A digital-based forging automatic control method and system

Abstract

The present invention discloses a digital-based forging automatic control method and system, which relates to the field of automated control technology, including: collecting data on a forging blank, performing forging fitting, and establishing a fitting control result mapped with the forging control; heating the forging blank, positioning the heated forging blank to a forging device, and controlling the forging device to perform a forging process using control parameters of the forging fitting; establishing a unidirectional forging chain based on the fitting control result; activating a pressure sensor and a temperature sensor to perform data monitoring of the forging process and establish a monitoring data set; inputting the monitoring data set and the unidirectional forging chain into a following fitting network to establish a following feedback result; and performing automatic forging control based on the following feedback result. The present invention solves the technical problems of insufficient forging precision and instability in the forging process in the prior art, realizes precise control and optimization of the forging process, and achieves the technical effect of improving forging operation stability and product quality.

Description

Technical Field

[0001] The present invention relates to the field of automatic control technology, and in particular to a digital-based forging automatic control method and system. Background Art

[0002] As an important plastic forming process, forging is widely used in aviation, automobiles, energy and other fields. However, the traditional forging process relies on empirical parameters and manual adjustments, and suffers from problems such as insufficient forging precision and unstable process. Traditional forging equipment usually adopts a fixed process parameter setting method and lacks real-time analysis of the material properties, geometric shape and actual working conditions of the forging blank, resulting in the forging effect being difficult to meet high-precision requirements. Since the process involves multiple variables (such as pressure, temperature, etc.) and their dynamic changes, the existing control methods are mostly open-loop control or closed-loop control with hysteresis response, lacking the ability to provide real-time feedback and adjustment for complex working conditions, resulting in poor operational stability. Existing processes generally lack systematic management and intelligent analysis of forging data, making it impossible to achieve accurate modeling and dynamic optimization of the forging process, affecting the consistency and quality of forged products. Summary of the Invention

[0003] The present application provides a digital-based forging automatic control method and system for solving the technical problems of insufficient forging accuracy and instability in the forging process in the prior art.

[0004] In view of the above problems, the present application provides a digital-based forging automatic control method and system.

[0005] The first aspect of the present application provides a digital-based forging automatic control method, the method comprising:

[0006] After data collection on the forging blank, forging fitting is performed to establish a fitting control result mapped with the forging control; after the forging blank is heated in a heating furnace, the heated forging blank is positioned on the forging equipment, and the forging equipment is controlled by the control parameters of the forging fitting to perform forging processing; according to the fitting control result, a same-direction forging chain is established, and the same-direction forging chain is a fitting chain for forging in the same direction; the pressure sensor and the temperature sensor are activated to perform data monitoring of the forging process and establish a monitoring data set; the monitoring data set and the same-direction forging chain are input into the following fitting network to establish a following feedback result; and automatic forging control is performed through the following feedback result.

[0007] The second aspect of the present application provides a digital forging automatic control system, the system comprising:

[0008] a forging fitting module, wherein the forging fitting module collects data on the forging blank, performs forging fitting, and establishes a fitting control result mapped with the forging control; a forging processing module, wherein the forging processing module positions the heated forging blank to the forging equipment after heating the forging blank in a heating furnace, and controls the forging equipment to perform forging processing through the control parameters of the forging fitting; a same-direction forging chain establishment module, wherein the same-direction forging chain establishment module establishes a same-direction forging chain according to the fitting control result, and the same-direction forging chain is a fitting chain for forging in the same direction; a data monitoring module, wherein the data monitoring module activates the pressure sensor and the temperature sensor to perform data monitoring of the forging processing process and establishes a monitoring data set; a following feedback result establishment module, wherein the following feedback result establishment module inputs the monitoring data set and the same-direction forging chain into the following fitting network to establish a following feedback result; and a forging automatic control module, wherein the forging automatic control module performs forging automatic control through the following feedback result.

[0009] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0010] After collecting data on the forging blank, the present application performs forging fitting and establishes a fitting control result mapped with the forging control; after heating the forging blank in a heating furnace, the heated forging blank is positioned on the forging equipment, and the forging equipment is controlled by the control parameters of the forging fitting to perform forging processing; according to the fitting control result, a same-direction forging chain is established, and the same-direction forging chain is a fitting chain for forging in the same direction; the pressure sensor and the temperature sensor are activated to perform data monitoring of the forging process and establish a monitoring data set; the monitoring data set and the same-direction forging chain are input into a following fitting network to establish a following feedback result; and automatic forging control is performed through the following feedback result. The present invention solves the technical problems of insufficient forging accuracy and instability in the forging process in the prior art. Through data acquisition, forging fitting and control mapping, fitting control results are generated, and the forging process is implemented in combination with the positioning and control parameters of the heated billet. By establishing a same-direction forging chain and activating sensor monitoring data, a monitoring data set is generated, which is input into a follow-up fitting network, feedback results are obtained and automatic control is performed, thereby realizing precise control and optimization of the forging process and achieving the technical effect of improving the stability of the forging operation and the quality of the product. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] 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.

[0012] Figure 1 A schematic flow chart of a digital-based forging automatic control method provided in an embodiment of the present application.

[0013] Figure 2 A schematic structural diagram of a digital forging automatic control system provided in an embodiment of the present application.

[0014] Explanation of the reference numerals: forging fitting module 11 , forging processing module 12 , same-direction forging chain establishing module 13 , data monitoring module 14 , follow-up feedback result establishing module 15 , forging automatic control module 16 . DETAILED DESCRIPTION

[0015] The present application provides a digital-based forging automatic control method and system to solve the technical problems of insufficient forging accuracy and instability in the forging process in the prior art. Through data acquisition, forging fitting and control mapping, fitting control results are generated, and the forging process is implemented in combination with the positioning and control parameters of the heated billet. By establishing a same-direction forging chain and activating sensor monitoring data, a monitoring data set is generated, input into a follow-up fitting network, feedback results are obtained and automatic control is performed, thereby realizing precise control and optimization of the forging process and achieving the technical effect of improving the stability of the forging operation and the quality of the product.

[0016] 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 some 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 creative efforts are within the scope of protection of this application.

[0017] It should be noted that any variations of the terms "include" and "have" are intended to cover non-exclusive inclusions. For example, a process, method, system, product or server that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or modules that are not clearly listed or are inherent to these processes, methods, products or devices.

[0018] Example 1, as Figure 1 As shown, the present application provides a digital-based forging automatic control method, the method comprising:

[0019] Step S100: After collecting data on the forging blank, perform forging fitting to establish a fitting control result corresponding to the forging control map.

[0020] In the examples of this application, data collection is first performed on the forging billet to obtain its material information and geometric features. Material information is obtained through chemical composition analysis and material property testing. These tests involve measuring the chemical composition of the billet using a spectrometer (such as an X-ray fluorescence spectrometer) or measuring its mechanical properties through methods such as hardness and tensile testing. Geometric features are acquired through methods such as 3D scanning, laser scanning, or computed tomography (CT). These technologies can accurately record the billet's external dimensions and surface profile.

[0021] Next, forging fitting is performed. Specifically, the collected billet data is input into a digital model space. Within this digital space, a computer simulates the forging process, creating a virtual forging environment. This environment includes simulated forging machine parameters (such as pressure, temperature, speed, etc.) as well as the deformation of the billet during the forging process. All forging process factors (such as temperature rise, pressure application, and deformation) are simulated in this virtual model, using numerical methods such as finite element analysis (FEA) to simulate the physical and mechanical behavior of the forging process.

[0022] After the simulation in digital space, parameter optimization is performed to select the optimal forging control parameters. Specifically, by using optimization algorithms, such as genetic algorithms, multiple forging parameters are searched and adjusted to find the control parameters that produce the best forging results. These parameters include applied pressure, forging speed, heating temperature, etc., aiming to maximize the deformation effect of the blank while avoiding excessive deformation, cracks, or other defects.

[0023] Finally, the control parameters and corresponding forging results obtained through the simulation and optimization process are the fitting control results.

[0024] Step S200: After heating the forging blank in a heating furnace, the heated forging blank is positioned on a forging device, and the forging device is controlled by forging fitting control parameters to perform forging processing.

[0025] In an embodiment of the present application, the forging blank is transferred to a heating furnace for heating until a preset temperature is reached, and then the heated forging blank is positioned to a forging device, and the forging device is controlled by the control parameters determined by the aforementioned forging fitting to perform forging processing.

[0026] Step S300: establishing a same-direction forging chain according to the fitting control result, wherein the same-direction forging chain is a fitting chain of same-direction forging.

[0027] In an embodiment of the present application, according to the fitting control result, the process of establishing a same-direction forging chain is that during the forging process, for each processing surface of the blank (such as surface A, surface B, surface C, and surface D), according to the fitting control result, the forging parameters and order of each surface in a certain direction are determined in sequence to form a continuous forging chain. The same-direction forging chain refers to the process of performing forging operations on the blank in the same direction. For example, when the blank is forged in the A direction, the same-direction forging chain will gradually perform the forging action according to the control parameters in the fitting control result. For other directions of the blank, such as surface B, surface C, and surface D, respective same-direction forging chains are also established.

[0028] Step S400: activating the pressure sensor and the temperature sensor to perform data monitoring of the forging process and establishing a monitoring data set.

[0029] In the present embodiment, a pressure sensor and a temperature sensor are activated to monitor the applied pressure and the temperature changes of the blank in real time during the forging process. The pressure sensor collects mechanical data during each forging process, recording the pressure, and the temperature sensor detects the surface temperature of the blank in real time. Data detection by the pressure and temperature sensors generates a monitoring data set. The monitoring data set includes the actual pressure and temperature of the blank during the forging process.

[0030] Step S500: inputting the monitoring data set and the same-direction forging chain into a following fitting network to establish a following feedback result.

[0031] Furthermore, in the method provided in the embodiment of the application, the step of inputting the monitoring data set and the unidirectional forging chain into a following fitting network to establish a following feedback result further includes:

[0032] Acquire the current forging step, and locate the chain node of the same-direction forging chain based on the current forging step; call the fitting control result of the chain node, use the fitting layer of the following fitting network to fit the forging effect of the monitoring data set, and establish the node fitting result; perform deviation analysis based on the fitting control result and the node fitting result through the analysis layer of the following fitting network, and establish the deviation analysis result; when the preview learning layer receives the deviation analysis result, perform preview tracking analysis, and perform the next round of node positioning according to the chain node, and establish the tracking feedback result according to the next round of node positioning result and the preview tracking analysis result.

[0033] In an embodiment of the present application, the current forging step is first obtained, the processing stage and direction of the blank are clarified, and the chain nodes of the same-direction forging chain are located based on the forging step. Specifically, the control system of the forging equipment reads the currently executed forging step, and according to the current step, the blank surface currently being forged is further identified. The same-direction forging chain corresponding to the surface is determined based on the acquired blank surface information. The same-direction forging chain is a set of forging control parameters established for the blank surface, in which each chain node corresponds to a specific control point in the forging process, such as different pressures.

[0034] Next, the fitting control result of the chain node is called to obtain the fitting control result of the corresponding chain node. The forging effect of the monitoring data set is then fitted using the fitting layer following the fitting network. Specifically, in the fitting layer, forging simulation is performed on the real-time monitoring data using digital simulation methods such as finite element analysis. Based on the monitoring data and the processing context of the chain node (such as forging direction and operation sequence), the fitting layer simulates the forging effect and quantifies the impact of the current operation on processing results such as blank deformation and temperature distribution. Finally, the fitting layer outputs the node fitting result, which reflects the actual effect of the current forging operation.

[0035] Then, the analysis layer following the fitting network performs a deviation analysis based on the fitting control results and node fitting results. The analysis layer uses the absolute difference calculation method to compare the node fitting results with the fitting control results of the chain nodes item by item, and calculates the difference between the actual and target parameters. For example, the deviation between the actual pressure and the target pressure, as well as the deviation range between the actual temperature and the target temperature, are analyzed to determine the deviation of key parameters in the forging process. Finally, the analysis layer generates the deviation analysis results. The deviation analysis results clearly indicate the specific differences between the actual operation and the target parameters in the current forging step. For example, it indicates that the current pressure is too low, resulting in insufficient deformation of the billet, or that the temperature is too high, which may cause local overheating.

[0036] The deviation analysis results are input into the preview learning layer to perform preview tracking analysis. Specifically, after receiving the deviation analysis results, the preview learning layer performs preview tracking analysis on the fitted control results of the next-wheel chain node by establishing compensation constraints, a lookback window dataset, and control deviation predictions. Combining the next-wheel node positioning results and prediction analysis, a tracking feedback result is generated, including margin compensation calculation and stability analysis. If the compensation stability evaluation result passes, the preview tracking analysis result is updated to generate the final feedback parameters; if not, a margin compensation ladder is established and the preview tracking analysis result is adjusted based on the compensation ladder distribution result, ultimately generating an optimized tracking feedback result.

[0037] Furthermore, in the method provided in the embodiment of the application, after the preview learning layer receives the deviation analysis result, performing preview tracking analysis further includes:

[0038] A compensation constraint is established based on the deviation analysis result; a backtracking window is established, and control backtracking of the current direction is performed through the backtracking window to establish a backtracking window data set; a control deviation prediction of the next round of node positioning results is performed through the backtracking window data set to establish a control deviation prediction result; a fitting control result of the next round of node positioning results is used as a preview point, and a preview tracking analysis is performed according to the compensation constraint and the control deviation prediction result.

[0039] In the present embodiment, compensation constraints are first established based on the deviation analysis results. By analyzing the deviation analysis results, the deviation ranges and change trends of key parameters (such as pressure and temperature) are extracted. Combined with historical operating data, statistical analysis methods are used to set upper and lower compensation limits. Specifically, based on the equipment's safe operating range and historical deviation ranges, adjustment limits for each parameter are determined. For example, the maximum pressure increase is 20%, and the minimum temperature decrease is 5°C. This creates clear compensation constraints, limiting the magnitude and direction of subsequent parameter adjustments and ensuring both effective and safe adjustments.

[0040] Next, a lookback window is established to perform retrospective analysis of historical control data for the current direction. Based on a sliding window algorithm, the lookback window extracts control parameter data from the most recent (e.g., 10) operation steps, including pressure, temperature, speed, and deformation, to form a lookback window dataset. This dataset reflects parameter change trends for the current operation direction, such as the cumulative degree of pressure deficiency over several consecutive cycles or the impact of temperature fluctuations on machining results.

[0041] Based on the lookback window dataset, control deviations are predicted for the next node. Time series forecasting methods (such as the ARIMA model) analyze historical parameter variations to generate control deviation forecasts. For example, the risk of pressure remaining below 10% or temperature exceeding the target by 5°C during the next operation can be predicted. These forecasts provide forward-looking data support, clarifying key parameters that may require adjustment and specific compensation strategies for the next node operation.

[0042] The fitted control results of the next node's positioning results are then used as preview points. Preview tracking analysis is performed in conjunction with compensation constraints and control deviation predictions. During this process, an optimization algorithm (such as gradient descent or genetic algorithm) comprehensively analyzes the compensation constraints and control deviation predictions, dynamically adjusting the operating parameters of the next node. For example, if the prediction shows a pressure deviation of 10%, and the compensation constraint allows a maximum adjustment of 15%, the optimization algorithm will allocate a reasonable adjustment range based on the preview points and ultimately generate parameter optimization recommendations. Preview tracking analysis dynamically generates adjustments that meet the machining objectives by comprehensively considering the prediction results and compensation constraints, ultimately resulting in the preview tracking analysis results.

[0043] Furthermore, in the method provided in the embodiment of the application, the establishing of the tracking feedback result based on the next round of node positioning result and the preview tracking analysis result further includes:

[0044] Establishing margin compensation according to the deviation analysis result; performing compensation stability analysis on the margin compensation to establish a stability evaluation result; and establishing a tracking feedback result after updating the preview tracking analysis result through the margin compensation if the stability evaluation result is a passing result.

[0045] In an embodiment of the present application, in order to effectively compensate for the deviation analysis results during the forging operation, margin compensation is first established. The compensation value is calculated based on the deviation value and the safety adjustment range of the equipment through a linear proportional calculation method. For example, if the pressure deviation is -10% and the set safety proportional factor is 0.8, the pressure margin compensation is -8%; for a temperature deviation of +5°C, the safety adjustment range allowed by the equipment is 0.6 times, and the temperature margin compensation is +3°C. This method ensures that the compensation value is proportional to the deviation while remaining within the safety range, thereby completing the establishment of margin compensation.

[0046] Next, a compensation stability analysis is performed on the established margin compensation. Using a historical data matching method, the currently calculated margin compensation value is compared with the device's historical operating data to verify whether the current compensation value is within the historical compensation range and whether stable operation can be achieved under similar conditions. For example, by querying the device's historical records, it is determined whether the device has experienced oscillation under conditions similar to a pressure compensation of -8%. At the same time, a comparison is made to determine whether a historical temperature compensation of +3°C has caused overheating or uneven processing. If the calculated compensation value is consistent with the stable parameter range in historical operation, a "pass" stability evaluation result is generated, indicating that the current margin compensation can be safely executed.

[0047] Finally, a tracking feedback result is established based on the stability evaluation results. When the stability evaluation result is "passed," the parameter superposition method is used to combine the margin compensation value with the preview tracking analysis results to generate updated operating parameters. For example, if the preview tracking analysis results indicate that the pressure needs to be increased by 10%, and the margin compensation is -8%, the final pressure adjustment value is +2%. Similarly, if the preview tracking analysis results indicate that the temperature needs to be reduced by 5°C, and the margin compensation is +3°C, the final adjustment value is -2°C. After superimposing all parameters, a tracking feedback result is generated, clarifying the adjustment value of each parameter. These results are transmitted in real time to the forging equipment's control system to guide the next operation.

[0048] Furthermore, the method provided in the application embodiment also includes:

[0049] If the stability evaluation result is a failure result, a margin compensation ladder is established according to the stability evaluation result; a compensation ladder distribution of the margin compensation is performed using the margin compensation ladder; and after the preview tracking analysis result is updated according to the compensation ladder distribution result, a tracking feedback result is established.

[0050] In the embodiment of the present application, when the stability evaluation result is "failed", in order to avoid damage to the billet and equipment caused by excessive parameter adjustment, a margin compensation ladder is established according to the stability evaluation result, and the compensation adjustment is gradually completed through step-by-step implementation. First, the large compensation value that cannot be implemented in a single time is decomposed into multiple small compensations using the step-by-step linear decomposition method to form a compensation ladder. For example, if the pressure needs to be increased by 20%, but the single safe adjustment range of the equipment is 10%, and considering the possible local deformation of the billet under high pressure, the compensation is decomposed into two steps, the first step increases by 10%, and the second step increases the remaining 10%. Similarly, if the temperature needs to be reduced by 5°C, but a one-time cooling may cause the billet surface to cool too quickly and cause microcracks, it will be divided into two steps, the first step reduces by 3°C, and the second step reduces by 2°C. This decomposition not only complies with the safe operation limits of the equipment, but also ensures the protection of the billet during the adjustment process.

[0051] After establishing the compensation steps, they are distributed and implemented using a priority distribution algorithm. Priority is given to correcting deviations in the current step, and the remaining compensation is allocated to subsequent steps. For example, the first step of compensation (such as a 10% pressure increase) is implemented in the current step to quickly resolve the main deviation; the second step of compensation (such as a 10% pressure increase) is allocated to the next step for implementation, gradually completing the adjustment. For temperature adjustment, the first step of a 3°C temperature reduction is implemented slowly in the current step to avoid sudden cooling damage to the billet surface, while the second step of a 2°C temperature reduction is allocated to the next step for completion. This distribution method combines the operating status of the equipment, the processing requirements of the billet, and the scope of the deviation to ensure safe and effective compensation distribution.

[0052] After the compensation step distribution is completed, the preview tracking analysis results are updated based on the compensation distribution results. Through the parameter optimization superposition method, the distribution value of the compensation step is combined with the adjustment suggestions in the preview tracking analysis results to dynamically generate new operation adjustment parameters. For example, if the preview tracking analysis recommends a 5% increase in the current pressure, and the compensation step requires a 10% increase in compensation in the first step, the final adjustment value is determined to be +10%, taking into account the billet protection and equipment limitations, and the first step compensation is completed first. For temperature adjustment, if the preview tracking analysis recommends a current reduction of 2°C, and the first step of the compensation step requires a reduction of 3°C, the temperature is ultimately reduced by 3°C based on the adjustment limit. This parameter update ensures the coordination of the preview analysis and compensation requirements while avoiding excessive one-time adjustments.

[0053] Finally, combined with the updated parameters, a tracking feedback result is generated. This tracking feedback result clarifies the adjustment plan for the current and subsequent steps, for example, "Increase pressure by 10% and reduce temperature by 3°C in the current step; increase pressure by 10% and reduce temperature by 2°C in the next step." This feedback result is transmitted to the forging equipment control system via a real-time parameter update mechanism to guide actual operation. By establishing a compensation ladder, implementing it in a distributed manner, and then updating the preview tracking analysis results, step-by-step compensation adjustment is achieved, ensuring dynamic stability of the process while avoiding potential damage to the equipment and blank.

[0054] Step S600: performing automatic forging control based on the following feedback result.

[0055] In the embodiments of this application, automatic forging control based on feedback refers to real-time adjustments to equipment parameters (such as pressure, temperature, and speed) during the forging operation based on the feedback. Specifically, based on the feedback, for example, a 10% pressure increase or a 3°C temperature decrease, the operating parameters of the forging equipment are adjusted in real time through a closed-loop control mechanism to ensure that these parameters reach their target values. Throughout the entire process, the equipment continuously monitors the operating status and makes fine adjustments based on the feedback to ensure precise execution, thereby achieving automatic control of the forging process.

[0056] Furthermore, the method provided in the application embodiment also includes:

[0057] The production control data of the forging equipment is read to establish a production control data set; the production control data set is controlled and analyzed in a preset period to establish a feedback report; and the feedback report is sent to a control administrator.

[0058] In an embodiment of the present application, the production control data of the equipment is first read through a sensor, including parameters such as pressure, temperature and speed, and a production control data set is established based on the read production control data.

[0059] Next, statistical analysis is performed on the production control data set within a preset period (e.g., every hour). The average, maximum, and minimum values of various parameters are calculated to determine whether the equipment is operating within the normal range. If a parameter exceeds the predetermined normal range, an abnormality is identified. A feedback report is then generated based on the analysis results, summarizing any operational issues. For example, the report might indicate that a temperature is too high.

[0060] Finally, the feedback report will be sent to the control administrator via email to ensure that the administrator can obtain the operating status of the equipment in a timely manner and take corresponding actions to ensure the smooth progress of the production process.

[0061] Furthermore, the method provided in the application embodiment also includes:

[0062] Activate the vibration sensor, use the vibration sensor to monitor the vibration of the forging equipment, and establish the vibration monitoring results; perform auxiliary abnormality authentication of the forging equipment based on the vibration monitoring results, and establish the auxiliary abnormality authentication results; perform joint abnormality analysis of the feedback report through the auxiliary abnormality authentication results, and send the joint abnormality analysis results to the control administrator.

[0063] In this embodiment, a vibration sensor is first activated. This sensor is installed in a key part of the equipment to monitor its vibration. The vibration sensor transmits real-time data to the monitoring system, generating vibration monitoring results, including information such as the frequency, amplitude, and duration of the vibration. This data can be used to determine whether the equipment is experiencing abnormal vibration, which is often a precursor to equipment failure.

[0064] Based on the vibration monitoring results, auxiliary anomaly identification is then performed. Using a threshold determination method, the vibration data is compared with the preset normal operating range. If the vibration data exceeds the normal range, it is marked as an anomaly. Furthermore, rule-based analysis is used, combined with other sensor data (such as temperature and pressure), to verify whether the vibration is a precursor to equipment failure. For example, if vibration and temperature or pressure anomalies occur simultaneously, this combination may be a sign of equipment overload or mechanical failure, resulting in an auxiliary anomaly identification result.

[0065] The auxiliary anomaly identification results are then compared with other equipment monitoring data (such as temperature and pressure) through joint anomaly analysis. Correlation analysis examines the relationship between vibration anomalies and other parameters to determine whether multiple sensor data show anomalies simultaneously. For example, the simultaneous occurrence of vibration anomalies with excessive temperature or pressure fluctuations is considered an indicator of a potential equipment failure, such as overload, loose components, or other mechanical issues. This analysis generates a joint anomaly analysis result, providing a detailed description of the equipment failure.

[0066] Finally, the joint abnormality analysis results are sent to the control administrator via email to ensure that the administrator can receive fault warnings in time and take appropriate measures, such as equipment adjustments, repairs or component replacements, to ensure the stability and safety of the forging process.

[0067] In the embodiments of the present application, in summary, the embodiments of the present application have at least the following technical effects:

[0068] After collecting data on the forging blank, the present application performs forging fitting and establishes a fitting control result mapped with the forging control; after heating the forging blank in a heating furnace, the heated forging blank is positioned on the forging equipment, and the forging equipment is controlled by the control parameters of the forging fitting to perform forging processing; according to the fitting control result, a same-direction forging chain is established, and the same-direction forging chain is a fitting chain for forging in the same direction; the pressure sensor and the temperature sensor are activated to perform data monitoring of the forging process and establish a monitoring data set; the monitoring data set and the same-direction forging chain are input into a following fitting network to establish a following feedback result; and automatic forging control is performed through the following feedback result. The present invention solves the technical problems of insufficient forging accuracy and instability in the forging process in the prior art. Through data acquisition, forging fitting and control mapping, fitting control results are generated, and the forging process is implemented in combination with the positioning and control parameters of the heated billet. By establishing a same-direction forging chain and activating sensor monitoring data, a monitoring data set is generated, which is input into a follow-up fitting network, feedback results are obtained and automatic control is performed, thereby realizing precise control and optimization of the forging process and achieving the technical effect of improving the stability of the forging operation and the quality of the product.

[0069] The second embodiment is based on the same inventive concept as the digital forging automatic control method in the above embodiment. Figure 2 As shown, the present application provides a digital forging automatic control system. The system and method embodiments in the present application are based on the same inventive concept. The system includes:

[0070] a forging fitting module 11, wherein the forging fitting module 11 collects data on the forging blank, performs forging fitting, and establishes a fitting control result mapped with the forging control; a forging processing module 12, wherein the forging processing module 12 positions the heated forging blank to the forging equipment after heating the forging blank in a heating furnace, and controls the forging equipment to perform forging processing through the control parameters of the forging fitting; a same-direction forging chain establishment module 13, wherein the same-direction forging chain establishment module 13 establishes a same-direction forging chain according to the fitting control result, and the same-direction forging chain is a fitting chain for forging in the same direction; a data monitoring module 14, wherein the data monitoring module 14 activates the pressure sensor and the temperature sensor to perform data monitoring of the forging process and establishes a monitoring data set; a following feedback result establishment module 15, wherein the following feedback result establishment module 15 inputs the monitoring data set and the same-direction forging chain into the following fitting network to establish a following feedback result; a forging automatic control module 16, wherein the forging automatic control module 16 performs forging automatic control through the following feedback result.

[0071] Furthermore, the system is also used to implement the following functions:

[0072] Acquire the current forging step, and locate the chain node of the same-direction forging chain based on the current forging step; call the fitting control result of the chain node, use the fitting layer of the following fitting network to fit the forging effect of the monitoring data set, and establish the node fitting result; perform deviation analysis based on the fitting control result and the node fitting result through the analysis layer of the following fitting network, and establish the deviation analysis result; when the preview learning layer receives the deviation analysis result, perform preview tracking analysis, and perform the next round of node positioning according to the chain node, and establish the tracking feedback result according to the next round of node positioning result and the preview tracking analysis result.

[0073] Furthermore, the system is also used to implement the following functions:

[0074] A compensation constraint is established based on the deviation analysis result; a backtracking window is established, and control backtracking of the current direction is performed through the backtracking window to establish a backtracking window data set; a control deviation prediction of the next round of node positioning results is performed through the backtracking window data set to establish a control deviation prediction result; a fitting control result of the next round of node positioning results is used as a preview point, and a preview tracking analysis is performed according to the compensation constraint and the control deviation prediction result.

[0075] Furthermore, the system is also used to implement the following functions:

[0076] Establishing margin compensation according to the deviation analysis result; performing compensation stability analysis on the margin compensation to establish a stability evaluation result; and establishing a tracking feedback result after updating the preview tracking analysis result through the margin compensation if the stability evaluation result is a passing result.

[0077] Furthermore, the system is also used to implement the following functions:

[0078] If the stability evaluation result is a failure result, a margin compensation ladder is established according to the stability evaluation result; a compensation ladder distribution of the margin compensation is performed using the margin compensation ladder; and after the preview tracking analysis result is updated according to the compensation ladder distribution result, a tracking feedback result is established.

[0079] Furthermore, the system is also used to implement the following functions:

[0080] The production control data of the forging equipment is read to establish a production control data set; the production control data set is controlled and analyzed in a preset period to establish a feedback report; and the feedback report is sent to a control administrator.

[0081] Furthermore, the system is also used to implement the following functions:

[0082] Activate the vibration sensor, use the vibration sensor to monitor the vibration of the forging equipment, and establish the vibration monitoring results; perform auxiliary abnormality authentication of the forging equipment based on the vibration monitoring results, and establish the auxiliary abnormality authentication results; perform joint abnormality analysis of the feedback report through the auxiliary abnormality authentication results, and send the joint abnormality analysis results to the control administrator.

[0083] 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. 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.

[0084] 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 shall be included in the scope of protection of the present application.

[0085] 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 digital-based forging automatic control method, characterized in that: The method comprises: After collecting data on the forging blank, perform forging fitting and establish the fitting control result with the forging control mapping; After heating the forging blank in a heating furnace, positioning the heated forging blank on a forging device, and controlling the forging device to perform forging processing by controlling the forging fitting control parameters; Establishing a same-direction forging chain according to the fitting control result, wherein the same-direction forging chain is a fitting chain of forging in the same direction; Activate pressure sensors and temperature sensors to perform data monitoring of the forging process and establish a monitoring data set; Inputting the monitoring data set and the same-direction forging chain into a following fitting network to establish a following feedback result; Automatic forging control is performed by following the feedback result; The step of inputting the monitoring data set and the same-direction forging chain into a following fitting network and establishing a following feedback result includes: Obtaining a current forging step, and locating a chain node of a same-direction forging chain based on the current forging step; Calling the fitting control result of the chain node, using the fitting layer of the follow-up fitting network to perform forging effect fitting of the monitoring data set, and establishing a node fitting result; Performing a deviation analysis based on the fitting control result and the node fitting result through the analysis layer of the follow-up fitting network to establish a deviation analysis result; After receiving the deviation analysis result, the preview learning layer performs preview tracking analysis, and performs next-round node positioning according to the chain node, and establishes tracking feedback results according to the next-round node positioning result and the preview tracking analysis result.

2. A digital forging automatic control method according to claim 1, characterized in that: After receiving the deviation analysis result, the preview learning layer performs preview tracking analysis, including: establishing compensation constraints based on the deviation analysis results; Establishing a lookback window, performing control backtracking of the current direction through the lookback window, and establishing a lookback window data set; Perform control deviation prediction on the next round of node positioning results using the backtracking window data set to establish a control deviation prediction result; The fitting control result of the next round node positioning result is used as the preview point, and the preview tracking analysis is performed according to the compensation constraint and the control deviation prediction result.

3. A digital forging automatic control method according to claim 2, characterized in that: The following feedback result is established based on the next round of node positioning result and the preview tracking analysis result, including: establishing margin compensation according to the deviation analysis result; Performing compensation stability analysis on the margin compensation to establish a stability evaluation result; If the stability evaluation result is a passing result, the preview tracking analysis result is updated through the margin compensation to create a tracking feedback result.

4. A digital forging automatic control method according to claim 3, characterized in that: The method further comprises: If the stability evaluation result is a failure result, establishing a margin compensation ladder according to the stability evaluation result; Performing compensation step distribution of the margin compensation by the margin compensation step; After the preview tracking analysis result is updated according to the compensation step distribution result, a tracking feedback result is established.

5. The digital-based forging automatic control method according to claim 1, characterized in that: The method further comprises: Reading the production control data of the forging equipment and establishing a production control data set; Perform control analysis on the production control data set at a preset period and create a feedback report; The feedback report is sent to the control administrator.

6. A digital forging automatic control method according to claim 5, characterized in that: The method further comprises: activating a vibration sensor, using the vibration sensor to monitor the vibration of the forging equipment, and establishing a vibration monitoring result; Perform auxiliary abnormality authentication on the forging equipment based on the vibration monitoring result, and establish an auxiliary abnormality authentication result; A joint anomaly analysis of the feedback report is performed based on the auxiliary anomaly authentication result, and the joint anomaly analysis result is sent to the control administrator.

7. A digital forging automatic control system, characterized in that: The system is used to execute the digital-based forging automatic control method according to claims 1 to 6, and the system includes: a forging fitting module, which collects data on the forging blank, performs forging fitting, and establishes a fitting control result mapped with the forging control; a forging processing module, which, after heating the forging blank in a heating furnace, positions the heated forging blank on a forging device, and controls the forging device to perform forging processing according to control parameters of forging fitting; a same-direction forging chain establishing module, wherein the same-direction forging chain establishing module establishes a same-direction forging chain according to the fitting control result, wherein the same-direction forging chain is a fitting chain for forging in the same direction; a data monitoring module, wherein the data monitoring module activates a pressure sensor and a temperature sensor to perform data monitoring of the forging process and establishes a monitoring data set; A tracking feedback result establishing module, wherein the tracking feedback result establishing module inputs the monitoring data set and the same-direction forging chain into a tracking fitting network to establish a tracking feedback result; A forging automatic control module is provided, wherein the forging automatic control module performs forging automatic control according to the following feedback result.

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