend data aims to smooth short-term fluctuations in the quality feedback data caused by measurement errors or instantaneous changes in operating conditions, making the quality trend line more stable and facilitating the observation of long-term patterns. The window size of the moving average can be adjusted according to the data sampling frequency and the desired smoothness. Outlier detection is performed on the trend data after moving average processing to identify and remove anomalous quality data points affected by environmental interference (such as sudden airflow, external impact, or momentary sensor malfunction). If these outliers are not removed, they may mislead the judgment of the quality trend, leading to incorrect adjustments of the compensation strategy parameters. Outlier detection yields more stable and reliable quality trend data. Finally, based on the clean residual deviation signal and stable quality trend data, the adjustment effect of each online adaptive compensation strategy parameter is evaluated. This evaluation process is based on more accurate input data, thereby improving the reliability of the evaluation results.

[0040] In one embodiment, assuming the handling robot is gripping a high-temperature metal billet, spectral analysis reveals a significant 50Hz low-frequency vibration component in the residual deviation signal fed back by its gripping force sensor. This is identified as mechanical resonance caused by the internal motor drive mechanism of the gripper. Simultaneously, a sharp noise peak is observed near 200kHz, which is determined to be electromagnetic interference generated by surrounding high-frequency induction heating equipment. First, the residual deviation signal is high-pass filtered, with a cutoff frequency set slightly above 50Hz to eliminate the low-frequency vibration caused by the mechanical resonance. Then, the high-pass filtered signal is notched, with the notch frequency precisely set at 200kHz to eliminate electromagnetic interference noise, resulting in a clean residual deviation signal. Meanwhile, when acquiring the trend of changes in the forging's quality feedback data, some instantaneous jumps are found in the original data. To address this, a 5-point moving average window is used to smooth the data and eliminate these instantaneous fluctuations. In the smoothed data, statistical methods (based on interquartile range or standard deviation) detected a few data points that significantly deviated from the normal range. By comparing these with environmental monitoring data (workshop vibration sensors, electromagnetic radiation monitors), it was confirmed that these outliers were caused by external environmental interference. These outliers were subsequently removed, resulting in stable quality trend data. Finally, based on this clean residual deviation signal and stable quality trend data, the system can accurately evaluate the adjustment effect of the current online adaptive compensation strategy parameters, determining whether adjusting a certain gain parameter effectively reduced the residual deviation, or whether adjusting a certain time constant improved the stability of the quality trend. This precise evaluation provides a reliable basis for adjusting the step size and convergence factor of the subsequent iterative optimization algorithm, ensuring the rapid optimization and stable operation of the compensation strategy.

[0041] It should be noted that when the trend of the data after moving average processing exceeds the fluctuation range of the quality data, the system will further acquire the environmental vibration intensity and electromagnetic interference intensity corresponding to that data point. When an anomaly exceeding the expected fluctuation range is detected, the system will not immediately classify it as environmental interference, but will collect additional environmental information as an auxiliary basis for judgment. The environmental vibration intensity can be acquired by vibration sensors installed on the robot or gripper, while the electromagnetic interference intensity can be obtained by electromagnetic field sensors or noise analysis of the control signal.

[0042] In one embodiment, assume a handling robot is clamping a batch of steel billets of a specific grade, with a furnace temperature of 1200°C and a clamping pressure of 500N applied by the gripper. The system first acquires these operating parameters. Based on a preset material property database, combined with the billet temperature of 1200°C and the clamping pressure of 500N, the system calculates that under these conditions, the reasonable fluctuation range of the quality feedback data for this grade of steel billet is ±5%, due to the inhomogeneity of the material's microstructure or local stress concentration. In subsequent quality feedback data processing, if a point appears in the quality trend data after moving average, with a deviation reaching +8%, exceeding the preset ±5% fluctuation range, the system will not immediately classify it as an anomaly and remove it. Instead, it will query the environmental vibration sensor data and electromagnetic interference sensor data at the time the data point occurred. If it is found that the environmental vibration intensity suddenly increased by 30% at that moment, and the electromagnetic interference intensity also exceeded the normal threshold, then the system will confirm, based on this environmental interference evidence, that the +8% deviation is an anomaly caused by environmental interference and remove it from the quality trend data. Conversely, if the intensity of environmental vibration and electromagnetic interference is within the normal range, the +8% deviation will be considered as a real fluctuation caused by the billet material or process itself and will be retained.

[0043] See Figure 2 , Figure 2 This is a schematic diagram of a gripping force control system for a handling robot according to one embodiment of this application. The gripping force control system 200 for the handling robot includes: The first acquisition module 210 is used to acquire the clamping force signal of the first clamping force sensing element, the driving parameters of the clamping drive mechanism, and the displacement information of the clamping jaws. The second acquisition module 220 is used to obtain the reference clamping force based on the driving parameters and displacement information; The detection module 230 is used to detect the deviation between the clamping force signal and the reference clamping force to obtain the target deviation; The compensation module 240 is used to compensate the clamping force signal according to the target deviation and the preset online adaptive compensation strategy to obtain the compensated clamping force signal. The control module 250 is used to control the clamping force of the clamping device drive mechanism on the high-temperature metal billet based on the compensated clamping force signal.

[0044] It should be noted that the information interaction and execution process between the above modules are based on the same concept as the method embodiments of this application. For details on their specific functions and technical effects, please refer to the method embodiments section, and they will not be repeated here.

[0045] It will be understood by those skilled in the art that all or some of the steps and systems in the methods disclosed above can be implemented as software, firmware, hardware, and suitable combinations thereof. Some or all of the physical components can be implemented as software executed by a processor, such as a central processing unit, digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as an application-specific integrated circuit. Such software can be distributed on a computer-readable medium, which can include computer storage media (or non-transitory media) and communication media (or transient media). As is known to those skilled in the art, the term computer storage media includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information (such as computer-readable instructions, data structures, program modules, or other data). Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technologies, CD-ROM, digital versatile disc (DVD) or other optical disc storage, magnetic cartridges, magnetic tape, disk storage or other magnetic storage devices, or any other medium that can be used to store desired information and is accessible to a computer. Furthermore, as is known to those skilled in the art, communication media typically include computer-readable instructions, data structures, program modules, or other data in modulated data signals such as carrier waves or other transmission mechanisms, and may include any information delivery medium.

[0046] The foregoing has provided a detailed description of the preferred embodiments of this application. However, this application is not limited to the above-described embodiments. Those skilled in the art can make various equivalent modifications or substitutions without departing from the spirit of this application. All such equivalent modifications or substitutions are included within the scope defined in this application.

Claims

1. A method for controlling the gripping force of a handling robot, characterized in that, include: Acquire the clamping force signal from the first clamping force sensing element, the driving parameters of the clamping drive mechanism, and the displacement information of the clamping jaws; The reference clamping force is obtained based on the driving parameters and the displacement information; The deviation trend between the clamping force signal and the reference clamping force is detected to obtain the target deviation; The clamping force signal is compensated according to the target deviation and the preset online adaptive compensation strategy to obtain the compensated clamping force signal; Based on the compensated clamping force signal, the clamping force of the clamping device drive mechanism on the high-temperature metal billet is controlled.

2. The method according to claim 1, characterized in that, The step of compensating the clamping force signal according to the target deviation and a preset online adaptive compensation strategy to obtain the compensated clamping force signal includes: Obtain quality feedback data for forgings; The quality feedback data is correlated with the compensated clamping force signal to obtain quality feedback data; The effectiveness of the online adaptive compensation strategy is evaluated based on the quality feedback data to obtain the evaluation result; Based on the evaluation results, adjust the parameters of the online adaptive compensation strategy; The clamping force signal is compensated based on the adjusted parameters of the online adaptive compensation strategy and the target deviation to obtain the compensated clamping force signal, and the clamping force of the clamping device drive mechanism on the high-temperature metal billet is controlled.

3. The method according to claim 2, characterized in that, The step of adjusting the parameters of the online adaptive compensation strategy based on the evaluation results includes: Obtain the rate and direction of change of the mass feedback data of the forging; Based on the rate of change and the direction, the update step size of the parameters of the online adaptive compensation strategy is dynamically adjusted to obtain the target step size; Obtain the transient deviation between the clamping force signal and the reference clamping force; The parameters of the online adaptive compensation strategy are adjusted based on the transient deviation, the evaluation result, and the target step size.

4. The method according to claim 3, characterized in that, The step of adjusting the parameters of the online adaptive compensation strategy based on the transient deviation, the evaluation result, and the target step size includes: After acquiring the transient deviation, the transient deviation signal is subjected to multi-band filtering to separate the vibration component corresponding to the preset mechanical resonance frequency and the noise component corresponding to the preset electromagnetic interference frequency. Based on the vibration components, the component of the transient deviation signal caused by mechanical resonance is suppressed; Based on the noise component, the electromagnetic interference-induced component in the transient deviation signal is eliminated to obtain a pure transient deviation signal; The parameters of the online adaptive compensation strategy are adjusted based on the purified transient deviation signal, the evaluation results, and the target step size.

5. The method according to claim 4, characterized in that, The step of adjusting the parameters of the online adaptive compensation strategy based on the transient deviation, the evaluation result, and the target step size includes: After acquiring the pure transient deviation signal, the batch information and furnace temperature information of the currently clamped billet are acquired; based on the batch information, the typical force-displacement curve and elastic modulus range of the corresponding batch of billet at the current furnace temperature are queried from the preset billet material property database. Based on the typical force-displacement curve, the elastic modulus range, and the displacement information, the theoretical transient deviation range caused by changes in billet material properties is calculated. The pure transient deviation signal is compared with the theoretical transient deviation range to distinguish transient deviations caused by system degradation. The parameters of the online adaptive compensation strategy are pre-adjusted based on the transient deviation caused by system degradation, the evaluation results, and the target step size.

6. The method according to claim 5, characterized in that, The step of pre-adjusting the parameters of the online adaptive compensation strategy based on the transient deviation caused by system degradation, the evaluation result, and the target step size includes: After obtaining the transient deviations caused by system degradation that are distinguished, the billet temperature, clamping speed and environmental vibration intensity under the current clamping conditions are obtained. Based on the billet temperature, the clamping speed, and the environmental vibration intensity, the dominant and secondary degradation modes of the system under the current working condition are identified from the preset working condition-degradation mode association rule library. Based on the dominant mode and the secondary mode, as well as the amplitude and trend of the transient deviation caused by system degradation, determine the set of parameters of the compensation strategy that need to be adjusted and the initial adjustment direction of each parameter. Based on the set of compensation strategy parameters, the initial adjustment direction, and the preset parameter interaction suppression rules, the online adaptive compensation strategy parameters are coordinated and adjusted to obtain the coordinated and adjusted compensation strategy parameters. The parameters of the online adaptive compensation strategy are adjusted based on the collaboratively adjusted compensation strategy parameters, the evaluation results, and the target step size.

7. The method according to claim 6, characterized in that, The step of coordinating the online adaptive compensation strategy parameters according to the set of compensation strategy parameters, the initial adjustment direction, and the preset parameter interaction suppression rules to obtain the coordinatingly adjusted compensation strategy parameters includes: The residual deviation between the clamping force signal and the reference clamping force, as well as the changing trend of the mass feedback data of the forging, are obtained. Based on the residual deviation and the rate of change of the trend, the step size and convergence factor of the preset iterative optimization algorithm are dynamically adjusted. Based on the step size and convergence factor of the dynamically adjusted preset iterative optimization algorithm, the set of compensation strategy parameters, the initial adjustment direction, and the preset parameter interaction suppression rules, the online adaptive compensation strategy parameters are coordinated and adjusted to obtain the coordinated adjusted compensation strategy parameters.

8. The method according to claim 7, characterized in that, After acquiring the residual deviation between the clamping force signal and the reference clamping force, and the changing trend of the forging's quality feedback data, the process includes: The residual deviation signal between the clamping force signal and the reference clamping force is subjected to high-pass filtering to eliminate the low-frequency vibration component caused by mechanical resonance. The residual deviation signal after high-pass filtering is subjected to notch filtering to eliminate specific frequency noise components caused by electromagnetic interference, thereby obtaining a clean residual deviation signal. The data on the changing trend are processed by a moving average to smooth out instantaneous fluctuations; Outlier detection is performed on the trend data after the moving average processing to identify and remove abnormal quality data points affected by environmental interference, so as to obtain stable quality trend data. The adjustment effect of each parameter of the online adaptive compensation strategy is evaluated based on the purified residual deviation signal and the stable quality trend data.

9. The method according to claim 8, characterized in that, The process of performing outlier detection on the trend data after the moving average processing to identify and remove abnormal quality data points affected by environmental interference, thereby obtaining stable quality trend data, includes: Obtain the clamping pressure under the current clamping condition; Based on the billet temperature and the clamping pressure, identify the range of quality data fluctuations caused by changes in the microstructure of the billet material or local stress concentration under the current working conditions. When the data of the change trend after the moving average processing exceeds the fluctuation range of the quality data, the environmental vibration intensity and electromagnetic interference intensity corresponding to the data point are obtained. Based on the environmental vibration intensity and the electromagnetic interference intensity, when the data point is an outlier caused by environmental interference, the outlier caused by environmental interference is removed.

10. A clamping force control system for a handling robot, characterized in that, include: The first acquisition module is used to acquire the clamping force signal of the first clamping force sensing element, the driving parameters of the clamping drive mechanism, and the displacement information of the clamping jaws. The second acquisition module is used to obtain a reference clamping force based on the driving parameters and the displacement information; The detection module is used to detect the deviation between the clamping force signal and the reference clamping force to obtain the target deviation; The compensation module is used to compensate the clamping force signal according to the target deviation and the preset online adaptive compensation strategy to obtain the compensated clamping force signal; The control module is used to control the clamping force of the clamping device drive mechanism on the high-temperature metal billet based on the compensated clamping force signal.

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