A method and system for temperature control of an aluminum alloy heat treatment process

Abstract

The present application relates to the technical field of temperature regulation, and in particular to a temperature control method and system for an aluminum alloy heat treatment process. The method comprises: obtaining the positions, pipeline pressures and temperatures of different temperature measurement nodes on the surface of an aluminum alloy component; generating a directed interference weight matrix based on the relative position distribution of each pair of temperature measurement nodes; determining the local heat exchange stability index of each temperature measurement node according to the pipeline pressure and temperature of each temperature measurement node during the spray cooling process; selecting non-source nodes and constructing an expected spray state vector according to the corresponding component cross-sectional thickness and temperature of each temperature measurement node; combining the expected spray state vector and the directed interference weight matrix to predict the equivalent flooding interference strength; and evaluating the flooding risk according to the equivalent flooding interference strength and the local heat exchange stability index of the non-source nodes, and then issuing corresponding control instructions. The present application improves the temperature control accuracy and response speed of the aluminum alloy heat treatment process.

Description

Technical Field

[0001] This invention relates to the field of temperature control technology, specifically to a temperature control method and system for the heat treatment process of aluminum alloys. Background Technology

[0002] Thick, complex-section high-strength aluminum alloy components are widely used in aerospace equipment manufacturing. The array spray quenching process after solution treatment is a necessary process that determines the final properties of the material. In the array spray quenching environment, the thick-walled areas of the component have a large heat capacity, requiring forced cooling with high-pressure water flow; while the thin-walled areas have a small heat capacity, requiring slower cooling to prevent thermal stress cracking.

[0003] In systems sharing a single array of nozzles, when high-pressure water spray is applied to thick-walled areas, the fluid overflows into surrounding non-target areas along the three-dimensional contours of the component. Once these overflowing water flows converge, they disrupt the original heat exchange and insulation of thin-walled areas, causing localized cooling rates to exceed the material's tolerance limits and triggering cracking. Existing control methods primarily rely on temperature sensors in each zone for post-event closed-loop feedback. Due to heat transfer hysteresis, the water flow often overflows long before the temperature drops rapidly, resulting in significant delays in prevention. Furthermore, traditional three-dimensional fluid dynamics simulations for predicting water flow are extremely time-consuming, failing to meet the hundreds of milliseconds of online feedback cycles required by industrial control systems. Using simple summation of spatial linear distances to estimate the water flow impact fails to reflect the obstruction and attenuation of the fluid by the three-dimensional steps of the component and the secondary flow characteristics. Therefore, the temperature control effect during the heat treatment of aluminum alloys is poor. Summary of the Invention

[0004] To address the problem of poor temperature control in existing methods for controlling the temperature during aluminum alloy heat treatment, the present invention aims to provide a temperature control method and system for aluminum alloy heat treatment. The specific technical solution adopted is as follows: In a first aspect, the present invention provides a temperature control method for an aluminum alloy heat treatment process, the method comprising the following steps: Obtain the location, pipeline pressure, and temperature of different temperature measurement nodes on the surface of aluminum alloy components; Based on the relative position distribution of each pair of temperature measuring nodes, a directed interference weight matrix is ​​generated to characterize the unidirectional interference intensity when water flows along the surface of the component; based on the pipeline pressure and temperature of each temperature measuring node during the spray cooling process, the local heat transfer stability index of each temperature measuring node is determined. Based on the component cross-sectional thickness and temperature corresponding to each temperature measuring node, non-source nodes are screened and an expected water spray state vector is constructed; combined with the expected water spray state vector and the directed interference weight matrix, the equivalent diffuse interference intensity of each temperature measuring node is predicted. Based on the equivalent spillover interference intensity and local heat transfer stability index of the non-source nodes, the spillover risk is assessed; based on the spillover risk, corresponding control commands are issued.

[0005] Preferably, the step of generating a directed interference weight matrix characterizing the unidirectional interference intensity as water flow spreads along the surface of a component, based on the relative positional distribution of each pair of temperature measuring nodes, includes: Obtain the three-dimensional coordinates of each temperature measurement node, where the direction of gravity is the positive direction of the vertical axis of the coordinate system; Two temperature measurement nodes form a node pair; one temperature measurement node in each node pair is used as the starting node and the other temperature measurement node is used as the target node. Traverse all node pairs and calculate the shortest path length between the starting node and the target node, as well as the height difference between the target node and the starting node along the direction of gravity. For any pair of nodes, if the pair of nodes satisfies a preset first condition, then the negative correlation mapping value of the shortest path length corresponding to the pair of nodes and the corresponding height difference are fused to obtain the unidirectional water flow interference weight from the starting node to the target node of the pair of nodes; if the pair of nodes does not satisfy the preset first condition, then the unidirectional water flow interference weight from the starting node to the target node of the pair of nodes is determined based on the negative correlation mapping value of the shortest path length corresponding to the pair of nodes. The first preset condition is: the target node is located within a preset inverted cone angle space with the starting node as the vertex and the direction of gravity as the central axis, and the shortest path length between the target node and the starting node is less than a preset lateral expansion limit; Based on the unidirectional water flow interference weights of all node pairs, the directed interference weight matrix is ​​constructed.

[0006] Preferably, determining the local heat transfer stability index of each temperature measuring node based on the pipeline pressure and temperature at each temperature measuring node during the spray cooling process includes: The dispersion of the pressure sequence and the dispersion of the transient cooling rate sequence at each temperature measurement node are evaluated respectively; the pressure sequence is obtained by arranging the pipeline pressure in time sequence, and the transient cooling rate sequence is obtained by arranging the transient cooling rate in time sequence, wherein the transient cooling rate is determined based on temperature. By combining the dispersion of the pressure sequence and the dispersion of the transient cooling rate sequence at the same temperature measurement node, the local heat transfer stability index of the corresponding temperature measurement node is obtained.

[0007] Preferably, the step of filtering non-source nodes based on the component cross-sectional thickness and temperature corresponding to each temperature measurement node includes: Obtain the cross-sectional thickness value corresponding to each temperature measurement node and the current average cooling rate; Temperature measurement nodes whose component cross-sectional thickness is greater than a preset thickness classification threshold and whose average cooling rate is lower than a preset cooling rate threshold are marked as expected activation sources, and all temperature measurement nodes other than the expected activation sources are marked as non-source nodes.

[0008] Preferably, the expected water spray state vector includes: The number of elements in the expected water spray state vector is the same as the number of temperature measurement nodes; the elements corresponding to the expected start-up source in the expected water spray state vector are set to the preset water pressure increment constant, and the elements corresponding to the non-source nodes in the expected water spray state vector are set to zero.

[0009] Preferably, the step of combining the expected water spray state vector with the directed interference weight matrix to predict the equivalent diffuse interference intensity of each temperature measurement node includes: The product of the normalized empirical constant characterizing the viscous properties of the water medium flow and the transpose of the directed interference weight matrix is ​​calculated. Based on the product and the expected water spray state vector, the equivalent diffuse interference intensity of each temperature measurement node is obtained.

[0010] Preferably, the degree of dispersion is the variance of all data in the corresponding sequence.

[0011] Preferably, the assessment of spillover risk based on the equivalent spillover interference intensity and local heat transfer stability index of non-source nodes includes: For any non-source node: Based on the equivalent diffuse interference intensity and local heat transfer stability index of the non-source node, the risk value of exceeding the cooling rate limit for the non-source node is obtained. The maximum value of the cold rate exceeding the limit risk value of all non-source nodes is taken as the overflow risk value, which is used to characterize the overflow risk.

[0012] Preferably, the step of issuing corresponding control instructions based on the spillover risk includes: When the overflow risk value is less than or equal to the preset safety limit value, a continuous high-pressure spraying command is issued to the corresponding expected start-up source. When the overflow risk value exceeds the preset safety limit value, the continuous spraying command is interrupted, and the continuous spraying command is reorganized into a pulse spraying command with a zero-pressure venting pause interval before being issued.

[0013] In a second aspect, the present invention provides a temperature control system for an aluminum alloy heat treatment process, the system being used to implement the method described in the first aspect, the system comprising: The acquisition module is used to acquire the location, pipeline pressure, and temperature of different temperature measurement nodes on the surface of aluminum alloy components; The analysis module is used to generate a directed interference weight matrix that characterizes the unidirectional interference intensity when water flows along the surface of a component, based on the relative position distribution of each pair of temperature measuring nodes; and to determine the local heat transfer stability index of each temperature measuring node based on the pipeline pressure and temperature of each temperature measuring node during the spray cooling process. The prediction module is used to filter non-source nodes and construct the expected water spray state vector based on the component cross-sectional thickness and temperature corresponding to each temperature measurement node; and combine the expected water spray state vector with the directed interference weight matrix to predict the equivalent diffuse interference intensity of each temperature measurement node. The control module is used to assess the spillover risk based on the equivalent spillover interference intensity and local heat transfer stability index of the non-source nodes; and to issue corresponding control commands based on the spillover risk.

[0014] The present invention has at least the following beneficial effects: This invention acquires the positions, pipeline pressures, and temperatures of different temperature measurement nodes on the surface of aluminum alloy components. It constructs a directed interference weight matrix to characterize the unidirectional interference intensity when water flows along the component surface, and determines the local heat transfer stability index of each temperature measurement node. Furthermore, it filters out non-source nodes and constructs a predicted water spray state vector. Combining this vector with the directed interference weight matrix, it predicts the equivalent overflow interference intensity of each temperature measurement node, thus achieving a feedforward quantitative assessment of the water flow interference effect that has not yet occurred. Finally, it assesses the overflow risk based on the equivalent overflow interference intensity of non-source nodes and the local heat transfer stability index, and issues corresponding control commands based on this risk. This method overcomes the control lag problem caused by the reliance on post-event closed-loop feedback in traditional temperature control methods. It eliminates the need for time-consuming three-dimensional fluid dynamics simulations and can predict the interference risk of water overflow on the heat transfer state of non-target areas in advance, while meeting the requirements of industrial online feedback cycles. This improves the temperature control accuracy and response speed of the aluminum alloy heat treatment process and reduces the risk of component cracking due to excessive local cooling rates. Attached Figure Description

[0015] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 A flowchart of a temperature control method for an aluminum alloy heat treatment process provided in an embodiment of the present invention; Figure 2 This is a structural block diagram of a temperature control system for an aluminum alloy heat treatment process provided in an embodiment of the present invention. Detailed Implementation

[0017] To further illustrate the technical means and effects adopted by the present invention to achieve the intended purpose, the following detailed description of a temperature control method and system for an aluminum alloy heat treatment process based on the present invention, in conjunction with the accompanying drawings and preferred embodiments, is provided below.

[0018] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0019] The following description, in conjunction with the accompanying drawings, details a specific scheme for a temperature control method and system for an aluminum alloy heat treatment process provided by the present invention.

[0020] An example of a temperature control method for aluminum alloy heat treatment process: This embodiment proposes a temperature control method for the heat treatment process of aluminum alloys, such as... Figure 1 As shown, a temperature control method for an aluminum alloy heat treatment process according to this embodiment includes the following steps: Step S1: Obtain the location, pipeline pressure, and temperature of different temperature measurement nodes on the surface of the aluminum alloy component.

[0021] Multiple temperature measurement nodes are distributed on the surface of the aluminum alloy component. Each temperature measurement node is equipped with a temperature sensor to collect temperature data from the surface of the aluminum alloy component. The number of temperature measurement nodes on the surface of the aluminum alloy component is determined by the implementer based on the size of the aluminum alloy component, which will not be elaborated further here. Since this embodiment involves matrix operations on spatial data, it is necessary to ensure complete alignment of the input data in the vector dimension. To this end, an array of high-pressure water valves that are physically mapped to the temperature measurement nodes are pre-configured, and each water valve is equipped with an independent pressure sensor to collect pipeline pressure data. A three-dimensional digital model of the aluminum alloy component is obtained. For any temperature measurement node on the surface of the aluminum alloy component, the high-pressure water valve with the smallest Euclidean distance from the temperature measurement node is selected as the target high-pressure water valve for that temperature measurement node based on the coordinates of the three-dimensional model. The pipeline pressure data of the target high-pressure water valve is used as the pipeline pressure of that temperature measurement node. The acquisition frequency of pipeline pressure data and temperature data can be set to once every 0.1 seconds.

[0022] Using the above methods, we can obtain the location of each temperature measuring node on the surface of the aluminum alloy component, the pipeline pressure of each temperature measuring node at each moment, and the temperature of each temperature measuring node at each sampling moment.

[0023] Step S2: Based on the relative position distribution of each pair of temperature measuring nodes, generate a directed interference weight matrix that characterizes the unidirectional interference intensity when water flows along the surface of the component; determine the local heat transfer stability index of each temperature measuring node according to the pipeline pressure and temperature of each temperature measuring node during the spray cooling process.

[0024] Using the direction of gravity as the positive direction of the vertical axis of the coordinate system, the three-dimensional coordinates of each temperature measuring node are obtained based on the three-dimensional digital model of the aluminum alloy component. Each temperature measuring node has its own unique index number. Using the existing surface mesh geodesic search algorithm, for each temperature measuring node, the shortest path length from the temperature measuring node to each other temperature measuring node along the surface mesh of the component is calculated, which is taken as the shortest path length between the temperature measuring node and each other temperature measuring node. The difference between the vertical coordinate of the temperature measuring node and the vertical coordinate of each other temperature measuring node is recorded as the height difference of each other temperature measuring node relative to the temperature measuring node along the direction of gravity.

[0025] Each pair of temperature measuring nodes forms a node pair, and multiple node pairs can be obtained. One temperature measuring node in each pair is designated as the starting node, and the other as the target node. By traversing all node pairs, the shortest path length between the starting and target nodes in each pair, as well as the height difference between the target node and the starting node along the direction of gravity, can be calculated using the above method.

[0026] For any node pair, if the node pair satisfies a preset first condition, the negative correlation mapping value of the shortest path length corresponding to the node pair and the corresponding height difference are fused to obtain the unidirectional water flow interference weight from the starting node to the target node of the node pair. The preset first condition is: the target node is located within a preset inverted cone angle space with the starting node as the vertex and the gravity direction as the central axis, and the shortest path length between the target node and the starting node is less than a preset lateral expansion limit. The preset inverted cone angle space can be a central axis angle less than 15 degrees. When the node pair satisfies the preset first condition, it indicates that the target node is located above the starting node and is very close. At this time, fluid overflow not only relies on surface spread but also generates a strong buoyancy wave due to steam expansion caused by the heat source. The preset lateral expansion limit is 1.5 times the average installation spacing between the array water valves. As a specific implementation, a specific calculation formula for the unidirectional water flow interference weight is given. The unidirectional water flow interference weight from the starting node i to the target node j can be expressed as: in, This represents the unidirectional water flow interference weight from starting node i to target node j. Represents the natural constant. This represents the shortest path length between the starting node i and the target node j. This represents the height difference between the target node j and the starting node i along the direction of gravity. This represents a highly normalized characteristic constant. Represents the distance scale constant. This represents the gravitational acceleration interference coefficient.

[0027] The characteristic height normalization constant is used to map the absolute height between two temperature measuring nodes to a dimensionless percentage value, thereby normalizing the height value. Specifically, the characteristic height normalization constant is the range between the maximum and minimum heights of the component's 3D model along the Z-axis. The distance scale constant is the average installation spacing between the array of water valves, and the gravity acceleration interference coefficient is used to quantify the kinetic energy increment of the downward-flowing fluid; its value typically ranges from 1.2 to 1.5, and is set to 1.3 in this embodiment.

[0028] This is a negative correlation mapping value for the shortest path length, used to characterize the intensity attenuation caused by surface friction and flow around the target node. The longer the shortest path between the starting node i and the target node j, the better. The closer the value is to 0, the greater the distance between the two temperature measuring nodes, and the less likely the fluids are to interfere with each other. The additional interference increment generated by the upward buoyancy impact is used to characterize this part. The addition of 0.01 to the denominator prevents the formula from having a denominator of 0 if the aluminum alloy component being processed happens to be a stepped plate or large flat plate with a certain thickness but a flat surface. It should be noted that the dimension of 0.01 here is consistent with the dimension of the characteristic height normalization constant. The unidirectional water flow interference weight is determined by combining the negative correlation mapping value of the shortest path length and the additional interference increment generated by the upward buoyancy impact, ensuring that the interference weight receives a true numerical amplification within a specific short-distance upper interval.

[0029] If the node pair does not meet the preset first condition, it means that the water flow can only interfere through frictional propagation via surface sliding. In this case, it is further determined whether the height difference between the target node j and the starting node i along the direction of gravity is greater than 0. If the height difference between the target node j and the starting node i along the direction of gravity is greater than 0, it means that the target node is located above. At this point, the unidirectional water flow interference weight is determined based on the negative correlation mapping value of the shortest path length corresponding to the node pair and the ascent attenuation factor, i.e.: ,in, The climbing attenuation factor is represented by a value of (0.3, 0.5), which characterizes the difficulty of fluid overcoming gravity and spreading upwards. As a specific example, the climbing attenuation factor can be 0.4. If the height difference between the target node j and the starting node i along the direction of gravity is less than or equal to 0, then the unidirectional water flow interference weight from the starting node to the target node is determined only based on the negative correlation mapping value of the shortest path length corresponding to the node pair. As a specific implementation method, the specific calculation formula for the unidirectional water flow interference weight is given. The unidirectional water flow interference weight from the starting node i to the target node j can be expressed as: .

[0030] Using the above method, we can obtain two unidirectional water flow interference weights for each node pair. The reason why a node pair has two unidirectional water flow interference weights is that the results calculated by the above method are different when different nodes in the node pair are taken as the starting node or the target node.

[0031] Furthermore, based on the unidirectional water flow interference weights of all node pairs, a directed interference weight matrix is ​​constructed. The directed interference weight matrix is ​​a matrix with dimension 1. The matrix, where, This represents the number of temperature measurement nodes. The element in the i-th row and j-th column of the directed interferometric weight matrix is... For elements in the directed interference weight matrix where the row number and column number are equal (e.g., the element at row i, column i, or row j, column j), their value is assigned to zero. Using this method, a complete directed interference weight matrix can be obtained, which quantifies the degree of obstruction and connectivity of water flow propagation between pairs of temperature measurement nodes on the surface of the aluminum alloy component.

[0032] For the current time period, in chronological order, for each temperature measuring node, the pipeline pressure at all sampling moments within the current time period is arranged to obtain a pressure sequence for each node. The temperature at each measuring node at all sampling moments within the current time period is also arranged to obtain a temperature sequence. A first-order difference algorithm is used to calculate the absolute value of the derivative of the temperature with time, which is taken as the transient cooling rate. In chronological order, all transient cooling rates for each temperature measuring node within the current time period are arranged to obtain a transient cooling rate sequence for each node. Each temperature measuring node has a corresponding pressure sequence and a transient cooling rate sequence within the current time period. The current time period is the set of all historical moments with a time interval less than or equal to a preset duration, plus the current moment. The preset duration can be 2 seconds, but the implementer can set it according to specific circumstances in practical applications.

[0033] Next, the dispersion of the pressure sequence and the dispersion of the transient cooling rate sequence at each temperature measurement node are evaluated. The dispersion can be characterized by variance. In actual industrial control, if the water valve outputs stably and the temperature decreases linearly and uniformly, the theoretically calculated values ​​of both variances will approach zero.

[0034] Furthermore, by combining the dispersion of the pressure sequence and the dispersion of the transient cooling rate sequence at the same temperature measurement node, the local heat transfer stability index of the corresponding temperature measurement node is obtained.

[0035] As a specific implementation method, a specific formula for calculating the local heat transfer stability index is given. The local heat transfer stability index of the j-th temperature measurement node can be expressed as: in, This represents the local heat transfer stability index of the j-th temperature measurement node. This represents the variance of all data in the pressure sequence at the j-th temperature measurement node. This represents the pressure noise floor bias constant. This represents the variance of all data in the transient cooling rate sequence of the j-th temperature measurement node. This represents the temperature noise floor bias constant. This represents the normalization function.

[0036] As one specific implementation method, the data is normalized using maximum and minimum values. The maximum and minimum values ​​used in the maximum and minimum value normalization can be determined by statistically analyzing data obtained from historical experiments. This method is an existing method and will not be elaborated further here. As another implementation method, other existing data normalization methods can also be used for processing.

[0037] The introduction of pressure noise floor bias constant and temperature noise floor bias constant in the above formula for calculating the local heat transfer stability index takes into account that the variance may be 0. The pressure noise floor bias constant and temperature noise floor bias constant can be 0.0001.

[0038] The variance of all data in the pressure sequence of the j-th temperature measuring node is used to characterize the dispersion of the pressure sequence of the j-th temperature measuring node. The larger the variance, the more discrete the data distribution in the pressure sequence, and the more intense the external interference fluctuations applied at the water channel end. The variance of all data in the transient cooling rate sequence of the j-th temperature measuring node is used to characterize the dispersion of the transient cooling rate sequence of the j-th temperature measuring node. The larger the variance, the more discrete the data distribution in the transient cooling rate sequence, and the more intense the rupture and fluctuation of the actual heat transfer state (i.e., vapor film stability) on the component surface. This represents the actual magnitude of external pressure disturbances after adding hardware noise floor support; This represents the actual internal heat transfer response fluctuations after adding hardware noise floor support. When the component is in a stable cooling period... and If the value is extremely small, the formula will degenerate into... The constant proportion.

[0039] Characterizing external interference, It represents the oscillations exhibited by the water itself. When the external water pressure fluctuates greatly... The surface temperature is relatively high, while the surface cooling rate fluctuates very little. When the temperature is extremely low, the local heat transfer stability index is relatively high, indicating that the heat transfer state in that area is exceptionally stable and extremely difficult to be interrupted by external water flow. Conversely, if the local heat transfer stability index approaches zero, it indicates that even extremely small water pressure fluctuations will cause violent fluctuations in the cooling rate, and the vapor film on the surface of the j-th temperature measuring node is on the verge of rupture, with extremely low ability to withstand interference from external water flow.

[0040] Using the above methods, the local heat transfer stability index of each temperature measurement node can be obtained.

[0041] Step S3: Based on the component cross-sectional thickness and temperature corresponding to each temperature measuring node, non-source nodes are screened and an expected water spray state vector is constructed; combined with the expected water spray state vector and the directed interference weight matrix, the equivalent diffuse interference intensity of each temperature measuring node is predicted.

[0042] After obtaining the directed interference weight matrix and the local heat transfer stability index, this embodiment addresses the limitation of simple linear distance summation algorithms in calculating the true energy distribution of fluid flow around and after splitting. Instead of relying on extremely time-consuming three-dimensional fluid dynamics iterative simulation, it uses the directed interference weight matrix to perform an algebraic equivalent solution for the superposition and spread process of multi-source water flow. To overcome the severe lag in traditional methods that only detect temperature changes after the water valve is activated, this embodiment first constructs a state of expected command that has not yet been issued. Then, it inputs this expected state into the equation to perform a virtual pre-simulation, obtaining the equivalent spillover interference intensity, thereby achieving feedforward quantization of the water flow interference that has not yet occurred.

[0043] Specifically, the cross-sectional thickness values ​​of each temperature measurement node are obtained, and the average of all transient cooling rates within the current time period for each temperature measurement node is taken as the current average cooling rate of each temperature measurement node. Temperature measurement nodes whose component cross-sectional thickness is greater than a preset thickness classification threshold and whose average cooling rate is lower than a preset cooling rate threshold are marked as expected activation sources. The areas where expected activation sources are located not only have thicker components, but also where the current actual cooling progress is lagging behind the target requirements. All temperature measurement nodes other than expected activation sources are marked as non-source nodes. The preset thickness classification threshold is used to distinguish the thickness of the component cross-section. The preset thickness classification threshold can be set to 50 mm. The preset cooling rate threshold is a process constant preset according to the aluminum alloy grade and its phase transformation kinetic characteristics. Its value is generally between 15℃ / s and 50℃ / s. This value is the minimum cooling rate to ensure that the material does not experience a significant decrease in performance. For thick components, this value is usually taken as the lower limit of the process requirements, such as 15℃ / s, to balance cooling efficiency and thermal stress control. In this embodiment, the preset cooling rate threshold is set to 25℃ / s. In specific applications, its value can be set according to the specific situation.

[0044] Then, create a dimension of A one-dimensional vector is denoted as the expected water spray state vector. Each element in the expected water spray state vector corresponds one-to-one with the index number of a temperature measuring node. For example, the first element of the expected water spray state vector corresponds to the data of the first temperature measuring node, the second element corresponds to the data of the second temperature measuring node, the third element corresponds to the data of the third temperature measuring node, and so on. The element is the first The data corresponds to each temperature measurement node. Specifically, the method for obtaining this data is as follows: the elements corresponding to the expected source in the expected water spray state vector are set to a preset water pressure increment constant; the elements corresponding to non-source nodes in the expected water spray state vector are set to zero. The preset water pressure increment constant is the data value of the increase in water pressure in the rated pipeline output of a single nozzle when the industrial water valve is opened to its maximum opening, excluding units. The expected water spray state vector is used to characterize the distribution of the total strong cooling intervention actions planned by the control system in the next instruction.

[0045] Furthermore, based on the directed interference weight matrix and the expected water spray state vector, the equivalent diffuse interference intensity of each temperature measurement node is obtained.

[0046] As a specific implementation method, the equivalent diffuse interference intensity of each temperature measurement node can be determined in the following way, and the equivalent diffuse interference intensity of each temperature measurement node can be expressed as: in, This represents the equivalent diffuse interference intensity matrix. The dimension is An identity matrix whose main diagonal is all 1s. A normalized empirical constant characterizing the viscous properties of water flow. This represents the directed interference weight matrix; T represents the transpose. This represents the operation of finding the matrix inverse; This represents the expected water spray state vector.

[0047] The normalized empirical constant characterizing the viscous properties of water flow is generally between 0.1 and 0.5. In this embodiment, the normalized empirical constant characterizing the viscous properties of water flow is 0.3.

[0048] A system attenuation characteristic matrix was constructed, incorporating the self-damping and interconnectivity relationships of all temperature measurement nodes. Inverting this matrix mathematically yields the final sweep coefficient distribution for all other nodes when any input source in the network diffuses infinitely outwards and reaches a steady state. Subsequently, a matrix multiplication was performed between the inverted matrix and the expected water jet state vector. This multiplication operation nonlinearly accumulates all independently activated expected high-pressure water flows after they have been impeded and attenuated by surface undulations.

[0049] The dimension of the equivalent diffuse interference intensity matrix is The j-th element in the equivalent diffuse interference intensity matrix is ​​the equivalent diffuse interference intensity of the j-th temperature measuring node. The equivalent diffuse interference intensity quantifies the total interference intensity that the high-speed water flow sprayed out when the water valve array actually performs the expected water spray state vector action, after passing through the surface step obstruction, flow diversion and multi-source convergence accumulation, will eventually affect and press on the temperature measuring node.

[0050] Step S4: Assess the spillover risk based on the equivalent spillover interference intensity and local heat transfer stability index of the non-source nodes; and issue corresponding control commands based on the spillover risk.

[0051] Industrial equipment is prone to water overflow when executing simple normally-open commands. This embodiment generates a risk index system by combining the equivalent overflow interference intensity obtained above with the local heat transfer stability index. Based on this index system, the continuous water flow is actively blocked through a duty cycle mechanism, providing a time window for natural drainage of the component surface in a semi-open spray environment, thereby mitigating hydrodynamic interference through mechanical action.

[0052] Based on the above characteristics, for any non-source node: the cooling rate exceeding risk value of the non-source node is obtained according to its equivalent diffuse interference intensity and local heat transfer stability index. Specifically, the ratio of the equivalent diffuse interference intensity to the local heat transfer stability index of the non-source node is used as the cooling rate exceeding risk value of the non-source node. In this way, the cooling rate exceeding risk value of each non-source node can be obtained.

[0053] The equivalent overflow interference intensity characterizes the total interference of external water flow energy to be applied to the non-source node, and the local heat transfer stability index characterizes the internal heat transfer stability benchmark of the non-source node against water flow impact, which is derived from actual measurements.

[0054] The greater the equivalent spillover interference intensity and the smaller the local heat transfer stability index, the stronger the expected external interference and the worse the stability of the vapor film itself, resulting in a significant increase in the risk value of exceeding the cooling rate limit. The cooling rate exceeding the limit risk value calculated in the above way eliminates the safety blind spot that is easily misjudged by relying solely on external water pressure, and quantifies the absolute probability of failure.

[0055] The maximum value of the cooling rate exceeding the limit risk value of all non-source nodes is taken as the overflow risk value, which is used to characterize the overflow risk. When the overflow risk value is less than or equal to the preset safety limit value, it means that the simulated interference experienced by the weakest node in the entire field has not yet crossed the safety red line, and the expected water spray state vector constructed in step S3 is safe. Therefore, a normally open command is immediately issued to the array water valve of the expected opening source. Since the cutoff logic is not triggered, the target water valve will maintain a continuous high-pressure output state to ensure that the thick cross-section area achieves the maximum cooling efficiency.

[0056] When the overflow risk value exceeds the preset safety limit, it indicates that a situation of uncontrolled cooling in the surrounding area due to water overflow is about to occur. At this time, the normally open spray command originally scheduled to be continuously sent to the water valve is interrupted. Duty cycle modulation is added to the timing output of the controller to generate a pulse water flow command with a fixed zero-pressure venting pause interval. Specifically, the originally continuous power supply drive signal is re-cut into alternating opening time windows and forced closing time windows. The opening time window can be 0.5 seconds for high-pressure water spray, and the closing time window can be 0.2 seconds for zero-pressure water stop. The recombined pulse command is sent to the corresponding array water valve actuator. When the water valve executes the pulse action, the nozzle is forcibly closed to cut off the water supply. In the semi-open array spray environment, the components are not submerged in liquid. Therefore, the mechanical water cut-off window period gained by closing the water valve can cause the water that originally spread and accumulated along the surface of the component to lose its source of kinetic energy and naturally drip away to lower places under the action of gravity. This physical drainage action eliminates the additional impact on the vapor film in the surrounding sensitive areas and resolves the risk of overflow.

[0057] As a specific implementation method, the preset safety limit value can be set in the following way: Through offline quenching destructive testing, the interference of overflowing water flow at the temperature measurement node is artificially increased. The critical risk value corresponding to the moment when the vapor film on the surface of the node ruptures and the cooling rate curve shows an uncontrolled sharp drop (or when the sample develops microcracks) is monitored and recorded. The preset safety limit value is set to 80%~90% of this critical risk value; for example, the preset safety limit value can be 2.0.

[0058] After the above action command is issued and maintained for a preset duration, the timeline judgment logic is executed to determine whether to continue the loop.

[0059] Specifically, the safe termination temperature for quenching aluminum alloys is read in advance from the material process parameter library. For example, the safe termination temperature for quenching aluminum alloys is... Get all data at the current time. The temperature returned by each temperature measurement node sensor is used to determine the current time of all... If the temperatures returned by all temperature measuring nodes are less than or equal to the safe termination temperature of aluminum alloy quenching, and if the result shows that the temperature of any one temperature measuring node is still higher than the safe termination temperature of aluminum alloy quenching, it means that the quenching and cooling task of the entire component is not yet complete. At this time, a first-in-first-out (FIFO) data queue sliding update mode is adopted to push the time axis forward by a preset time. After the time axis is pushed forward, it returns to step S2 and seamlessly connects to the subsequent analysis and processing steps. The queue sliding mechanism, rather than the clearing and resetting mechanism, is used to achieve real-time protection. If the judgment result confirms that all... If the instantaneous temperature of each temperature measuring node is less than or equal to the safe termination temperature of aluminum alloy quenching, it indicates that the overall component has completed sufficient solution cooling and reached the safe conditions for removal. At this time, a forced lockout command is immediately issued to the water circuit hardware, including the main water supply pump and all array water valves.

[0060] Thus, by using the above method, intelligent control of the heat treatment process of aluminum alloys has been achieved.

[0061] This embodiment acquires the positions, pipeline pressures, and temperatures of different temperature measurement nodes on the surface of aluminum alloy components. It constructs a directed interference weight matrix to characterize the unidirectional interference intensity when water flows along the component surface, and determines the local heat transfer stability index of each temperature measurement node. Furthermore, it filters out non-source nodes and constructs a predicted water spray state vector. Combining this vector with the directed interference weight matrix, it predicts the equivalent overflow interference intensity of each temperature measurement node, thus achieving a feedforward quantitative assessment of the water flow interference effect that has not yet occurred. Finally, it assesses the overflow risk based on the equivalent overflow interference intensity of non-source nodes and the local heat transfer stability index, and issues corresponding control commands based on this risk. This method overcomes the control lag problem caused by the reliance on post-event closed-loop feedback in traditional temperature control methods. It eliminates the need for time-consuming three-dimensional fluid dynamics simulation and can predict the interference risk of water overflow on the heat transfer state of non-target areas in advance, while meeting the requirements of industrial online feedback cycles. This improves the temperature control accuracy and response speed of the aluminum alloy heat treatment process and reduces the risk of component cracking due to excessive local cooling rates.

[0062] An embodiment of a temperature control system for an aluminum alloy heat treatment process: See Figure 2 The diagram illustrates a structural block diagram of a temperature control system for an aluminum alloy heat treatment process according to an embodiment of the present invention. The system may include an acquisition module, an analysis module, a prediction module, and a control module.

[0063] The acquisition module is used to acquire the location, pipeline pressure, and temperature of different temperature measurement nodes on the surface of the aluminum alloy component. The analysis module is used to generate a directed interference weight matrix that characterizes the unidirectional interference intensity when water flows along the surface of a component, based on the relative position distribution of each pair of temperature measuring nodes; and to determine the local heat transfer stability index of each temperature measuring node based on the pipeline pressure and temperature of each temperature measuring node during the spray cooling process. The prediction module is used to filter non-source nodes and construct the expected water spray state vector based on the component cross-sectional thickness and temperature corresponding to each temperature measurement node; and combine the expected water spray state vector with the directed interference weight matrix to predict the equivalent diffuse interference intensity of each temperature measurement node. The control module is used to assess the spillover risk based on the equivalent spillover interference intensity and local heat transfer stability index of the non-source nodes; and to issue corresponding control commands based on the spillover risk.

[0064] It should be understood that Figure 2 The structural block diagram and modules of the temperature control system for the aluminum alloy heat treatment process shown can be implemented in various ways. For example, in some embodiments, the system and its modules can be implemented by hardware, software, or a combination of both. The hardware portion can be implemented using dedicated logic; the software portion can be stored in memory and executed by appropriate instructions, such as a microprocessor or dedicated hardware. Those skilled in the art will understand that the above-described methods and systems can be implemented using computer-executable instructions and / or included in processor control code, for example, on a carrier medium such as a disk, CD, or DVD-ROM, a programmable memory such as read-only memory (firmware), or a data carrier such as an optical or electronic signal carrier. The systems and modules of this specification can be implemented not only by hardware circuits such as very large-scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, or programmable hardware devices such as field-programmable gate arrays, programmable logic devices, etc., but also by software executed by various types of processors, or by a combination of the above-described hardware circuits and software (e.g., firmware).

[0065] For more details about the above modules, please refer to other parts of this manual; they will not be repeated here.

[0066] It should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A temperature control method for an aluminum alloy heat treatment process, characterized in that, The method includes the following steps: Obtain the location, pipeline pressure, and temperature of different temperature measurement nodes on the surface of aluminum alloy components; Based on the relative position distribution of each pair of temperature measuring nodes, a directed interference weight matrix is ​​generated to characterize the unidirectional interference intensity when water flows along the surface of the component; based on the pipeline pressure and temperature of each temperature measuring node during the spray cooling process, the local heat transfer stability index of each temperature measuring node is determined. Based on the component cross-sectional thickness and temperature corresponding to each temperature measuring node, non-source nodes are screened and an expected water spray state vector is constructed; combined with the expected water spray state vector and the directed interference weight matrix, the equivalent diffuse interference intensity of each temperature measuring node is predicted. Based on the equivalent spillover interference intensity and local heat transfer stability index of the non-source nodes, the spillover risk is assessed; based on the spillover risk, corresponding control commands are issued.

2. The temperature control method for an aluminum alloy heat treatment process according to claim 1, characterized in that, The directed interference weight matrix, which characterizes the unidirectional interference intensity as water flow spreads along the surface of a component, is generated based on the relative position distribution of each pair of temperature measuring nodes. This includes: Obtain the three-dimensional coordinates of each temperature measurement node, where the direction of gravity is the positive direction of the vertical axis of the coordinate system; Two temperature measurement nodes form a node pair; one temperature measurement node in each node pair is used as the starting node and the other temperature measurement node is used as the target node. Traverse all node pairs and calculate the shortest path length between the starting node and the target node, as well as the height difference between the target node and the starting node along the direction of gravity. For any pair of nodes, if the pair of nodes satisfies a preset first condition, then the negative correlation mapping value of the shortest path length corresponding to the pair of nodes and the corresponding height difference are fused to obtain the unidirectional water flow interference weight from the starting node to the target node of the pair of nodes; if the pair of nodes does not satisfy the preset first condition, then the unidirectional water flow interference weight from the starting node to the target node of the pair of nodes is determined based on the negative correlation mapping value of the shortest path length corresponding to the pair of nodes. The first preset condition is: the target node is located within a preset inverted cone angle space with the starting node as the vertex and the direction of gravity as the central axis, and the shortest path length between the target node and the starting node is less than a preset lateral expansion limit; Based on the unidirectional water flow interference weights of all node pairs, the directed interference weight matrix is ​​constructed.

3. The temperature control method for an aluminum alloy heat treatment process according to claim 1, characterized in that, The determination of the local heat transfer stability index of each temperature measuring node based on the pipeline pressure and temperature at each temperature measuring node during the spray cooling process includes: The dispersion of the pressure sequence and the dispersion of the transient cooling rate sequence at each temperature measurement node are evaluated respectively; the pressure sequence is obtained by arranging the pipeline pressure in time sequence, and the transient cooling rate sequence is obtained by arranging the transient cooling rate in time sequence, wherein the transient cooling rate is determined based on temperature. By combining the dispersion of the pressure sequence and the dispersion of the transient cooling rate sequence at the same temperature measurement node, the local heat transfer stability index of the corresponding temperature measurement node is obtained.

4. The temperature control method for an aluminum alloy heat treatment process according to claim 1, characterized in that, The step of filtering non-source nodes based on the component cross-sectional thickness and temperature corresponding to each temperature measurement node includes: Obtain the cross-sectional thickness value corresponding to each temperature measurement node and the current average cooling rate; Temperature measurement nodes whose component cross-sectional thickness is greater than a preset thickness classification threshold and whose average cooling rate is lower than a preset cooling rate threshold are marked as expected activation sources, and all temperature measurement nodes other than the expected activation sources are marked as non-source nodes.

5. The temperature control method for an aluminum alloy heat treatment process according to claim 4, characterized in that, The expected water spray state vector includes: The number of elements in the expected water spray state vector is the same as the number of temperature measurement nodes; the elements corresponding to the expected start-up source in the expected water spray state vector are set to the preset water pressure increment constant, and the elements corresponding to the non-source nodes in the expected water spray state vector are set to zero.

6. The temperature control method for an aluminum alloy heat treatment process according to claim 1, characterized in that, The step of combining the expected water spray state vector with the directed interference weight matrix to predict the equivalent diffuse interference intensity of each temperature measurement node includes: The product of the normalized empirical constant characterizing the viscous properties of the water medium flow and the transpose of the directed interference weight matrix is ​​calculated. Based on the product and the expected water spray state vector, the equivalent diffuse interference intensity of each temperature measurement node is obtained.

7. The temperature control method for an aluminum alloy heat treatment process according to claim 3, characterized in that, The degree of dispersion is the variance of all data in the corresponding sequence.

8. The temperature control method for an aluminum alloy heat treatment process according to claim 1, characterized in that, The assessment of spillover risk based on the equivalent spillover interference intensity and local heat transfer stability index of non-source nodes includes: For any non-source node: Based on the equivalent diffuse interference intensity and local heat transfer stability index of the non-source node, the risk value of exceeding the cooling rate limit for the non-source node is obtained. The maximum value of the cold rate exceeding the limit risk value of all non-source nodes is taken as the overflow risk value, which is used to characterize the overflow risk.

9. The temperature control method for an aluminum alloy heat treatment process according to claim 8, characterized in that, Based on the aforementioned spillover risk, the issuance of corresponding control commands includes: When the overflow risk value is less than or equal to the preset safety limit value, a continuous high-pressure spraying command is issued to the corresponding expected start-up source. When the overflow risk value exceeds the preset safety limit value, the continuous spraying command is interrupted, and the continuous spraying command is reorganized into a pulse spraying command with a zero-pressure venting pause interval before being issued.

10. A temperature control system for an aluminum alloy heat treatment process, the system being used to implement the method of claim 1, characterized in that, The system includes: The acquisition module is used to acquire the location, pipeline pressure, and temperature of different temperature measurement nodes on the surface of aluminum alloy components; The analysis module is used to generate a directed interference weight matrix that characterizes the unidirectional interference intensity when water flows along the surface of a component, based on the relative position distribution of each pair of temperature measuring nodes; and to determine the local heat transfer stability index of each temperature measuring node based on the pipeline pressure and temperature of each temperature measuring node during the spray cooling process. The prediction module is used to filter non-source nodes and construct the expected water spray state vector based on the component cross-sectional thickness and temperature corresponding to each temperature measurement node; and combine the expected water spray state vector with the directed interference weight matrix to predict the equivalent diffuse interference intensity of each temperature measurement node. The control module is used to assess the spillover risk based on the equivalent spillover interference intensity and local heat transfer stability index of the non-source nodes; and to issue corresponding control commands based on the spillover risk.

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