Intelligent parameter control method of necking forming ball wrapping equipment

Abstract

The invention discloses an intelligent parameter control method of necking forming ball wrapping equipment, and relates to the technical field of intelligent equipment. Comprising the following steps: S1, establishing a material thermodynamic characteristic database, and determining a corresponding relationship among yield strength-temperature-strain rate of a metal material in different processing stages; s2, real-time thermal stress compensation is carried out on the sensor data, and a cooperative control curve between pressure and temperature is optimized; and S3, according to the real-time deformation data of the metal material, the flow speed and direction of the cooling medium are dynamically adjusted. The temperature change in the machining process is monitored in real time, thermal stress compensation is conducted, therefore, it is ensured that the mechanical stress does not exceed the critical value, irreversible plastic deformation of the material is avoided, meanwhile, a cooperative control curve between pressure and temperature is optimized, the technological requirements are met, and meanwhile the production cost is reduced. The defects are effectively inhibited; and the energy consumption is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent equipment, and in particular to an intelligent parameter control method for a shrinkage forming ball encapsulation equipment. Background Art

[0002] Neck forming is a metalworking process that reduces the diameter of one end of a tubular or annular workpiece to create the desired shape. This process is widely used to manufacture various types of pipe fittings, valves, and other parts requiring specific shapes and dimensional accuracy. Spherical coating, on the other hand, involves coating certain mechanical components with a spherical material to impart wear resistance or other special properties.

[0003] Traditional necking and ball-encapsulating equipment typically relies on manual setting and adjustment of process parameters such as pressure, temperature, and speed. This method is not only inefficient but also makes it difficult to ensure consistent product quality. Differences in operator experience and skill often lead to varying product quality. Furthermore, with increasing demands for product precision, traditional manual adjustment methods are increasingly unable to meet the demands of modern manufacturing.

[0004] The Chinese invention patent with publication number CN119407408A discloses a method for controlling the three-dimensional shrinkage rate of motorcycle muffler welding, which includes: (1) constructing a thermal-mechanical coupling model of the welding process; (2) determining the "heated stress reduction zone"; (3) collecting welding data in real time through sensors to monitor the welding temperature field and stress-strain changes; (4) processing and analyzing the monitoring data using a machine learning algorithm to establish a mapping relationship between welding parameters and three-dimensional shrinkage deformation; (5) transmitting the monitoring data to the control system in real time, comparing it with a preset threshold, and dynamically adjusting the welding process parameters and the "heated stress reduction zone" parameters based on the model prediction results and real-time data feedback to reduce welding stress and accurately control the three-dimensional shrinkage deformation.

[0005] When adjusting the parameters of the aforementioned and similar parameter adjustment methods for shrink-molding and ball-encapsulating equipment, existing shrink-molding and ball-encapsulating equipment, in pursuit of efficient production, often accelerates production by increasing mold pressure or raising processing temperature. While this significantly improves production efficiency, it can also lead to quality issues such as material deformation or surface cracking, which can affect product consistency and quality. Furthermore, during the shrink-molding process, the high temperature environment inside the mold causes significant thermal stress on the sensor, resulting in inaccurate or unstable readings, thus affecting the final consistency and quality of the product. Summary of the Invention

[0006] The object of the present invention is to provide an intelligent parameter control method for a necking forming ball encapsulation device to solve the problems raised in the above background technology.

[0007] To achieve the above-mentioned purpose, the present invention provides the following technical solution: an intelligent parameter control method for a shrinkage forming ball encapsulation device, comprising: S1: Acquire sensor data through a pressure sensor array and an infrared spectrum temperature measurement unit, and establish a material thermodynamic property database based on the sensor data to determine the corresponding relationship between the yield strength, temperature, and strain rate of the metal material at different processing stages; S2: Based on the corresponding relationship between the yield strength, temperature and strain rate of the metal material at different processing stages, the sensor data is subjected to real-time thermal stress compensation, and the coordinated control curve between pressure and temperature is optimized, including: S2.1: Perform real-time thermal stress compensation: Based on the material's corresponding temperature and strain rate data, determine the material's internal real-time thermal stress caused by temperature changes. Adjust the magnitude of the mechanical stress based on the comparison between the material's combined stress and the critical stress at which irreversible plastic deformation begins. S2.2: Optimize the coordinated control curve: Based on the corresponding relationship between the yield strength, temperature, and strain rate of the metal material at different processing stages, obtain a balance formula between the adjustment costs of pressure and temperature, determine the minimum energy loss corresponding to the pressure / temperature adjustment process, and adjust the magnitude of the mechanical stress using the adjustment method corresponding to the minimum energy loss; S3: Adjust the flow rate and injection direction of the cooling medium according to the strain rate and temperature gradient of the metal material.

[0008] Furthermore, the corresponding relationship between the yield strength, temperature and strain rate of metal materials at different processing stages is determined, including: S1.1: Acquiring sensor data: A pressure sensor array and an infrared spectrum temperature measurement unit are arranged inside the working chamber, and the pressure sensor array and the infrared spectrum temperature measurement unit simultaneously acquire sensor data; S1.2: Constructing a material thermodynamic property database: Based on the sensor data, construct a four-dimensional tensor storage architecture, including a time axis, a space axis, a process parameter axis, and a material axis; S1.3: Perform relationship modeling: Based on the four-dimensional tensor storage architecture, determine the yield strength-temperature-strain rate relationship modeling, specifically:

[0009] in: is the yield strength of the material during deformation, is the stress level coefficient, is the material constant, is the strain rate, is the activation energy required for the material to undergo plastic deformation, is the gas constant, is the temperature data when the material is deformed, is the stress index.

[0010] Furthermore, sensor data is obtained, including: M1: Setting up orthogonal redundant pressure arrays: According to the internal structure of the working chamber, multiple groups of mutually perpendicular sensor arrays are set up inside the working chamber, and each group of the sensor arrays is provided with multiple redundant nodes; M2: Redundant data fusion: Obtaining the three-dimensional pressure field distribution through the sensor array, determining the pressure distribution unevenness, and adjusting the pressure according to the pressure distribution unevenness; M3: Calibrate the infrared temperature measurement system: Obtain the internal temperature data of the working chamber through the infrared spectrum temperature measurement unit.

[0011] Furthermore, the spacing between two adjacent redundant nodes is set to 1 / 8 of the inner diameter of the working cavity, and the installation angles include an X-axis array, a Y-axis array and a Z-axis array, the X-axis array is set to the main deformation direction, the Y-axis array is set to the direction perpendicular to the material flow, and the Z-axis array is set to the thickness direction.

[0012] Furthermore, pressure adjustment is performed, including: M2.1: Monitor the axial pressure gradient of the X-axis array: Determine the strain rate by acquiring the pressure sensor data on the X-axis array, and perform pressure compensation for the hydraulic system based on the change in the strain rate. The strain rate is obtained using the formula:

[0013] in: is the strain rate, is the pressure change between two adjacent measuring points, is the physical distance between two adjacent measuring points, is the elastic modulus; M2.2: Monitor the pressure distribution on the Y-axis parting surface: Acquire the pressure sensor data on the Y-axis array, construct a real-time data matrix, and determine the unevenness evaluation value based on the real-time data matrix. Compare the unevenness evaluation value with the preset evaluation value and adjust the mold clamping force based on the comparison result. Specifically: When the unevenness evaluation value is greater than a preset evaluation value, the mold clamping force is adjusted according to the obtained adjustment amount of the mold clamping force; otherwise, the mold clamping force is not adjusted; M2.3: Monitor the radial pressure decay along the Z axis. Obtain the measured pressure gradient by acquiring data from the pressure sensors on the Z axis array. Compare the measured pressure gradient with the theoretical pressure gradient to determine the gradient deviation. Compare the gradient deviation with the preset deviation. Based on the comparison results, optimize the material heat treatment process. Specifically: When the gradient deviation is not greater than the preset deviation, the pressure decay is normal and there are no defects inside the material. Otherwise, the pressure decay is abnormal, there are cracks or loose structure inside the material, and the heat treatment process of the material needs to be optimized.

[0014] Furthermore, the formula for obtaining the unevenness evaluation value is specifically:

[0015] in: is the unevenness evaluation value, is the average pressure value in the real-time data matrix, is the maximum pressure value in the real-time data matrix, is the minimum pressure value in the real-time data matrix; The specific formula for obtaining the adjustment amount of the mold clamping force is:

[0016] in: is the adjustment amount of the mold clamping force, is the empirical correction coefficient, is the maximum pressure value in the real-time data matrix, is the minimum pressure value in the real-time data matrix, is the equivalent clamping area of the mold.

[0017] Furthermore, the magnitude of the mechanical stress is adjusted, including: N1: Obtain real-time thermal stress: Determine the real-time thermal stress by the maximum local temperature difference between different locations within the same time slice, specifically:

[0018] in: is the internal real-time thermal stress of the material caused by temperature changes, is the elastic modulus, is the linear expansion coefficient, is the maximum local temperature difference; N2: Determine the adjustment stress: Compare the comprehensive stress on the material with the critical stress at which irreversible plastic deformation begins, and adjust the magnitude of the mechanical stress based on the comparison results, specifically: When the comprehensive stress on the material is less than the critical stress at which irreversible plastic deformation begins to occur, the magnitude of the mechanical stress is not adjusted. Otherwise, the upper limit adjustment of the mechanical stress is determined according to the real-time thermal stress, specifically:

[0019] in: is the internal stress of the material caused by external mechanical loads, is the comprehensive stress on the material, It is the internal real-time thermal stress of the material caused by temperature changes.

[0020] Furthermore, the balance formula between the pressure and temperature adjustment costs is as follows:

[0021] in: is the pressure adjustment weight coefficient, is the actual external pressure value applied, is the optimal pressure value, is the temperature adjustment weight coefficient, is the temperature data when the material is deformed, For the optimal temperature.

[0022] Furthermore, the flow rate of the cooling medium is adjusted according to the strain rate of the metal material, specifically:

[0023] in: is the flow rate of coolant, is the initial flow rate of the coolant, is the proportionality coefficient, is the strain rate, is the target strain rate; According to the temperature gradient of the metal material, adjust the spray direction of the coolant medium, specifically:

[0024] in: is the spray angle of the coolant nozzle, is the rate of change of temperature in the Y-axis direction, is the rate of change of temperature in the X-axis direction, is the angle correction.

[0025] Compared with the prior art, the present invention has the following beneficial effects: First, the present invention monitors temperature changes during processing in real time and compensates for thermal stress based on the temperature changes, thereby ensuring that mechanical stress does not exceed the critical value and avoiding irreversible plastic deformation of the material. Simultaneously, by optimizing the coordinated control curve between pressure and temperature, defects are effectively suppressed and energy consumption is reduced while meeting process requirements, thereby improving product consistency and process stability. Second, the sensor array of the present invention uses at least three groups of orthogonally distributed redundant measurement nodes, which not only improves the accuracy of data acquisition but also increases fault tolerance. The sensor array is placed in the working chamber and works in conjunction with an infrared spectroscopy temperature measurement unit to acquire pressure and temperature data, thereby helping to understand the relationship between the yield strength, temperature, and strain rate of metal materials at different processing stages. Third: The present invention dynamically adjusts the flow rate and direction of the cooling medium based on the surface curvature and temperature gradient of the metal material, which helps to control the cooling rate and reduce the risk of thermal stress cracks. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 It is a structural schematic diagram of the CNC plunger sliding shoe closing machine of the present invention; Figure 2 A schematic diagram of the process for adjusting mechanical stress in the present invention; Figure 3 This is a schematic diagram of the process for pressure adjustment in the present invention; Figure 4 This is the pressure-depth variation curve in the present invention. DETAILED DESCRIPTION

[0027] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0028] Since the existing shrinkage forming ball encapsulation equipment, in the pursuit of efficient production, mostly speeds up production by increasing mold pressure or raising processing temperature, although this can greatly improve production efficiency, it will also lead to quality problems such as material deformation or surface cracks, which will affect the consistency and high quality of the product. At the same time, during the shrinkage forming process, due to the high temperature environment inside the mold, the sensor will experience significant thermal stress, which will lead to inaccurate or unstable readings, thus affecting the final consistency and high quality of the product. The technical solution of the present application uses an orthogonal redundant pressure sensor array and an infrared spectroscopy temperature measurement unit to collect three-dimensional pressure field and temperature field data in real time, and construct a four-dimensional space-time process database. At the same time, according to the real-time thermal stress compensation and pressure-temperature collaborative optimization algorithm, it dynamically adjusts the hydraulic pressure, mold clamping force and the flow and injection direction of the cooling medium, thereby achieving high-precision closed-loop control of the material forming process, effectively suppressing defects and reducing energy consumption, and improving product consistency and process stability.

[0029] Example 1 refer to Figure 1 and Figure 2 This embodiment provides an intelligent parameter control method for a shrinking molding ball-wrapping device. The intelligent parameter control method is based on Figure 1 The CNC plunger shoe closing machine (NPM-10T-D) provided in the article is used as an example to explain the specific steps, including the following: Step S1: Distribute a heat-shielded pressure sensor array and an infrared spectroscopy temperature measurement unit within the working chamber of a CNC plunger shoe closing machine. It is noteworthy that the pressure sensor array in this embodiment includes at least three groups of orthogonally distributed redundant measurement nodes. Furthermore, corresponding sensor data is acquired through the pressure sensor array and infrared spectroscopy temperature measurement unit to establish a material thermodynamic property database, thereby capturing and storing the corresponding relationships between the yield strength, temperature, and strain rate of metal materials at different processing stages. The details are as follows: Step S1.1: Get sensor data. Figure 1 The working chamber of the CNC plunger shoe closing machine shown is equipped with a heat shielding pressure sensor array and an infrared spectrum temperature measurement unit to obtain corresponding sensor data. The details are as follows: Step M1: Set up an orthogonal redundant pressure array. That is, the working chamber of the CNC plunger shoe closing machine in this embodiment is a dual-station composite structure, which includes a CNC pre-closing station and a CNC spinning station. Specifically, the CNC pre-closing station adopts a multi-stage stroke pressure head design, and the mold closing and extrusion actions are completed by the same servo electric cylinder. The CNC spinning station is equipped with a three-roller self-centering system, adopts a thrust ball and roller bearing composite structure, and adopts a position / force dual control mode. In other words, the working chamber of the CNC plunger shoe closing machine in this embodiment is a closed cavity composed of a die and a punch.

[0030] Furthermore, the die in this embodiment is a fixed end, the cavity diameter is 200mm, and the depth is 50mm. The punch is a movable end, which forms a cavity with a thickness of 2mm after closing with the die. The parting surface is the contact plane between the die and the punch. Specifically, three groups of mutually perpendicular sensor arrays (X / Y / Z axis directions) are arranged inside the mold tool cavity, wherein each group of sensor arrays is provided with three redundant nodes, and forms a 3*3 orthogonal grid. Furthermore, the spacing between adjacent nodes is set to 1 / 8 of the inner diameter of the mold. The installation angle includes an X-axis array, a Y-axis array, and a Z-axis array, specifically: The X-axis array, with the primary deformation direction, is located along the centerline of the concave mold cavity (parallel to the sheet feed direction). The node coordinates (in the concave mold coordinate system) are: (0, -75mm, 0), (0, 0, 0), and (0, 75mm, 0). In other words, three pressure sensors are located at each node, forming a 120° circular distribution.

[0031] The Y-axis array is perpendicular to the material flow and is located along the circumference of the die parting surface (perpendicular to the material flow plane). The node coordinates are: (-50mm, 0, 0), (0, 0, 0), and (50mm, 0, 0). In other words, the sensor normal is at a 45° angle to the parting surface.

[0032] The Z-axis array is in the thickness direction and is set between the end face of the punch and the closed surface of the die. The node coordinates (punch coordinate system) are: (25mm, 25mm, -2mm), (25mm, 25mm, 0) and (25mm, 25mm, +2mm).

[0033] Furthermore, on the die side, a 0.5mm deep groove is machined on the parting surface to embed the sensor module, while on the punch side, an elastic contact gasket is installed at the corresponding position. A 200μm thick Al2O3-TiC ceramic coating is sprayed on the sensor surface.

[0034] Step M2: Redundant Data Fusion. This involves acquiring a three-dimensional pressure field distribution using the sensor array installed in step M1, including axial pressure gradient data, planar pressure distribution, and pressure decay curves. Furthermore, based on the acquired three-dimensional pressure field distribution, the degree of pressure distribution nonuniformity is determined, and pressure adjustments are made based on this nonuniformity.

[0035] Step M3: Calibrate the infrared temperature measurement system. This involves using the infrared spectral temperature measurement unit installed in step S1.1 to capture the distribution of local high-temperature areas (800°C) and low-temperature areas (650°C) within the working chamber. This means obtaining temperature data within the working chamber using the infrared spectral temperature measurement unit.

[0036] Step S1.2: Construct a material thermodynamic property database. This involves constructing a four-dimensional tensor storage architecture based on the sensor data (e.g., temperature, strain rate, and pressure) acquired in step S1.1, with the four axes being the T, S, C, and M axes.

[0037] Specifically, the T-axis is the time axis, which directly records timestamp data based on the sensor's sampling frequency. The S-axis is the spatial axis, which determines the grid position based on the spatial coordinates of the pressure / temperature sensor. The C-axis is the process parameter axis, which acquires temperature, strain rate, and stress data in real time. The M-axis is the material axis, which associates material grade tags with sensor data to achieve multi-material data isolation.

[0038] Step S1.3: Perform relationship modeling. That is, based on the four-dimensional tensor storage architecture constructed in step S1.2, with temperature data and strain rate data as input features and yield strength as output feature, the yield strength-temperature-strain rate relationship modeling is obtained. Specifically,

[0039] in: is the yield strength of the material during deformation, is the stress level coefficient, is the material constant, is the strain rate, is the activation energy required for the material to undergo plastic deformation, is the gas constant, is the temperature data when the material is deformed, is the stress index.

[0040] Step S2: Based on the yield strength-temperature-strain rate relationship of the metal material at different processing stages obtained in step S1.3, real-time thermal stress compensation is performed on the sensor data. The compensation coefficient can be dynamically adjusted based on the current thermal expansion of the mold. At the same time, the coordinated control curve between pressure and temperature is optimized through a reinforcement learning model. The details are as follows: Step S2.1: Perform real-time thermal stress compensation. This involves obtaining temperature and strain rate data to determine the corresponding yield strength, comparing the determined yield strength with the total stress, and determining the mechanical stress adjustment amount and the upper limit of the mechanical stress adjustment amount. The hydraulic system pressure is adjusted based on the determined upper limit of the mechanical stress adjustment amount. The details are as follows: Step N1: Obtain real-time thermal stress. That is, based on the thermocouple data at different locations within the same time slice, obtain the maximum local temperature difference and determine the real-time thermal stress magnitude based on the maximum local temperature difference. Specifically,

[0041] in: is the internal real-time thermal stress of the material caused by temperature changes, is the elastic modulus, is the linear expansion coefficient, is the maximum local temperature difference; In the specific implementation process, the temperature of the center point of the slice is 850℃, and the temperature of the edge point is 800℃, so the maximum local temperature difference is 50℃. At the same time, the elastic modulus is 110*10 3 MPa, linear expansion coefficient is 8.6*10 -6 K -1 , so the internal real-time thermal stress caused by temperature change is 47.3MPa.

[0042] Step N2: Determine the adjustment stress. That is, based on the comparison between the comprehensive stress on the material and the critical stress at which the material begins to undergo irreversible plastic deformation, adjust the magnitude of the mechanical stress. Specifically: When the combined stress on the material is not greater than the critical stress at which the material begins to undergo irreversible plastic deformation, the upper limit of the mechanical stress adjustment is obtained based on the internal real-time thermal stress caused by the temperature change determined in step N1. Otherwise, no mechanical stress adjustment is performed.

[0043] Furthermore, the formula for obtaining the upper limit adjustment of mechanical stress is as follows:

[0044] in: is the internal stress of the material caused by external mechanical loads, is the comprehensive stress on the material, It is the internal real-time thermal stress of the material caused by temperature changes.

[0045] Furthermore, the pressure adjustment amount of the hydraulic system is determined based on the obtained upper limit adjustment amount of the mechanical stress, specifically:

[0046] in: is the newly applied force of the mechanical stress, is the internal stress of the material caused by external mechanical loads, is the actual contact area between the force-applying surface and the material.

[0047] During the specific implementation process, the comprehensive stress on the material is 106.7MPa, and the internal real-time thermal stress caused by temperature change is 47.3MPa, so the upper limit adjustment of mechanical stress is 59.4MPa. Furthermore, the actual contact area between the force-applying surface and the material is 0.01m 2 , so the new applied force of mechanical stress is 0.594MPa. In other words, the hydraulic system reduces the pressure from the initial value (for example, 1MPa) to 0.594MPa.

[0048] Step S2.2: Optimize the coordinated control curve. This is based on the yield strength-temperature-strain rate relationship of the metal material at different processing stages obtained in step S1.3, and the physical rationality between thermal stress compensation and coordinated control is obtained. In other words, while satisfying the yield strength-temperature-strain rate relationship in step S1.3, the adjustment costs of pressure and temperature are balanced, specifically:

[0049] in: is the pressure adjustment weight coefficient, is the actual external pressure value applied, is the optimal pressure value, is the temperature adjustment weight coefficient, is the temperature data when the material is deformed, For the optimal temperature.

[0050] During the specific implementation, the temperature data when the material deformed was 850°C, the actual applied external pressure value was 1 MPa, and the strain rate was 0.5s -1 , the material constant is 4.5*10 12 s -1, the stress level coefficient is 0.012MPa, the stress exponent is 5.2, and the activation energy required for plastic deformation of the material is 320KJ / mol. Therefore, the yield strength of the material during deformation is 106.7MPa.

[0051] Furthermore, the internal real-time thermal stress of the material caused by temperature changes is 47.3 MPa, so the comprehensive stress on the material is 147.3 MPa, which is much greater than the yield strength (106.7 MPa) of the material during deformation. Therefore, the pressure adjustment amount of the hydraulic system can be adjusted from the two aspects of temperature and strain rate.

[0052] Furthermore, when the temperature is lowered to 840°C, the internal real-time thermal stress of the material caused by temperature change is 37.8 MPa, so the internal stress of the material caused by the external mechanical load is 68.9 MPa, that is, the newly applied force of the mechanical stress is 0.689 MPa.

[0053] Furthermore, increasing the strain rate to 0.6s -1 At this time, the yield strength of the material during deformation is 115 MPa, so the internal stress of the material caused by the external mechanical load is 67.7 MPa, that is, the new applied force of the mechanical stress is 0.677 MPa.

[0054] Specifically, when the temperature is lowered to 840°C, the energy consumption increases by 5% according to the balance formula corresponding to the adjustment cost of pressure and temperature. -1 According to the balance formula for adjusting pressure and temperature, energy consumption increased by 8%. Therefore, in this embodiment, the new applied mechanical stress was adjusted to 0.689 MPa by lowering the temperature to 840°C.

[0055] Step S3: By acquiring the sensor data in real time, the real-time deformation data of the metal material is determined, including the surface curvature and temperature gradient, and the flow rate and direction of the cooling medium are dynamically adjusted according to the surface curvature and temperature gradient.

[0056] Furthermore, the flow rate of the coolant is adjusted according to the strain rate of the metal material, specifically:

[0057] in: is the flow rate of coolant, is the initial flow rate of the coolant, is the proportionality coefficient, is the strain rate, is the target strain rate; Furthermore, the spray angle of the coolant medium is adjusted according to the temperature gradient of the metal material, specifically:

[0058] in: is the spray angle of the coolant nozzle, is the rate of change of temperature in the Y-axis direction, is the rate of change of temperature in the X-axis direction, is the angle correction.

[0059] During the specific implementation, the target strain rate was set to 0.5s -1 , the initial flow rate of the coolant is set to 10L / s. When the strain rate of the metal material is 0.8s -1 , the coolant flow rate needs to be increased to 10.03 L / s. Furthermore, when the temperature gradient of the metal material is (5, 0) °C / mm, the spray angle of the coolant nozzle is 10°, that is, the spray angle of the coolant nozzle is deflected 10° to the right.

[0060] Example 2 refer to Figure 3 and Figure 4 This embodiment provides an intelligent parameter control method for a shrinkage molding ball-wrapping device. Its specific implementation method is the same as that of Example 1. The difference is that the pressure distribution unevenness is determined based on the obtained three-dimensional pressure field distribution, and the pressure is adjusted according to the unevenness of the pressure distribution. The present invention is illustrated below with reference to the specific implementation method of this embodiment.

[0061] The sensor array set in step M1 acquires a three-dimensional pressure field distribution, including axial pressure gradient data, planar pressure distribution, and pressure decay curves. Furthermore, based on the acquired three-dimensional pressure field distribution, the pressure distribution nonuniformity is determined, and pressure adjustment is performed based on the pressure distribution nonuniformity. The details are as follows: Step M2.1: Perform X-axis array axial pressure gradient monitoring. Specifically, three measurement points are set at 50mm intervals in the direction of plunger motion, and the pressure at each measurement point is measured. Specifically, the pressure sensor acquires pressure data at a frequency of 1kHz and a recording time of 200ms. The strain rate is then determined from this pressure sensor data. Furthermore, based on the changes in the acquired strain rate, pressure compensation in the hydraulic system is triggered to prevent material overload.

[0062] In this embodiment, the strain rate is obtained by the formula:

[0063] in: is the strain rate, is the pressure change between two adjacent measuring points, is the physical distance between two adjacent measuring points, is the elastic modulus; In the specific implementation process, the pressure between two adjacent measuring points is 92MPa and 85MPa respectively, and the physical distance between the two measuring points is 50mm. At the same time, the elastic modulus of the material is 210000MPa, so the corresponding strain rate is 0.0067s -1 .

[0064] Furthermore, when the strain rate changes suddenly, the hydraulic system performs pressure compensation to prevent the material from being overloaded. Specifically, when the strain rate changes from 0.01s -1 Increased to 0.15s -1 When loading, the hydraulic system performs pressure compensation to prevent material overload.

[0065] Step M2.2: Monitor the pressure distribution on the Y-axis parting surface. Nine piezoresistive pressure sensors (e.g., Kistler 9132A) are embedded in the parting surface of the sliding shoe and arranged in a 3x3 grid with a node spacing of 30 mm. Furthermore, the pressure sensors acquire pressure sensor data at a frequency of 1 kHz, a resolution of 0.1 MPa, and a range of 0-200 MPa. Based on the acquired pressure sensor data, a real-time data matrix is generated. Furthermore, based on this real-time data matrix, a nonuniformity assessment calculation is performed, specifically:

[0066] Among them: is the unevenness evaluation value, is the average pressure value in the real-time data matrix, is the maximum pressure value in the real-time data matrix, is the minimum pressure value in the real-time data matrix; Furthermore, the obtained unevenness evaluation value is compared with a preset evaluation value (which can be set according to actual needs, so it is not specifically explained in this embodiment). When the obtained unevenness evaluation value is greater than the preset evaluation value, the pressure distribution is balanced by adjusting the mold clamping force.

[0067] In this embodiment, when the obtained unevenness evaluation value is greater than the preset evaluation value, the mold clamping force is adjusted to achieve a balanced pressure distribution. Specifically, the adjustment amount of the mold clamping force is:

[0068] in: is the adjustment amount of the mold clamping force, is the empirical correction coefficient, is the maximum pressure value in the real-time data matrix, is the minimum pressure value in the real-time data matrix, is the equivalent clamping area of the mold.

[0069] In the process of specific implementation, the real-time data matrix obtained is MPa, the average pressure value in the real-time data matrix is 84.67 MPa, the maximum pressure value is 90 MPa, and the minimum pressure value is 78 MPa. In other words, the corresponding unevenness evaluation value is 14.3%. Furthermore, the preset evaluation value in this embodiment is set to 15%, which means that the mold clamping force does not need to be adjusted in this embodiment.

[0070] Step M2.3: Monitor the Z-axis radial pressure decay. This involves acquiring the time series of pressure values from each sensor along the Z-axis during loading (e.g., mechanical pressurization, hydraulic shock, etc.) on the CNC plunger shoe closing machine. Furthermore, a Z-axis pressure decay curve (pressure-depth curve) is plotted based on the acquired steady-state pressure values to obtain the measured pressure gradient. Specifically, the following is the calculation:

[0071] in: is the measured pressure gradient, is the pressure at the end depth point, is the pressure at the starting depth, is the depth difference between the ending depth point and the starting depth point.

[0072] Furthermore, the measured pressure gradient is compared with the theoretical pressure gradient to determine the gradient deviation, specifically:

[0073] in: is the gradient deviation, is the theoretical pressure gradient, is the measured pressure gradient.

[0074] Furthermore, the obtained gradient deviation is compared with a preset deviation (which can be set according to actual needs and is not specifically described in this embodiment). Based on the comparison result, it is determined whether cracks or structural looseness occur, thereby optimizing the heat treatment process of the material (for example, reducing the cooling rate to avoid thermal stress cracks). Specifically, When the obtained gradient deviation is not greater than the preset deviation, it means that the pressure decay is normal and there are no defects inside the material. Otherwise, the pressure decay is abnormal and there are cracks or loose structure inside the material.

[0075] In the process of specific implementation, the following Figure 4 The pressure-depth curve shown in Figure 1 shows a measured pressure gradient of -0.74 MPa / mm. Meanwhile, the theoretical pressure gradient in this embodiment is -5.2 MPa / mm, resulting in a gradient deviation of 85.8%. Furthermore, the preset deviation is set to 30%. That is, the obtained gradient deviation (85.8%) is much greater than the preset deviation (30%), indicating cracks or a loose structure within the material. This suggests that the material's heat treatment process needs to be optimized, such as adjusting the casting process (reducing the cooling rate to 5°C / s and increasing the holding time to 30 seconds) while increasing the molding pressure (from 80 MPa to 100 MPa).

[0076] Although embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is limited by the accompanying embodiments and their equivalents.

Claims

1. An intelligent parameter control method for a necking molding ball-wrapping device, characterized in that: Includes: S1: Acquire sensor data through a pressure sensor array and an infrared spectrum temperature measurement unit, and establish a material thermodynamic property database based on the sensor data to determine the corresponding relationship between the yield strength, temperature, and strain rate of the metal material at different processing stages; S2: Based on the corresponding relationship between the yield strength, temperature and strain rate of the metal material at different processing stages, the sensor data is subjected to real-time thermal stress compensation, and the coordinated control curve between pressure and temperature is optimized, including: S2.1: Perform real-time thermal stress compensation: Based on the material's corresponding temperature and strain rate data, determine the material's internal real-time thermal stress caused by temperature changes. Adjust the magnitude of the mechanical stress based on the comparison between the material's combined stress and the critical stress at which irreversible plastic deformation begins. S2.2: Optimize the coordinated control curve: Based on the corresponding relationship between the yield strength, temperature, and strain rate of the metal material at different processing stages, obtain a balance formula between the adjustment costs of pressure and temperature, determine the minimum energy loss corresponding to the pressure / temperature adjustment process, and adjust the magnitude of the mechanical stress using the adjustment method corresponding to the minimum energy loss; S3: Adjust the flow rate and injection direction of the cooling medium according to the strain rate and temperature gradient of the metal material.

2. The intelligent parameter control method for the shrinking molding ball wrapping equipment according to claim 1 is characterized in that: Determine the corresponding relationship between the yield strength, temperature and strain rate of metal materials at different processing stages, including: S1.1: Acquiring sensor data: A pressure sensor array and an infrared spectrum temperature measurement unit are arranged inside the working chamber, and the pressure sensor array and the infrared spectrum temperature measurement unit simultaneously acquire sensor data; S1.2: Constructing a material thermodynamic property database: Based on the sensor data, construct a four-dimensional tensor storage architecture, including a time axis, a space axis, a process parameter axis, and a material axis; S1.3: Perform relationship modeling: Based on the four-dimensional tensor storage architecture, determine the yield strength-temperature-strain rate relationship modeling, specifically: in: is the yield strength of the material during deformation, is the stress level coefficient, is the material constant, is the strain rate, is the activation energy required for the material to undergo plastic deformation, is the gas constant, is the temperature data when the material is deformed, is the stress index.

3. The intelligent parameter control method for the shrinking molding ball wrapping equipment according to claim 2 is characterized in that: Get sensor data, including: M1: Setting up an orthogonal redundant pressure array: According to the internal structure of the working chamber, multiple groups of mutually perpendicular sensor arrays are set up inside the working chamber, and each group of the sensor arrays is provided with multiple redundant nodes; M2: Redundant data fusion: Obtaining the three-dimensional pressure field distribution through the sensor array, determining the pressure distribution unevenness, and adjusting the pressure according to the pressure distribution unevenness; M3: Calibrate the infrared temperature measurement system: Obtain the internal temperature data of the working chamber through the infrared spectrum temperature measurement unit.

4. The intelligent parameter control method for the shrinking molding ball wrapping equipment according to claim 3 is characterized in that: The spacing between two adjacent redundant nodes is set to 1 / 8 of the inner diameter of the working cavity. At the same time, the installation angle includes an X-axis array, a Y-axis array and a Z-axis array. The X-axis array is set to the main deformation direction, the Y-axis array is set to the direction perpendicular to the material flow, and the Z-axis array is set to the thickness direction.

5. The intelligent parameter control method for the ball-wrapping equipment for shrinking and forming according to claim 3 or 4, characterized in that: Perform pressure adjustments, including: M2.1: Monitor the axial pressure gradient of the X-axis array: Determine the strain rate by acquiring the pressure sensor data on the X-axis array, and perform pressure compensation for the hydraulic system based on the change in the strain rate. The strain rate is obtained using the formula: in: is the strain rate, is the pressure change between two adjacent measuring points, is the physical distance between two adjacent measuring points, is the elastic modulus; M2.2: Monitor the pressure distribution on the Y-axis parting surface: Acquire the pressure sensor data on the Y-axis array, construct a real-time data matrix, and determine the unevenness evaluation value based on the real-time data matrix. Compare the unevenness evaluation value with the preset evaluation value and adjust the mold clamping force based on the comparison result. Specifically: When the unevenness evaluation value is greater than a preset evaluation value, the mold clamping force is adjusted according to the obtained adjustment amount of the mold clamping force; otherwise, the mold clamping force is not adjusted; M2.3: Monitor the radial pressure decay along the Z axis. Obtain the measured pressure gradient by acquiring data from the pressure sensors on the Z axis array. Compare the measured pressure gradient with the theoretical pressure gradient to determine the gradient deviation. Compare the gradient deviation with the preset deviation. Based on the comparison results, optimize the material heat treatment process. Specifically: When the gradient deviation is not greater than the preset deviation, the pressure decay is normal and there are no defects inside the material. Otherwise, the pressure decay is abnormal, there are cracks or loose structure inside the material, and the heat treatment process of the material needs to be optimized.

6. The intelligent parameter control method for the shrinking molding ball wrapping equipment according to claim 5 is characterized in that: The formula for obtaining the unevenness evaluation value is specifically: in: is the unevenness evaluation value, is the average pressure value in the real-time data matrix, is the maximum pressure value in the real-time data matrix, is the minimum pressure value in the real-time data matrix; The specific formula for obtaining the adjustment amount of the mold clamping force is: in: is the adjustment amount of the mold clamping force, is the empirical correction coefficient, is the maximum pressure value in the real-time data matrix, is the minimum pressure value in the real-time data matrix, is the equivalent clamping area of the mold.

7. The intelligent parameter control method for the shrinking molding ball wrapping equipment according to claim 1 is characterized in that: Adjust the magnitude of mechanical stress, including: N1: Obtain real-time thermal stress: Determine the real-time thermal stress by the maximum local temperature difference between different locations within the same time slice, specifically: in: is the internal real-time thermal stress of the material caused by temperature changes, is the elastic modulus, is the linear expansion coefficient, is the maximum local temperature difference; N2: Determine the adjustment stress: Compare the comprehensive stress on the material with the critical stress at which irreversible plastic deformation begins, and adjust the magnitude of the mechanical stress based on the comparison results, specifically: When the comprehensive stress on the material is less than the critical stress at which irreversible plastic deformation begins to occur, the magnitude of the mechanical stress is not adjusted. Otherwise, the upper limit adjustment of the mechanical stress is determined according to the real-time thermal stress, specifically: in: is the internal stress of the material caused by external mechanical loads, is the comprehensive stress on the material, It is the internal real-time thermal stress of the material caused by temperature changes.

8. The intelligent parameter control method for the ball-wrapping equipment for shrinking molding according to claim 1 is characterized in that: The balance formula between pressure and temperature adjustment costs is: in: is the pressure adjustment weight coefficient, is the actual external pressure value applied, is the optimal pressure value, is the temperature adjustment weight coefficient, is the temperature data when the material is deformed, For the optimal temperature.

9. The intelligent parameter control method for the ball-wrapping equipment for shrinking molding according to claim 1 is characterized in that: According to the strain rate of the metal material, the flow rate of the cooling medium is adjusted as follows: in: is the flow rate of coolant, is the initial flow rate of the coolant, is the proportionality coefficient, is the strain rate, is the target strain rate; According to the temperature gradient of the metal material, adjust the spray direction of the coolant medium, specifically: in: is the spray angle of the coolant nozzle, is the rate of change of temperature in the Y-axis direction, is the rate of change of temperature in the X-axis direction, is the angle correction.

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