Automatic forging and pressing control method and system based on digitization

Abstract

The invention discloses an automatic forging and pressing control method and system based on digitization, and relates to the technical field of automatic control, and the method comprises the following steps: carrying out data acquisition on a forging blank, executing forging and pressing fitting, and establishing a fitting control result mapped with forging and pressing control; the forging blank is heated, the heated forging blank is positioned to forging and pressing equipment, and the forging and pressing equipment is controlled to conduct forging and pressing treatment through the control parameters of forging and pressing fitting; establishing a homodromous forging chain according to a fitting control result; activating a pressure sensor and a temperature sensor to execute data monitoring in the forging and pressing process, and establishing a monitoring data set; inputting the monitoring data set and the same-direction forging chain into a following fitting network, and establishing a following feedback result; and automatic forging and pressing control is performed by following a feedback result. The technical problems that in the forging and pressing process in the prior art, the forging and pressing precision is insufficient and unstable are solved, precise control and optimization of the forging and pressing process are achieved, and the technical effect of improving the forging and pressing operation stability and the product quality is achieved.

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, automobile, energy and other fields. However, the traditional forging process relies on empirical parameters and manual adjustment, and there are problems such as insufficient forging precision and unstable process. Traditional forging equipment usually adopts a fixed process parameter setting method, 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 adjust real-time feedback for complex working conditions, resulting in poor operational stability. The existing process generally lacks systematic management and intelligent analysis of forging data, and cannot 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, which are used to solve 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: After data collection of the forging blank, forging fitting is performed to establish a fitting control result mapped with the forging control; after heating the forging blank in a heating furnace, the heated forging blank is positioned to the forging equipment, and the forging equipment is controlled to perform forging processing by the control parameters of the forging fitting; a same-direction forging chain is established according to the fitting control result, 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 by the following feedback result.

[0006] The second aspect of the present application provides a digital forging automatic control system, the system comprising: A forging fitting module, after 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, after the forging processing module heats the forging blank in a heating furnace, the forging processing module positions the heated forging blank to the forging equipment, and controls the forging equipment to perform forging processing through the control parameters of the forging fitting; a same-direction forging chain establishment module, 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, 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 follow-up feedback result establishment module, the follow-up feedback result establishment module inputs the monitoring data set and the same-direction forging chain into the follow-up fitting network to establish a follow-up feedback result; a forging automatic control module, the forging automatic control module performs forging automatic control through the follow-up feedback result.

[0007] One or more technical solutions provided in this application have at least the following technical effects or advantages: After data collection 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 to the forging equipment, and the forging equipment is controlled to perform forging processing by the control parameters of the forging fitting; a same-direction forging chain is established according to the fitting control result, and the same-direction forging chain is a fitting chain for forging in the same direction; a pressure sensor and a 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 follow-up fitting network to establish a follow-up feedback result; and automatic forging control is performed by the follow-up feedback result. The present invention solves the technical problems of insufficient forging accuracy and instability in the forging process in the prior art. The present invention generates fitting control results through data acquisition, forging fitting and control mapping, and implements forging processing in combination with the positioning and control parameters of the heated blank. The present invention establishes a unidirectional forging chain and activates sensor monitoring data to generate a monitoring data set, which is input into a follow-up fitting network to obtain feedback results and perform automatic control, thereby achieving precise control and optimization of the forging process and achieving the technical effect of improving the stability of forging operation and product quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the description of the embodiments will be briefly introduced below. 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.

[0009] Figure 1A schematic flow chart of a digital-based forging automatic control method provided in an embodiment of the present application.

[0010] Figure 2 A schematic diagram of the structure of a digital forging automatic control system provided in an embodiment of the present application.

[0011] 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

[0012] 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 unidirectional forging chain and activating sensor monitoring data, a monitoring data set is generated, which is input into a follow-up fitting network to obtain feedback results and perform automatic control, thereby achieving precise control and optimization of the forging process and achieving the technical effect of improving the stability of forging operations and product quality.

[0013] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0014] 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 explicitly listed, but may include other steps or modules that are not explicitly listed or inherent to these processes, methods, products or devices.

[0015] Embodiment 1, as Figure 1 As shown, the present application provides a digital-based forging automatic control method, the method comprising: Step S100: After collecting data on the forging blank, forging fitting is performed to establish a fitting control result corresponding to the forging control mapping.

[0016] In the embodiment of the present application, data collection is first performed on the forging blank to obtain the material information and geometric shape characteristics of the forging blank. The material information is obtained through chemical composition analysis and material performance testing, which involves using a spectrometer (such as an X-ray fluorescence spectrometer) to determine the chemical composition of the blank, or measuring its mechanical properties through hardness testing, tensile testing, etc. The geometric shape characteristics are obtained by three-dimensional scanning, laser scanning, or computer tomography (CT), which can accurately record the shape size and surface contour of the blank.

[0017] Next, forging fitting is performed. Specifically, the collected billet data is input into a digital model space. In this digital space, a virtual forging environment is constructed by computer simulation of the forging process. This environment includes the parameters of the simulated forging machine (such as pressure, temperature, speed, etc.) and the deformation of the billet during the forging process. At this point, all factors of the forging process (such as temperature rise, pressure application, and deformation) are simulated in this virtual model, and numerical methods such as finite element analysis (FEA) are used to simulate the physical and mechanical behavior of the forging process.

[0018] After the simulation in the digital space, the next step is to optimize the parameters and select the best 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 can produce the best forging results. These parameters include applied pressure, forging speed, heating temperature, etc., which are designed to maximize the deformation effect of the blank while avoiding excessive deformation, cracks or other defects.

[0019] Finally, the control parameters and corresponding forging results obtained through the simulation and optimization process are the fitting control results.

[0020] 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.

[0021] 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 control parameters determined by the aforementioned forging fitting to perform forging processing.

[0022] 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 forging in the same direction.

[0023] 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.

[0024] Step S400: activating the pressure sensor and the temperature sensor to perform data monitoring of the forging process and establish a monitoring data set.

[0025] In the embodiment of the present application, the pressure sensor and the temperature sensor are activated to monitor the applied pressure and the temperature change of the blank in real time during the forging process. The pressure sensor collects mechanical data during each forging and records the pressure. The temperature sensor detects the temperature of the blank surface in real time. The pressure sensor and the temperature sensor perform data detection to obtain a monitoring data set. The monitoring data set includes the actual pressure during forging and the temperature of the blank during the forging process.

[0026] Step S500: inputting the monitoring data set and the same-direction forging chain into a following fitting network to establish a following feedback result.

[0027] Furthermore, in the method provided in the embodiment of the application, the step of inputting the monitoring data set and the same-direction forging chain into a following fitting network to establish a following feedback result further includes: Acquire the current forging step, and locate the chain nodes 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 locate the next round of nodes 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.

[0028] 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 forging step currently being executed is read by the control system of the forging equipment, and the blank surface currently being forged is further identified according to the current step. The same-direction forging chain corresponding to the surface is determined through 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.

[0029] Next, the fitting control result of the chain node is called to obtain the fitting control result of the corresponding chain node. Then, the fitting layer following the fitting network is used to fit the forging effect of the monitoring data set. Specifically, in the fitting layer, the forging simulation is performed on the real-time monitoring data through digital simulation-based methods, such as finite element analysis. The fitting layer is based on the monitoring data and combines the processing background of the chain node (such as forging direction and operation sequence) to simulate the forging effect and quantify the impact of the current operation on the 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.

[0030] Then, the analysis layer that follows the fitting network performs deviation analysis based on the fitting control results and node fitting results. Through the analysis layer, the node fitting results and the fitting control results of the chain nodes are compared item by item using the absolute difference calculation method to calculate the difference between the actual and target parameters. For example, the deviation value 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 obtain the deviation of the key parameters in the forging process. Finally, the analysis layer generates the deviation analysis results. The deviation analysis results clearly point out the specific differences between the actual operation and the target parameters in the current forging step. For example, it indicates that the current low pressure leads to insufficient deformation of the billet, or that the high temperature may cause local overheating.

[0031] The deviation analysis results are input to 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 fitting control results of the next-wheel chain nodes by establishing compensation constraints, backtracking window data sets, and control deviation predictions. Combined with the next-wheel node positioning results and prediction analysis, the tracking feedback results are generated, including the calculation of margin compensation 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, and finally an optimized tracking feedback result is generated.

[0032] Furthermore, in the method provided in the embodiment of the application, after the preview learning layer receives the deviation analysis result, the preview tracking analysis is performed, which further includes: 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; 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; the fitting control result of the next round of node positioning results is used as a preview aiming point, and a preview aiming and tracking analysis is performed according to the compensation constraint and the control deviation prediction result.

[0033] In the embodiment of the present application, compensation constraints are first established based on the deviation analysis results. By parsing the deviation analysis results, the deviation range and change trend of key parameters (such as pressure, temperature, etc.) are extracted, and the upper and lower limits of compensation are set using statistical analysis methods in combination with historical operation data. Specifically, based on the safe operating range of the equipment and the historical deviation amplitude, the adjustment amplitude limit of each parameter is determined, for example, the maximum value of the pressure increase is 20%, and the minimum value of the temperature drop is 5°C, thereby forming a clear compensation constraint, limiting the amplitude and direction of subsequent parameter adjustments, and ensuring that the adjustment is both effective and safe.

[0034] Next, a backtracking window is established to perform backtracking analysis on the historical control data in the current direction. The backtracking window is based on a sliding window algorithm, which extracts the control parameter data in the most recent (e.g., 10) operation steps, including pressure, temperature, speed, deformation, etc., to form a backtracking window data set. The backtracking window data set reflects the parameter change trend in the current operation direction, such as the accumulation of insufficient pressure in the past several consecutive rounds of operation, or the impact of temperature fluctuations on the processing results.

[0035] Based on the backtracking window data set, the control deviation of the next round of nodes is predicted. Through time series prediction methods (such as ARIMA model), the historical parameter change pattern is analyzed to generate control deviation prediction results. For example, it is predicted that the pressure may continue to be less than 10% in the next round of operation, or the temperature may exceed the target value by 5°C. These prediction results provide forward-looking data support to clarify the parameters that may need to be adjusted in the next round of node operations and the specific compensation direction.

[0036] Then, the fitting control result of the next round of node positioning result is used as the preview point, combined with the compensation constraint and control deviation prediction results, to perform preview tracking analysis. In this process, the compensation constraint and control deviation prediction results are comprehensively analyzed through optimization algorithms (such as gradient descent method or genetic algorithm), and the operating parameters of the next round of nodes are dynamically adjusted. For example, if the prediction shows that the pressure deviation is 10%, and the maximum adjustment range allowed by the compensation constraint is 15%, the optimization algorithm will allocate a reasonable adjustment range in combination with the preview point, and finally generate parameter optimization suggestions. The preview tracking analysis dynamically generates adjustment results that meet the processing goals by comprehensively considering the prediction results and compensation constraints, and finally obtains the preview tracking analysis results.

[0037] Furthermore, in the method provided in the embodiment of the application, the establishing of the tracking feedback result according to the next round of node positioning result and the preview tracking analysis result also includes: Establish margin compensation according to the deviation analysis result; perform compensation stability analysis on the margin compensation to establish a stability evaluation result; if the stability evaluation result is a passing result, update the preview tracking analysis result through the margin compensation to establish a tracking feedback result.

[0038] In an embodiment of the present application, in the forging operation, in order to effectively compensate for the deviation analysis results, margin compensation is first established. The compensation value is calculated based on the deviation value and the safe 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 margin compensation for pressure is -8%; for a temperature deviation of +5°C, the safe 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 safe range, thereby completing the establishment of margin compensation.

[0039] Then, the established margin compensation is analyzed for compensation stability. Using the historical data matching method, the currently calculated margin compensation value is compared with the historical operation data of the equipment 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 equipment history records, it is determined whether the equipment has experienced equipment oscillation under conditions similar to pressure compensation -8%; at the same time, it is compared whether the historical temperature compensation +3°C causes overheating or uneven processing. If the calculated compensation value is consistent with the stable parameter range in the historical operation, a "passed" stability evaluation result is generated, indicating that the current margin compensation can be safely executed.

[0040] Finally, the follow-up feedback result is established based on the stability evaluation result. When the stability evaluation result is "passed", the parameter superposition method is used to combine the margin compensation value with the preview tracking analysis result to generate updated operating parameters. For example, if the preview tracking analysis result shows that the pressure needs to be increased by 10% and the margin compensation is -8%, the final adjustment value of the pressure is +2%; similarly, if the preview tracking analysis result shows that the temperature needs to be reduced by 5°C and the margin compensation is +3°C, the final adjustment is -2°C. After superimposing all parameters, the follow-up feedback result is generated, the adjustment value of each parameter is clarified, and these results are transmitted to the control system of the forging equipment in real time to guide the next operation.

[0041] Furthermore, the method provided in the application embodiment also includes: If the stability evaluation result is a failed result, a margin compensation ladder is established according to the stability evaluation result; a compensation ladder distribution of the margin compensation is performed through the margin compensation ladder; after the preview tracking analysis result is updated according to the compensation ladder distribution result, a tracking feedback result is established.

[0042] In the embodiment of the present application, when the stability evaluation result is "failed", in order to avoid damage to the blank and the equipment due to 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 at a single time is decomposed into multiple small compensations using a 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 blank 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 surface of the blank to cool too quickly and cause microcracks, it is 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 blank during the adjustment process.

[0043] After the compensation ladder is established, each compensation ladder is distributed and implemented through the priority distribution algorithm. Priority is given to correcting the deviation of the current step, and the remaining compensation is allocated to the subsequent steps. For example, the first step compensation (such as a 10% increase in pressure) is implemented in the current step to resolve the main deviation as soon as possible; the second step compensation (such as a 10% increase in pressure) is allocated to the next step for implementation to gradually complete the adjustment. For temperature adjustment, the first step of cooling by 3°C is implemented slowly in the current step to avoid sudden cooling damage to the surface of the billet, and the second step of cooling by 2°C is allocated to the next step. This distribution method combines the operating status of the equipment, the processing requirements of the billet, and the scope of influence of the deviation to ensure that the compensation distribution is safe and effective.

[0044] After the compensation step distribution is completed, the preview tracking analysis results are updated according to 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 the first step compensation, the final adjustment value is determined to be +10% based on 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 finally reduced by 3°C according to the adjustment limit. This parameter update ensures the coordination of the preview analysis and compensation requirements while avoiding excessive one-time adjustments.

[0045] Finally, combined with the updated parameters, a follow-up feedback result is generated. The follow-up feedback result clarifies the adjustment plan for the current step and the subsequent steps, such as "the current step increases the pressure by 10% and the temperature decreases by 3°C; the next step increases the pressure by 10% and the temperature decreases by 2°C". The feedback result is transmitted to the forging equipment control system through the real-time parameter update mechanism to guide the actual operation. By establishing the compensation ladder, implementing the distribution, and then updating the preview follow-up analysis results, the step-by-step adjustment of the compensation is achieved to ensure the dynamic stability of the processing while avoiding potential damage to the equipment and the blank.

[0046] Step S600: Automatic forging control is performed by following the feedback result.

[0047] In the embodiment of the present application, automatic forging control by following the feedback results refers to real-time adjustment of equipment parameters (such as pressure, temperature, speed, etc.) according to the following feedback results during the forging operation. Specifically, according to the following feedback results, such as a 10% increase in pressure and a 3°C decrease in temperature, the operating parameters of the forging equipment are adjusted in real time through a closed-loop control mechanism to ensure that these parameters reach the expected target values. Throughout the process, the equipment continuously monitors the operating status and makes fine adjustments based on the feedback results to ensure that the operation is accurately performed, thereby achieving automatic control of the forging process.

[0048] Furthermore, the method provided in the application embodiment also includes: 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 under a preset period to establish a feedback report; and the feedback report is sent to a control administrator.

[0049] In the embodiment of the present application, the production control data of the equipment is first read by the sensor, including parameters such as pressure, temperature and speed, and a production control data set is established based on the read production control data.

[0050] Next, statistical analysis is performed on the production control data set within a preset period (e.g., every hour) to calculate the average, maximum, and minimum values ​​of each parameter to determine whether the equipment is operating within the normal range. If a parameter exceeds the predetermined normal range, it is considered abnormal. Then, a feedback report is generated based on the analysis results to summarize the problems encountered in the operation of the equipment. For example, the report may indicate that a temperature is too high.

[0051] 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.

[0052] Furthermore, the method provided in the application embodiment also includes: Activate the vibration sensor, use the vibration sensor to perform vibration monitoring of the forging equipment, and establish the vibration monitoring result; perform auxiliary abnormality authentication of the forging equipment based on the vibration monitoring result, and establish the auxiliary abnormality authentication result; perform joint abnormality analysis of the feedback report through the auxiliary abnormality authentication result, and send the joint abnormality analysis result to the control administrator.

[0053] In the embodiment of the present application, the vibration sensor is first activated. The sensor is installed at the key part of the equipment to monitor the vibration of the equipment. The vibration sensor transmits real-time data to the monitoring system to form vibration monitoring results, including information such as the frequency, amplitude and duration of the vibration. Through these data, it can be determined whether the equipment has abnormal vibration, which is usually a precursor to equipment failure.

[0054] Based on the vibration monitoring results, auxiliary abnormality authentication is then performed. The vibration data is compared with the preset normal working range through the threshold judgment method. If the vibration data exceeds the normal range, it is marked as abnormal. In addition, the rule-based analysis method is combined with other sensor data (such as temperature, pressure, etc.) to verify whether the vibration is a precursor to equipment failure. For example, if vibration and temperature or pressure anomalies occur at the same time, it is judged that this combination may be a sign of equipment overload or mechanical failure, and then an auxiliary abnormality authentication result is generated.

[0055] The auxiliary anomaly certification results are then compared and analyzed with other equipment monitoring data (such as temperature, pressure, etc.) through joint anomaly analysis. The correlation analysis method is used to check the relationship between vibration anomalies and other parameters to determine whether multiple sensor data are abnormal at the same time. For example, when vibration anomalies occur at the same time as excessive temperature or pressure fluctuations, it is considered to be an indication of potential equipment failure, which may be overload, loose components or other mechanical problems. Through this analysis, a joint anomaly analysis result is generated, providing a detailed description of the equipment failure.

[0056] 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.

[0057] In the embodiments of the present application, in summary, the embodiments of the present application have at least the following technical effects: After data collection 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 to the forging equipment, and the forging equipment is controlled to perform forging processing by the control parameters of the forging fitting; a same-direction forging chain is established according to the fitting control result, and the same-direction forging chain is a fitting chain for forging in the same direction; a pressure sensor and a 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 follow-up fitting network to establish a follow-up feedback result; and automatic forging control is performed by the follow-up feedback result. The present invention solves the technical problems of insufficient forging accuracy and instability in the forging process in the prior art. The present invention generates fitting control results through data acquisition, forging fitting and control mapping, and implements forging processing in combination with the positioning and control parameters of the heated blank. The present invention establishes a unidirectional forging chain and activates sensor monitoring data to generate a monitoring data set, which is input into a follow-up fitting network to obtain feedback results and perform automatic control, thereby achieving precise control and optimization of the forging process and achieving the technical effect of improving the stability of forging operation and product quality.

[0058] Embodiment 2 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, and the system and method embodiments in the present application are based on the same inventive concept. The system includes: A forging fitting module 11, after 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, after the forging blank is heated in a heating furnace, the forging processing module 12 positions the heated forging blank to the forging equipment, 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, 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, the data monitoring module 14 activates the pressure sensor and the temperature sensor to perform data monitoring of the forging processing process and establishes a monitoring data set; a follow-up feedback result establishment module 15, the follow-up feedback result establishment module 15 inputs the monitoring data set and the same-direction forging chain into the follow-up fitting network to establish a follow-up feedback result; a forging automatic control module 16, the forging automatic control module 16 performs forging automatic control through the follow-up feedback result.

[0059] Furthermore, the system is also used to implement the following functions: Acquire the current forging step, and locate the chain nodes 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 locate the next round of nodes 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.

[0060] Furthermore, the system is also used to implement the following functions: 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; 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; the fitting control result of the next round of node positioning results is used as a preview aiming point, and a preview aiming and tracking analysis is performed according to the compensation constraint and the control deviation prediction result.

[0061] Furthermore, the system is also used to implement the following functions: Establish margin compensation according to the deviation analysis result; perform compensation stability analysis on the margin compensation to establish a stability evaluation result; if the stability evaluation result is a passing result, update the preview tracking analysis result through the margin compensation to establish a tracking feedback result.

[0062] Furthermore, the system is also used to implement the following functions: If the stability evaluation result is a failed result, a margin compensation ladder is established according to the stability evaluation result; a compensation ladder distribution of the margin compensation is performed through the margin compensation ladder; after the preview tracking analysis result is updated according to the compensation ladder distribution result, a tracking feedback result is established.

[0063] Furthermore, the system is also used to implement the following functions: 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 under a preset period to establish a feedback report; and the feedback report is sent to a control administrator.

[0064] Furthermore, the system is also used to implement the following functions: Activate the vibration sensor, use the vibration sensor to perform vibration monitoring of the forging equipment, and establish the vibration monitoring result; perform auxiliary abnormality authentication of the forging equipment based on the vibration monitoring result, and establish the auxiliary abnormality authentication result; perform joint abnormality analysis of the feedback report through the auxiliary abnormality authentication result, and send the joint abnormality analysis result to the control administrator.

[0065] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description and does not represent the advantages and disadvantages of the embodiments. And the above-mentioned specific embodiments of this specification are described. The processes depicted in the accompanying drawings do not necessarily require the specific order and continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0066] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

[0067] This specification and drawings are merely exemplary illustrations of the present application and are deemed to cover any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, a person skilled in the art may make various modifications and variations to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application intends to include these modifications and variations.

Claims

1. A digital forging automatic control method, characterized in that: The method comprises: After data collection of the forging blank, forging fitting is performed to establish the fitting control result mapped with the forging control; After heating the forging blank in a heating furnace, positioning the heated forging blank to 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 the pressure sensor and the temperature sensor 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.

2. A digital forging automatic control method as claimed in claim 1, characterized in that: 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: Acquire a current forging step, and locate 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 fit the forging effect of the monitoring data set, and establishing the node fitting result; Performing deviation analysis based on the fitting control result and the node fitting result through the analysis layer of the follow-up fitting network, and establishing 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.

3. A digital forging automatic control method as claimed in claim 2, 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 backtracking window, performing control backtracking in the current direction through the backtracking window, and establishing a backtracking window data set; Perform control deviation prediction of next round node positioning results through 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.

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

5. A digital forging automatic control method as claimed in claim 4, 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.

6. A digital forging automatic control method as claimed in 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.

7. A digital forging automatic control method as claimed in claim 6, characterized in that: The method further comprises: activating a vibration sensor, using the vibration sensor to perform vibration monitoring of the forging equipment, and establishing a vibration monitoring result; Perform auxiliary abnormality authentication of 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.

8. A digital forging automatic control system, characterized in that: The system comprises: A forging fitting module, which performs forging fitting after collecting data on the forging blank, 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 to a forging device, and controls the forging device to perform forging processing through a control parameter of 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, 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 establish a monitoring data set; A follow-up feedback result establishment module, wherein the follow-up feedback result establishment module inputs the monitoring data set and the same-direction forging chain into a follow-up fitting network to establish a follow-up 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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