Aluminum profile dynamic extrusion control system and method

Abstract

The invention discloses an aluminum profile dynamic extrusion control system and method, which can solve the problems that the internal temperature of an aluminum bar is difficult to accurately obtain in the traditional extrusion technology, and the speed and temperature regulation are disjointed, the system comprises an image acquisition module, a thermal imaging device is used for obtaining a thermal distribution diagram after the aluminum bar is heated and cooled and separated, and a shallow layer characteristic diagram is generated; the temperature change data analysis module calculates the temperature change rate; the internal temperature modeling module calculates the internal actual temperature by combining the specification of an aluminum bar and the like, the internal temperature verification module is matched to ensure the precision, and the heating strategy generation module generates an optimal heating strategy by means of machine learning. The extrusion speed regulation and control module dynamically corrects the extrusion speed; the temperature field optimization module adjusts process parameters through simulation; the monitoring feedback module monitors and processes exceptions in real time; the method has the advantages that accurate sensing of the internal temperature of the aluminum bar and full-process dynamic cooperative regulation and control can be achieved, the product quality stability and the production efficiency are improved, and energy consumption is reduced.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of aluminum processing and automation control, and more particularly to a dynamic extrusion control system and method for aluminum profiles. BACKGROUND

[0002] With the wide application of aluminum profiles in the industrial field, the extrusion process in the production process plays a decisive role in the quality and performance of the profiles. However, the traditional extrusion control system still has certain limitations in practical application. For example, in terms of extrusion speed control, the system has limited adaptability to the characteristics of aluminum materials, the state of the die and the real-time changes in the production process, especially when the resistance of the extrusion cylinder or the extrusion die changes, or the characteristics of the aluminum materials differ, the actual extrusion speed of the extrusion machine is prone to deviate from the set value, thereby affecting the stability of product quality.

[0003] In terms of temperature control, in ordinary aluminum hot extrusion production, the phenomenon of uneven distribution of material temperature is common, which may lead to defects in size, shape, organization and performance of the extruded parts. Although there are some isothermal extrusion control methods in the prior art, these methods mostly rely on temperature detection at the die outlet, and thermal imaging or infrared temperature measurement technology can only obtain the surface temperature of the profile, and cannot accurately judge the internal temperature distribution. This limitation makes it difficult to achieve precise and targeted temperature control, and also limits the intelligent response ability of the system to rapidly changing process requirements.

[0004] In addition, during the entire extrusion process, the comprehensive real-time monitoring and collaborative control ability of various parameters is insufficient. The existing technology has limited performance in equipment operation anomaly detection and production quality problem early warning, resulting in low production efficiency and resource waste problems. The existence of these problems indicates that there is still room for improvement in the current technology in realizing efficient, stable and intelligent aluminum profile extrusion control. SUMMARY

[0005] In view of the deficiencies in the prior art, the purpose of the present application is to overcome the core technical contradiction that the prior art cannot accurately obtain or infer the actual temperature inside the profile by relying on surface temperature measurement, and to realize dynamic, collaborative and intelligent control of extrusion speed, temperature and other key process parameters, thereby comprehensively improving product quality stability, production efficiency and effectively reducing energy consumption.

[0006] To achieve the above purpose, the present application provides the following technical solutions: A dynamic extrusion control system for aluminum profiles, comprising: The image acquisition module acquires the thermal distribution maps at two different moments after the aluminum bar is heated and after the aluminum bar is partially pulled into the cooling device for cooling and separation, extracts shallow feature information from the thermal distribution maps to generate a thermal distribution shallow feature map; The temperature change data analysis module extracts aluminum bar surface temperature data from the thermal distribution shallow feature map, and calculates the temperature reduction rate and the temperature rise rate of the aluminum bar according to the aluminum bar surface temperature data, the heat dissipation coefficient, and the time interval between the two times of shooting after cooling treatment; The internal temperature modeling module constructs a mathematical model according to the temperature reduction rate and the temperature rise rate, combines the aluminum bar specifications and environmental parameters, and calculates the actual internal temperature of the aluminum bar; The heating strategy generation module constructs an aluminum bar heating data set according to multiple sets of actual internal temperatures of the aluminum bar, heating times, and heating box temperatures, trains a machine learning model to obtain a mapping relationship between the optimal heating time and the heating box temperature at the required temperature of the aluminum bar, and generates a heating strategy for the aluminum bar; The extrusion speed regulation module dynamically adjusts the motor drive parameters to correct the extrusion speed according to the deviation value between the set extrusion speed and the actual extrusion speed; The temperature field optimization module detects the profile extrusion temperature by an infrared thermometer during the profile extrusion process, reestablishes the profile temperature distribution through numerical simulation, and dynamically adjusts the extrusion speed and the heating power through thermal coupling simulation analysis; The monitoring feedback module monitors the extrusion length and temperature change in real time, automatically stops when the extrusion speed exceeds the preset range, and compensates the temperature of the aluminum bar.

[0007] Further, the cooling device is a water cooling device or an air cooling device or a fog cooling device, and further comprises an internal temperature verification module, which derives an aluminum bar internal temperature reference value for verification according to heat transfer data in the cooling device, compares the reference value with the calculated actual internal temperature of the aluminum bar, and if the absolute value of the difference between the two is less than or equal to a preset threshold, it is determined that the calculation of the actual internal temperature of the aluminum bar is accurate, and the heating strategy is generated, otherwise, the actual internal temperature of the aluminum bar after heating and cooling is re-judged.

[0008] Further, when the cooling device is a water cooling device, the internal temperature verification module calculates the heat released by the aluminum bar according to the cooling water inlet temperature, the cooling water outlet temperature, and the mass of the cooling water in contact with the surface of the aluminum bar collected by the inlet temperature sensor, the outlet temperature sensor, and the flowmeter in the water cooling device, respectively, calculates the theoretical temperature reduction value of the aluminum bar according to the mass of the part pulled out of the aluminum bar and the specific heat capacity of the aluminum bar, calculates the first reference value of the internal temperature of the aluminum bar according to the surface temperature of the aluminum bar after heating, the heat released by the aluminum bar, and the theoretical temperature reduction value of the aluminum bar, and compares the first reference value with the actual internal temperature of the aluminum bar.

[0009] Further, when the cooling device is a wind cooling device, the internal temperature verification module obtains the inlet air temperature, the outlet air temperature and the inlet air volume through the temperature sensor at the air inlet, the temperature sensor at the air outlet and the air volume meter of the wind cooling device respectively, calculates the air mass passing through the wind cooling device during the cooling process according to the wind cooling time, calculates the heat absorbed by the cooling air, and calculates the theoretical temperature reduction value of the aluminum bar according to the mass of the part of the aluminum bar pulled out and the specific heat capacity of the aluminum bar, calculates the second reference value of the internal temperature of the aluminum bar according to the surface temperature of the aluminum bar after heating, the heat absorbed by the cooling air and the theoretical temperature reduction value of the aluminum bar, and compares the second reference value with the actual internal temperature of the aluminum bar.

[0010] Further, when the cooling device is a mist cooling device, the internal temperature verification module continuously shoots the surface mist cooling area of the aluminum bar after the aluminum bar is separated from the mist cooling device through the visual camera, synchronously obtains the environmental data at the shooting time of each frame of image, calculates the pixel proportion of the residual area of the mist droplets in the mist cooling area as the residual rate, determines that the mist droplets are completely dried when the residual rate is less than a preset threshold value, records the time stamp at this time and calculates the mist droplet complete drying time, synchronously obtains the average value of the environmental data in the mist droplet complete drying time, derives the third reference value of the internal temperature of the aluminum bar according to the mist droplet complete drying time and the average value of the environmental data, and compares the third reference value with the actual internal temperature of the aluminum bar.

[0011] Further, the temperature field optimization module includes a dynamic adjustment strategy, and the dynamic adjustment strategy includes an infrared temperature measurement unit, a temperature modeling unit and a simulation analysis unit. The infrared temperature measurement unit accurately concentrates the test light area of the infrared thermometer at the profile die exit, and collects the temperature value of the profile surface in real time. The temperature modeling unit constructs a three-dimensional model of the profile through the image shot by the visual camera, and judges the temperature value of each point on the profile surface through numerical simulation according to the profile characteristics, the extrusion speed and the three-dimensional model. The simulation analysis unit dynamically adjusts the extrusion speed and the heating power to keep the die exit temperature of each point on the profile surface uniform and consistent through thermal coupling simulation analysis combined with the preset temperature control requirement.

[0012] Further, the extrusion speed regulation module includes a speed detection unit, a speed regulation unit and a warning triggering unit. The speed detection unit analyzes the current extruded profile length in real time through the image shot by the visual camera, and calculates the actual extrusion speed according to the profile length. The speed regulation unit compares the actual extrusion speed with the preset extrusion speed to obtain a deviation value, and dynamically corrects the extrusion speed according to the deviation value. The pre-warning triggering unit sends an audible and visual alarm instruction to a pre-warning device when the deviation value exceeds a preset threshold range.

[0013] Further, the monitoring feedback module comprises a temperature compensation unit and a feedback control unit. The temperature compensation unit automatically stops and compensates the temperature of the aluminum bar when the actual extrusion speed exceeds the preset range. The feedback control unit restarts the extruder according to the state of the aluminum bar after temperature compensation.

[0014] Further, the heating strategy generation module comprises a heating model construction unit and a heating parameter calculation unit. The heating model construction unit constructs a heating model by multivariate linear regression algorithm based on the aluminum bar heating data set. The heating parameter calculation unit calculates the required heating time and heating box temperature to meet the required temperature of the aluminum bar by reverse solving in the heating model.

[0015] An aluminum profile dynamic extrusion control method, comprising the following steps: An image acquisition step, in which a thermal distribution map at two different time points after heating of the aluminum bar and after the aluminum bar is partially pulled away from the cooling device for cooling and separation is obtained by a thermal imaging device, and shallow feature information is extracted from the thermal distribution map to generate a thermal distribution shallow feature map; A temperature change data analysis step, in which aluminum bar surface temperature data are extracted according to the thermal distribution shallow feature map, and the temperature reduction rate and the temperature rise rate of the aluminum bar are calculated according to the aluminum bar surface temperature data, the heat dissipation coefficient, and the time interval between the two times of shooting after cooling treatment; An internal temperature modeling step, in which a mathematical model is constructed according to the temperature reduction rate and the temperature rise rate, and the actual temperature inside the aluminum bar is calculated in combination with the aluminum bar specifications and environmental parameters; A heating strategy generation step, in which an aluminum bar heating data set is constructed according to a plurality of groups of actual temperatures inside the aluminum bar, heating time, and heating box temperature, a mapping relationship between the optimal heating time and the heating box temperature at the required temperature of the aluminum bar is obtained by machine learning model training, and a heating strategy of the aluminum bar is generated; An extrusion speed regulation step, in which the deviation value between the set extrusion speed and the actual extrusion speed is used to dynamically adjust the motor driving parameters to correct the extrusion speed; A temperature field optimization step, in which the profile exit temperature is detected by an infrared thermometer during the profile extrusion process, the profile temperature distribution is reconstructed by numerical simulation, and the extrusion speed and heating power are dynamically adjusted by thermal coupling simulation analysis; A monitoring feedback step, in which the extrusion length and temperature change are monitored in real time, and the aluminum bar is automatically stopped and temperature-compensated when the extrusion speed exceeds the preset range.

[0016] The beneficial effects of the present application: 1. Through two-stage heat distribution acquisition and temperature change rate modeling, two heat distribution maps of the aluminum bar after heating and cooling off are obtained according to the thermal imaging device, the shallow temperature characteristics are extracted, the temperature reduction and rising rate are calculated combined with the heat dissipation coefficient, time interval and other parameters, a mathematical model is constructed and coupled with the aluminum bar specifications and environmental parameters, the internal actual temperature is accurately back calculated, at the same time, special internal temperature verification logic is designed for different cooling methods such as water cooling, air cooling and fog cooling, to ensure that the internal temperature calculation error is controlled within the preset threshold; On this basis, through machine learning model training multiple groups of aluminum bar internal temperature, heating time and heating box temperature data, the mapping relationship among the three is established, and the generated heating strategy can automatically match the optimal heating parameters according to the target temperature, which not only avoids the blindness of traditional empirical heating, but also realizes efficient use of energy.

[0017] 2. Through multi-module cooperation to realize dynamic regulation and control of the whole process, on the one hand, the extrusion speed regulation module analyzes the profile length in real time through a visual camera to calculate the actual speed, dynamically corrects the motor parameters after comparing with the set value, and responds to speed abnormalities in time through a warning trigger unit; on the other hand, the temperature field optimization module combines infrared temperature measurement and thermal coupling simulation to detect the profile temperature out of the mold in real time and reconstruct the temperature distribution, and adjusts the extrusion speed and heating power to ensure that the temperature of each point on the profile surface is uniform; in addition, the monitoring feedback module links the extrusion length and temperature data in real time, automatically stops when the speed exceeds the preset range, and warms up the aluminum bar, this cooperative regulation and control mode not only solves the drawbacks of traditional technology that speed and temperature are fought separately, but also quickly adapts to process fluctuations, significantly improves the profile size precision and performance stability, reduces the downtime loss caused by abnormal parameters, and effectively improves the production efficiency. BRIEF DESCRIPTION OF DRAWINGS

[0018] Figure 1 is the overall flowchart in the present application; Figure 2 is the internal temperature detection and heating strategy generation flowchart in the present application; Figure 3 is the extrusion process regulation and monitoring feedback flowchart in the present application. DETAILED DESCRIPTION

[0019] The present application will be further described in detail below in combination with the drawings and examples. Wherein the same parts are denoted by the same reference numerals. It should be noted that the words "front", "back", "left", "right", "up" and "down" used in the following description refer to the directions in the drawings, and the words "bottom surface" and "top surface", "inner" and "outer" refer to the directions towards or away from the geometric center of a particular part.

[0020] Due to the lack of real-time and accurate perception of the actual internal temperature of the aluminum profile during the traditional extrusion production process, it is difficult to optimize the key process parameters such as extrusion speed and heating temperature, which affects the product quality, production efficiency and energy consumption. The control system realizes dynamic and accurate regulation and control from aluminum bar preheating to profile extrusion by integrating multi-source data acquisition, advanced numerical simulation and intelligent control algorithm.

[0021] As shown in Figure 1 The present application provides an aluminum profile dynamic extrusion control system, which comprises an image acquisition module, a temperature change data analysis module, an internal temperature modeling module, an internal temperature verification module, a heating strategy generation module, an extrusion speed regulation module, a temperature field optimization module and a monitoring feedback module. Specifically, the image acquisition module includes a heating zone heat distribution acquisition unit and a cooling and dynamic temperature measurement unit. The heating zone heat distribution acquisition unit is used to obtain the heat distribution image of the aluminum bar surface after a predetermined time of heating. At this time, the predetermined time is relatively short, and the aluminum bar surface temperature is below 100 degrees Celsius, which facilitates subsequent cooling judgment of the internal temperature. Then the heat distribution image is analyzed by a heat distribution shallow feature extractor of a first convolutional neural network model. The first convolutional neural network model is a U-Net variant structure, and its encoder part is composed of multiple convolutional layers and pooling layers, which are used to extract low-level visual features of the heat distribution image, such as surface temperature gradient, contour edge, local hot or cold spot distribution and texture heterogeneity. The decoder part maps these features back to a feature map with the same size as the original image through upsampling and jump connection, thereby obtaining a heat distribution shallow feature map. Before that, this model has been trained offline through thousands of real heat distribution image datasets containing aluminum bars of different sizes and different models under various heating conditions, and cross-validation has been performed to ensure its accuracy and robustness in shallow feature extraction, in order to overcome the potential influence of aluminum bar surface oxidation, roughness change and environmental light interference on feature extraction.

[0022] The cooling and dynamic temperature measurement unit is used to cool the aluminum bar locally and for a short time, and to synchronously obtain the surface temperature data before and after cooling and the related heat exchange parameters during cooling. The cooling device can be configured as water cooling, air cooling or fog cooling mode according to actual needs. After the aluminum bar is partially pulled out by the aluminum bar pulling device and enters the cooling device for a preset time of cooling, the cooling device stops running, and the pulling device pulls out or stops the aluminum bar. At this time, the thermal imaging acquisition device immediately acquires 5 frames of heat distribution images of the part of the aluminum bar pulled out and cooled. The image processing controller receives these images, removes the blurred images caused by device micro-vibration or thermal smoke interference through an image stability evaluation algorithm, and retains at least 3 clear images. As shown in Figure 2As shown, the temperature change data analysis module analyzes the collected thermal distribution image, accurately converts the content in the image into corresponding surface temperature values, and calculates the surface average temperature of the part of the aluminum bar being pulled out, denoted as ; at the same time, the system records relevant parameters during the cooling process, wherein the cooling device includes water cooling, air cooling and mist cooling, and one of the cooling methods is selected, if water cooling is selected, records , if air cooling is selected, records ; if mist cooling is selected, the drying time of the mist droplets needs to be analyzed, and after the measurement is completed, the aluminum bar pulling device pulls out the aluminum bar and then remains stationary or only needs to be separated from the cooling device, and is naturally placed for a predetermined time , and then the thermal imaging equipment takes thermal distribution image of the part of the aluminum bar again, and after image screening and temperature analysis, the average temperature of the surface of the aluminum bar at this time is calculated, denoted as , in this process, the surface average temperature of the aluminum bar when it is not cooled is obtained from the thermal distribution image of the heating zone thermal distribution collection unit, and the temperature related rates are calculated through , , , and , wherein the temperature reduction rate represents the average temperature drop per second of the aluminum bar surface temperature from to in the cooling process, that is , the temperature rise rate represents the average temperature rise per second of the surface temperature of the aluminum bar from to within time after the aluminum bar is separated from the cooling device, and the temperature change in this process is determined by the comprehensive effect of the heat transferred from the inside of the aluminum bar to the surface and the heat dissipation from the surface of the aluminum bar to the air, that is .

[0023] The internal temperature modeling module calculates the and calculated by the cooling and dynamic temperature measurement unit, and calculates the actual temperature of the aluminum bar inside according to the heat conduction law and material characteristics. In this module, a transient heat conduction model based on Fourier heat conduction law is preset, and the physical parameters of the aluminum bar, such as the geometric shape of the aluminum bar, the specific heat capacity , density , thermal conductivity and surface convective heat transfer coefficient are considered. Since there is a dynamic balance between the heat transfer from the inside of the aluminum bar to the surface and the surface heat dissipation, the module uses a simplified model based on inverse heat conduction algorithm, which first uses To reverse-engineer the instantaneous temperature gradient between the surface and interior of the aluminum rod during cooling, and then combine... To correct for the accumulation and release of internal heat, the internal temperature of the aluminum rod is calculated as follows: ,in The diameter of the aluminum rod is [missing information]. The thermal conductivity coefficient of the aluminum rod is stored in the system's material database. The thermal conductivity coefficient is different for each type of aluminum rod with different contents. It is obtained by looking up a table or interpolating based on the different contents of the aluminum rod and the temperature range.

[0024] The internal temperature verification module verifies the actual internal temperature of the aluminum rod calculated by the internal temperature modeling module based on the heat exchange parameters obtained from different cooling methods. Independent verification was conducted to ensure the accuracy of the calculation results. Specifically: When the cooling device uses water cooling, the internal temperature verification module first calculates the heat released by the aluminum rod during the cooling process based on the cooling water flow rate and temperature rise measured in the cooling water circulation system. The calculation formula is: ,in The specific heat capacity of water, The total mass of cooling water sprayed onto the surface of the aluminum rod during the cooling process is obtained by measuring and integrating the flow rate. To raise the temperature of cooling water .

[0025] Secondly, based on the specific heat capacity of the aluminum rod and the mass of the drawn portion, the theoretical calculation of the aluminum rod's release... Temperature drop caused by heat ,Right now ,in This refers to the specific heat capacity of the aluminum rod. This refers to the mass of the portion of the aluminum rod that is pulled out.

[0026] Next, the surface temperature after heating with an aluminum rod for n seconds. Calculate the actual internal temperature of the aluminum rod derived through water cooling. ,Right now ,in This is a correction value used to compensate for the hysteresis and non-uniformity in the heat transfer process from the interior of the aluminum rod to the surface. It was obtained through fitting a large amount of preliminary experimental data and numerical simulation calibration, and its value is related to the diameter of the aluminum rod, the initial temperature, and the cooling intensity.

[0027] Finally, The actual internal temperature of the aluminum rod calculated by the internal temperature modeling module. The comparison is performed; if the absolute value of the difference between the two is ≤3℃ (threshold), then a judgment is made. If the reading is accurate, the system continues to execute subsequent steps; if the absolute value of the difference is greater than 3℃ (threshold), the system will trigger an early warning signal, indicating that there is a deviation in the internal temperature calculation of the aluminum rod, and will remeasure or adaptively adjust the parameters of the internal temperature calculation model according to the preset logic.

[0028] When the cooling device uses air cooling, the internal temperature verification module first calculates the heat absorbed by the cooling air during the cooling process based on the cooling airflow and temperature rise measured in the cooling air circulation system. The calculation formula is: ,in The specific heat capacity of air. The total mass of air passing through the fan shroud during the cooling process is calculated by measuring the airflow with an anemometer and combining it with the blowing time. The air density is taken as 1.2 kg / m³. The temperature is increased by the cooling air.

[0029] Secondly, based on the calculation of the aluminum rod's heat capacity, theoretically, the aluminum rod will release... Temperature drop caused by heat ,in .

[0030] Next, the actual internal temperature of the aluminum rod is derived. ,Right now ,in This is a correction value for air-cooled conditions. It is also obtained through fitting a large amount of experimental data and numerical simulation calibration. For 6061 aluminum alloy, its empirical range is usually set to 8℃ to 12℃, and it is dynamically selected in the system according to the specific working conditions by looking up a table.

[0031] Finally, and The comparison is performed; if the absolute value of the difference between the two is ≤3℃ (threshold), then a judgment is made. Accurate; if the absolute value of the difference is greater than 3℃ (threshold), an early warning signal will be triggered and corresponding correction or adjustment operations will be performed.

[0032] The internal temperature verification module further derives the surface temperature of the aluminum rod at the end of the mist cooling process using a complex thermo-mass coupling model based on the complete drying time of the mist droplets, the latent heat of vaporization of the mist droplets, and environmental parameters. This thermo-mass coupling model is solved numerically or using a pre-trained machine learning model. Based on the derived surface temperature of the aluminum rod, the inverse heat conduction algorithm is used again to deduce the actual internal temperature of the aluminum rod, denoted as . .

[0033] Finally, Calculated with the internal temperature modeling module For comparison, if the absolute value of the difference is ≤ 3℃ (threshold value), it is determined that the measured value is accurate; if the absolute value of the difference is > 3℃ (threshold value), a warning signal is triggered and corresponding correction or adjustment operations are performed.

[0034] a heating strategy generation module that constructs and applies an aluminum bar heating model according to accurate data provided by the aluminum bar internal temperature acquisition module to achieve accurate control of the target internal temperature of the aluminum bar before entering the extruder, the module including a heating model construction unit and a heating parameter calculation unit; The heating model construction unit is used to collect a large amount of experimental data and construct a heating model. After the internal temperature verification unit determines that the measured value is accurate, the system will continuously collect multiple sets of experimental data, each set of data including the specification parameters of the aluminum bar, including the diameter , length , model, heating temperature of the heating furnace, aluminum bar heating time and corresponding aluminum bar internal actual temperature , wherein the heating temperature is collected in real time by a K-type thermocouple array arranged in multiple regions inside the heating furnace and the average value is taken to ensure uniformity of the furnace temperature monitoring, the time experienced before the first thermal distribution image collection by the heating zone thermal distribution acquisition unit, to ensure the representativeness and accuracy of the model, at least 50 sets of effective data of each specification of aluminum bar are collected, covering the typical change range of heating temperature, heating time and aluminum bar size.

[0035] The system receives and processes these data, and adopts a multiple linear regression algorithm to construct a heating model, the input variables of the heating model being the heating temperature of the heating furnace and the heating time of the aluminum bar, and the output variable being the internal actual temperature of the aluminum bar, at the same time, the specification parameters of the aluminum bar (diameter , , model) are used as correction variables of the model to reflect the differences in heat capacity and heat conduction characteristics of aluminum bars of different specifications, through least squares fitting analysis of historical data, the mathematical expression of the heating model is obtained: wherein are model coefficients, these coefficients are accurately calculated by an iterative optimization algorithm that minimizes the sum of squared residuals, and in the fitting process, the determination The coefficient must reach above 0.95 to ensure that the model fits the actual internal temperature of the aluminum rod well and has high prediction accuracy. This heating model clearly and quantitatively reflects the quantitative relationship between the target internal temperature of the aluminum rod and the heating time, the internal temperature of the heating box, and the specifications of the aluminum rod.

[0036] The heating parameter calculation unit sets the target internal temperature that the aluminum rod must reach before entering the extrusion press, based on the extrusion production line's process requirements for aluminum alloy profiles. This unit will And the specifications of the aluminum rod to be heated ( , The model number (e.g., model number) is input into the heating model, which then calculates the result satisfying the given conditions using a reverse engineering algorithm. Required furnace heating temperature setting Set heating time with aluminum rod According to the calculation and The system sends precise control signals to the temperature control system and timing device of the heating furnace, thereby achieving closed-loop precise control of the aluminum rod heating process and ensuring that the aluminum rod reaches the predetermined internal actual temperature when it enters the extruder.

[0037] like Figure 3 As shown, the purpose of the extrusion speed control module is to precisely control the extrusion speed of the extruder and perform real-time monitoring and abnormal early warning. This module includes a speed detection unit, a speed control unit, and an early warning triggering unit. The speed detection unit includes a high-precision laser tachometer or encoder to detect the current extrusion bar advance speed of the extruder in real time and feed this data back to the system. In addition, this unit also includes a product extrusion speed detection unit, which is usually a tachometer set behind the die outlet to detect the product extrusion speed of the profile in real time. The system calculates the difference between the currently set extrusion speed and the real-time feedback current product extrusion speed. If the absolute value of this difference exceeds the preset difference threshold range, that is, when the actual product extrusion speed deviates from the set value by more than ±5% (threshold), the early warning triggering module immediately sends an early warning control information to the early warning device. The early warning device reminds the operator that the extruder may need to be inspected and maintained through audible and visual alarms, touch screen interface prompts, or remote notifications. For example, checking whether there are impurities blocking the extrusion mechanism and extrusion die, whether the extrusion cylinder seal is good, or the extrusion bar is worn, to ensure that the extruder can operate stably and ensure the geometric dimensional accuracy and surface quality of the profile product.

[0038] The temperature field optimization module accurately infers the internal temperature of the profile through real-time thermo-mechanical coupling simulation and dynamically adjusts the extrusion process parameters accordingly. This module includes a dynamic adjustment strategy, which in turn includes an infrared temperature measurement unit, a temperature modeling unit, and a simulation analysis unit. The infrared temperature measurement unit is used during the profile extrusion process to perform non-contact detection of the profile's extrusion surface temperature in real time using multiple high-precision infrared thermometers installed outside the extrusion die. The thermometer's test light area is precisely focused and limited to a specific micro-region at the profile's extrusion opening to ensure the accuracy and regional specificity of the measured temperature. To maintain high sensitivity to profile temperature changes and ensure conservative control, the system selects the extreme temperature value within the range from the temperature data obtained from multiple temperature measurement points as the current extrusion surface temperature. .

[0039] The temperature modeling unit is housed in a high-performance industrial computer, and this unit receives real-time feedback from the infrared temperature measurement unit. The extrusion speed control module provides real-time extrusion speed. The unit takes into account the material properties of the profile to be extruded (including model, instantaneous specific heat capacity, thermal conductivity, coefficient of thermal expansion, rheological stress model, etc., which are stored in the material database and dynamically updated with temperature) and the geometric parameters of the die. Based on these input data, the unit uses an integrated real-time inverse heat conduction thermo-mechanical coupling finite element simulation model to perform high-precision simulation and prediction of the internal temperature field, deformation rate, stress-strain distribution and microstructure evolution of the profile. The simulation model adopts adaptive mesh generation technology to locally refine the mesh at the die outlet and the plastic deformation region of the profile in order to accurately capture severe deformation and temperature gradient. The physical basis of the model includes: heat conduction equation, deformation heat generation, material constitutive relation and contact heat transfer.

[0040] The heat conduction equation considers solid-phase conduction, surface convection heat transfer, and radiation heat transfer; deformation heat generation, during plastic deformation, most of the mechanical work is converted into heat, and its generation rate is related to the deformation rate and rheological stress; material constitutive relations, using a rheological stress model, describe the stress, strain, strain rate, and temperature sensitivity of aluminum alloys under high temperature and large deformation conditions; contact heat transfer, accurately simulating the frictional heat generation and contact heat transfer between the profile and the mold.

[0041] Before extrusion begins, the simulation model loads the CAD geometric data of the die and the initial state of the material. During the extrusion process, the real-time reverse heat conduction and thermo-coupling finite element simulation model continuously receives real-time data. and extrusion speed As boundary conditions and driving parameters, the temperature field distribution inside the profile is corrected and updated in reverse through iterative solutions or data assimilation techniques such as Kalman filtering. By establishing a physical model correlation between the externally measurable surface temperature and the internally unmeasurable temperature field, the actual internal temperature field of the profile can be accurately predicted. The output of this unit is a real-time internal temperature distribution map on the profile cross-section. (and the predicted internal temperature values ​​for key areas).

[0042] The heating parameter calculation unit calculates the real-time temperature output from the profile internal temperature simulation and prediction unit. Based on preset temperature control requirements, the extrusion speed is adjusted. The cooling intensity and heating intensity are dynamically coordinated and regulated. The preset temperature control requirements include that the internal temperature of the profile at the mold exit point must be maintained at the specified target temperature, and the internal temperature difference of the profile cross section must be less than the threshold.

[0043] when The actual temperature in key areas inside the display profile is higher than that of the display material. Sometimes When the extrusion speed exceeds the allowable range, indicating an overheated area inside, a command is sent to the extrusion speed control module to appropriately reduce the extrusion speed. This reduces the generation of deformation heat and increases the residence time of the profile in the mold and cooling zone, promoting heat dissipation. Simultaneously, control signals are sent to the external cooler and loading unit to enhance localized or overall cooling of the profile, i.e., increasing the flow rate of cooling water / air / mist or lowering its temperature, or activating additional cooling devices to accelerate the dissipation of internal heat and reduce the internal temperature.

[0044] when The actual temperature of key areas inside the display profile is lower than that of the display material. If the internal cooling of the profile is too rapid, a command will be sent to the extrusion speed control module to appropriately increase the extrusion speed. To increase the generation of deformation heat and reduce the residence time of the profile in the cooling zone, control signals will be sent to the heating furnace or mold heater to heat the profile locally or as a whole. For example, the mold exit area or profile will be preheated or reheated by induction heating coils to ensure that the internal temperature of the profile is kept within the ideal range when it is extruded from the mold, thus achieving true isothermal extrusion.

[0045] Furthermore, to ensure the stability of the extrusion process and product quality, the extrusion speed is... Strict limits are imposed, and the system monitors the length of the extruded profile in real time. Combining the real-time internal temperature of the profile with the target temperature, the system calculates and adjusts the extrusion speed using a model predictive control algorithm. This model predictive control algorithm considers the dynamic response of the extruder, the thermophysical properties of the material, the die characteristics, and the response delay of the cooling / heating system to optimize the extrusion speed and temperature over a future period. This maximizes production efficiency while meeting temperature targets. The core control objective of the algorithm is to minimize the internal temperature of the profile at any given time. To minimize deviations and ensure that the extrusion speed is within the preset process window.

[0046] For example, let the unit time be... , The time is 1 second, and the system will determine the time based on the current and predicted internal temperature gradient, as well as the profile length and the current extrusion speed. Predicting the future unit of time Internally, temperature changes at key points within the profile; if the predicted temperature changes cause the internal temperature of the profile to exceed [a certain threshold]... If the temperature is within ±3℃, the controller will adjust the extrusion speed. and cooling / heating intensity, when the calculated extrusion speed When the internal temperature exceeds the preset adjustment range and cannot be maintained within the target range by cooling / heating, the system will automatically shut down the extruder and initiate a temperature replenishment operation for the aluminum rod. This operation includes sending a command to the aluminum rod heating optimization module to heat the aluminum rod in the extrusion cylinder through a heating furnace. After the profile temperature rises back to the appropriate range, the extruder will be restarted for production, thereby ensuring the stability of the profile extrusion process and product quality.

[0047] Based on the dynamic extrusion control system for aluminum profiles, a corresponding dynamic extrusion control method for aluminum profiles was designed, including the following steps: The image acquisition step involves using a thermal imaging device to acquire thermal distribution images at two different times: after the aluminum rod is heated and after part of the aluminum rod is pulled into a cooling device for cooling and separation. Shallow feature information is extracted from the thermal distribution images to generate a shallow feature map of thermal distribution. The temperature change data analysis step involves extracting the surface temperature data of the aluminum rod based on the shallow feature map of the heat distribution, and then calculating the temperature decrease rate and temperature rise rate of the aluminum rod based on the surface temperature data of the aluminum rod, the heat dissipation coefficient, and the time interval between the two shots after the cooling treatment. The internal temperature modeling steps involve constructing a mathematical model based on the rate of temperature decrease and the rate of temperature increase, and then calculating the actual internal temperature of the aluminum rod by combining the aluminum rod specifications and environmental parameters. The heating strategy generation step involves constructing an aluminum rod heating dataset based on the actual internal temperature, heating time, and heating box temperature of multiple sets of aluminum rods. A machine learning model is then trained to obtain the mapping relationship between the optimal heating time and the heating box temperature at the required temperature of the aluminum rod, thereby generating the heating strategy for the aluminum rod. The extrusion speed control step involves dynamically adjusting the motor drive parameters to correct the extrusion speed based on the deviation between the set extrusion speed and the actual extrusion speed. The temperature field optimization step involves detecting the extrusion temperature of the profile using an infrared thermometer during the extrusion process, reconstructing the profile temperature distribution through numerical simulation, and dynamically adjusting the extrusion speed and heating power through thermo-coupling simulation analysis. The monitoring and feedback process monitors the extrusion length and temperature changes in real time. When the extrusion speed exceeds the preset range, the machine automatically stops and performs temperature compensation on the aluminum rod.

[0048] The above are merely preferred embodiments of the present invention. The scope of protection of the present invention is not limited to the above embodiments. All technical solutions falling within the scope of the present invention's concept are within the scope of protection of the present invention. It should be noted that for those skilled in the art, any improvements and modifications made without departing from the principle of the present invention should also be considered within the scope of protection of the present invention.

Claims

1. A dynamic extrusion control system for an aluminum profile, characterized by: The application relates to a temperature field optimization method for aluminum profile extrusion, which comprises the following steps: An image acquisition module acquires thermal distribution maps at two different moments after an aluminum rod is heated and after the aluminum rod is partially pulled into a cooling device for cooling and separation, extracts shallow feature information from the thermal distribution maps to generate thermal distribution shallow feature maps; A temperature change data analysis module extracts aluminum rod surface temperature data from the thermal distribution shallow feature maps, and calculates the temperature reduction rate and the temperature rise rate of the aluminum rod according to the aluminum rod surface temperature data, the heat dissipation coefficient and the time interval between the two shooting times after cooling treatment; An internal temperature modeling module constructs a mathematical model according to the temperature reduction rate and the temperature rise rate, calculates the actual temperature inside the aluminum rod in combination with the aluminum rod specifications and environmental parameters; A heating strategy generation module constructs an aluminum rod heating data set according to multiple groups of actual temperatures inside the aluminum rod, heating time and heating box temperature, trains a machine learning model to obtain the mapping relationship between the optimal heating time and the heating box temperature at the required temperature of the aluminum rod, and generates a heating strategy for the aluminum rod; An extrusion speed regulation module dynamically adjusts motor driving parameters to correct the extrusion speed according to the deviation value between the set extrusion speed and the actual extrusion speed; A temperature field optimization module detects the profile ejection temperature during profile extrusion through an infrared thermometer, reestablishes the profile temperature distribution through numerical simulation, and dynamically adjusts the extrusion speed and the heating power through thermal coupling simulation analysis; A monitoring feedback module monitors the extrusion length and the temperature change in real time, automatically stops and compensates the temperature of the aluminum rod when the extrusion speed exceeds the preset range.

2. The dynamic extrusion control system for aluminum profiles according to claim 1, characterized in that: The cooling device is a water cooling device or an air cooling device or a fog cooling device, and further comprises an internal temperature verification module, which derives an aluminum rod internal temperature reference value for verification according to heat transfer data in the cooling device, compares the reference value with the actual temperature inside the aluminum rod, and determines that the actual temperature inside the aluminum rod is calculated accurately and generates a heating strategy when the absolute value of the difference between the two is less than or equal to a preset threshold, otherwise, the actual temperature inside the aluminum rod after heating and cooling is rejudged.

3. The dynamic extrusion control system for aluminum profiles according to claim 2, characterized in that: When the cooling device is a water cooling device, the internal temperature verification module calculates the heat released by the aluminum rod according to the cooling water inlet temperature, the cooling water outlet temperature and the cooling water mass in contact with the aluminum rod surface collected by the inlet temperature sensor, the outlet temperature sensor and the flowmeter in the water cooling device respectively, calculates the theoretical temperature reduction value of the aluminum rod according to the mass of the pulled-out part of the aluminum rod and the specific heat capacity of the aluminum rod, calculates the first reference value of the internal temperature of the aluminum rod according to the surface temperature of the aluminum rod after heating, the heat released by the aluminum rod and the theoretical temperature reduction value of the aluminum rod, and compares the first reference value with the actual temperature inside the aluminum rod.

4. The dynamic extrusion control system for aluminum profiles according to claim 2, characterized in that: When the cooling device is a wind cooling device, the internal temperature verification module obtains the inlet air temperature, the outlet air temperature and the inlet air volume through the temperature sensors at the air inlet and the air outlet of the wind cooling device and the air volume meter, respectively, calculates the air mass passing through the wind cooling device during the cooling process according to the wind cooling time, calculates the heat absorbed by the cooling air, and calculates the theoretical temperature reduction value of the aluminum bar according to the mass of the part of the aluminum bar pulled out and the specific heat capacity of the aluminum bar, calculates the second reference value of the internal temperature of the aluminum bar according to the surface temperature of the aluminum bar after heating, the heat absorbed by the cooling air and the theoretical temperature reduction value of the aluminum bar, and compares the second reference value with the actual internal temperature of the aluminum bar.

5. The dynamic extrusion control system for aluminum profiles according to claim 2, characterized in that: When the cooling device is a mist cooling device, the internal temperature verification module continuously shoots the surface mist cooling area of the aluminum bar after the aluminum bar leaves the mist cooling device through a visual camera, synchronously obtains the environmental data at the shooting time of each frame of image, calculates the pixel proportion of the residual area of the mist droplets in the mist cooling area as a residual rate, determines that the mist droplets are completely dried when the residual rate is less than a preset threshold value, records the time stamp at this time and calculates the mist droplet complete drying time, synchronously obtains the average value of the environmental data in the mist droplet complete drying time, derives the third reference value of the internal temperature of the aluminum bar according to the mist droplet complete drying time and the average value of the environmental data, and compares the third reference value with the actual internal temperature of the aluminum bar.

6. The dynamic extrusion control system for aluminum profiles according to any one of claims 3-5, characterized in that: The temperature field optimization module includes a dynamic adjustment strategy, the dynamic adjustment strategy includes an infrared temperature measurement unit, a temperature modeling unit and a simulation analysis unit; The infrared temperature measurement unit accurately concentrates the test light area of the infrared thermometer at the profile die outlet, and collects the temperature value of the profile surface in real time; The temperature modeling unit constructs a three-dimensional model of the profile through the images shot by the visual camera, and judges the temperature value of each point on the profile surface through numerical simulation according to the profile characteristics, the extrusion speed and the three-dimensional model; The simulation analysis unit dynamically adjusts the extrusion speed and the heating power to keep the die-out temperature of each point on the profile surface uniform and consistent through thermal coupling simulation analysis combined with the preset temperature control requirement.

7. The dynamic extrusion control system for aluminum profiles according to claim 6, characterized in that: The extrusion speed regulation module includes a speed detection unit, a speed regulation unit and a pre-warning triggering unit; The speed detection unit analyzes the current extruded profile length in real time through the images shot by the visual camera, and calculates the actual extrusion speed according to the profile length; The speed regulation unit compares the actual extrusion speed with the preset extrusion speed to obtain a deviation value, and dynamically corrects the extrusion speed according to the deviation value; The pre-warning triggering unit sends an audible and light alarm instruction to the pre-warning device when the deviation value exceeds the preset threshold range.

8. The dynamic extrusion control system for aluminum profiles according to claim 7, characterized in that: The monitoring feedback module includes a temperature compensation unit and a feedback control unit; The temperature compensation unit automatically stops and compensates the temperature of the aluminum bar when the actual extrusion speed exceeds the preset range; The feedback control unit restarts the extruder according to the state of the aluminum bar after temperature compensation.

9. The dynamic extrusion control system for aluminum profiles according to claim 8, characterized in that: The heating strategy generation module includes a heating model construction unit and a heating parameter calculation unit; The heating model construction unit constructs a heating model through a multiple linear regression algorithm according to the aluminum bar heating data set; The heating parameter estimation unit inversely solves a heating model according to the aluminum bar required temperature to obtain the heating time and the heating box temperature required to meet the aluminum bar required temperature.

10. A method of dynamic extrusion control of an aluminium profile, characterized in that: The method comprises the following steps: An image acquisition step, in which a thermal distribution map at two different time points after the aluminum bar is heated and after the aluminum bar is partially pulled into a cooling device for cooling and separation is obtained by a thermal imaging device, and shallow feature information is extracted from the thermal distribution map to generate a thermal distribution shallow feature map; A temperature change data analysis step, in which aluminum bar surface temperature data are extracted according to the thermal distribution shallow feature map, and a temperature reduction rate and a temperature rise rate of the aluminum bar are calculated according to the aluminum bar surface temperature data, a heat dissipation coefficient, and a time interval between two times of shooting after cooling treatment; An internal temperature modeling step, in which a mathematical model is constructed according to the temperature reduction rate and the temperature rise rate, and the actual temperature inside the aluminum bar is calculated in combination with aluminum bar specifications and environmental parameters; A heating strategy generation step, in which an aluminum bar heating data set is constructed according to a plurality of groups of actual temperatures inside the aluminum bar, heating times, and heating box temperatures, a mapping relationship between optimal heating times and heating box temperatures at the aluminum bar required temperature is obtained by machine learning model training, and a heating strategy of the aluminum bar is generated; An extrusion speed regulation step, in which a motor driving parameter is dynamically adjusted to correct the extrusion speed according to a deviation value of a set extrusion speed and an actual extrusion speed; A temperature field optimization step, in which a profile extrusion temperature is detected by an infrared thermometer during profile extrusion, a profile temperature distribution is re-modeled by numerical simulation, and the extrusion speed and the heating power are dynamically adjusted by thermal coupling simulation analysis; A monitoring feedback step, in which the extrusion length and the temperature change are monitored in real time, and the aluminum bar is automatically compensated for temperature when the extrusion speed exceeds a preset range.

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