A method and system for pressure self-adaptive adjustment of multi-material forging

Abstract

The present application relates to the technical field of metal casting and powder metallurgy, and particularly relates to a pressure self-adaptive adjusting method and system for multi-material forging, wherein the method comprises the following steps: step 1, initializing a forging equipment and a parameter acquisition system; step 2, acquiring multi-parameter data in a real-time manner during the forging process; step 3, calculating a dynamic deformation resistance of the material based on the real-time parameters; step 4, generating an adaptive pressure adjusting value according to the dynamic deformation resistance; step 5, controlling a pressure executing mechanism to output a target pressure value; step 6, continuously monitoring and terminating the forging process, and repeating steps 2 to 5 until a die displacement reaches a preset forging completion position, a central processing unit sends a termination signal, the pressure executing mechanism gradually unloads pressure to complete the forging process, and all parameters and adjustment records are stored for quality analysis. Through accurate pressure control and a closed-loop feedback mechanism, defects caused by excessively high or low pressure are avoided, the waste product rate is significantly reduced, and the qualified rate of products is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of metal casting and powder metallurgy, and particularly relates to a pressure self-adaptive adjusting method and system for multi-material forging. BACKGROUND

[0002] In the process of metal casting and powder metallurgy forging, multi-material forging technology is widely used in the manufacture of composite parts, such as parts using different metal material combinations in the automotive and aerospace fields. Multi-material forging involves the simultaneous forming of two or more materials at high temperature and high pressure, which may have different physical properties such as deformation resistance, thermal expansion coefficient and flowability. In the prior art, the pressure control of the forging process usually relies on preset fixed parameters or simple feedback control based on a single material. For example, the traditional method pre-sets a pressure curve, adjusts the pressure value based on experience, or uses a pressure sensor to monitor the state of a single material during the forging process, and uses a proportional-integral-derivative control algorithm for fine tuning. However, these methods have significant limitations in the context of multi-material forging.

[0003] The difference in material properties during multi-material forging can lead to uneven deformation. For example, materials with higher hardness require higher pressure to deform adequately, while materials with lower hardness may over-deform under the same pressure, resulting in cracks or porosity. The prior art cannot identify and adapt to the deformation requirements of different materials in real time, resulting in stress concentration, poor interface bonding or shape deviation in forged parts. Secondly, the existing pressure control methods lack the ability to respond to dynamic changes in materials. During the forging process, material temperature, strain rate and microstructure change in real time, but the existing technology is usually based on static models or offline calculations, and cannot dynamically adjust the pressure during the forging process. For example, when the material temperature fluctuates due to environmental factors, fixed pressure parameters may cause the material to overheat or cool too quickly, affecting the mechanical properties of the final product. In addition, existing methods have deficiencies in parameter acquisition, often relying only on a few sensors (such as pressure sensors) and ignoring other key parameters such as real-time deformation resistance and temperature distribution of the material, resulting in low control accuracy. These problems not only reduce the efficiency of forging, but also increase the scrap rate, especially in high-end manufacturing fields such as precision instruments or high-performance composite material parts, where defects can cause serious safety hazards.

[0004] Therefore, there is an urgent need for a method that can adaptively adjust the pressure in real time to solve the problem of uneven deformation and dynamic changes in multi-material forging. The present application proposes an adaptive method based on multi-parameter real-time monitoring and dynamic pressure adjustment, which realizes accurate control of pressure by integrating various sensors and algorithms. SUMMARY

[0005] In order to achieve the above object, the present application provides a pressure self-adaptive adjusting method for multi-material forging, comprising the following steps:

[0006] Step 1: initializing the forging equipment and the parameter acquisition system, the forging equipment comprising an upper die, a lower die and a pressure executing mechanism, the parameter acquisition system comprising a temperature sensor, a displacement sensor and a pressure sensor, the temperature sensor being installed on the contact surface of the forging die, the displacement sensor being installed on the die moving part, the pressure sensor being integrated in the pressure executing mechanism, the sampling frequency of the parameter acquisition system being dynamically adjusted according to the material type and the forging speed, and the preloaded basic characteristic data of the multi-material comprising the theoretical deformation resistance and the heat conduction coefficient of the material;

[0007] Step 2: real-time acquisition of multi-parameter data in the forging process, the temperature sensor measuring the material surface temperature in a non-contact manner, the displacement sensor measuring the displacement of the die through the laser ranging principle and calculating the real-time strain value of the material based on the displacement, the pressure sensor reading the output pressure value of the pressure executing mechanism, all the acquired parameters being transmitted to the central processing unit through the data bus for filtering and normalization processing, and the synchronization of the parameter acquisition being realized through a hardware triggering mechanism;

[0008] Step 3: calculation of the dynamic deformation resistance of the material based on the real-time parameters, the central processing unit using the acquired real-time temperature value, real-time strain value and real-time pressure value to calculate the dynamic deformation resistance through a material constitutive model, the material constitutive model being constructed based on the stress-strain relationship and temperature dependence of the material, the calculation process comprising adjusting the flow stress coefficient of the material according to the real-time temperature value, calculating the instantaneous deformation resistance using the incremental method in combination with the real-time strain value, and generating a deviation value by comparing the dynamic deformation resistance with the theoretical deformation resistance;

[0009] Step 4: generation of an adaptive pressure adjusting value according to the dynamic deformation resistance, the central processing unit generating the adaptive pressure adjusting value based on the dynamic deformation resistance deviation value using a proportional-integral control algorithm, the proportional coefficient and the integral coefficient of the proportional-integral control algorithm being dynamically adjusted according to the material type and the forging stage, the adjustment process being realized through a lookup table, the lookup table being constructed based on historical optimization data, and the calculation of the adaptive pressure adjusting value comprising calculating the difference between the dynamic deformation resistance deviation value and the target deformation resistance, applying the proportional-integral control algorithm to output a pressure compensation amount, and superimposing the pressure compensation amount with the current pressure value to generate a target pressure value;

[0010] Step 5: The control pressure actuator outputs the target pressure value, the central processing unit converts the target pressure value into a control signal and sends it to the pressure actuator, the pressure actuator adopts an electro-hydraulic servo system, the control signal drives the servo valve to adjust the hydraulic flow through a digital-analog converter to output the target pressure value, and the pressure sensor feedbacks the actual pressure value in real time to form a closed loop control. If the error between the actual pressure value and the target pressure value exceeds the threshold value, steps 2 to 5 are repeated for iterative adjustment.

[0011] Step 6: Continuously monitor and terminate the forging process, repeat steps 2 to 5 until the mold displacement reaches the preset forging completion position, the central processing unit sends a termination signal, the pressure actuator gradually unloads the pressure to complete the forging process, and all parameters and adjustments are recorded for quality analysis.

[0012] Preferably, the initialization of the forging equipment and the parameter acquisition system in step 1 also includes: the temperature sensor is an infrared temperature measuring instrument installed near the contact surface of the upper and lower dies, the sampling frequency is set to 100 times per second according to the forging speed, the displacement sensor is a laser displacement sensor installed on the die guide column to measure the relative displacement of the die, the initial material thickness is obtained through a calibration program, the pressure sensor is integrated in the hydraulic cylinder, the measurement range covers the maximum working pressure of the forging machine, and the basic characteristic data of multiple materials are loaded from the material library, including the deformation resistance curves of aluminum alloy and steel at different temperatures. The material library is constructed by historical experimental data, and the central processing unit executes a self-checking program to verify the connection state of the sensor and the pressure actuator during the initialization process.

[0013] Preferably, the real-time acquisition of multi-parameter data in step 2 also includes: the temperature sensor measures the material surface temperature through infrared radiation, and the measurement value is subjected to environmental temperature compensation processing, which is realized by referring to the data of the environmental temperature sensor; the displacement sensor measures the displacement amount through laser reflection principle, the real-time strain value calculation formula is the ratio of real-time displacement amount and initial material thickness; the pressure sensor directly outputs a voltage signal, and the voltage signal is converted into a pressure value after analog-digital conversion; the data bus adopts CAN bus, the central processing unit uses moving average filtering method to process data to reduce noise, and the hardware trigger mechanism ensures that the temperature, strain and pressure data are aligned at the same time stamp through a synchronous clock signal.

[0014] Preferably, the step 3 of calculating the dynamic deformation resistance of the material based on the real-time parameters further comprises: the material constitutive model adopts a Johnson-Cook material model, the Johnson-Cook material model includes a strain hardening term, a strain rate term and a temperature term, and the process of calculating the dynamic deformation resistance includes first querying a material property data table according to the real-time temperature value to obtain a temperature softening coefficient, the material property data table is constructed by pre-experiment calibration, secondly combining the real-time strain value to calculate the strain hardening term, the strain hardening term is obtained by the product of the strain value and the material hardening coefficient, and finally multiplying the temperature softening coefficient, the strain hardening term and the material constant to obtain the dynamic deformation resistance, the material constant includes a strength coefficient and a strain rate sensitivity coefficient, the material constant is determined by multiple groups of forging experiments regression analysis, and the deviation value is calculated by the absolute difference between the dynamic deformation resistance and the theoretical deformation resistance.

[0015] Preferably, the step 4 of generating the adaptive pressure adjustment value according to the dynamic deformation resistance further comprises: the proportional coefficient and the integral coefficient of the proportional-integral control algorithm are set to higher values in the initial stage of forging to quickly respond, and are set to lower values in the stable stage of forging to reduce oscillation, the higher value and the lower value are determined by optimization experiment, the look-up table is based on material type and forging stage index, the look-up table update is realized by real-time learning algorithm, the real-time learning algorithm uses the recursive least squares method to adjust the proportional coefficient and the integral coefficient, the calculation of the pressure compensation amount includes a proportional term and an integral term, the proportional term is the product of the dynamic deformation resistance deviation value and the proportional coefficient, and the integral term is the product of the integral of the dynamic deformation resistance deviation value and the integral coefficient, and the target pressure value is obtained by the algebraic sum of the current pressure value and the pressure compensation amount.

[0016] Preferably, the step 5 of controlling the pressure actuator to output the target pressure value further comprises: the electro-hydraulic servo system includes a hydraulic pump, a servo valve and a hydraulic cylinder, the control signal is converted into an analog voltage signal by a digital-to-analog converter to drive the servo valve, the servo valve adjusts the hydraulic flow to control the output pressure of the hydraulic cylinder, the pressure sensor feeds back the actual pressure value in real time, the closed-loop control adopts a negative feedback mechanism, the threshold value is set according to the material tolerance and the forging accuracy requirement, the threshold value is determined by the statistical process control method, and the central processing unit monitors the error change rate in real time in the iterative adjustment process, and if the error change rate exceeds the preset limit value, the sampling frequency and the control parameters are adjusted.

[0017] Preferably, the step 6 of continuously monitoring and terminating the forging process further comprises: the forging completion position is determined by three-dimensional modeling software according to product design specifications, the central processing unit compares the die displacement amount with the forging completion position in real time, when the die displacement amount reaches the forging completion position, the central processing unit generates a termination signal, the pressure actuator gradually unloads the pressure through a linear pressure reduction curve, the linear pressure reduction curve is dynamically generated according to the material type and the forging history data, all parameters and adjustment records are stored in the non-volatile memory, the storage format includes time series data and event logs, and the quality analysis is performed by comparing the stored data with the standard quality indicators.

[0018] Preferably, the material constitutive model in step 3 further comprises a temperature compensation factor, which is calculated by the ratio of the real-time temperature value to the reference temperature value, the reference temperature value is set as the material recrystallization temperature, the material recrystallization temperature is determined by thermal analysis experiment, the strain hardening model is used when calculating the instantaneous deformation resistance, the strain hardening model adopts Voce hardening law, the parameters of Voce hardening law are calibrated by uniaxial tensile test, and the incremental method calculation comprises correlating the strain increment with the stress increment, the strain increment is obtained by the difference between the real-time strain value and the strain value at the previous time step.

[0019] Preferably, the proportional-integral control algorithm in step 4 further comprises an anti-saturation mechanism, which is realized by limiting the growth of the integral term, the limit value is set according to the maximum output capacity of the pressure actuator, the construction of the lookup table comprises optimizing the proportional coefficient and the integral coefficient using a neural network model, the neural network model is obtained by training historical forging data, the training process uses a back propagation algorithm, and the calculation of the pressure compensation amount further comprises a differential term to improve the dynamic response, the differential term is obtained by the product of the change rate of the dynamic deformation resistance deviation value and the differential coefficient, and the differential coefficient is adjusted according to the material strain rate sensitivity.

[0020] Correspondingly, the embodiment of the present application also provides a pressure self-adaptive adjustment system for multi-material forging, comprising a memory configured to store instructions, and a processor configured to call the instructions from the memory and to realize the pressure self-adaptive adjustment method for multi-material forging as any one of the embodiments of the present application when executing the instructions.

[0021] The beneficial effects of the present application are:

[0022] 1. By the method of the present application, multi-parameter real-time monitoring technology is used to monitor the state of different materials such as deformation resistance, temperature, strain rate, etc. at the same time, and the deformation process of each material is accurately controlled through dynamic pressure adjustment. This can effectively avoid the situation that the material with high hardness cannot be fully deformed due to insufficient pressure, or the material with low hardness cracks and pores due to excessive pressure, thereby ensuring the quality and precision of the composite parts.

[0023] 2. Through the method of the present application, a variety of parameters can be obtained in real time, and the pressure is dynamically adjusted based on these data to ensure the optimal forming state of the material. This method can effectively respond to temperature changes caused by environmental fluctuations, avoid performance degradation of the material due to overheating or overcooling, and thus improve the mechanical properties and stability of the product.

[0024] 3. The present application integrates various sensors such as temperature sensors, strain sensors and force sensors, etc., which can comprehensively monitor the state of the material and provide real-time feedback and adjustment based on multiple parameters. This multi-parameter collaborative control method can significantly improve the precision of pressure control, thereby reducing the scrap rate and defects in the production process, especially in high-end manufacturing fields such as the production of precision instruments or high-performance composite parts, which can effectively avoid safety hazards caused by defects.

[0025] 4. Through the method of the present application, based on real-time monitoring data and dynamic algorithms, adaptive adjustment can be achieved, and the system can automatically optimize the pressure setting according to the real-time changes of the material, greatly enhancing the adaptability and intelligent level of the system. This feature makes the forging process more flexible and suitable for the production of various complex multi-material composite parts.

[0026] 5. The present application significantly improves production efficiency and yield by precise pressure control and real-time feedback of material state, reduces waste caused by excessive deformation or insufficient deformation, and reduces energy waste caused by inaccurate control, thereby improving resource utilization efficiency and effectively reducing production costs. BRIEF DESCRIPTION OF DRAWINGS

[0027] In order to more clearly illustrate the technical solutions in the present application or prior art, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, for those skilled in the art, other drawings can also be obtained based on these drawings without creative labor.

[0028] Fig. 1 is a step flow chart of the method of the present application;

[0029] Fig. 2 is a step flow chart of the method of the present application for continuous monitoring and termination of the forging process. DETAILED DESCRIPTION

[0030] The present application will be described in detail below in conjunction with the drawings and specific embodiments. It should be noted here that in order to make the embodiments more detailed, the following embodiments are the best, preferred embodiments, and other alternative ways can also be used by those skilled in the art to implement them; and the drawings are only used to more specifically describe the embodiments, and are not intended to specifically limit the present application.

[0031] Referring to Figs. 1-2 The embodiment of the present application provides a pressure self-adaptive adjustment method for multi-material forging. Before the forging process starts, the forging equipment and the parameter acquisition system are initialized. The forging equipment is composed of an upper die, a lower die and a pressure execution mechanism. Displacement, temperature and pressure sensors are used to monitor the displacement, surface temperature and internal pressure of the die, respectively. The temperature sensor is installed on the contact surface of the die, the displacement sensor measures the movement of the die through the laser ranging principle, and the pressure sensor is integrated in the pressure execution mechanism. By dynamically adjusting the sampling frequency, the acquisition system can obtain sufficiently accurate data according to the characteristics of different materials and the forging speed.

[0032] During the forging process, the temperature sensor measures the surface temperature of the material in real time through a non-contact method, the displacement sensor calculates the strain of the material according to the displacement of the die, and the pressure sensor measures the output pressure of the execution mechanism. All data are transmitted to the central processing unit through the data bus for filtering, normalization and synchronization processing. The synchronization is ensured by a hardware trigger mechanism to ensure the accuracy and real-time performance of the data.

[0033] According to the real-time collected temperature, strain and pressure data, the central processing unit calculates the dynamic deformation resistance of the material using a material constitutive model. The model is based on the stress-strain relationship of the material and takes into account the effect of temperature on the flow stress. During the calculation process, the temperature change adjusts the material flow stress coefficient, and the strain is calculated by the incremental method to calculate the instantaneous deformation resistance. By comparing the calculated dynamic deformation resistance with the theoretical deformation resistance, a deviation value is generated, which provides a basis for subsequent pressure regulation.

[0034] The central processing unit generates an adaptive pressure regulation value using a proportional-integral control algorithm based on the deviation value of the dynamic deformation resistance. The proportional coefficient and integral coefficient of the algorithm are dynamically adjusted according to different material types and forging stages, and this adjustment process is realized through a lookup table, which is constructed based on historical optimization data. During the calculation process, the difference between the deviation value and the target deformation resistance will affect the pressure compensation amount, and finally the compensation amount is added to the current pressure value to generate a new target pressure value.

[0035] The central processing unit converts the target pressure value into a control signal and sends it to the pressure execution mechanism, which adjusts the hydraulic flow through an electro-hydraulic servo system to output the required target pressure. The servo valve is driven through a digital-to-analog converter, and the real-time feedback pressure value is used to form a closed-loop control. If the error between the actual pressure value and the target pressure value exceeds the preset threshold value, the system will re-enter the pressure regulation link and continue data acquisition and processing to ensure the accuracy and stability of the pressure control.

[0036] During the entire forging process, the system will continuously monitor various parameters until the mold displacement reaches the preset completion position. When the set conditions are met, the central processing unit sends a termination signal, gradually unloads the pressure, and completes the forging process. At the same time, all collected parameters and pressure adjustment records will be stored for subsequent quality analysis and process optimization.

[0037] Through the collection and processing of real-time data from multiple sensors, the deformation and state of each material during the forging process can be accurately mastered, ensuring that the uneven deformation of different materials is effectively controlled. Real-time data calculation and proportional-integral control algorithms are used to enable the system to dynamically adjust the pressure based on the real-time state of different materials. This adaptive capability makes the forging process less affected by environmental changes, improving the stability and reliability of the forging process.

[0038] In one possible implementation, first, the temperature sensor is an infrared thermometer, which is installed near the contact surface of the upper and lower molds. The infrared thermometer can accurately measure the temperature changes on the material surface non-contact, and its sampling frequency is dynamically set to 100 data per second according to the forging speed. This can track the temperature changes in real time and effectively deal with the temperature fluctuations caused by different forging speeds, thereby providing accurate temperature data for subsequent deformation resistance calculation.

[0039] The displacement sensor is a laser displacement sensor, which is installed on the mold guide column. This sensor can accurately measure the relative displacement of the mold and calculate the real-time strain value of the material based on the displacement. The selection of the laser displacement sensor makes the measurement process contactless, avoiding errors or interference caused by direct contact. The relative displacement data of the mold plays a key role in the forging process, accurately reflecting the deformation process and stress distribution of the material.

[0040] During the initialization process, the system also obtains the initial material thickness data through a calibration program. This program ensures that the initial state of the material is accurately recorded, providing the necessary reference for subsequent dynamic deformation resistance calculation. The pressure sensor is integrated into the hydraulic cylinder, with a measurement range covering the maximum working pressure of the forging machine. This pressure sensor accurately detects the output pressure of the pressure actuator, providing key feedback data for the entire process and ensuring the accuracy of pressure control.

[0041] The basic characteristic data of multiple materials comes from a specialized material library that contains the deformation resistance curves of aluminum alloys and steel at different temperatures. This material library is built from a large amount of historical experimental data and can provide material responses under different working conditions to support real-time calculation of deformation resistance prediction and correction. By loading these data, the system can accurately control and adjust under different material conditions.

[0042] In addition, during the initialization phase, the central processing unit also performs a self-checking program to ensure the correct connection status of the sensors and the pressure actuator. This self-checking process can detect the working status of the system hardware in real time, avoiding process instability or loss of control due to hardware failure.

[0043] In one possible implementation, first, the temperature sensor measures the temperature of the material surface through infrared radiation. This measurement method can avoid direct contact with the material, thereby reducing measurement errors and improving real-time monitoring efficiency. To ensure the accuracy of the measurement value, the data collected by the temperature sensor needs to be compensated for the ambient temperature. This compensation is achieved by referencing the ambient temperature sensor data, i.e., using real-time data from the ambient temperature sensor to correct the material surface temperature to eliminate the influence of ambient temperature fluctuations on the measurement results, ensuring more accurate temperature values.

[0044] The displacement sensor measures the displacement of the mold using the principle of laser reflection. The laser displacement sensor can accurately track the displacement of the mold relative to the material during the forging process. Based on the real-time displacement data, the system calculates the real-time strain value, i.e., the ratio of the real-time displacement to the initial material thickness, thereby obtaining the strain state of the material during the forging process. This data is crucial for controlling the material deformation process.

[0045] The pressure sensor detects the pressure value generated during the forging process by directly outputting a voltage signal. After analog-to-digital conversion, the voltage signal can be converted into the corresponding pressure value, providing accurate pressure feedback. This ensures that the system can accurately measure and adjust the working pressure of the forging machine.

[0046] The data bus uses CAN bus, a communication protocol widely used in industrial automation, which ensures that the data collected by each sensor can be efficiently and stably transmitted to the central processing unit. Through this bus system, sensor data can be quickly collected and processed uniformly.

[0047] The central processing unit uses a moving average filtering method to process data to reduce the influence of noise caused by external interference or sensor precision limitations. Through this method, data fluctuations can be smoothed, making the system's response to signal changes more stable and reducing the impact of sudden noise on system judgment.

[0048] The hardware trigger mechanism ensures that temperature, strain, and pressure data can be aligned at the same timestamp through a synchronous clock signal. The purpose of this is to ensure that all parameter data is collected synchronously within the same time window, avoiding inconsistencies or errors caused by time misalignment, thereby improving the accuracy and usability of the data.

[0049] Through temperature compensation, laser measurement, pressure sensor output and data filtering, etc., the accuracy and stability of various data can be significantly improved, providing a reliable basis for subsequent pressure regulation.

[0050] In one possible implementation, first, the Johnson-Cook material model is used to describe the stress-strain behavior of the material during dynamic deformation. The model includes three key terms: strain hardening term, strain rate term and temperature term. Through these terms, the deformation resistance of the material during forging can be accurately simulated due to changes in strain, temperature and strain rate.

[0051] The first step in calculating the dynamic deformation resistance is to query the temperature softening coefficient from the material property data table according to the real-time temperature value. The material property data table is constructed after calibration by pre-experiment, ensuring that the temperature-related data is consistent with the actual material performance. The temperature softening coefficient characterizes the influence of temperature on the material resistance, and the resistance of the material at high temperature usually decreases, so this coefficient is an important factor that cannot be ignored in the calculation of dynamic deformation resistance.

[0052] Next, the real-time strain value is used to calculate the strain hardening term. The strain hardening term is obtained by multiplying the real-time strain value with the material hardening coefficient. The hardening coefficient of the material is obtained by regression analysis of experimental data in the study of the mechanical properties of the material, reflecting the change of the hardening degree of the material with the increase of plastic deformation.

[0053] Finally, the temperature softening coefficient, strain hardening term and material constant are multiplied to obtain the dynamic deformation resistance. The material constant includes the strength coefficient and the strain rate sensitivity coefficient, and these two constants are also determined by regression analysis of multiple forging experiments. The strength coefficient describes the basic strength of the material, and the strain rate sensitivity coefficient reflects the influence of strain rate on the material performance.

[0054] After the dynamic deformation resistance is calculated, the system calculates the deviation value by the absolute difference between the dynamic deformation resistance and the theoretical deformation resistance. The calculation of the deviation value helps to verify the accuracy of the model and calibrate the calculation results, so as to ensure more accurate pressure regulation in the forging process.

[0055] Through the Johnson-Cook material model combined with real-time parameters, the dynamic response of the material during forging can be more accurately simulated, and more accurate deformation resistance values can be provided. The embodiment of the present application considers three factors of temperature, strain and strain rate, so that the calculation results can better reflect the complex changes in the actual forging process, avoiding errors caused by ignoring a single factor.

[0056] In one possible implementation, a proportional-integral control algorithm is used to adjust the pressure value based on the deviation of the dynamic deformation resistance. The settings of the proportional and integral coefficients of the proportional-integral control algorithm have different requirements at different stages. In the initial stage of forging, the proportional and integral coefficients are set to higher values to ensure that the system can quickly respond to dynamic changes, especially in the early stage of the material entering the forging process, quickly adjusting to the appropriate pressure level is crucial to avoid excessive deformation or damage. After entering the stable stage of forging, the proportional and integral coefficients are then lowered to reduce system oscillation and over-adjustment, ensuring stable forging process.

[0057] These higher and lower values are determined through optimization experiments, which ensure that the control parameters can maximize the efficiency and stability of the forging process. The use of lookup tables plays a key role in this step. The lookup table is indexed based on material type and forging stage, and can provide the best control parameters for the corresponding material and stage. To cope with the changes in the forging process, the lookup table is also updated through a real-time learning algorithm. The real-time learning algorithm uses the recursive least squares method to adjust the proportional and integral coefficients, ensuring that the system can automatically optimize parameters according to changes in actual operating conditions, thereby improving control accuracy and response speed.

[0058] The calculation of the pressure compensation amount includes a proportional term and an integral term. The proportional term is obtained by multiplying the deviation of the dynamic deformation resistance by the proportional coefficient, and the integral term is obtained by multiplying the integral of the deviation of the dynamic deformation resistance by the integral coefficient. After combining the two, the resulting pressure compensation amount can effectively adjust the current pressure value. Finally, the target pressure value is obtained by algebraically adding the current pressure value and the pressure compensation amount, which ensures that the system can maintain accurate and stable pressure control in a dynamic environment.

[0059] In one possible implementation, first, the core components of the electro-hydraulic servo system include a hydraulic pump, a servo valve, and a hydraulic cylinder. The hydraulic pump provides hydraulic oil flow, and the servo valve adjusts the hydraulic flow according to the control signal to control the output pressure of the hydraulic cylinder. In order to achieve high-precision pressure control, the control signal needs to be converted by a digital-to-analog converter (DAC) to convert the digital signal into an analog voltage signal, so that the servo valve can adjust the hydraulic flow according to the analog voltage signal to accurately control the pressure output of the hydraulic cylinder.

[0060] The pressure sensor plays an important role throughout the process. By monitoring the actual output pressure of the hydraulic cylinder in real time, the pressure sensor feeds back data to the system. This feedback signal is used in the negative feedback mechanism of closed-loop control. Under the negative feedback mechanism, when there is a deviation between the output pressure and the target pressure value, the system will automatically adjust the control parameters to reduce the error and ensure that the target pressure is maintained during the forging process.

[0061] To ensure the accuracy of the pressure regulation process, the system sets a threshold, which is set according to material tolerances and forging precision requirements. These thresholds are determined through statistical process control (SPC), which helps to establish reasonable tolerance ranges by analyzing historical data, process fluctuations and other factors, thereby avoiding quality problems caused by excessive or insufficient pressure.

[0062] During the iterative adjustment process, the central processing unit (CPU) is responsible for real-time monitoring of error change rate. Error change rate refers to the change in the difference between actual pressure value and target pressure value. If the error change rate exceeds the preset limit, the central processing unit will adjust the sampling frequency and control parameters in a timely manner according to the feedback signal. By adjusting the sampling frequency, data can be collected more frequently to ensure a more timely response to system dynamics, while adjusting the control parameters can optimize the pressure regulation process and improve control accuracy.

[0063] Through the electro-hydraulic servo system and real-time pressure feedback, accurate pressure control can be achieved, ensuring that the pressure in the forging process is highly consistent with the target pressure value, avoiding uneven material deformation or quality problems caused by unstable pressure.

[0064] In one possible implementation, first, the forging completion position is accurately determined according to product design specifications through three-dimensional modeling software. Three-dimensional modeling software can take into account the specific dimensions, shape and processing requirements of the product, and then generate the corresponding forging completion position. This position refers to the final position of the die during the forging process, marking the completion of the forging process.

[0065] The central processing unit (CPU) monitors the displacement of the die in real time and compares it with the forging completion position. Through devices such as displacement sensors, the actual displacement of the die is constantly fed back to the central processing unit. When the displacement of the die reaches the preset forging completion position, the central processing unit generates a termination signal indicating that the forging process is about to end.

[0066] Once the termination signal is received, the pressure actuator begins to gradually unload the pressure, which is achieved through a linear pressure reduction curve. The linear pressure reduction curve is a rule for reducing pressure, which is dynamically generated in actual operation according to the type of material and forging historical data. The characteristics of each material are different, so the rate of unloading pressure needs to be adjusted according to the processing requirements of different materials, while historical data provides a reference for the generation of the pressure reduction curve to ensure that the process of unloading pressure is smooth and does not affect product quality.

[0067] In addition, all operation parameters and adjustment records are stored in non-volatile memory to ensure data persistence and subsequent review. The storage format includes time series data and event logs, the time series data records the specific operation data at each time point, and the event logs record the occurrence time of each important event and the related operation. These data provide the basis for subsequent quality analysis, by comparing the stored data with the standard quality indicators, the stability of the forging process and the quality of the product can be evaluated, and potential quality problems can be found in time.

[0068] In one possible implementation, first, the calculation of the temperature compensation factor is based on the ratio of the real-time temperature value and the reference temperature value. The real-time temperature value is obtained by real-time monitoring by the temperature sensor, reflecting the instantaneous temperature of the material during the forging process. The reference temperature value is set as the recrystallization temperature of the material, which is the temperature at which the material undergoes significant structural changes at high temperatures, and is usually determined by thermal analysis experiments. According to this ratio, the temperature compensation factor can consider the effect of temperature on the deformation resistance of the material, especially in the high-temperature forging process, the effect of temperature on the hardness and flowability of the material is significant. By introducing the temperature compensation factor, the material constitutive model can be dynamically adjusted to more accurately reflect the deformation characteristics of the material under actual working conditions.

[0069] In the calculation of instantaneous deformation resistance, a strain hardening model is used, which describes the relationship between stress and strain during the deformation process. Specifically, Voce hardening law is used, which is a widely used strain hardening model in high-temperature processing, and can well predict the change of stress with strain increase during the forging process. The parameters of Voce hardening law are calibrated by uniaxial tensile test, and the stress and strain data of the material in the test are used to fit the parameters of the model, to ensure that the strain hardening model can accurately reflect the strain hardening characteristics of the specific material.

[0070] In order to improve the accuracy of the calculation, the incremental method is used to calculate the change of strain and stress. In the incremental method, the strain increment is obtained by the difference between the real-time strain value and the strain value at the previous time step. By continuously updating the relationship between strain increment and stress increment, the incremental method can accurately track the strain and stress state of the material in each calculation step, thereby realizing efficient calculation and real-time adjustment of instantaneous deformation resistance.

[0071] The embodiment of the present application can dynamically adjust and real-time feedback the change of deformation resistance, ensure the stable progress of forging process under different temperature and strain conditions, avoid the pressure instability caused by inaccurate material constitutive relationship, and improve the stability and consistency of the forging process.

[0072] In one possible implementation, the core of the anti-windup mechanism is to limit the growth of the integral term in the control process. When the control variable of the system approaches or reaches the maximum output capacity of the pressure actuator, the growth of the integral term can lead to over-control, which may cause system instability or saturation phenomenon. To avoid this situation, the limit value is set according to the maximum output capacity of the pressure actuator, ensuring that the control signal always remains within a reasonable range. Through this mechanism, the system can effectively avoid over-reaction when approaching the maximum output, thereby improving the accuracy and stability of the control.

[0073] The construction of the lookup table utilizes a neural network model to optimize the proportional and integral coefficients. Through training with historical forging data, the neural network can automatically learn and adjust the parameters of the control algorithm in complex multi-material forging processes. The training process of the neural network uses the backpropagation algorithm, which is an optimization method that gradually reduces the prediction error by adjusting the weights of the neural network. In this way, the proportional and integral coefficients in the lookup table can adapt to the characteristics of different materials, deformation states, and processing conditions, thereby achieving precise control of the forging process.

[0074] To improve the dynamic response of the control system, a derivative term is introduced into the calculation of the pressure compensation. The role of the derivative term is to adjust the control output according to the rate of change of the dynamic deformation resistance deviation value, so that the system can respond more quickly to sudden changes in the deformation process. The derivative coefficient is adjusted according to the strain rate sensitivity of the material, because different materials exhibit different sensitivity in their mechanical properties at different strain rates. In this way, the system can flexibly adjust the derivative response under different strain rate conditions, improving the dynamic control ability.

[0075] Through the embodiments of the present application, the pressure adaptive adjustment method can achieve more efficient and accurate control in multi-material forging processes, thereby improving production efficiency and product quality, reducing the rate of defective products, and improving the stability and reliability of the overall production process.

[0076] Correspondingly, the embodiments of the present application also provide a pressure adaptive adjustment system for multi-material forging, which includes a memory configured to store instructions, and a processor configured to call the instructions from the memory and implement the pressure adaptive adjustment method for multi-material forging as described in any of the embodiments of the present application when executing the instructions.

[0077] The following will be described in detail through embodiments: Embodiments

[0078] In this example, a 800-ton hydraulic forging press is used to form a composite engine bracket component. The component is composed of aluminum alloy and steel, where the aluminum alloy serves as the lightweight portion and the steel serves as the high-strength support portion. The forging process is performed in a high-temperature environment with an initial temperature of 400°C.

[0079] Step 1: Initialize the forging equipment and parameter acquisition system:

[0080] The initialization process includes configuring the forging press hardware and software systems. The upper and lower dies of the forging equipment are made of H13 tool steel, and the die contact surfaces are coated with a titanium nitride coating to reduce heat loss. The pressure actuator is an electro-hydraulic servo system, including a hydraulic pump, a servo valve, and a hydraulic cylinder, with a maximum working pressure of 800 MPa. The parameter acquisition system includes:

[0081] Temperature sensor: An infrared temperature measuring instrument is selected and installed near the contact surface of the upper and lower dies, 10 mm away from the material surface, to measure the surface temperature in a non-contact manner. The sampling frequency is dynamically adjusted according to the material type and forging speed: for aluminum alloy, the forging speed is relatively high (about 50 mm / s), and the sampling frequency is set to 100 times per second; for steel, the forging speed is relatively low (about 30 mm / s), and the sampling frequency is set to 80 times per second. Dynamic adjustment is based on the thermal conductivity coefficient of the material and the deformation rate, achieved through a table lookup method, with table data coming from historical optimization records.

[0082] Displacement sensor: A laser displacement sensor is used, installed on the die guide column to measure the relative displacement of the die. The initial material thickness is obtained through a calibration program: before forging, the laser sensor is used to scan the material surface, and the average value of multiple measurements is taken as the initial thickness (in this example, the aluminum alloy thickness is 20 mm and the steel thickness is 15 mm).

[0083] Pressure sensor: Integrated in the hydraulic cylinder, the measurement range is 0-1000 MPa, covering the maximum working pressure of the forging press.

[0084] The basic characteristic data of the pre-loaded multi-material is loaded from the material library, which is constructed from historical experimental data, including:

[0085] Theoretical deformation resistance: for aluminum alloy AA6061, the theoretical deformation resistance at 400°C is 150 MPa; for steel AISI1045, the theoretical deformation resistance at 400°C is 300 MPa. The data comes from uniaxial compression tests under a strain rate of 0.1 s -1 .

[0086] Thermal conductivity coefficient: aluminum alloy is 180 W / m·K, steel is 48 W / m·K, data comes from thermal physical property database.

[0087] During the initialization process, the central processing unit (using an industrial PC with an Intel i7 CPU running a real-time operating system) executes a self-checking program to verify the connection status of all sensors and pressure actuators. The self-checking program includes signal loop testing and response time checking to ensure that the temperature sensor error is less than ±1°C, the displacement sensor error is less than ±0.01 mm, and the pressure sensor error is less than ±0.5% FS.

[0088] Step 2: Real-time acquisition of multi-parameter data during forging process:

[0089] During the forging process, multi-parameter data is collected synchronously:

[0090] Temperature measurement: The infrared thermometer detects the infrared radiation intensity of the material surface and converts it into a temperature value. The measured value is processed with environmental temperature compensation: an additional environmental temperature sensor is used to monitor the workshop environmental temperature (in this case, 25°C), and the compensation formula is: compensated temperature = measured temperature + k x (environmental temperature - reference temperature), where k is the compensation coefficient (k = 0.05 for aluminum alloy and k = 0.03 for steel), and the reference temperature is set to 20°C. This eliminates the interference of environmental thermal radiation.

[0091] Displacement and strain measurement: The laser displacement sensor emits a laser beam and receives the reflected signal to calculate the displacement. The real-time strain value is calculated by the formula: real-time strain value = (real-time displacement value) / (initial material thickness). For example, when the mold displacement is 5 mm, the real-time strain value of aluminum alloy is 5 / 20 = 0.25, and the real-time strain value of steel is 5 / 15 ≈ 0.33.

[0092] Pressure measurement: The pressure sensor outputs a voltage signal (0-10V), which is converted to a digital pressure value by an analog-digital converter (16-bit resolution) with the conversion formula: pressure value = (digital reading x 1000) / 65535 MPa.

[0093] All collected parameters are transmitted to the central processing unit through the CAN bus at a transmission rate of 1 Mbps. The central processing unit uses a moving average filtering method to process the data: taking the arithmetic mean of the last 10 sampling values to reduce noise. The synchronization of parameter acquisition is achieved through a hardware trigger mechanism: using a synchronous clock signal (generated by the central processing unit, frequency 1 kHz) to ensure that temperature, strain, and pressure data are aligned at the same timestamp. For example, the timestamp deviation of each sampling point is less than 1 ms.

[0094] Step 3: Calculate the dynamic deformation resistance of the material based on real-time parameters:

[0095] The central processing unit uses the Johnson-Cook material model to calculate the dynamic deformation resistance. The Johnson-Cook model expression is:

[0096] ;

[0097] in:

[0098] Dynamic deformation resistance (MPa);

[0099] Real-time strain value (unitless);

[0100] strain rate (s) -1 ), calculated by the time derivative of the real-time strain value;

[0101] For reference strain rate;

[0102] The temperature is dimensionless. T represents the real-time temperature value (°C). At room temperature (25°C). The melting point of the material (580°C for aluminum alloy, 1460°C for steel);

[0103] A, B, C, n, and m are material constants, calibrated through preliminary experiments: for aluminum alloys, A = 150 MPa, B = 100 MPa, n = 0.25, C = 0.02, m = 1.0; for steel, A = 300 MPa, B = 400 MPa, n = 0.15, C = 0.06, m = 1.1.

[0104] Calculation process:

[0105] The temperature softening coefficient (i.e., the coefficient in the model) is obtained by querying the material property data table based on the real-time temperature value. (Item). For example, when the real-time temperature of the aluminum alloy is 450°C, The temperature softening coefficient is 1-0.77¹≈0.23.

[0106] Based on the real-time strain values, the strain hardening term (i.e., the (A+Bεⁿ) term in the model) is calculated. For example, when the real-time strain value of the aluminum alloy is 0.25, the strain hardening term is 150+100×0.25. 0.25 ≈200MPa.

[0107] Calculate the strain rate term (i.e., in the model) Item), reference strain rate Set to 0.1s -1 For example, when the strain rate When, the strain rate term is .

[0108] The temperature softening coefficient, strain hardening term and strain rate term are multiplied to obtain the dynamic deformation resistance. For example, for aluminum alloy, σ = 200 x 1.03 x 0.23 ≈ 47.4 MPa.

[0109] The dynamic deformation resistance is compared with the theoretical deformation resistance to generate a deviation value. Deviation value = |dynamic deformation resistance - theoretical deformation resistance|. For example, the aluminum alloy deviation value is |47.4 - 150| = 102.6 MPa.

[0110] The material constants are determined by regression analysis of multiple sets of forging experiments: the stress-strain curve is fitted using the least squares method, and the fitting error is less than 5%. The temperature compensation factor is calculated by the ratio of the real-time temperature value to the reference temperature value, and the reference temperature value is set to the material recrystallization temperature (300°C for aluminum alloy and 700°C for steel), which is determined by thermal analysis experiments (differential scanning calorimetry). In the incremental method, the strain increment is obtained by the difference between the real-time strain value and the previous time step strain value, and the time step is the sampling interval (0.01 s).

[0111] Step 4: Generate adaptive pressure adjustment value according to dynamic deformation resistance:

[0112] The central processing unit generates an adaptive pressure adjustment value using a proportional-integral control algorithm. The proportional-integral control algorithm expression is:

[0113] Output = K_p x e(t) + K_i x ∫e(t)dt;

[0114] Where:

[0115] e(t) is the dynamic deformation resistance deviation value;

[0116] K_p is the proportional coefficient;

[0117] K_i is the integral coefficient.

[0118] The proportional coefficient and integral coefficient are dynamically adjusted according to the material type and forging stage:

[0119] In the initial stage of forging (0-30% stroke), a higher value is used: for aluminum alloy, K_p = 0.5, K_i = 0.1; for steel, K_p = 0.8, K_i = 0.2.

[0120] In the stable stage of forging (30-100% stroke), a lower value is used: for aluminum alloy, K_p = 0.2, K_i = 0.05; for steel, K_p = 0.4, K_i = 0.1.

[0121] The higher value and lower value are determined by optimization experiments: the gradient descent method is used to iteratively optimize to minimize pressure fluctuations.

[0122] The adjustment process is implemented through a lookup table, which is built based on historical optimization data with index keys of material type and forging stage (expressed in percentage of displacement). The lookup table is updated through a real-time learning algorithm: the proportional coefficient and integral coefficient are adjusted using recursive least squares method with a forgetting factor of 0.99 to adapt to material changes.

[0123] Calculation of pressure compensation:

[0124] Proportional term: P term = K_p x e(t).

[0125] Integral term: I term = K_i x Σ e(t) x Δt, where Δt is the sampling interval.

[0126] Pressure compensation = P term + I term.

[0127] Target pressure value = current pressure value + pressure compensation.

[0128] An anti-windup mechanism is implemented by limiting the growth of the integral term: the upper limit of the integral term is set to 10% of the maximum output pressure of the pressure actuator (i.e. 80 MPa), and the lower limit is set to 0. A derivative term is added to improve dynamic response: derivative term = K_d x de(t) / dt, where K_d is the derivative coefficient (K_d = 0.01 for aluminum alloy, K_d = 0.02 for steel), adjusted according to material strain rate sensitivity. The strain rate sensitivity is determined through strain rate change tests.

[0129] Step 5: Control the pressure actuator to output the target pressure value:

[0130] The central processing unit converts the target pressure value into a control signal (0-10V analog signal) and drives the servo valve through a digital-to-analog converter (16-bit resolution). The electro-hydraulic servo system includes:

[0131] Hydraulic pump: variable pump, maximum flow 100 L / min.

[0132] Servo valve: Moog D661 series, frequency response 100 Hz.

[0133] Hydraulic cylinder: cylinder diameter 200 mm, stroke 500 mm.

[0134] The servo valve adjusts the hydraulic flow according to the control signal, and the flow is proportional to the signal voltage. The pressure sensor feeds back the actual pressure value in real time to form a closed-loop control. The threshold is set according to the material tolerance and forging accuracy requirements: threshold = target pressure value x tolerance rate. The tolerance rate is determined by statistical process control methods: for aluminum alloy, the tolerance rate is 5%; for steel, the tolerance rate is 3%. For example, when the target pressure value is 500 MPa, the aluminum alloy threshold is 25 MPa.

[0135] If the error between the actual pressure value and the target pressure value exceeds the threshold value, steps 2 to 5 are repeated for iterative adjustment. During the iteration, the central processing unit monitors the error change rate (the slope of the error change over time) in real time, and if the error change rate exceeds a preset limit value (for example, 10 MPa / s), the sampling frequency (increased to 150 Hz) and the control parameters (such as increasing K_p) are adjusted.

[0136] Step 6: Continuous monitoring and termination of the forging process:

[0137] Steps 2 to 5 are repeated until the mold displacement reaches the preset forging completion position. The forging completion position is determined according to the product design specifications by three-dimensional modeling software (such as SolidWorks): in this example, the forging completion position of the engine support is that the mold displacement reaches 30 mm. The central processing unit compares the mold displacement with the forging completion position in real time, and when the displacement is ≥ 30 mm, a termination signal is generated.

[0138] The pressure actuator gradually unloads the pressure through a linear pressure reduction curve: the pressure reduction curve is dynamically generated according to the material type and forging history data. For example, the pressure reduction curve for aluminum alloy is that the pressure is linearly reduced from the current value to 0 within 2s; the pressure reduction curve for steel is that the pressure is linearly reduced to 0 within 3s. All parameters and adjustments are recorded in the non-volatile memory (SSD, 1TB), and the storage format includes time series data (CSV format) and event logs (XML format). Quality analysis is performed by comparing the stored data with standard quality indicators: for example, the deformation resistance deviation should be less than 10%, and the pressure fluctuation should be less than 5%.

[0139] The present application encompasses any alternative, modification, equivalent method and scheme made on the essence and scope of the present application. In order to make the public have a thorough understanding of the present application, specific details are described in the following preferred embodiments of the present application, and the present application can also be fully understood without the description of these details to those skilled in the art. In addition, in order to avoid unnecessary confusion to the essence of the present application, well-known methods, processes, procedures, elements and circuits, etc. are not described in detail.

[0140] The above is only the preferred embodiment of the present application, and it should be noted that for ordinary skilled in the art, without departing from the principles of the present application, a number of improvements and refinements can also be made, which should be considered as the protection scope of the present application.

Claims

1. A method for adaptive pressure adjustment in multi-material forging, characterized in that, Includes the following steps: Step 1: Initialize the forging equipment and parameter acquisition system. The forging equipment includes an upper die, a lower die, and a pressure actuator. The parameter acquisition system includes a temperature sensor, a displacement sensor, and a pressure sensor. The temperature sensor is installed on the contact surface of the forging die, the displacement sensor is installed on the moving parts of the die, and the pressure sensor is integrated into the pressure actuator. The sampling frequency of the parameter acquisition system is set to be dynamically adjusted according to the material type and forging speed. The basic characteristic data of multiple materials are preloaded, including the theoretical deformation resistance and thermal conductivity of the materials. Step 2: Real-time acquisition of multi-parameter data during the forging process. Temperature sensors measure the surface temperature of the material in a non-contact manner. Displacement sensors measure the displacement of the mold using the laser ranging principle and calculate the real-time strain value of the material based on the displacement. Pressure sensors read the output pressure value of the pressure actuator. All acquired parameters are transmitted to the central processing unit via the data bus for filtering and normalization. The synchronization of parameter acquisition is achieved through a hardware triggering mechanism. Step 3: Calculate the dynamic deformation resistance of the material based on real-time parameters. The central processing unit uses the collected real-time temperature, strain and pressure values ​​to calculate the dynamic deformation resistance through the material constitutive model. The material constitutive model is built based on the stress-strain relationship and temperature dependence of the material. The calculation process includes adjusting the material's flow stress coefficient according to the real-time temperature value, calculating the instantaneous deformation resistance using the incremental method in combination with the real-time strain value, and comparing the dynamic deformation resistance with the theoretical deformation resistance to generate a deviation value. Step 4: Generate an adaptive pressure adjustment value based on the dynamic deformation resistance. The central processing unit uses a proportional-integral control algorithm to generate a pressure compensation amount based on the dynamic deformation resistance deviation value, and then superimposes the pressure compensation amount with the current pressure value to generate the target pressure value. Step 5: Control the pressure actuator to output the target pressure value. The central processing unit converts the target pressure value into a control signal and sends it to the pressure actuator. The pressure actuator adopts an electro-hydraulic servo system. The control signal drives the servo valve to adjust the hydraulic flow and output the target pressure value through a digital-to-analog converter. The pressure sensor provides real-time feedback of the actual pressure value to form a closed-loop control. If the error between the actual pressure value and the target pressure value exceeds the threshold, steps 2 to 5 are repeated for iterative adjustment. Step 6: Continuously monitor and terminate the forging process. Repeat steps 2 to 5 until the die displacement reaches the preset forging completion position. The central processing unit issues a termination signal, and the pressure actuator gradually unloads the pressure to complete the forging process. All parameters and adjustment records are stored for quality analysis.

2. The adaptive pressure adjustment method for multi-material forging according to claim 1, characterized in that, Step 1, which initializes the forging equipment and parameter acquisition system, also includes: an infrared thermometer as the temperature sensor, installed near the contact surface of the upper and lower dies, with a sampling frequency set to 100 data acquisitions per second based on the forging speed; a laser displacement sensor installed on the die guide pillar to measure the relative displacement of the die; initial material thickness obtained through a calibration procedure; a pressure sensor integrated into the hydraulic cylinder, with a measurement range covering the maximum working pressure of the forging press; and basic characteristic data of multiple materials loaded from a material library, including deformation resistance curves of aluminum alloys and steel at different temperatures. The material library is constructed using historical experimental data. During initialization, the central processing unit executes a self-test procedure to verify the connection status of the sensors and pressure actuators.

3. The adaptive pressure adjustment method for multi-material forging according to claim 1, characterized in that, Step 2, which involves real-time acquisition of multi-parameter data during the forging process, also includes: The temperature sensor measures the surface temperature of the material through infrared radiation, and the measured value is compensated for the ambient temperature. Ambient temperature compensation is achieved by referencing ambient temperature sensor data. The displacement sensor measures displacement using the principle of laser reflection. The real-time strain value is calculated as the ratio of the real-time displacement to the initial material thickness. The pressure sensor directly outputs a voltage signal, which is converted from analog to digital to obtain the pressure value. The data bus uses a CAN bus. The central processing unit uses a moving average filtering method to process the data to reduce noise. The hardware triggering mechanism ensures that the temperature, strain, and pressure data are aligned at the same timestamp by using a synchronous clock signal.

4. The adaptive pressure adjustment method for multi-material forging according to claim 1, characterized in that, Step 3, which calculates the dynamic deformation resistance of the material based on real-time parameters, also includes: using the Johnson-Cook material model as the material constitutive model. The Johnson-Cook material model includes strain hardening, strain rate, and temperature terms. The process of calculating the dynamic deformation resistance includes first obtaining the temperature softening coefficient by querying the material property data table based on the real-time temperature value. The material property data table is constructed through pre-experiment calibration. Second, the strain hardening term is calculated by combining the real-time strain value. The strain hardening term is obtained by multiplying the strain value by the material hardening coefficient. Finally, the strain hardening term, strain rate term, and temperature term are multiplied together to obtain the dynamic deformation resistance. The material constants include the strength coefficient and the strain rate sensitivity coefficient. The material constants are determined through regression analysis of multiple sets of forging experiments. The deviation value is calculated by the absolute difference between the dynamic deformation resistance and the theoretical deformation resistance.

5. The adaptive pressure adjustment method for multi-material forging according to claim 1, characterized in that, Step 4, generating adaptive pressure adjustment values ​​based on dynamic deformation resistance, further includes: setting the proportional coefficient and integral coefficient of the proportional-integral control algorithm to higher values ​​in the initial forging stage for rapid response, and setting them to lower values ​​in the stable forging stage to reduce oscillations. The higher and lower values ​​are determined through optimization experiments. The lookup table is based on the material type and forging stage index. The lookup table is updated through a real-time learning algorithm, which uses recursive least squares to adjust the proportional coefficient and integral coefficient. The calculation of the pressure compensation includes a proportional term and an integral term. The proportional term is the product of the dynamic deformation resistance deviation value and the proportional coefficient, and the integral term is the product of the integral of the dynamic deformation resistance deviation value and the integral coefficient. The target pressure value is obtained by the algebraic sum of the current pressure value and the pressure compensation.

6. The adaptive pressure adjustment method for multi-material forging according to claim 1, characterized in that, Step 5, controlling the output target pressure value of the pressure actuator, also includes: the electro-hydraulic servo system includes a hydraulic pump, a servo valve, and a hydraulic cylinder. The control signal is converted into an analog voltage signal by a digital-to-analog converter to drive the servo valve. The servo valve adjusts the hydraulic flow to control the output pressure of the hydraulic cylinder. The pressure sensor provides real-time feedback of the actual pressure value. The closed-loop control adopts a negative feedback mechanism. The threshold is set according to the material tolerance and forging accuracy requirements. The threshold is determined by a statistical process control method. During the iterative adjustment process, the central processing unit monitors the error change rate in real time. If the error change rate exceeds the preset limit, the sampling frequency and control parameters are adjusted.

7. The adaptive pressure adjustment method for multi-material forging according to claim 1, characterized in that, Step 6, which involves continuously monitoring and terminating the forging process, also includes: The forging completion position is determined using 3D modeling software based on the product design specifications. The central processing unit compares the die displacement with the forging completion position in real time. When the die displacement reaches the forging completion position, the central processing unit generates a termination signal. The pressure actuator gradually unloads pressure through a linear pressure reduction curve, which is dynamically generated based on material type and forging history data. All parameters and adjustment records are stored in non-volatile memory, with storage formats including time-series data and event logs. Quality analysis is performed by comparing the stored data with standard quality indicators.

8. The method for adaptive pressure adjustment in multi-material forging according to claim 1, characterized in that, In step 3, the material constitutive model also includes a temperature compensation factor. The temperature compensation factor is calculated by the ratio of the real-time temperature value to the reference temperature value. The reference temperature value is set as the material recrystallization temperature, which is determined by thermal analysis experiments. When calculating the instantaneous deformation resistance, a strain hardening model is used. The strain hardening model adopts Voce's hardening law. The parameters of Voce's hardening law are calibrated by uniaxial tensile tests. The incremental method calculation includes correlating the strain increment with the stress increment. The strain increment is obtained by the difference between the real-time strain value and the strain value of the previous time step.

9. The adaptive pressure adjustment method for multi-material forging according to claim 1, characterized in that, Step 4 of the proportional-integral control algorithm also includes an anti-saturation mechanism, which is achieved by limiting the growth of the integral term. The limit value is set according to the maximum output capacity of the pressure actuator. The construction of the lookup table includes optimizing the proportional coefficient and integral coefficient using a neural network model. The neural network model is obtained by training historical forging data. The training process uses the backpropagation algorithm. The calculation of the pressure compensation also includes a differential term to improve the dynamic response. The differential term is obtained by multiplying the rate of change of the dynamic deformation resistance deviation value with the differential coefficient. The differential coefficient is adjusted according to the material strain rate sensitivity.

10. A pressure adaptive adjustment system for multi-material forging, characterized in that, The system includes a memory configured to store instructions, a processor configured to retrieve the instructions from the memory, and, when executing the instructions, to implement a multi-material forging pressure adaptive adjustment method as described in any one of claims 1-9.

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