A dynamic compensation control system and method for high-precision trimming die

Abstract

The present invention discloses a dynamic compensation control system and method for a high-precision trimming die, which relates to the field of mechanical control technology. The system includes: a trimming die monitoring module for real-time monitoring and obtaining a trimming die monitoring data stream; an upper die compensation analysis module for performing health expectation deviation compensation analysis on the trimming upper die and generating an upper die compensation feature decision; a lower die compensation analysis module for performing compensation analysis on the trimming lower die and generating a lower die compensation feature decision; a guide compensation analysis module for generating a guide compensation feature decision; a fixed compensation analysis module for performing compensation control analysis on the trimming fixed component and generating a fixed compensation feature decision; and a trimming compensation execution module for performing compensation control on the trimming die. The present invention solves the technical problem in the prior art that the production quality of the trimming die is unstable and difficult to meet the product precision requirements, and achieves the technical effect of accurate dynamic compensation of the trimming die, thereby significantly improving the working precision and stability of the trimming die.

Description

Technical Field

[0001] The present invention relates to the field of mechanical control technology, and in particular to a dynamic compensation control system and method for a high-precision trimming die. Background Art

[0002] In modern manufacturing, trimming dies play a vital role in the production of numerous products. In fields such as automotive manufacturing, electronic equipment production, and aerospace parts processing, the requirements for edge precision and quality of components are becoming increasingly stringent. Traditional trimming dies face numerous challenges during use, and the trimming process is prone to problems such as dimensional deviation, irregular shape, and poor surface quality.

[0003] The existing technology has the technical problem that the production quality of the trimming die is unstable and it is difficult to meet the product precision requirements. Summary of the Invention

[0004] The present application provides a dynamic compensation control system and method for a high-precision trimming die, which is used to solve the technical problem in the prior art that the production quality of the trimming die is unstable and difficult to meet the product precision requirements.

[0005] In view of the above problems, the present application provides a dynamic compensation control system and method for a high-precision trimming die.

[0006] In a first aspect of the present application, a dynamic compensation control system for a high-precision trimming die is provided, the system comprising:

[0007] A trimming die monitoring module, the trimming die monitoring module is used to monitor the trimming die in real time and obtain a trimming die monitoring data stream, wherein the trimming die includes a trimming upper die, a trimming lower die, a trimming guide assembly and a trimming fixing assembly; an upper die compensation analysis module, the upper die compensation analysis module performs a health expectation deviation compensation analysis on the trimming upper die based on an upper die health expectation prediction channel in combination with the trimming die monitoring data stream, and generates an upper die compensation feature decision; a lower die compensation analysis module, the lower die compensation analysis module performs a compensation analysis on the trimming lower die based on a multi-feature lower die compensation factor and the trimming die monitoring data stream, and generates a lower die compensation feature decision; a guide compensation analysis module, the The guide compensation analysis module performs compensation analysis on the trimming guide component based on the guide compensation decision dual channel in combination with the trimming die monitoring data stream to generate a guide compensation feature decision; the fixed compensation analysis module performs compensation control analysis on the trimming fixed component based on the cutting workpiece fixing force perception data and the cutting workpiece fixing position perception data in combination with the trimming die monitoring data stream to generate a fixed compensation feature decision; the trimming compensation execution module is used to perform compensation control on the trimming die based on the upper die compensation feature decision, the lower die compensation feature decision, the guide compensation feature decision and the fixed compensation feature decision.

[0008] A second aspect of the present application provides a dynamic compensation control method for a high-precision trimming die, the method comprising:

[0009] Monitor the trimming die in real time to obtain a trimming die monitoring data stream, wherein the trimming die includes an upper trimming die, a lower trimming die, a trimming guide assembly, and a trimming fixed assembly; based on the upper die health expectation prediction channel, combined with the trimming die monitoring data stream, perform health expectation deviation compensation analysis on the upper trimming die to generate an upper die compensation feature decision; based on the multi-feature lower die compensation factor and the trimming die monitoring data stream, perform compensation analysis on the trimming lower die to generate a lower die compensation feature decision; based on the guide compensation decision dual channel, combine the trimming die monitoring data stream to perform compensation analysis on the trimming guide assembly to generate a guide compensation feature decision; based on the cutting workpiece fixing force perception data and the cutting workpiece fixing position perception data, combine the trimming die monitoring data stream to perform compensation control analysis on the trimming fixed assembly to generate a fixed compensation feature decision; based on the upper die compensation feature decision, the lower die compensation feature decision, the guide compensation feature decision, and the fixed compensation feature decision, perform compensation control on the trimming die.

[0010] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0011] The trimming die is monitored in real time to obtain a trimming die monitoring data stream. Based on the upper die health expectation prediction channel, an upper die compensation feature decision is generated. Compensation analysis of the lower die is performed based on the multi-feature lower die compensation factor and the trimming die monitoring data stream to generate a lower die compensation feature decision. A guide compensation feature decision is generated based on the dual guide compensation decision channels. A fixed compensation feature decision is generated based on the sensing data of the workpiece fixing force and the workpiece fixing position. Compensation control of the trimming die is performed based on the upper die compensation feature decision, the lower die compensation feature decision, the guide compensation feature decision, and the fixed compensation feature decision. This achieves precise dynamic compensation of the trimming die, significantly improving the working accuracy and stability of the trimming die. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0013] Figure 1 A schematic structural diagram of a dynamic compensation control system for a high-precision trimming die provided in an embodiment of the present application;

[0014] Figure 2 A flow chart of a dynamic compensation control method for a high-precision trimming die provided in an embodiment of the present application.

[0015] Explanation of the accompanying reference numerals: trimming die monitoring module 10 , upper die compensation analysis module 20 , lower die compensation analysis module 30 , guide compensation analysis module 40 , fixed compensation analysis module 50 , trimming compensation execution module 60 . DETAILED DESCRIPTION

[0016] The present application provides a dynamic compensation control system and method for a high-precision trimming die, which is used to solve the technical problem in the prior art that the production quality of the trimming die is unstable and difficult to meet the product precision requirements.

[0017] The following will be combined with the accompanying drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making any creative work are within the scope of protection of this application. Example 1

[0018] like Figure 1As shown, the present application provides a dynamic compensation control system for a high-precision trimming die, the system comprising:

[0019] The trimming die monitoring module 10 is used to monitor the trimming die in real time and obtain a trimming die monitoring data stream, wherein the trimming die includes an upper trimming die, a lower trimming die, a trimming guide assembly and a trimming fixing assembly.

[0020] Specifically, the trimming die monitoring module 10 integrates a series of advanced and highly sensitive monitoring instruments and technologies, including but not limited to high-precision displacement sensors, pressure sensors, temperature sensors, and image acquisition devices. These sensors and devices are placed at key locations on the trimming die to ensure that the operating status of each part of the trimming die can be fully and accurately captured. For the trimming upper die, the pressure distribution, surface wear, and slight displacement changes during operation are monitored. For the trimming lower die, close attention is paid to its deformation under pressure, temperature changes, and internal stress accumulation. For the trimming guide assembly, its guiding accuracy, smoothness of movement, and tightness of fit with other parts of the mold are accurately measured. For the trimming fixing assembly, the magnitude of its fixing force on the workpiece, the accuracy of the fixing position, and its stability during continuous operation are obtained in real time. Through these advanced monitoring methods, the trimming die monitoring module 10 can collect data at an extremely high frequency and quickly convert this data into a trimming die monitoring data stream. These data streams contain rich and detailed information, such as the real-time operating parameters of each component, status change trends, and possible abnormal fluctuations. It provides a crucial, accurate and reliable data basis for subsequent analysis, compensation and control, and is one of the key links to ensure the high-precision and high-efficiency operation of the trimming die.

[0021] The upper die compensation analysis module 20 performs health expectation deviation compensation analysis on the trimming upper die based on the upper die health expectation prediction channel and in combination with the trimming die monitoring data stream to generate an upper die compensation feature decision.

[0022] Specifically, the upper die compensation analysis module 20 is a key component for analyzing deviations from the expected health of the trimming upper die. Drawing on extensive experimental data, historical operating records, and specialized knowledge, a model was constructed that simulates the expected health state of the trimming upper die under different operating conditions. This model takes into account various factors, such as the trimming die's material properties, operating environment, and frequency of use, to accurately predict the upper die's ideal health state. The module receives real-time information from the trimming die monitoring data stream. This data stream contains numerous real-time parameters related to the trimming upper die, such as its temperature distribution, pressure variations, vibration amplitude, and frequency. These real-time parameters extracted from the monitoring data stream are then input into the constructed upper die health prediction model. The model then calculates and analyzes these parameters and outputs the predicted expected health state of the trimming upper die under the current circumstances. To determine the deviation between the actual health state of the trimming upper die and the expected health state, the module conducts a detailed comparison of the predicted expected health state with the actual monitored upper die state, accurately quantifying the degree and specific direction of the deviation. Based on the analysis results of this deviation from the expected health condition, the compensation strategies and algorithms preset in the module will be triggered, thereby generating a compensation feature decision for the upper die. For example, if the analysis finds that the temperature of the upper die is too high and exceeds the expected health condition, the compensation feature decision will instruct to adjust the working parameters of the upper die, such as reducing the stamping speed to reduce frictional heat; or prompt to check whether the cooling system is operating normally, whether maintenance or replacement of related components is required, etc. For example, if the vibration frequency of the upper die is detected to be abnormal, the decision will require adjusting the fixing method of the upper die or checking the tightness of related connecting parts. In this way, the upper die compensation analysis module 20 can promptly detect the deviation of the health condition of the trimming upper die and generate corresponding compensation decisions to ensure that the trimming upper die always maintains a high-precision and stable working state, extend its service life, and improve the quality and efficiency of the trimming process.

[0023] The lower die compensation analysis module 30 performs compensation analysis on the trimming lower die based on the multi-feature lower die compensation factor and the trimming die monitoring data stream to generate a lower die compensation feature decision.

[0024] Specifically, the die compensation analysis module operates based on multi-feature die compensation factors and the trimming die monitoring data stream. These multi-feature die compensation factors include die deformation, uneven die force, residual die material, heat impact, and die displacement. The trimming die monitoring data stream contains various relevant data from the actual operation of the trimming die. This monitoring data is then comprehensively considered using the multi-feature die compensation factors. For example, die deformation can lead to reduced machining accuracy, uneven die force can affect die life and product quality, residual die material can interfere with subsequent processing, heat impact can alter the die's physical properties, and die displacement can lead to inaccurate trimming positioning. By establishing a specific model, the module quantifies and processes these factors, assigning appropriate weights to each compensation factor. Combined with the specific data in the monitoring data stream, the module then calculates the specific characteristics and degree of die compensation required. The resulting die compensation feature decisions include specific measures such as die adjustment, repair, or replacement. For example, if analysis reveals significant lower mold deformation, the decision is to reshape or replace the mold. If the force applied to the mold is uneven, the mold structure or processing parameters need to be adjusted to improve the force distribution. If there is residual material in the mold, a cleaning process or improved demolding methods are needed. If the problem is thermal, the cooling system needs to be optimized or the processing technology needs to be adjusted to control the temperature. If the mold is displaced, the positioning device needs to be calibrated or the fixing method needs to be strengthened. Through the operation of the lower mold compensation analysis module 30, problems with the trimming lower mold can be promptly identified and corresponding compensation decisions generated to improve the performance of the trimming lower mold, ensure processing quality, and extend the life of the mold.

[0025] The guide compensation analysis module 40 performs compensation analysis on the trimming guide component based on the guide compensation decision dual channel and in combination with the trimming die monitoring data stream to generate a guide compensation feature decision.

[0026] Specifically, the dual-channel guide compensation decision includes the upper and lower die guide fit compensation decision channel and the guide state abnormality compensation decision channel. The upper and lower die guide fit compensation decision channel focuses on the fit between the upper and lower dies through the guide components during the trimming process. The established model will take into account factors such as the relative position of the upper and lower dies, motion synchronization, and contact pressure distribution. When the trimming die monitoring data stream shows that there is a deviation in the fit of the upper and lower dies, such as inaccurate relative position resulting in the trimming size not meeting the requirements, or poor motion synchronization affecting processing efficiency and quality, the model of this channel will analyze the cause and extent of the deviation and calculate the necessary compensation adjustments, such as adjusting the installation position or angle of the guide component to improve the fit accuracy of the upper and lower dies. The guide state abnormality compensation decision channel focuses on monitoring whether the state of the guide component itself is abnormal. The model considers parameters such as guide component wear, deformation, temperature changes, and vibration. If the trimming die monitoring data stream indicates severe wear, excessive deformation, excessive temperature, or abnormal vibration, the model in this channel will evaluate the impact of this abnormal condition on the trimming guide based on pre-defined standards and strategies and determine appropriate compensation measures, which may include replacing worn parts, enhancing cooling, or adding shock absorbers. By combining the analysis results of these two channels with the real-time information provided by the trimming die monitoring data stream, a comprehensive model is established, ultimately generating guide compensation feature decisions that effectively improve the performance of the trimming guide assembly and enhance the trimming process accuracy and stability.

[0027] The fixed compensation analysis module 50 performs compensation control analysis on the trimming fixing component based on the cutting workpiece fixing force sensing data and the cutting workpiece fixing position sensing data, combined with the trimming die monitoring data stream, to generate a fixed compensation feature decision.

[0028] Specifically, the module acquires data on the workpiece's clamping force and position. The clamping force data reflects the clamping force and stability of the fixture assembly on the workpiece, while the position data clarifies whether the workpiece is accurately and securely positioned within the fixture assembly. Simultaneously, the module receives a stream of trimming die monitoring data, which contains a wealth of information, including the operating status of the trimming fixture assembly and the operating parameters of related components. The module then comprehensively processes and analyzes this data. By comparing the actual clamping force data with a preset ideal clamping force range, the module determines whether the current clamping force is sufficient and stable. Insufficient clamping force can cause workpiece displacement during trimming, affecting machining accuracy; excessive clamping force can damage the workpiece. The position data is compared with standard clamping position requirements to verify the workpiece's correct position. Position deviation can lead to inaccurate trimming positions, thus affecting product quality. The module also integrates other relevant information from the trimming die monitoring data stream, such as fixture wear and temperature fluctuations, to further assess the overall operating condition of the fixture assembly. Using an optimal control model, with the goals of minimizing fixture errors, improving system stability, and reducing energy consumption, the optimal compensation control strategy is determined. The required compensation adjustment amount and method are calculated, ultimately generating fixture compensation feature decisions. These decisions include adjusting the fixture force, recalibrating the fixture position, replacing worn fixture components, and optimizing the fixture assembly's structure or operating parameters. This ensures that the trimming fixture assembly securely and accurately secures the workpiece, providing a stable and reliable foundation for trimming operations, ensuring machining quality and production efficiency.

[0029] The trimming compensation execution module 60 is used to perform compensation control on the trimming die based on the upper die compensation feature decision, the lower die compensation feature decision, the guide compensation feature decision and the fixed compensation feature decision.

[0030] Specifically, the trimming compensation execution module 60, as the key execution link in the entire trimming die compensation control system, undertakes the important task of converting various compensation feature decisions into actual compensation control actions. Upon receiving an upper die compensation feature decision, the trimming compensation execution module 60 precisely controls the relevant actuators based on the specific decision content, such as the adjustment requirements for the upper die operating parameters, to adjust the upper die's pressure, motion speed, and position. For lower die compensation feature decisions, the module performs corresponding compensation operations on the lower die's temperature, force distribution, and displacement, including adjusting the cooling system, modifying the support structure, or optimizing processing parameters. When processing guide compensation feature decisions, the trimming compensation execution module 60 controls the guide assembly's adjustment device to precisely control the guide assembly's position, angle, and lubrication status to ensure guidance accuracy and stability. For fixed compensation feature decisions, the module operates the fixed assembly's control components to effectively execute the magnitude of the fixed force, calibration of the fixed position, and replacement or repair of fixed components. To implement these compensation control actions, the trimming compensation execution module 60 works closely with a series of actuators, sensors, and controllers. It will monitor the execution effect of the compensation action in real time and compare the feedback information with the expected compensation target. If necessary, it will make further fine-tuning or corrections to ensure that the trimming die can achieve the expected high precision, high stability and high-quality processing requirements after compensation control.

[0031] In one possible implementation, the upper die compensation analysis module 20 further includes:

[0032] According to the trimming die monitoring data stream, the associated data of the trimming die are integrated to construct an upper die monitoring state matrix; the cutting workpiece feature data and the workpiece cutting upper die control decision corresponding to the trimming die are retrieved; based on the upper die basic data set of the trimming die, combined with the cutting workpiece feature data and the workpiece cutting upper die control decision, the health expectation state of the trimming die is predicted according to the upper die health expectation prediction channel to obtain an upper die health expectation matrix; an upper die health expectation deviation compensation model is constructed, and the upper die health expectation deviation compensation model is embedded in the upper die compensation analysis module; based on the upper die monitoring state matrix and the upper die health expectation matrix, the upper die compensation feature decision is obtained according to the upper die health expectation deviation compensation model.

[0033] Specifically, the system first acquires the trimming die monitoring data stream. This data stream contains a large amount of raw data related to the trimming die's operating process. Then, through data screening and extraction methods, it selects data directly related to the trimming die from this massive data set. This data includes various parameters, such as temperature changes, pressure magnitude and distribution, displacement, vibration frequency, and amplitude. This selected related data is organized and classified, grouping similar or related data. Finally, the grouped and organized data is arranged and combined in a matrix format to construct a die monitoring status matrix that comprehensively and accurately reflects the real-time status of the trimming die. Each row and column of this matrix represents a specific type of data—for example, the first row represents temperature data, the second row represents pressure data, and so on—while each column corresponds to data values ​​at different time points or sampling points. By constructing this die monitoring status matrix, the complex and dispersed trimming die-related data can be integrated into a structured form that is easy to analyze and process, providing a clear and accurate data foundation for subsequent health status prediction, compensation analysis, and other operations.

[0034] The system retrieves the characteristic data of the workpiece being cut by the current trimming die from a stored database or related dataset. This workpiece characteristic data includes material information, such as material type (e.g., steel, aluminum, plastic), physical properties such as hardness, strength, and toughness, as well as properties such as surface roughness and thermal conductivity. Structural characteristic data may include workpiece shape (e.g., flat plate, curved plate, complex three-dimensional structure), dimensions (length, width, thickness), and structural complexity (presence of holes, protrusions, grooves, etc.). The system also retrieves the upper die control decision for cutting the workpiece—operational instructions for the upper die during the cutting process, developed based on process requirements, production standards, and prior experience. These include control parameters such as upper die speed, stroke, applied pressure, and dwell time. By accessing this workpiece characteristic data and the upper die control decision, the system provides a more comprehensive and accurate reference for subsequent upper die state prediction and compensation analysis, ensuring the precision and quality of the trimming process, improving production efficiency, and increasing product qualification rates.

[0035] First, the upper die basic dataset for the trimming upper die contains basic information such as the die's initial design parameters, material properties, and manufacturing process. Next, the upper die basic dataset is combined with workpiece feature data (including material type, hardness, and shape and structure) and the upper die control decisions for workpiece cutting (e.g., specific control parameters such as the upper die's cutting speed, pressure, and stroke). The model established in the upper die health expectation prediction channel is a complex system that integrates mechanical principles, materials science knowledge, and data analysis techniques. This mechanical model, constructed using finite element analysis, simulates the stress distribution and deformation during the interaction between the upper die and the workpiece. Utilizing deep learning neural networks in machine learning, the model captures the potential relationships between various input factors and the upper die's health status by learning from a large amount of historical data. The model uses this integrated dataset as input. Based on the workpiece material properties, the model predicts the friction and impact forces experienced by the upper die during the cutting process and, combined with structural characteristics, calculates the location and extent of stress concentrations. The dynamic response and energy consumption of the upper die are simulated based on the speed and pressure parameters used in the control decisions. Through comprehensive calculations and analysis, the model derives the upper die's expected health status under specific operating conditions. These conditions include predicted wear values ​​for various parts of the upper die, the magnitude and direction of deformation, the distribution of internal stresses, and the location and development trends of potential fatigue cracks. Organizing and presenting these rich prediction results in a matrix format creates the upper die health expectation matrix. The rows of the matrix can represent different health indicators, such as wear, deformation, stress, and fatigue; the columns can correspond to different regions or time points in the upper die. Each matrix element quantifies the expected health value of the corresponding region at a specific time. This meticulous and comprehensive process provides accurate and forward-looking predictions for assessing the future health of the upper die, providing solid data support for subsequent maintenance and optimization decisions.

[0036] To construct a compensation model for deviations from the upper die health expectation, a detailed comparison and in-depth analysis of the acquired upper die health expectation matrix and the actual monitored upper die status data are required. The differences between the two are measured along multiple dimensions, including comparisons of predicted wear with measured wear, expected deformation with actual deformation, and assessment of the deviation between the predicted and monitored stress distributions. Statistical metrics such as mean and standard deviation are used to describe the central tendency and dispersion of deviations, thereby identifying the key factors contributing to the deviations. Based on the quantified deviations, a specific compensation strategy is developed. This involves adjusting the upper die's mechanical structure, such as replacing severely worn components and repairing deformed areas, and reoptimizing cutting process parameters, such as increasing or decreasing cutting speed or applying pressure. When developing the compensation strategy, various practical constraints, such as cost constraints, production schedule requirements, and equipment operability, are considered to ensure its feasibility and effectiveness. The constructed upper die health expectation deviation compensation model is implemented programmatically in the form of algorithms and code, and embedded into the upper die compensation analysis module. During the embedding process, it was necessary to ensure that the model was compatible with the interface of the upper die compensation analysis module, accurately receiving relevant data inputs for the upper die, and outputting the calculated compensation decisions to the module's subsequent processing flow. A series of tests and verifications were also conducted to ensure that the embedded model could operate stably and accurately, and work in conjunction with the other components of the upper die compensation analysis module to effectively compensate for and optimize the upper die's health status. Through this detailed and rigorous construction and embedding process, the upper die health expectation deviation compensation model became a core component of the upper die compensation analysis module, playing a key role in ensuring the normal operation of the trimming die and improving production quality.

[0037] Based on the upper die monitoring state matrix and upper die health expectation matrix obtained above, these two matrices are input into the upper die health expectation deviation compensation model. The model accurately quantifies the degree of deviation between the upper die's actual state and the expected state. Based on these quantified deviations, these deviations are converted into specific compensation requirements. These requirements involve multiple aspects, such as fine-tuning the upper die structure, changing the working environment conditions (such as temperature and humidity), and adjusting the fit accuracy between the upper die and other components. These compensation requirements are then screened and ranked according to pre-defined priority and feasibility criteria. For example, if upper die wear has seriously affected product accuracy, replacing the worn components is prioritized; if the temperature deviation is only minor, optimizing the cooling system is the first step to address it. Finally, all analysis and screening results are combined to generate an upper die compensation feature decision. This decision clearly specifies the specific compensation action to be taken, including the specific operation steps, the required resources and time, and the expected results. This ensures that the upper die can be restored to its ideal health state as quickly as possible, safeguarding the normal operation of the trimming die and product quality.

[0038] In one possible implementation, the upper die compensation analysis module 20 further includes:

[0039] Deviation identification is performed based on the upper mold monitoring state matrix and the upper mold health expectation matrix to obtain the upper mold monitoring expected deviation matrix; cutting impact evaluation is performed based on the upper mold monitoring expected deviation matrix to generate the upper mold deviation cutting impact degree; it is determined whether the upper mold deviation cutting impact degree is greater than / equal to the predetermined upper mold deviation cutting impact degree; if the upper mold deviation cutting impact degree is greater than / equal to the predetermined upper mold deviation cutting impact degree, the upper mold health expectation deviation compensation model is activated; the upper mold monitoring expected deviation matrix is ​​input into the upper mold health expectation deviation compensation model to generate the upper mold compensation feature decision.

[0040] Specifically, the acquired upper mold monitoring state matrix and the upper mold health expectation matrix are compared and calculated element by element to identify deviations between them. Through appropriate mathematical operations, such as difference calculations and ratio calculations, a new matrix, the upper mold monitoring expectation deviation matrix, is obtained. Each element in this matrix quantitatively represents the degree of deviation between the upper mold's actual monitoring state and the health expectation state at the corresponding location or parameter.

[0041] When evaluating cutting impact, each element in the upper die monitoring expected deviation matrix is ​​first carefully analyzed. These elements may represent deviations such as upper die position deviation, temperature deviation, pressure deviation, and deformation deviation. Each deviation element is assigned a different weight based on its corresponding physical parameter and its importance in the cutting process. For example, a deviation in upper die position may have a significant impact on cutting accuracy and therefore receive a higher weight; whereas a minor temperature deviation may receive a relatively lower weight. By comprehensively considering all deviation elements and their weights, a weighted summation method is applied to calculate the upper die deviation cutting impact. This metric reflects the overall potential impact of upper die deviation on the cutting process, ultimately generating a quantitative upper die deviation cutting impact that intuitively reflects the impact of upper die deviation on cutting results in terms of accuracy, quality, and efficiency.

[0042] The specific value for the upper die deviation cutting impact is determined. This predetermined value is typically a threshold set based on a combination of factors, including past experience, industry standards, product quality requirements, and equipment performance. The calculated current upper die deviation cutting impact is then compared with this predetermined value. If the upper die deviation cutting impact is greater than or equal to the predetermined upper die deviation cutting impact, it indicates that the upper die deviation has reached a critical level, significantly impacting cutting quality, efficiency, and cost.

[0043] The system immediately triggers a command to activate the upper die health expected deviation compensation model. Upon awakening, the model begins initialization and data preparation. Next, the upper die monitoring expected deviation matrix is ​​accurately input into this activated compensation model. Upon receiving the deviation matrix, the model rapidly initiates its complex internal calculation and analysis mechanisms, ultimately generating an accurate and feasible upper die compensation feature decision. This decision details key information such as the compensation method, magnitude, timing, and sequence, providing clear guidance for subsequent upper die adjustments and repairs, ensuring the rapid restoration of the upper die to its normal working state and ensuring cutting quality and production efficiency.

[0044] In one possible implementation, the lower die compensation analysis module 30 further includes:

[0045] The multi-feature lower die compensation factors include lower die deformation, uneven force on the lower die, residual lower die material, lower die thermal impact and lower die displacement; the associated data of the trimming die is integrated according to the trimming die monitoring data stream to generate a lower die monitoring block; based on the multi-feature lower die compensation factors, a lower die compensation decision is made according to the lower die monitoring block to generate a multi-feature lower die compensation decision; the multi-feature lower die compensation decisions are integrated to obtain the lower die compensation feature decision.

[0046] Specifically, the multi-feature lower die compensation factor is a key factor used to evaluate and adjust the state of the trimming lower die. Lower die deformation refers to the change in shape of the lower die due to factors such as pressure and temperature during use, which may lead to a decrease in cutting accuracy. Uneven force on the lower die means that the forces borne by various parts of the lower die are inconsistent, which will affect the service life and cutting effect of the lower die, and easily cause local wear or breakage too quickly. Lower die material residue means that after the cutting is completed, some raw materials are still attached to the lower die, which will interfere with subsequent cutting operations and affect product quality. Lower die thermal impact refers to the heat generated during the cutting process that causes the temperature of the lower die to change, thereby causing changes in its physical properties, such as hardness and strength, which in turn affects the working performance of the lower die. Lower die displacement means that the lower die has moved or offset during work, which will destroy the originally set cutting position and accuracy, causing the product to not meet the requirements.

[0047] Data directly related to the trimming die is filtered from the trimming die monitoring data stream and distinguished and extracted based on specific identifiers, tags, or data fields. The extracted trimming die-related data is classified and organized, grouping them according to data type, such as deformation-related data, force data, and heat-affected data. These classified and organized data groups are then integrated and correlated. For example, deformation data and force data for the die at the same point in time or within the same work cycle are correlated to form a complete dataset that reflects the state of the die under specific working conditions. Finally, this integrated and correlated dataset is stored and managed as an independent die monitoring block. This block provides centralized, organized, and targeted data support for subsequent analysis and decision-making, enabling more accurate assessment of the die's working condition and the development of appropriate compensation strategies.

[0048] Analyze multi-feature die compensation factors, specifically conducting in-depth research on factors such as die deformation, uneven die force, residual die material, heat impact, and die displacement. Define the normal range and abnormal threshold for each factor, as well as its specific impact on die performance and cutting quality. Align the data in the die monitoring block with these compensation factors. For die deformation, compare the monitored deformation data with the normal deformation range. For uneven force, analyze the difference between the force distribution and ideal uniform force. For material residue, determine the amount and location of the residue. For thermal impact, evaluate the effect of temperature changes on the die. For die displacement, measure the value and direction of displacement. Based on these comparison and analysis results, formulate preliminary compensation decisions for each compensation factor. Combine the preliminary decisions for each compensation factor to form a multi-feature die compensation decision.

[0049] Each multi-feature die compensation decision is individually reviewed and analyzed. Understand the specific issues addressed by each decision, the proposed compensation measures, and the expected effects. Then, evaluate the interrelationships between these compensation decisions. Some decisions reinforce each other and can work synergistically to more effectively resolve die issues; others may conflict or constrain certain aspects, requiring coordination and balancing. For mutually reinforcing decisions, integrate them into a unified compensation solution. For example, if one decision involves increasing cooling measures to mitigate thermal impact, while another involves adjusting processing parameters to reduce uneven stress, these two decisions can be implemented together to achieve better compensation results. For conflicting decisions, balance them based on the actual situation. For example, one decision may require a significant investment of time and resources for a comprehensive die repair, while another recommends temporary adjustments to quickly resume production. In this case, the optimal solution may need to be selected by comprehensively considering factors such as production schedule, cost, and repair effectiveness. During the integration process, the overall performance and operating environment of the die should also be considered. Ensure that the compensation feature decisions not only address the specific issues identified, but also do not adversely affect other aspects of the die and are adaptable to the actual operating conditions on the production line. Through comprehensive analysis, coordination, and optimization, the individual multi-feature die compensation decisions are integrated into a complete, coordinated, and feasible die compensation feature decision. This provides clear and comprehensive guidance for subsequent die compensation operations to maximize die performance and production efficiency.

[0050] In one possible implementation, the lower die compensation analysis module 30 further includes:

[0051] The lower die monitoring block is feature identified according to the multi-feature lower die compensation factor to obtain lower die deformation monitoring data; a lower die deformation cutting impact evaluation model and a lower die deformation compensation decision model are constructed according to the multi-feature lower die compensation factor; based on the lower die deformation monitoring data and according to the lower die deformation cutting impact evaluation model, a lower die deformation cutting impact evaluation coefficient is obtained; it is determined whether the lower die deformation cutting impact evaluation coefficient is greater than / equal to a lower die deformation cutting impact evaluation threshold; if the lower die deformation cutting impact evaluation coefficient is greater than / equal to the lower die deformation cutting impact evaluation threshold, the lower die deformation monitoring data is input into the lower die deformation compensation decision model to obtain a lower die deformation compensation decision; the lower die deformation compensation decision is added to the multi-feature lower die compensation decision, and the lower die compensation decision is continued according to the multi-feature lower die compensation factor and the lower die monitoring block to obtain the multi-feature lower die compensation decision.

[0052] Specifically, the specific factors included in the multi-feature lower die compensation factor are identified, such as lower die deformation, uneven force, material residue, thermal effects, and displacement. Utilizing relevant monitoring technologies and equipment, comprehensive and detailed monitoring of the lower die monitoring area is performed. Monitoring methods and means may involve a variety of sensors, measuring instruments, or technical methods to accurately obtain data related to the multi-feature lower die compensation factor. During the monitoring process, special attention is paid to features and indicators that can reflect the deformation of the lower die. For example, by measuring the displacement changes of specific parts of the lower die, the degree of deformation can be understood; the stress distribution of the lower die can be analyzed to determine whether the force is uniform; the presence of material residue can be detected; and temperature changes during operation and their impact on deformation can be monitored. The various monitoring data are integrated and analyzed to extract key information that accurately describes the deformation of the lower die, namely the lower die deformation monitoring data.

[0053] To construct an evaluation model for the impact of die deformation on cutting, we identify the key factors influencing die deformation. Beyond the previously mentioned multi-feature die compensation factors, such as die deformation, uneven stress, material residue, thermal effects, and displacement, we also need to further analyze which specific factors have the most significant impact on die deformation cutting. Through monitoring and measurement in actual production, we acquire a large amount of die deformation cutting data, including die deformation under different working conditions, stress conditions, temperature changes, material properties, and post-cut product quality. We analyze this collected data to extract features relevant to the impact of die deformation on cutting. We select an appropriate model structure and use a decision tree algorithm to analyze the multi-feature die compensation factors. These factors serve as input features for the decision tree, automatically determining node partitioning criteria based on the data's characteristics and distribution. For example, if die deformation is a key feature, the decision tree might first partition nodes based on the magnitude of the deformation, dividing the data into different subsets. In this way, a decision tree is constructed that can predict the impact of die deformation on cutting based on multi-feature die compensation factors. The selected model is trained using the collected data. By adjusting the model parameters, the model is able to predict the impact of die deformation on cutting as accurately as possible based on the input features. Cross-validation and other techniques are used to evaluate the model's performance and perform necessary optimizations. The trained model is then validated using an independent test dataset to assess its accuracy, reliability, and generalization capabilities. The die deformation compensation decision model is constructed to determine specific goals for die deformation compensation, such as controlling deformation within a certain range and improving product dimensional accuracy. Various constraints in actual production, such as cost constraints and equipment capacity, are also considered.

[0054] Similarly, a decision tree algorithm is used to construct a die deformation compensation decision model. A large amount of sample data is collected, including die deformation characteristics (such as deformation amount, location, and speed), multi-feature die compensation factors (such as stress, material properties, and temperature), and corresponding compensation measures (such as adjusting the die structure and changing processing parameters). Data preprocessing, including data cleaning, missing value handling, and feature engineering (such as feature selection and scaling), is performed to enable the algorithm to better process the data. Key features relevant to die deformation compensation decisions are selected from the collected data and serve as input nodes for the decision tree. Starting from the root node, a feature that best partitions the dataset into different subsets is selected as the splitting feature. Based on the values ​​of the selected feature, the dataset is partitioned into multiple child nodes. The above steps are repeated for each child node, selecting a new splitting feature until a stopping condition is met, such as reaching a predetermined tree depth, a small number of samples in a child node, or sufficient node purity. The model is evaluated using a portion of data (the test set) that was not used in the decision tree construction. Common evaluation metrics include precision, recall, and F1 score. Through repeated experiments and optimization, combined with domain knowledge and experience, the lower die deformation cutting impact evaluation model and the lower die deformation compensation decision model are continuously improved so that they can accurately predict the deformation cutting impact based on the multi-feature lower die compensation factor and provide effective compensation decisions.

[0055] Using relevant monitoring equipment and technologies, we acquire monitoring data on die deformation. This data includes various parameters, including deformation amount, location, and speed. We clean, filter, and organize the collected raw monitoring data, removing outliers and erroneous data to ensure data accuracy and reliability. This pre-processed die deformation monitoring data is then input into the die deformation cutting impact assessment model to generate the corresponding results.

[0056] The calculated die deformation cutting impact evaluation coefficient is compared with a pre-set die deformation cutting impact evaluation threshold. If the evaluation coefficient is greater than or equal to the threshold, it indicates that the die deformation has a significant impact on cutting and requires compensation. The acquired die deformation monitoring data is input into the die deformation compensation decision model. Analysis and calculations are performed based on the input data, ultimately resulting in a compensation decision for the die deformation. This compensation decision is then added to the previous multi-feature die compensation decision. The die compensation decision is then analyzed and judged based on the multi-feature die compensation factor and die monitoring block. The final multi-feature die compensation decision is determined by comprehensively considering the previously determined compensation decision, the newly acquired monitoring data, and the compensation factor. The entire process is a dynamic, step-by-step optimization process designed to achieve precise die compensation and improve die performance and precision during the cutting process through continuous monitoring, analysis, and decision-making.

[0057] In one possible implementation, the guidance compensation analysis module 40 further includes:

[0058] According to the trimming die monitoring data stream, the associated data of the trimming guide component is integrated to generate the guide component angle feature point cloud, the guide component position feature point cloud and the guide component state feature information; according to the trimming die monitoring data stream, the upper die position feature point cloud and the lower die position feature point cloud are obtained; according to the upper die position feature point cloud and the lower die position feature point cloud, the matching offset detection is performed to generate the upper and lower die matching offset point cloud; based on the guide component angle feature point cloud, the guide component position feature point cloud and the upper and lower die matching offset point cloud, according to the upper and lower die guide matching compensation decision channel of the guide compensation dual channel, the upper and lower die guide matching compensation decision is obtained; based on the guide component state feature information, according to the guide state abnormality compensation decision channel of the guide compensation dual channel, the guide state abnormality compensation decision is obtained; the upper and lower die guide matching compensation decision and the guide state abnormality compensation decision are integrated to obtain the guide compensation feature decision.

[0059] Specifically, the trimming die monitoring data stream contains a large amount of complex data related to the trimming guide assembly. This data comes from various sensors installed on the trimming guide assembly, such as angle sensors, position sensors, and sensors for monitoring wear, vibration, shock, and environmental conditions. The system performs preliminary screening and classification on this data stream, extracting data related to the trimming guide assembly's angle. Further processing and analysis of this angle data, using principal component analysis, converts angle measurements at different time points and under different operating conditions into a series of coordinate points. These coordinate points together form a feature point cloud of the guide assembly's angle, which intuitively demonstrates the changing patterns and distribution of the guide assembly's angle. Similarly, data related to the guide assembly's position is selected from the data stream. After processing and calculation, the position measurements are converted into a feature point cloud, accurately displaying the guide assembly's positional movement and trajectory in space. For guide assembly status feature information, the system processes data from wear sensors, vibration sensors, shock sensors, and environmental monitoring sensors. For wear monitoring, the system analyzes data such as material loss and surface roughness changes acquired by these sensors to assess the extent of wear. Vibration sensor data is used to determine the frequency, amplitude, and pattern of vibrations, thereby understanding the vibration characteristics of the guide assembly. Shock monitoring data helps determine the intensity and timing of shock. Environmental monitoring data, including temperature and humidity, is used to assess the impact of the environment on the guide assembly. This data is combined to form comprehensive status profiles of the trimming guide assembly, including wear, vibration levels, shock exposure, and environmental conditions. This meticulous and comprehensive integration and analysis of the trimming die monitoring data stream generates a point cloud of guide assembly angle features, a point cloud of position features, and status features.

[0060] The monitoring data stream is preprocessed, including removing noise, erroneous data, and outliers. Then, the position data of the upper and lower dies at different moments are extracted according to chronological order and a specific sampling frequency. For the upper die, the position coordinates (x, y, and z coordinates in three-dimensional space) acquired at each sampling moment are combined to form a series of points. These points form a feature point cloud of the upper die position in three-dimensional space, reflecting the trajectory and distribution of the upper die's position changes during operation. Similarly, the position data of the lower die is processed in the same manner to produce a feature point cloud of the lower die position.

[0061] First, an in-depth analysis of the guide component's angular feature point cloud is performed. Mathematical statistical methods, such as calculating the mean, variance, and distribution of the angles, are used to understand the variation and stability of the guide component's angles. Pattern recognition techniques are also employed to identify possible abnormal angular patterns. For the guide component's positional feature point cloud, spatial geometric analysis and topological methods are used to determine the guide component's position range, movement trajectory, and deviation from the ideal position. A three-dimensional model of the position is also constructed to visually demonstrate the positional features. For the upper and lower mold offset point cloud, deviation analysis and error assessment algorithms are used to accurately calculate the magnitude, direction, and distribution of the offset. Furthermore, historical data or standard data is compared to determine whether the offset is within an acceptable range. The angle, position, and offset information obtained from these analyses is input into the upper and lower mold guide fit compensation decision channel within the dual-channel guide compensation system. This channel is built using a neural network, which is trained and optimized using a large amount of training data. This training data contains the guide component's angular features, positional features, upper and lower mold fit offset features, and the corresponding optimal compensation decisions under various conditions. By learning from this data, the neural network automatically extracts the complex patterns and relationships hidden within the data. When new angle feature point clouds, position feature point clouds, and upper and lower mold offset point cloud data are input into the channel, the neural network will quickly perform calculations and reasoning, evaluate and judge the current situation based on the knowledge and patterns it has learned, and output corresponding compensation decision recommendations.

[0062] Similarly, based on the state characteristic information of the guidance component, a compensation decision is obtained using the guidance state abnormality compensation decision channel constructed based on the neural network model. By comprehensively analyzing the various state characteristic information of the guidance component and based on the preset algorithm in the channel, a specific compensation decision for the guidance state abnormality is finally generated.

[0063] Evaluate compensation decisions for upper and lower die guide coordination and abnormal guide status, identifying any correlations and conflicts. Then, balance and coordinate based on production requirements, equipment performance, and cost, prioritizing any conflicts. Next, integrate and optimize compensation measures to form a comprehensive and unified guide compensation feature decision. Finally, review and verify the decision to ensure its feasibility, rationality, and effectiveness.

[0064] In one possible implementation, the fixed compensation analysis module 50 further includes:

[0065] Obtain the expected fixing force of the cutting workpiece and the expected fixing position of the cutting workpiece; perform deviation detection on the cutting workpiece fixing force sensing data according to the expected cutting workpiece fixing force to obtain the workpiece fixing force deviation detection result; perform deviation detection on the cutting workpiece fixing position sensing data according to the expected cutting workpiece fixing position to obtain the workpiece fixing position deviation detection result; perform associated data integration of the trimming fixing component according to the trimming die monitoring data stream to obtain the fixing component monitoring data; activate a pre-built fixing compensation correction model; input the workpiece fixing force deviation detection result, the workpiece fixing position deviation detection result and the fixing component monitoring data into the fixing compensation correction model to obtain the fixing compensation feature decision.

[0066] Specifically, we determine the desired clamping force and position for the workpiece during cutting. This step comprehensively considers multiple factors, including the workpiece's material, shape, size, cutting process requirements, previous practical experience, and theoretical calculations. Through in-depth analysis of these factors, we determine the desired clamping force and position that will ensure cutting quality and accuracy under ideal conditions.

[0067] Deviation detection is performed on the workpiece holding force sensor data based on the desired holding force. The real-time captured holding force sensor data is accurately compared with the pre-set holding force expectation. This comparison clearly identifies whether the sensor data is above or below the desired holding force, as well as the specific value and degree of deviation, thereby obtaining the workpiece holding force deviation detection result.

[0068] Deviation detection is performed on the fixed position sensing data of the workpiece according to the expected fixed position of the workpiece. The fixed position sensing data and the expected fixed position are compared in the same rigorous manner to determine whether there is a position deviation and the specific situation of the deviation, and the workpiece fixed position deviation detection result is obtained.

[0069] Data integration related to the trimming fixture assembly is performed based on the trimming die monitoring data stream. Key information related to the trimming fixture assembly, such as component pressure, temperature, and deformation, is extracted from the extensive monitoring data stream. This data is then filtered, organized, and comprehensively analyzed to obtain fixture monitoring data that comprehensively reflects the trimming fixture assembly's operating status. Throughout the entire process, after completing the collection, processing, and deviation detection of relevant data, and obtaining the workpiece fixture force deviation detection results, workpiece fixture position deviation detection results, and fixture monitoring data, the pre-built fixture compensation correction model is activated. This fixture compensation correction model, developed in previous work, is based on a neural network architecture and consists of numerous interconnected neurons forming a complex hierarchical structure. To construct this neural network model, a large amount of historical data related to workpiece fixtures was collected, including actual and expected values ​​of the fixture force and fixture position for different workpiece types, as well as corresponding compensation correction measures and final results. This data is then used to train the neural network. During training, the neural network continuously adjusts the connection weights between neurons to learn the patterns and regularities in the data. This allows it to accurately predict effective compensation and correction strategies based on newly input fixation force deviation detection results, fixation position deviation detection results, and fixation component monitoring data. Once the model is successfully activated, it is ready to receive subsequent input of various detection results and monitoring data for further processing and analysis to generate the final fixation compensation feature decision.

[0070] The workpiece clamping force deviation detection results, workpiece clamping position deviation detection results, and clamping component monitoring data obtained through preliminary processing and testing are prepared in a specific data format and sequence. This organized data is then input into a pre-built and trained clamping compensation correction model (neural network model), ultimately outputting a clamping compensation feature decision. This decision comprehensively considers multiple factors, including workpiece clamping force, clamping position deviation, and the status of the clamping components. The output clamping compensation feature decision includes detailed instructions such as specific adjustment parameters, such as adjusting the clamping force, correcting the offset of the clamping position, and replacing or repairing the clamping components. These decisions are designed to correct current clamping deviations, improve the workpiece clamping effect, and ensure the accuracy and quality of the cutting process. Example 2

[0071] Based on the same inventive concept as the dynamic compensation control system of a high-precision trimming die in the above embodiment, Figure 2 As shown, the present application provides a dynamic compensation control method for a high-precision trimming die. The method in the embodiment of the present application and the system embodiment are based on the same inventive concept. The method includes:

[0072] Step S100: monitoring the trimming die in real time to obtain a trimming die monitoring data stream, wherein the trimming die includes an upper trimming die, a lower trimming die, a trimming guide assembly, and a trimming fixing assembly.

[0073] Step S200: Based on the upper die health expectation prediction channel and in combination with the trimming die monitoring data stream, a health expectation deviation compensation analysis is performed on the trimming upper die to generate an upper die compensation feature decision.

[0074] Step S300: performing compensation analysis on the trimming die based on the multi-feature die compensation factor and the trimming die monitoring data stream to generate a die compensation feature decision.

[0075] Step S400: Based on the dual channels of guide compensation decision, the trimming guide component is subjected to compensation analysis in combination with the trimming die monitoring data stream to generate a guide compensation feature decision.

[0076] Step S500: Based on the cutting workpiece fixing force sensing data and the cutting workpiece fixing position sensing data, combined with the trimming die monitoring data stream, compensation control analysis is performed on the trimming fixing component to generate a fixing compensation feature decision.

[0077] Step S600: performing compensation control on the trimming die based on the upper die compensation feature decision, the lower die compensation feature decision, the guide compensation feature decision and the fixed compensation feature decision.

[0078] Furthermore, step S200 further includes:

[0079] According to the trimming die monitoring data stream, the associated data of the trimming die are integrated to construct an upper die monitoring state matrix; the cutting workpiece feature data and the workpiece cutting upper die control decision corresponding to the trimming die are retrieved; based on the upper die basic data set of the trimming die, combined with the cutting workpiece feature data and the workpiece cutting upper die control decision, the health expectation state of the trimming die is predicted according to the upper die health expectation prediction channel to obtain an upper die health expectation matrix; an upper die health expectation deviation compensation model is constructed, and the upper die health expectation deviation compensation model is embedded in the upper die compensation analysis module; based on the upper die monitoring state matrix and the upper die health expectation matrix, the upper die compensation feature decision is obtained according to the upper die health expectation deviation compensation model.

[0080] Furthermore, step S200 further includes:

[0081] Deviation identification is performed based on the upper mold monitoring state matrix and the upper mold health expectation matrix to obtain the upper mold monitoring expected deviation matrix; cutting impact evaluation is performed based on the upper mold monitoring expected deviation matrix to generate the upper mold deviation cutting impact degree; it is determined whether the upper mold deviation cutting impact degree is greater than / equal to the predetermined upper mold deviation cutting impact degree; if the upper mold deviation cutting impact degree is greater than / equal to the predetermined upper mold deviation cutting impact degree, the upper mold health expectation deviation compensation model is activated; the upper mold monitoring expected deviation matrix is ​​input into the upper mold health expectation deviation compensation model to generate the upper mold compensation feature decision.

[0082] Furthermore, step S300 further includes:

[0083] The multi-feature lower die compensation factors include lower die deformation, uneven force on the lower die, residual lower die material, lower die thermal impact and lower die displacement; the associated data of the trimming die is integrated according to the trimming die monitoring data stream to generate a lower die monitoring block; based on the multi-feature lower die compensation factors, a lower die compensation decision is made according to the lower die monitoring block to generate a multi-feature lower die compensation decision; the multi-feature lower die compensation decisions are integrated to obtain the lower die compensation feature decision.

[0084] Furthermore, step S300 further includes:

[0085] The lower die monitoring block is feature identified according to the multi-feature lower die compensation factor to obtain lower die deformation monitoring data; a lower die deformation cutting impact evaluation model and a lower die deformation compensation decision model are constructed according to the multi-feature lower die compensation factor; based on the lower die deformation monitoring data and according to the lower die deformation cutting impact evaluation model, a lower die deformation cutting impact evaluation coefficient is obtained; it is determined whether the lower die deformation cutting impact evaluation coefficient is greater than / equal to a lower die deformation cutting impact evaluation threshold; if the lower die deformation cutting impact evaluation coefficient is greater than / equal to the lower die deformation cutting impact evaluation threshold, the lower die deformation monitoring data is input into the lower die deformation compensation decision model to obtain a lower die deformation compensation decision; the lower die deformation compensation decision is added to the multi-feature lower die compensation decision, and the lower die compensation decision is continued according to the multi-feature lower die compensation factor and the lower die monitoring block to obtain the multi-feature lower die compensation decision.

[0086] Furthermore, step S400 further includes:

[0087] According to the trimming die monitoring data stream, the associated data of the trimming guide component is integrated to generate the guide component angle feature point cloud, the guide component position feature point cloud and the guide component state feature information; according to the trimming die monitoring data stream, the upper die position feature point cloud and the lower die position feature point cloud are obtained; according to the upper die position feature point cloud and the lower die position feature point cloud, the matching offset detection is performed to generate the upper and lower die matching offset point cloud; based on the guide component angle feature point cloud, the guide component position feature point cloud and the upper and lower die matching offset point cloud, according to the upper and lower die guide matching compensation decision channel of the guide compensation dual channel, the upper and lower die guide matching compensation decision is obtained; based on the guide component state feature information, according to the guide state abnormality compensation decision channel of the guide compensation dual channel, the guide state abnormality compensation decision is obtained; the upper and lower die guide matching compensation decision and the guide state abnormality compensation decision are integrated to obtain the guide compensation feature decision.

[0088] Furthermore, step S500 further includes:

[0089] Obtain the expected fixing force of the cutting workpiece and the expected fixing position of the cutting workpiece; perform deviation detection on the cutting workpiece fixing force sensing data according to the expected cutting workpiece fixing force to obtain the workpiece fixing force deviation detection result; perform deviation detection on the cutting workpiece fixing position sensing data according to the expected cutting workpiece fixing position to obtain the workpiece fixing position deviation detection result; perform associated data integration of the trimming fixing component according to the trimming die monitoring data stream to obtain the fixing component monitoring data; activate a pre-built fixing compensation correction model; input the workpiece fixing force deviation detection result, the workpiece fixing position deviation detection result and the fixing component monitoring data into the fixing compensation correction model to obtain the fixing compensation feature decision.

[0090] It should be noted that the order in which the embodiments of the present application are presented is for illustrative purposes only and does not necessarily represent the superiority or inferiority of the embodiments. Furthermore, the foregoing descriptions of specific embodiments of this specification are provided. Furthermore, the processes depicted in the accompanying drawings do not necessarily require the specific order or sequential sequence shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0091] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application should be included in the scope of protection of the present application.

[0092] This specification and drawings are merely illustrative of the present application and are intended to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Obviously, those skilled in the art may make various modifications and variations to this application without departing from the scope of this application. Thus, this application is intended to include such modifications and variations as fall within the scope of this application and its equivalents.

Claims

1. A dynamic compensation control system for a high-precision trimming die, characterized in that: The system comprises: A trimming die monitoring module, which is used to monitor the trimming die in real time and obtain a trimming die monitoring data stream, wherein the trimming die includes an upper trimming die, a lower trimming die, a trimming guide assembly, and a trimming fixing assembly; an upper die compensation analysis module, wherein the upper die compensation analysis module performs health expectation deviation compensation analysis on the trimming die based on the upper die health expectation prediction channel and in combination with the trimming die monitoring data stream, and generates an upper die compensation feature decision; a lower die compensation analysis module, which performs compensation analysis on the trimming die based on a multi-feature lower die compensation factor and the trimming die monitoring data stream, and generates a lower die compensation feature decision; A guide compensation analysis module, which performs compensation analysis on the trimming guide assembly based on a dual-channel guide compensation decision and combines the trimming die monitoring data stream to generate a guide compensation feature decision; A fixed compensation analysis module, which performs compensation control analysis on the trimming fixing component based on the cutting workpiece fixing force sensing data and the cutting workpiece fixing position sensing data in combination with the trimming die monitoring data stream to generate a fixed compensation feature decision; a trimming compensation execution module, the trimming compensation execution module being configured to perform compensation control on the trimming die based on the upper die compensation feature decision, the lower die compensation feature decision, the guide compensation feature decision, and the fixed compensation feature decision; The guide compensation analysis module performs compensation analysis on the trimming guide assembly based on the guide compensation decision dual channel and in combination with the trimming die monitoring data stream to generate a guide compensation feature decision, including: Performing correlation data integration of the trimming guide component according to the trimming die monitoring data stream to generate a guide component angle feature point cloud, a guide component position feature point cloud, and guide component state feature information; Obtaining an upper die position feature point cloud and a lower die position feature point cloud according to the trimming die monitoring data stream; Performing fit offset detection based on the upper die position feature point cloud and the lower die position feature point cloud to generate upper and lower die fit offset point clouds; The dual-channel guide compensation decision includes an upper and lower mold guide matching compensation decision channel and a guide state abnormality compensation decision channel; Based on the guide component angle feature point cloud, the guide component position feature point cloud and the upper and lower mold fit offset point cloud, and according to the upper and lower mold guide fit compensation decision channels in the guide compensation decision dual channels, an upper and lower mold guide fit compensation decision is obtained; Based on the state characteristic information of the guide component, a guide state abnormality compensation decision is obtained according to the guide state abnormality compensation decision channel in the guide compensation decision dual channel; The upper and lower mold guide matching compensation decision and the guide state abnormality compensation decision are integrated to obtain the guide compensation feature decision.

2. The system according to claim 1, wherein The upper die compensation analysis module performs health expectation deviation compensation analysis on the trimming upper die based on the upper die health expectation prediction channel and in combination with the trimming die monitoring data stream, and generates an upper die compensation feature decision, including: Integrate the associated data of the trimming upper die according to the trimming die monitoring data stream to construct an upper die monitoring state matrix; Retrieving the cutting workpiece feature data corresponding to the trimming die and the workpiece cutting upper die control decision; Based on the upper die basic data set of the trimming upper die, combined with the cutting workpiece feature data and the workpiece cutting upper die control decision, the health expectation state of the trimming upper die is predicted according to the upper die health expectation prediction channel to obtain an upper die health expectation matrix; Constructing an upper mold health expectation deviation compensation model, and embedding the upper mold health expectation deviation compensation model into the upper mold compensation analysis module; Based on the upper mold monitoring state matrix and the upper mold health expectation matrix, and according to the upper mold health expectation deviation compensation model, the upper mold compensation feature decision is obtained.

3. The system according to claim 2, wherein: Based on the upper mold monitoring state matrix and the upper mold health expectation matrix, and according to the upper mold health expectation deviation compensation model, obtaining the upper mold compensation feature decision includes: Perform deviation identification based on the upper mold monitoring state matrix and the upper mold health expectation matrix to obtain an upper mold monitoring expected deviation matrix; Perform cutting impact evaluation based on the upper die monitoring expected deviation matrix to generate an upper die deviation cutting impact degree; Determining whether the upper die deviation cutting influence degree is greater than / equal to a predetermined upper die deviation cutting influence degree; If the upper die deviation cutting influence is greater than / equal to the predetermined upper die deviation cutting influence, activating the upper die health expectation deviation compensation model; The upper mold monitoring expected deviation matrix is ​​input into the upper mold health expected deviation compensation model to generate the upper mold compensation feature decision.

4. The system according to claim 1, wherein: The lower die compensation analysis module performs compensation analysis on the trimming lower die based on the multi-feature lower die compensation factor and the trimming die monitoring data stream to generate a lower die compensation feature decision, including: The multi-feature lower die compensation factors include lower die deformation, uneven force on the lower die, residual lower die material, lower die thermal impact and lower die displacement; Performing data integration of the trimming lower die according to the trimming die monitoring data stream to generate a lower die monitoring block; Based on the multi-feature lower die compensation factor, a lower die compensation decision is made according to the lower die monitoring block to generate a multi-feature lower die compensation decision; The multi-feature lower die compensation decisions are integrated to obtain the lower die compensation feature decision.

5. The system according to claim 4, wherein: Based on the multi-feature lower die compensation factor, a lower die compensation decision is made according to the lower die monitoring block to generate a multi-feature lower die compensation decision, including: Performing feature recognition on the lower die monitoring block according to the multi-feature lower die compensation factor to obtain lower die deformation monitoring data; According to the multi-feature lower die compensation factors, a lower die deformation cutting impact evaluation model and a lower die deformation compensation decision model are constructed; Based on the lower die deformation monitoring data and according to the lower die deformation cutting impact evaluation model, a lower die deformation cutting impact evaluation coefficient is obtained; Determining whether the lower die deformation cutting impact evaluation coefficient is greater than / equal to a lower die deformation cutting impact evaluation threshold; If the lower die deformation cutting impact evaluation coefficient is greater than / equal to the lower die deformation cutting impact evaluation threshold, inputting the lower die deformation monitoring data into the lower die deformation compensation decision model to obtain a lower die deformation compensation decision; The lower die deformation compensation decision is added to the multi-feature lower die compensation decision, and the lower die compensation decision is continued according to the multi-feature lower die compensation factor and the lower die monitoring block to obtain the multi-feature lower die compensation decision.

6. The system according to claim 1, wherein: The fixing compensation analysis module performs compensation control analysis on the trimming fixing component based on the cutting workpiece fixing force sensing data and the cutting workpiece fixing position sensing data in combination with the trimming die monitoring data stream, and generates a fixing compensation feature decision, including: Obtaining an expected fixing force and a expected fixing position of a cutting workpiece; performing deviation detection on the cutting workpiece fixing force sensing data according to the cutting workpiece fixing force expectation to obtain a workpiece fixing force deviation detection result; performing deviation detection on the cutting workpiece fixed position sensing data according to the cutting workpiece fixed position expectation to obtain a workpiece fixed position deviation detection result; Performing data integration related to the trimming fixture components according to the trimming die monitoring data stream to obtain fixture component monitoring data; Activate pre-built fixed compensation correction models; The workpiece fixing force deviation detection result, the workpiece fixing position deviation detection result and the fixing component monitoring data are input into the fixing compensation correction model to obtain the fixing compensation feature decision.

7. A dynamic compensation control method for a high-precision trimming die, characterized in that: The method is applied to the system according to any one of claims 1 to 6, and the method comprises: Real-time monitoring of the trimming die to obtain a trimming die monitoring data stream, wherein the trimming die includes an upper trimming die, a lower trimming die, a trimming guide assembly, and a trimming fixing assembly; Based on the upper die health expectation prediction channel, combined with the trimming die monitoring data stream, the health expectation deviation compensation analysis of the trimming upper die is performed to generate an upper die compensation feature decision; Performing compensation analysis on the trimming die based on the multi-feature die compensation factor and the trimming die monitoring data stream to generate a die compensation feature decision; Based on the dual channels of guide compensation decision, the trimming guide component is subjected to compensation analysis in combination with the trimming die monitoring data stream to generate a guide compensation feature decision; Based on the cutting workpiece fixing force sensing data and the cutting workpiece fixing position sensing data, combined with the trimming die monitoring data stream, the compensation control analysis of the trimming fixing component is performed to generate a fixing compensation feature decision; Performing compensation control on the trimming die based on the upper die compensation feature decision, the lower die compensation feature decision, the guide compensation feature decision, and the fixed compensation feature decision; The guide compensation analysis module performs compensation analysis on the trimming guide assembly based on the guide compensation decision dual channel and in combination with the trimming die monitoring data stream to generate a guide compensation feature decision, including: Performing correlation data integration of the trimming guide component according to the trimming die monitoring data stream to generate a guide component angle feature point cloud, a guide component position feature point cloud, and guide component state feature information; Obtaining an upper die position feature point cloud and a lower die position feature point cloud according to the trimming die monitoring data stream; Performing fit offset detection based on the upper die position feature point cloud and the lower die position feature point cloud to generate upper and lower die fit offset point clouds; The dual-channel guide compensation decision includes an upper and lower mold guide matching compensation decision channel and a guide state abnormality compensation decision channel; Based on the guide component angle feature point cloud, the guide component position feature point cloud and the upper and lower mold fit offset point cloud, and according to the upper and lower mold guide fit compensation decision channels in the guide compensation decision dual channels, an upper and lower mold guide fit compensation decision is obtained; Based on the state characteristic information of the guide component, a guide state abnormality compensation decision is obtained according to the guide state abnormality compensation decision channel in the guide compensation decision dual channel; The upper and lower mold guide matching compensation decision and the guide state abnormality compensation decision are integrated to obtain the guide compensation feature decision.

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