Intelligent cooling control method and system for metal stamping die based on temperature monitoring

Abstract

The invention provides a metal stamping die intelligent cooling control method and system based on temperature monitoring, and relates to the field of temperature control, and the method comprises the steps: collecting the temperature of multiple measuring points of a die, reconstructing an internal temperature field, and recognizing an abnormal region; predicting the heat accumulation intensity of the next cycle; calculating the cooling influence coefficient of each cooling channel on the abnormal area; and based on this, an optimization target is constructed to solve the flow distribution value of each channel, and the cooling medium flow is adjusted accordingly. Accurate and self-adaptive cooling of the area with the abnormal temperature of the die is achieved, the cooling efficiency and the temperature uniformity of the die are improved, and the service life of the die is prolonged.

Description

Technical Field

[0001] This invention relates to temperature control technology, and more particularly to an intelligent cooling control method and system for metal stamping dies based on temperature monitoring. Background Technology

[0002] In the metal stamping process, the die temperature continuously rises due to continuous contact with the high-temperature blank and heat generated by plastic deformation. Excessive die temperature not only reduces product dimensional accuracy and surface quality but can also lead to die thermal fatigue, accelerated wear, and even premature failure, severely impacting production efficiency and die life. Therefore, effective die cooling control is crucial for ensuring stable stamping processes and product quality. Current conventional practices typically involve pre-setting several cooling channels inside the die and circulating a constant cooling medium (such as water or oil) for overall cooling. Some improved solutions place a small number of temperature sensors on the die surface or at key internal locations. By monitoring the temperature at single points or a few points, when the temperature exceeds a preset threshold, the flow rate of the overall cooling system is simply increased or the cooling medium temperature is decreased, attempting to achieve global cooling of the die.

[0003] However, the aforementioned conventional cooling control methods have significant drawbacks. Due to the localized and transient nature of heat generation during metal stamping, the temperature distribution across different areas of the mold is extremely uneven, and heat easily accumulates in specific areas (such as rounded corners, bosses, and other structurally complex locations), forming localized high-temperature zones. Existing methods, relying on a limited number of measuring points, cannot comprehensively and accurately reflect the complex three-dimensional temperature field distribution inside the mold, nor can they accurately identify the specific spatial location and intensity of localized heat accumulation areas. Coarse-grained cooling adjustments based on overall or a few point temperatures often lead to unreasonable allocation of cooling resources: overcooling non-critical areas may cause localized overcooling of the mold and generate thermal stress; while insufficient cooling is applied to high-temperature accumulation areas that truly require enhanced cooling, failing to achieve precise and efficient targeted cooling. This "one-size-fits-all" cooling approach struggles to control localized high temperatures while ensuring overall mold temperature uniformity, resulting in low cooling efficiency and potentially damaging the mold's structural integrity due to uneven thermal stress distribution. Summary of the Invention

[0004] This invention provides an intelligent cooling control method and system for metal stamping dies based on temperature monitoring, which can solve the problems in the prior art.

[0005] A first aspect of this invention provides an intelligent cooling control method for metal stamping dies based on temperature monitoring, comprising: Temperature values ​​at multiple measuring points of the metal stamping die during the stamping cycle are collected to form a temperature data sequence; Based on the temperature values ​​and locations of each measuring point in the temperature data sequence, the temperature gradient vector between the measuring points is calculated. The temperature gradient vector is then spatially integrated along the inside of the mold to reconstruct the temperature distribution field at locations where no measuring points are arranged inside the mold, thereby identifying the spatial location and heat accumulation intensity of abnormal temperature areas. Extract the temperature evolution trajectory of each measuring point in the temperature data sequence during the continuous stamping cycle, calculate the rate of change of the temperature evolution trajectory, and predict the heat accumulation intensity of the temperature anomaly area at the end of the next stamping cycle. Based on the spatial location of the temperature anomaly area and the spatial layout of each cooling channel, calculate the cooling influence coefficient of each cooling channel on the temperature anomaly area. Based on the cooling influence coefficient, the predicted value of heat accumulation intensity, and the overall temperature uniformity constraint of the mold, an optimization objective for flow distribution is constructed and the flow distribution value of each cooling channel is obtained by solving the problem. The flow rate of the cooling medium in each cooling channel is adjusted according to the flow distribution value, and the metal stamping die is cooled during the stamping cycle interval.

[0006] Based on the temperature values ​​and locations of each measuring point in the temperature data sequence, the temperature gradient vector between the measuring points is calculated. This temperature gradient vector is then spatially integrated along the interior of the mold to reconstruct the temperature distribution field at locations where no measuring points are located within the mold. This process identifies the spatial location and heat accumulation intensity of abnormal temperature areas, including: Based on the temperature values ​​and locations of each measuring point in the temperature data sequence, a mold space grid cell is constructed, the center coordinates and boundary coordinates of each grid cell are calculated, and the set of boundary measuring points of each grid cell is determined according to the distance relationship between the boundary coordinates and the measuring point locations. Extract the temperature values ​​of each measuring point in the boundary measuring point set, calculate the temperature gradient vector between measuring points in the boundary measuring point set, and extend the temperature gradient vector from the boundary coordinates to the center coordinates to obtain the internal temperature gradient field of each grid cell. The internal temperature gradient field is spatially integrated along the path from the boundary coordinates to the center coordinates inside the mold. The temperature value at the center coordinates of each grid cell is calculated. The temperature value and the center coordinates are combined to reconstruct the temperature distribution field at the location inside the mold where no measuring points are arranged. Extract grid cells in the temperature distribution field whose temperature values ​​exceed a preset temperature reference threshold, mark the center coordinates of the grid cells as the spatial location of the temperature anomaly region, and calculate the difference between the temperature value and the preset temperature reference threshold as the heat accumulation intensity.

[0007] The temperature evolution trajectory of each measuring point in the temperature data sequence during continuous stamping cycles is extracted, the rate of change of the temperature evolution trajectory is calculated, and the predicted heat accumulation intensity of the temperature anomaly region at the end of the next stamping cycle is calculated. The temperature values ​​of each measuring point in the temperature data sequence at the end of each stamping cycle in the continuous stamping cycle are obtained, and the temperature evolution trajectory of each measuring point is constructed in chronological order. Calculate the cross-correlation function between the temperature evolution trajectories of each measuring point within the temperature anomaly area, extract the time offset corresponding to the peak value of the cross-correlation function, identify heat source measuring points and conduction measuring points based on the time offset, and record the heat conduction delay time between heat source measuring points and conduction measuring points; Calculate the rate of change of the temperature evolution trajectory at the heat source measuring point, and based on the rate of change and the heat conduction delay time, extrapolate the predicted temperature value of the conduction measuring point at the end of the next stamping cycle; The location of conduction measurement points where the predicted temperature value exceeds the preset temperature reference threshold is identified. The spatial distribution density of conduction measurement points exceeding the threshold in the temperature anomaly area is statistically analyzed. The heat accumulation state in the temperature anomaly area is evaluated based on the deviation between the spatial distribution density and the predicted temperature value, and the predicted value of the heat accumulation intensity in the temperature anomaly area at the end of the next stamping cycle is obtained.

[0008] The rate of change of the temperature evolution trajectory at the heat source measuring point is calculated. Based on the rate of change and the heat conduction delay time, the predicted temperature value of the conducting measuring point at the end of the next stamping cycle is deduced, including: Extract the temperature value at the end of the continuous ramming cycle from the temperature evolution trajectory of the heat source measuring point, and calculate the temperature difference at the end of adjacent ramming cycles; The change in temperature difference is obtained by performing a second difference on the continuous temperature difference. When the change in temperature difference is positive, it is determined that the heat source measuring point is in an accelerated heating state. When the change in temperature difference is negative, it is determined that the heat source measuring point is in a decelerated heating state. For heat source measuring points in the accelerated heating state, the temperature values ​​of multiple ram cycles in the temperature evolution trajectory are extracted to calculate the rate of change; for heat source measuring points in the decelerated heating state, the temperature values ​​of a small number of ram cycles in the temperature evolution trajectory are extracted to calculate the rate of change. The rate of change of the heat source measuring point is applied to the time period corresponding to the heat conduction delay time. The temperature state of the heat source measuring point after the heat conduction delay time is deduced, and the deduced temperature state is assigned to the conduction measuring point as the predicted temperature value of the conduction measuring point at the end of the next stamping cycle.

[0009] Based on the spatial location of the temperature anomaly area and the spatial layout of each cooling channel, the cooling influence coefficient of each cooling channel on the temperature anomaly area is calculated, including: The spatial location and cooling medium flow direction of each cooling channel are obtained, the temperature gradient field of the temperature anomaly area is extracted, and the direction with the largest gradient value in the temperature gradient field is identified as the dominant heat diffusion direction. Calculate the spatial distance between each cooling channel and the temperature anomaly area based on the spatial location of each cooling channel and the spatial location of the temperature anomaly area. Calculate the spatial angle between the cooling medium flow direction and the dominant heat diffusion direction based on the cooling medium flow direction and the dominant heat diffusion direction of each cooling channel. Based on the spatial angle range, the cooling channels are divided into opposing cooling channels, lateral cooling channels, and forward cooling channels. The forward cooling channels are excluded to obtain the effective cooling channels. The opposing cooling intensity and the lateral cooling intensity are calculated based on the spatial distance and spatial angle of the effective cooling channels, and the coverage range of each effective cooling channel for the temperature anomaly area is determined. When the coverage areas of multiple effective cooling channels overlap, the opposing cooling intensity and lateral cooling intensity of each effective cooling channel within the overlapping area are superimposed to obtain the cooling influence coefficient of each effective cooling channel on the temperature anomaly area.

[0010] Based on the cooling influence coefficient, the predicted value of heat accumulation intensity, and the constraint of overall mold temperature uniformity, an optimization objective for flow distribution is constructed and the flow distribution values ​​for each cooling channel are obtained by solving for the following: Extract the cooling influence coefficient and predicted heat accumulation intensity of each cooling channel, calculate the target cooling capacity of each cooling channel and convert it into flow demand value; Extract the temperature fluctuation range and temperature gradient distribution of non-abnormal areas of the mold in the temperature distribution field, and determine the overall temperature uniformity constraint of the mold. Extract the heat conduction delay time of heat source measurement points and conduction measurement points in the temperature anomaly area, identify the dominant cooling channel corresponding to the heat source measurement point and the cooperative cooling channel corresponding to the conduction measurement point, and calculate the flow distribution timing offset between the dominant cooling channel and the cooperative cooling channel. Calculate the temperature disturbance range of each cooling channel to the non-abnormal area of ​​the mold based on the flow demand value. When the temperature disturbance range violates the overall temperature uniformity constraint of the mold, calculate the constraint correction coefficient. The flow demand value is used as the optimization variable, the constraint correction coefficient is used as the constraint condition, and the flow allocation time offset is used as the time allocation rule. The flow allocation optimization objective that minimizes the temperature disturbance range while satisfying the target cooling amount is constructed and solved iteratively. The iteration stops when the temperature disturbance range meets the overall temperature uniformity constraint of the mold. The flow distribution value of the dominant cooling channel in the current stamping cycle gap and the flow distribution value of the cooperative cooling channel in the delayed stamping cycle gap are obtained. The flow distribution value of each cooling channel is then obtained by summing them up.

[0011] Adjusting the cooling medium flow rate of each cooling channel according to the flow distribution value, and cooling the metal stamping die during the stamping cycle interval includes: Monitor the stamping cycle status, and mark the start time of the stamping cycle gap when the current stamping cycle ends; At the start of the stamping cycle gap, the opening of the flow control valve of the main cooling channel is adjusted according to the flow distribution value of the main cooling channel in the current stamping cycle gap, and the main cooling channel is started to deliver cooling medium to the metal stamping die. The start-up time of the co-cooling channel is calculated based on the flow distribution timing offset between the main cooling channel and the co-cooling channel and the start time of the stamping cycle gap. When the start-up time of the collaborative cooling channel is reached, the opening of the flow control valve of the collaborative cooling channel is adjusted according to the flow distribution value of the collaborative cooling channel in the delayed stamping cycle gap, and the collaborative cooling channel is started to deliver cooling medium to the metal stamping die. Monitor the stamping cycle status, and when the start of the next stamping cycle is detected, close the flow control valves of each cooling channel to stop cooling the metal stamping die.

[0012] A second aspect of the present invention provides an intelligent cooling control system for metal stamping dies based on temperature monitoring, comprising: The temperature acquisition unit is used to acquire the temperature values ​​of multiple measuring points of the metal stamping die during the stamping cycle, forming a temperature data sequence. The temperature field re-unit is used to calculate the temperature gradient vector between measuring points based on the temperature value and location of each measuring point in the temperature data sequence. The temperature gradient vector is then spatially integrated along the inside of the mold to reconstruct the temperature distribution field at locations where no measuring points are arranged inside the mold, and to identify the spatial location and heat accumulation intensity of temperature anomaly areas. The temperature prediction unit is used to extract the temperature evolution trajectory of each measuring point in the temperature data sequence during the continuous stamping cycle, calculate the rate of change of the temperature evolution trajectory, and predict the heat accumulation intensity of the temperature anomaly area at the end of the next stamping cycle. The cooling influence unit is used to calculate the cooling influence coefficient of each cooling channel on the temperature anomaly area based on the spatial location of the temperature anomaly area and the spatial layout of each cooling channel. The flow optimization unit is used to construct the flow distribution optimization objective and solve for the flow distribution value of each cooling channel based on the cooling influence coefficient, the predicted value of heat accumulation intensity and the overall temperature uniformity constraint of the mold. The cooling control unit is used to adjust the flow rate of the cooling medium in each cooling channel according to the flow distribution value, and to cool the metal stamping die during the stamping cycle interval.

[0013] A third aspect of the present invention provides an electronic device, comprising: processor; Memory used to store processor-executable instructions; The processor is configured to invoke instructions stored in the memory to execute the aforementioned method.

[0014] A fourth aspect of the present invention provides a computer-readable storage medium having stored thereon computer program instructions that, when executed by a processor, implement the aforementioned method.

[0015] In this embodiment, real-time multi-point monitoring of the internal temperature of the metal stamping die and reconstruction of the full-space temperature field can be achieved, accurately identifying abnormal temperature areas and their heat accumulation intensity; through continuous stamping cycle temperature evolution trajectory analysis, the heat accumulation state of the next cycle can be predicted; combined with the spatial layout of the cooling channel, the influence coefficient is calculated and the flow distribution is optimized to achieve directional and precise cooling of each area of ​​the die, ensuring the overall temperature uniformity of the die, improving the stamping forming quality and production efficiency, while reducing die loss and energy consumption caused by local overheating. Attached Figure Description

[0016] Figure 1 This is a schematic flowchart of an intelligent cooling control method for metal stamping dies based on temperature monitoring, according to an embodiment of the present invention. Figure 2 This is a flowchart of the flow allocation value calculation method according to an embodiment of the present invention. Detailed Implementation

[0017] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0018] The technical solution of the present invention will be described in detail below with reference to specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments.

[0019] Figure 1 This is a flowchart illustrating the intelligent cooling control method for metal stamping dies based on temperature monitoring, as described in an embodiment of the present invention. Figure 1 As shown, the method includes: Temperature values ​​at multiple measuring points of the metal stamping die during the stamping cycle are collected to form a temperature data sequence; Based on the temperature values ​​and locations of each measuring point in the temperature data sequence, the temperature gradient vector between the measuring points is calculated. The temperature gradient vector is then spatially integrated along the inside of the mold to reconstruct the temperature distribution field at locations where no measuring points are arranged inside the mold, thereby identifying the spatial location and heat accumulation intensity of abnormal temperature areas. Extract the temperature evolution trajectory of each measuring point in the temperature data sequence during the continuous stamping cycle, calculate the rate of change of the temperature evolution trajectory, and predict the heat accumulation intensity of the temperature anomaly area at the end of the next stamping cycle. Based on the spatial location of the temperature anomaly area and the spatial layout of each cooling channel, calculate the cooling influence coefficient of each cooling channel on the temperature anomaly area. Based on the cooling influence coefficient, the predicted value of heat accumulation intensity, and the overall temperature uniformity constraint of the mold, an optimization objective for flow distribution is constructed and the flow distribution value of each cooling channel is obtained by solving the problem. The flow rate of the cooling medium in each cooling channel is adjusted according to the flow distribution value, and the metal stamping die is cooled during the stamping cycle interval.

[0020] Based on the temperature values ​​and locations of each measuring point in the temperature data sequence, the temperature gradient vector between the measuring points is calculated. This temperature gradient vector is then spatially integrated along the interior of the mold to reconstruct the temperature distribution field at locations where no measuring points are located within the mold. This process identifies the spatial location and heat accumulation intensity of abnormal temperature areas, including: Based on the temperature values ​​and locations of each measuring point in the temperature data sequence, a mold space grid cell is constructed, the center coordinates and boundary coordinates of each grid cell are calculated, and the set of boundary measuring points of each grid cell is determined according to the distance relationship between the boundary coordinates and the measuring point locations. Extract the temperature values ​​of each measuring point in the boundary measuring point set, calculate the temperature gradient vector between measuring points in the boundary measuring point set, and extend the temperature gradient vector from the boundary coordinates to the center coordinates to obtain the internal temperature gradient field of each grid cell. The internal temperature gradient field is spatially integrated along the path from the boundary coordinates to the center coordinates inside the mold. The temperature value at the center coordinates of each grid cell is calculated. The temperature value and the center coordinates are combined to reconstruct the temperature distribution field at the location inside the mold where no measuring points are arranged. Extract grid cells in the temperature distribution field whose temperature values ​​exceed a preset temperature reference threshold, mark the center coordinates of the grid cells as the spatial location of the temperature anomaly region, and calculate the difference between the temperature value and the preset temperature reference threshold as the heat accumulation intensity.

[0021] The mold space grid is constructed using a three-dimensional cubic mesh generation method. Based on the mold's external dimensions, it is divided into several equally spaced units along the length, width, and height directions. The unit size is typically set to 10-20mm. The center coordinates of each grid unit are calculated using the arithmetic mean of the boundary vertex coordinates. The boundary coordinates consist of the coordinates of the center points of the unit's six faces. When determining the boundary measurement point set, all actually arranged temperature measurement points are traversed, and the Euclidean distance between each measurement point and the grid unit boundary coordinates is calculated. If the distance is less than a preset threshold, the measurement point is included in the boundary measurement point set of that unit. The preset threshold is generally 1.5 times the unit size, ensuring that each unit is associated with at least 3 valid measurement points.

[0022] The temperature gradient vector within the boundary measuring point set is calculated using the finite difference method. Any two measuring points in the boundary measuring point set are selected, and their temperature values ​​T are extracted. i and T j and spatial coordinates (x) i y i , z i ) and (x j y j , z j The temperature gradient vector is represented as... The gradient vectors of all pairs of measuring points in the boundary measuring point set are weighted and averaged, with the weighting coefficients inversely proportional to the distance between the measuring point pairs, to obtain the comprehensive temperature gradient vector at the boundary of the grid cell.

[0023] The vector extension process is based on the assumption of gradient field continuity, extending the combined temperature gradient vector at the boundary in a linearly decaying direction towards the center coordinates. The decay coefficient is set as a distance decay function, with the gradient magnitude increasing closer to the boundary and gradually decreasing closer to the center. By inserting several virtual nodes between the boundary and the center, a discrete representation of the internal temperature gradient field of this mesh cell is constructed.

[0024] Spatial integration is achieved using path integration. A straight path from the boundary coordinates to the center coordinates is selected, and the path is discretized into multiple small line segments. The product of the tangential component of the temperature gradient vector on each small line segment and the segment length is accumulated along the path. The temperature value of the measuring point closest to the starting point of the path in the set of boundary measuring points is used as the initial value for integration. After accumulating the integration increment, the temperature value at the center coordinates is obtained. The above integration process is performed on the six boundary surfaces of a single grid cell, and the arithmetic mean of the six results is taken as the final temperature value at the center of the cell.

[0025] After traversing all grid cells to calculate the center temperature, the center coordinates of each cell are combined with its corresponding temperature value to form a discrete data point set of a three-dimensional temperature field. A three-dimensional interpolation algorithm is then used to reconstruct the continuous temperature distribution field inside the mold. Identification of temperature anomaly regions is achieved by setting a preset temperature reference threshold. This threshold is determined based on the working temperature range of the mold material, typically set to 80%-90% of the material's phase transition temperature or the upper limit of its usable temperature. All grid cells in the temperature distribution field whose temperature values ​​exceed the threshold are extracted, and their center coordinates are recorded to form a spatial location set of temperature anomaly regions. The heat accumulation intensity is defined as the difference between the cell's center temperature value and the preset temperature reference threshold. A larger difference indicates a more severe degree of heat accumulation at that location, requiring priority cooling.

[0026] The temperature evolution trajectory of each measuring point in the temperature data sequence during continuous stamping cycles is extracted, the rate of change of the temperature evolution trajectory is calculated, and the predicted heat accumulation intensity of the temperature anomaly region at the end of the next stamping cycle is calculated. The temperature values ​​of each measuring point in the temperature data sequence at the end of each stamping cycle in the continuous stamping cycle are obtained, and the temperature evolution trajectory of each measuring point is constructed in chronological order. Calculate the cross-correlation function between the temperature evolution trajectories of each measuring point within the temperature anomaly area, extract the time offset corresponding to the peak value of the cross-correlation function, identify heat source measuring points and conduction measuring points based on the time offset, and record the heat conduction delay time between heat source measuring points and conduction measuring points; Calculate the rate of change of the temperature evolution trajectory at the heat source measuring point, and based on the rate of change and the heat conduction delay time, extrapolate the predicted temperature value of the conduction measuring point at the end of the next stamping cycle; The location of conduction measurement points where the predicted temperature value exceeds the preset temperature reference threshold is identified. The spatial distribution density of conduction measurement points exceeding the threshold in the temperature anomaly area is statistically analyzed. The heat accumulation state in the temperature anomaly area is evaluated based on the deviation between the spatial distribution density and the predicted temperature value, and the predicted value of the heat accumulation intensity in the temperature anomaly area at the end of the next stamping cycle is obtained.

[0027] In continuous stamping production, heat accumulation in areas of abnormal temperature can lead to unstable product quality. This embodiment provides a method for predicting the intensity of heat accumulation in areas of abnormal temperature by analyzing temperature data sequences, predicting the intensity of heat accumulation in these areas at the end of the next stamping cycle.

[0028] The temperature data acquisition device acquires the temperature data sequence of the stamping equipment surface at a preset sampling frequency. For the identified temperature anomaly area, the temperature value of each measuring point in the area at the end of each continuous stamping cycle is extracted. Assuming that n stamping cycles are continuously monitored and there are m measuring points in the temperature anomaly area, m temperature evolution trajectories of length n can be constructed, and each trajectory represents the temperature change trend of the corresponding measuring point with the stamping cycle.

[0029] Cross-correlation analysis was performed on the temperature evolution trajectories of each measuring point within the temperature anomaly region to calculate the cross-correlation function between the temperature evolution trajectories of measuring point i and measuring point j. The cross-correlation function was calculated using the sliding window method, with a window length of w stamping cycles and a window sliding step size of 1 stamping cycle. Within each window, the cross-correlation coefficient between the two trajectories was calculated to obtain the cross-correlation function. The time offset Δt corresponding to the peak value was extracted from the cross-correlation function. ij This offset reflects the delay time of heat conduction between measuring point i and measuring point j.

[0030] Based on the cross-correlation analysis results, heat source and conduction measurement points are identified. If the time offset corresponding to the peak value of the cross-correlation function between measurement point i and multiple measurement points is negative, it indicates that the temperature change at measurement point i leads that of other measurement points, and measurement point i is identified as a heat source measurement point. If the time offset corresponding to the peak value of the cross-correlation function between measurement point j and the heat source measurement points is positive, it indicates that the temperature change at measurement point j lags that of the heat source measurement points, and measurement point j is identified as a conduction measurement point. The heat conduction delay time Δt between the heat source measurement point s and each conduction measurement point c is recorded. sc .

[0031] Calculate the rate of change of the temperature evolution trajectory at the heat source measuring point. For the heat source measuring point s, its temperature at the end of the k-th stamping cycle is T. s(k) Then its rate of temperature change is calculated as v s(k) =[T s(k) -T s(k-1) ] / Δt, where Δt is the time interval between two adjacent stamping cycles. An exponential smoothing filter is applied to the temperature change rate sequence at the heat source measuring point to obtain the smoothed change rate v'. s(k) .

[0032] Based on the temperature change rate and heat conduction delay time at the heat source measuring point, the predicted temperature value at the conduction measuring point at the end of the next stamping cycle is derived. For the conduction measuring point c, its predicted temperature value T at the end of the (n+1)th stamping cycle is... c(n+1) Calculated as: T c(n+1) =T c(n) +v' s(n) ×α c ×Δt sc , where α cThe conduction coefficient reflects the degree of influence of temperature changes at the heat source measuring point on the conduction measuring point. The conduction coefficient is calculated by analyzing historical data, and the formula is: α c =[T c(k) -T c(k-1) ] / [T s (k-Δt sc )-T s (k-1-Δt sc )], take the average of multiple cycles.

[0033] Set a temperature reference threshold θ to identify the locations of conduction measurement points where the predicted temperature exceeds the threshold. For conduction measurement point c, if T c(n+1) If the temperature at the measuring point is greater than θ, an abnormal temperature will occur at the end of the next stamping cycle.

[0034] The spatial distribution density of conduction measurement points exceeding the threshold within the temperature anomaly region was statistically analyzed. The temperature anomaly region was divided into several grids, and the ratio of the number of conduction measurement points exceeding the threshold to the grid area within each grid was calculated to obtain the spatial distribution density ρ. i For each conduction measurement point c that exceeds the threshold, calculate the deviation δ of its predicted temperature from the threshold. c =(T c(n+1) -θ) / θ.

[0035] The heat accumulation status in temperature anomaly regions is assessed based on the degree of deviation between spatial distribution density and predicted temperature values. The heat accumulation intensity index I is calculated as: I = ∑(ρ i ×∑δ c The inner summation is performed on all conduction measurement points in grid i that exceed the threshold, while the outer summation is performed on all grids. This index comprehensively considers the spatial distribution of abnormal measurement points and the severity of temperature anomalies; a larger value indicates more severe heat accumulation.

[0036] This method extracts temperature evolution trajectories and analyzes heat conduction characteristics to accurately predict the intensity of heat accumulation in areas of temperature anomalies, effectively identifying potential hotspots. This method does not rely on complex models, requires minimal computation, and can be executed in real time. It improves the foresight and early warning capabilities of temperature monitoring during stamping production, helping to adjust process parameters promptly, prevent product quality problems caused by excessive heat accumulation, reduce scrap rates, improve production efficiency, extend mold life, and lower production costs.

[0037] The rate of change of the temperature evolution trajectory at the heat source measuring point is calculated. Based on the rate of change and the heat conduction delay time, the predicted temperature value of the conducting measuring point at the end of the next stamping cycle is deduced, including: Extract the temperature value at the end of the continuous ramming cycle from the temperature evolution trajectory of the heat source measuring point, and calculate the temperature difference at the end of adjacent ramming cycles; The change in temperature difference is obtained by performing a second difference on the continuous temperature difference. When the change in temperature difference is positive, it is determined that the heat source measuring point is in an accelerated heating state. When the change in temperature difference is negative, it is determined that the heat source measuring point is in a decelerated heating state. For heat source measuring points in the accelerated heating state, the temperature values ​​of multiple ram cycles in the temperature evolution trajectory are extracted to calculate the rate of change; for heat source measuring points in the decelerated heating state, the temperature values ​​of a small number of ram cycles in the temperature evolution trajectory are extracted to calculate the rate of change. The rate of change of the heat source measuring point is applied to the time period corresponding to the heat conduction delay time. The temperature state of the heat source measuring point after the heat conduction delay time is deduced, and the deduced temperature state is assigned to the conduction measuring point as the predicted temperature value of the conduction measuring point at the end of the next stamping cycle.

[0038] After obtaining the temperature evolution trajectory of each measuring point, the temperature correlation between the heat source measuring point and the conduction measuring point is predicted by collecting the temperature value sequence of the heat source measuring point at the end of 8 to 12 consecutive pressing cycles. Taking a typical heat source measuring point as an example, its temperature values ​​at the end of the n-5th to nth cycles are 178℃, 182℃, 187℃, 194℃, 202℃, and 211℃, respectively. The temperature difference between adjacent cycles is calculated, and the difference sequence is 4℃, 5℃, 7℃, 8℃, and 9℃.

[0039] A second-order difference operation is performed on the above temperature difference sequence, that is, the difference between adjacent temperature differences is calculated, resulting in a second-order difference sequence of 1℃, 2℃, 1℃, 1℃. When the second-order difference value is consistently positive, it indicates that the temperature rise is continuously increasing, and the heat source measuring point is determined to be in an accelerated heating state. Conversely, if the second-order difference value is negative, it indicates that the temperature rise is decreasing or even tending to stabilize, and is determined to be in a decelerated heating state.

[0040] For the heat source measuring points under accelerated heating conditions, temperature data from 8 to 10 consecutive pressing cycles are selected for linear or exponential fitting to obtain the rate of change over a longer time scale. For example, fitting the above temperature sequence using the least squares method yields a temperature change rate of 6.8℃ / cycle. This approach can capture the nonlinear trend during accelerated heating.

[0041] For heat source measurement points in a decelerating heating state, only the temperature data from the most recent 3 to 4 pressing cycles are selected to calculate the rate of change, avoiding interference from data from the early rapid heating phase in judging the current trend. The temperature difference between two adjacent cycles is used directly as the rate of change, or the average of the most recent 3 differences is taken to obtain the real-time rate of change for the current stage. Typically, this rate value is in the range of 2 to 5℃ / cycle.

[0042] The heat conduction delay time is calculated based on the thermal diffusivity of the mold material and the spatial distance between the heat source measuring point and the conduction measuring point. For a pair of measuring points 30 to 50 mm apart, the heat conduction delay time is typically 2 to 3 stamping cycles. The predicted temperature increment is obtained by multiplying the rate of change of the heat source measuring point by the number of cycles corresponding to the conduction delay. For example, if the rate of change is 6.8℃ / cycle and the conduction delay is 2 cycles, the predicted temperature increment is 13.6℃.

[0043] The current temperature value of the heat source measuring point is added to the predicted temperature increment to obtain the temperature state of the heat source measuring point after a delay time. Considering the attenuation of heat during conduction, a conduction attenuation coefficient of 0.65 to 0.85 is introduced to correct the temperature increment. The corrected temperature increment is 8.8 to 11.6℃. The corrected temperature state is assigned to the corresponding conduction measuring point as the predicted temperature value of that conduction measuring point at the end of the next stamping cycle. If the current temperature of the conduction measuring point is 165℃, its predicted temperature is 173.8 to 176.6℃, providing forward-looking data support for subsequent flow allocation optimization.

[0044] Based on the spatial location of the temperature anomaly area and the spatial layout of each cooling channel, the cooling influence coefficient of each cooling channel on the temperature anomaly area is calculated, including: The spatial location and cooling medium flow direction of each cooling channel are obtained, the temperature gradient field of the temperature anomaly area is extracted, and the direction with the largest gradient value in the temperature gradient field is identified as the dominant heat diffusion direction. Calculate the spatial distance between each cooling channel and the temperature anomaly area based on the spatial location of each cooling channel and the spatial location of the temperature anomaly area. Calculate the spatial angle between the cooling medium flow direction and the dominant heat diffusion direction based on the cooling medium flow direction and the dominant heat diffusion direction of each cooling channel. Based on the spatial angle range, the cooling channels are divided into opposing cooling channels, lateral cooling channels, and forward cooling channels. The forward cooling channels are excluded to obtain the effective cooling channels. The opposing cooling intensity and the lateral cooling intensity are calculated based on the spatial distance and spatial angle of the effective cooling channels, and the coverage range of each effective cooling channel for the temperature anomaly area is determined. When the coverage areas of multiple effective cooling channels overlap, the opposing cooling intensity and lateral cooling intensity of each effective cooling channel within the overlapping area are superimposed to obtain the cooling influence coefficient of each effective cooling channel on the temperature anomaly area.

[0045] After obtaining the spatial location of the temperature anomaly region, a quantitative correlation needs to be established between the cooling channels and the anomaly region. The centerline coordinate sequence of each cooling channel is read from the 3D model of the mold. This centerline consists of several spatial nodes, and the vector connecting adjacent nodes is defined as the local flow direction vector. For curved cooling channels, the flow direction vector of the channel segment closest to the centroid of the temperature anomaly region is selected as the representative flow direction of that channel. Simultaneously, in the reconstructed temperature distribution field, a 3D gradient calculation is performed on the temperature anomaly region to obtain the temperature gradient vector of each spatial point within the region. The direction of the vector with the largest gradient magnitude is determined as the dominant heat diffusion direction, indicating the main path for heat transfer to the surrounding environment.

[0046] Spatial distance is calculated using the Euclidean distance from the centerline of the cooling channel to the centroid of the temperature anomaly region. When the cooling channel is curved, the distance from each node on the channel centerline to the centroid of the anomaly region is calculated, and the minimum value is taken as the characteristic distance of the channel. The spatial angle is obtained by the dot product of the cooling medium flow direction vector and the dominant heat diffusion direction vector, with the angle ranging from 0 to 180 degrees. When the angle is less than 45 degrees, the cooling medium flow direction is basically consistent with the heat diffusion direction, and this channel is defined as a unidirectional cooling channel, whose cooling effect is weakened by heat transfer and is excluded in subsequent calculations. When the angle is between 45 and 135 degrees, it is defined as a lateral cooling channel, where there is a certain angle between the cooling medium flow direction and the heat diffusion direction. When the angle is greater than 135 degrees, it is defined as a counter-current cooling channel, where the cooling medium flow direction is opposite to the heat diffusion direction, and the cooling effect is most significant.

[0047] The calculation of counter-current cooling intensity comprehensively considers spatial distance and angle factors, employing a product of a distance attenuation function and an angle enhancement function. The distance attenuation function is set as a negative exponential function of spatial distance, ensuring stronger cooling effects with closer distances. The angle enhancement function is set as a negative value of the cosine of the angle, with the enhancement coefficient reaching its maximum when the angle approaches 180 degrees. The calculation of lateral cooling intensity introduces the sine of the angle as a correction factor, reflecting the efficiency difference between lateral and counter-current cooling. By setting a temperature influence radius, with the centroid of the temperature anomaly region as the center of a sphere, effective cooling channels with spatial distances smaller than the influence radius are included in the coverage area.

[0048] When multiple effective cooling channels are detected to have overlapping coverage areas, the superposition of cooling effects needs to be considered. Within the overlapping area, the temperature drop contribution of each channel is calculated separately, and the opposing and lateral cooling intensities are weighted and summed using weighted coefficients. These weighted coefficients are dynamically determined based on the relative position of the channel to the anomaly area; channels closer to the channel and with better angles are assigned higher weights. Finally, the weighted cooling intensity of each effective cooling channel is normalized to obtain a cooling influence coefficient ranging from 0 to 1. This coefficient quantitatively characterizes the cooling contribution capability of each cooling channel to a specific temperature anomaly area, providing fundamental data for subsequent flow allocation optimization.

[0049] like Figure 2 The diagram illustrates the flow chart for calculating the traffic allocation value in this embodiment.

[0050] Based on the cooling influence coefficient, the predicted value of heat accumulation intensity, and the constraint of overall mold temperature uniformity, an optimization objective for flow distribution is constructed and the flow distribution values ​​for each cooling channel are obtained by solving for the following: Extract the cooling influence coefficient and predicted heat accumulation intensity of each cooling channel, calculate the target cooling capacity of each cooling channel and convert it into flow demand value; Extract the temperature fluctuation range and temperature gradient distribution of non-abnormal areas of the mold in the temperature distribution field, and determine the overall temperature uniformity constraint of the mold. Extract the heat conduction delay time of heat source measurement points and conduction measurement points in the temperature anomaly area, identify the dominant cooling channel corresponding to the heat source measurement point and the cooperative cooling channel corresponding to the conduction measurement point, and calculate the flow distribution timing offset between the dominant cooling channel and the cooperative cooling channel. Calculate the temperature disturbance range of each cooling channel to the non-abnormal area of ​​the mold based on the flow demand value. When the temperature disturbance range violates the overall temperature uniformity constraint of the mold, calculate the constraint correction coefficient. The flow demand value is used as the optimization variable, the constraint correction coefficient is used as the constraint condition, and the flow allocation time offset is used as the time allocation rule. The flow allocation optimization objective that minimizes the temperature disturbance range while satisfying the target cooling amount is constructed and solved iteratively. The iteration stops when the temperature disturbance range meets the overall temperature uniformity constraint of the mold. The flow distribution value of the dominant cooling channel in the current stamping cycle gap and the flow distribution value of the cooperative cooling channel in the delayed stamping cycle gap are obtained. The flow distribution value of each cooling channel is then obtained by summing them up.

[0051] The cooling influence coefficient of the cooling channels characterizes the degree of influence of the cooling channels on the temperature field of the mold surface, and is obtained through a controlled flow step experiment. It is assumed that there are k cooling channels in the mold, and each cooling channel corresponds to a cooling influence coefficient matrix. The elements in the cooling influence coefficient matrix represent the temperature change at measuring points on the mold surface caused by a unit flow rate change, and the matrix size is consistent with the number of temperature data acquisition points. The predicted value of heat accumulation intensity in the temperature anomaly region is extracted; this predicted value reflects the heat accumulation state in the temperature anomaly region at the end of the next stamping cycle. Based on the cooling influence coefficient and the predicted value of heat accumulation intensity, the target cooling amount for each cooling channel is calculated. The formula for calculating the target cooling amount is: Q i = I hot × C i ×S i , where I hot C is the predicted value of heat accumulation intensity. i S is the average cooling influence coefficient of cooling channel i on the temperature anomaly area. i The proportion of the area covering the temperature anomaly zone in cooling aisle i. Convert the target cooling capacity to a flow rate requirement: F i = Q i / (ρ × c × ΔT), where ρ is the density of the coolant, c is the specific heat capacity of the coolant, and ΔT is the temperature rise of the coolant.

[0052] Temperature data from non-abnormal areas of the mold within the temperature distribution field is extracted, and the temperature fluctuation range, i.e., the difference between the highest and lowest temperatures in these non-abnormal areas, is calculated. The temperature gradient distribution is calculated using the central difference method, comprehensively considering the temperature change rates in both the horizontal and vertical directions. Based on production process requirements, constraints on the overall temperature uniformity of the mold are determined, including two indicators: the temperature fluctuation range not exceeding a preset threshold and the maximum temperature gradient not exceeding a preset threshold.

[0053] Based on the cross-correlation analysis results, heat source and conduction measuring points within the temperature anomaly area are identified, and the heat conduction delay time between the heat source and conduction measuring points is extracted. Cooling channels are sorted according to their cooling influence coefficients on the heat source and conduction measuring points. The cooling channel with the largest cooling influence coefficient is identified as the dominant cooling channel for the corresponding measuring point, and the cooling channel with the second largest cooling influence coefficient is identified as a co-cooling channel. For the dominant cooling channel corresponding to the heat source measuring point, its flow rate should be adjusted during the current stamping cycle interval; for the co-cooling channel corresponding to the conduction measuring point, its flow rate should be adjusted during the stamping cycle interval after a certain delay. The flow distribution timing offset between the dominant and co-cooling channels is calculated, based on the heat conduction delay time and the stamping cycle period.

[0054] Calculate the temperature disturbance range of each cooling channel on the non-abnormal area of ​​the mold based on the flow rate demand. The formula for calculating the temperature disturbance range γ is: γ = F i × max(C i (x, y)), where max(C i (x, y) represents the maximum cooling influence coefficient of cooling channel i on the non-abnormal area of ​​the mold. When the calculated temperature disturbance range exceeds the overall temperature uniformity constraint of the mold, the constraint correction coefficient is calculated: α. i = min(1, (R) t - R c ) / γ), where R t R represents the temperature fluctuation range. c This represents the temperature fluctuation range in the current non-abnormal area of ​​the mold.

[0055] Construct the objective function for traffic allocation optimization: min J = w1 × Σγ 2 + w2 × Σ(F i - F id ) 2 Where w1 and w2 are weighting coefficients, F id Here, F represents the flow rate requirement. The first term indicates the range of temperature disturbance to be minimized, and the second term indicates the target cooling capacity to be met. Optimization constraints include: 0 ≤ F i ≤ F i max, Σ F i ≤ F total F i ≤ F id × α i , where F i max is the maximum flow limit for cooling channel i, F total The total flow rate is limited. An iterative solution using gradient descent is employed, updating the temperature perturbation range and constraint correction coefficients after each iteration. Iteration stops when the temperature perturbation range satisfies the overall temperature uniformity constraint of the mold.

[0056] Based on the timing offset of the flow distribution, determine the flow distribution value of the primary cooling channel during the current stamping cycle interval and the flow distribution value of the co-cooling channel during the delayed stamping cycle interval. The flow distribution value of the primary cooling channel is directly adopted from the optimization result, while the flow distribution value of the co-cooling channel needs to be adjusted according to the timing offset. Summarize the flow distribution values ​​of all cooling channels to form a complete flow distribution scheme.

[0057] This method achieves precise allocation of cooling channel flow by comprehensively considering the cooling influence coefficient, predicted heat accumulation intensity, and overall mold temperature uniformity constraints. By employing a time-series allocation strategy of dominant and co-current cooling channels, it effectively solves the problem of heat conduction delay between heat source and conduction points, enabling targeted cooling of abnormal temperature areas while maintaining the uniformity of the overall mold temperature distribution. This avoids localized overcooling or overheating, improves the quality stability of stamped products, extends mold life, and reduces production costs.

[0058] Adjusting the cooling medium flow rate of each cooling channel according to the flow distribution value, and cooling the metal stamping die during the stamping cycle interval includes: Monitor the stamping cycle status, and mark the start time of the stamping cycle gap when the current stamping cycle ends; At the start of the stamping cycle gap, the opening of the flow control valve of the main cooling channel is adjusted according to the flow distribution value of the main cooling channel in the current stamping cycle gap, and the main cooling channel is started to deliver cooling medium to the metal stamping die. The start-up time of the co-cooling channel is calculated based on the flow distribution timing offset between the main cooling channel and the co-cooling channel and the start time of the stamping cycle gap. When the start-up time of the collaborative cooling channel is reached, the opening of the flow control valve of the collaborative cooling channel is adjusted according to the flow distribution value of the collaborative cooling channel in the delayed stamping cycle gap, and the collaborative cooling channel is started to deliver cooling medium to the metal stamping die. Monitor the stamping cycle status, and when the start of the next stamping cycle is detected, close the flow control valves of each cooling channel to stop cooling the metal stamping die.

[0059] During the actual cooling control execution phase, precise flow regulation is achieved by monitoring the stamping cycle status signal in real time. A position sensor installed at the end of the stamping press stroke continuously collects the displacement signal of the stamping head. When it is detected that the stamping head has completed its return stroke and stabilized at the top dead center position for more than 50 milliseconds, the current stamping cycle is determined to be over. At this moment, the start time t0 of the stamping cycle gap is recorded by the control system clock.

[0060] After the stamping cycle gap begins, the primary cooling channel that contributes the most to cooling the temperature anomaly region is activated first. The flow distribution value Q of this primary cooling channel is obtained from the optimization solution. mainBased on the flow-opening calibration curve of the flow control valve, the flow value is converted into the corresponding valve opening command. For example, when the flow distribution value is 15 liters / minute and the calibration curve is linear, if the flow rate when the valve is fully open is 30 liters / minute, then the target opening is calculated to be 50%. The control system sends an opening adjustment command to the electric regulating valve of the main cooling channel. The valve actuator completes the opening adjustment within 200 milliseconds, and the cooling medium immediately begins to flow through this channel into the cooling flow path inside the mold.

[0061] For collaborative cooling channels, a timing offset mechanism is introduced to avoid flow surges and temperature field disturbances caused by the simultaneous activation of multiple channels. The propagation time from the cooling wavefront of the dominant cooling channel to the boundary of the temperature anomaly region is pre-calculated using fluid dynamics simulation. This time is used as the flow distribution timing offset Δt, typically between 0.3 and 0.8 seconds. According to the formula t... start = t0 + Δt calculates the start-up time of the collaborative cooling channel. The control system sets a timer; when the system clock reaches t... start At that time, read the flow distribution value Q of the collaborative cooling channel. aux The valve opening is calculated using the same flow-to-opening conversion method, and an adjustment command is sent to the flow control valve of the co-cooling channel. At this time, the co-cooling channel and the main cooling channel work together to form a three-dimensional cooling field covering the abnormal temperature area.

[0062] During the cooling process, the control system continuously monitors the stamping cycle status signals. When the position sensor detects that the stamping head has begun to descend and the displacement speed exceeds 10 mm / s, it determines that the next stamping cycle is about to start. The control system immediately sends a shut-off command to the flow control valves of all operating cooling channels. Each valve closes completely within 100 milliseconds, stopping the delivery of cooling medium. This rapid response mechanism ensures that the cooling medium does not remain inside the die during the stamping process, avoiding the impact of coolant residue on the surface quality of the workpiece. At the same time, the control system resets the time stamp and flow accumulation value of this cooling execution, preparing for the cooling operation during the next stamping cycle interval.

[0063] Through the aforementioned time-sequential flow control strategy, precise intervention in the mold temperature field is achieved within the limited stamping cycle gap, effectively eliminating heat accumulation in abnormal temperature areas, maintaining the overall temperature distribution of the mold in a uniform and stable state, and ensuring consistent product quality in continuous stamping production.

[0064] A second aspect of the present invention provides an intelligent cooling control system for metal stamping dies based on temperature monitoring, the system comprising: The temperature acquisition unit is used to acquire the temperature values ​​of multiple measuring points of the metal stamping die during the stamping cycle, forming a temperature data sequence. The temperature field re-unit is used to calculate the temperature gradient vector between measuring points based on the temperature value and location of each measuring point in the temperature data sequence. The temperature gradient vector is then spatially integrated along the inside of the mold to reconstruct the temperature distribution field at locations where no measuring points are arranged inside the mold, and to identify the spatial location and heat accumulation intensity of temperature anomaly areas. The temperature prediction unit is used to extract the temperature evolution trajectory of each measuring point in the temperature data sequence during the continuous stamping cycle, calculate the rate of change of the temperature evolution trajectory, and predict the heat accumulation intensity of the temperature anomaly area at the end of the next stamping cycle. The cooling influence unit is used to calculate the cooling influence coefficient of each cooling channel on the temperature anomaly area based on the spatial location of the temperature anomaly area and the spatial layout of each cooling channel. The flow optimization unit is used to construct the flow distribution optimization objective and solve for the flow distribution value of each cooling channel based on the cooling influence coefficient, the predicted value of heat accumulation intensity and the overall temperature uniformity constraint of the mold. The cooling control unit is used to adjust the flow rate of the cooling medium in each cooling channel according to the flow distribution value, and to cool the metal stamping die during the stamping cycle interval.

[0065] A third aspect of the present invention provides an electronic device, comprising: processor; Memory used to store processor-executable instructions; The processor is configured to invoke instructions stored in the memory to execute the aforementioned method.

[0066] A fourth aspect of the present invention provides a computer-readable storage medium having stored thereon computer program instructions that, when executed by a processor, implement the aforementioned method.

[0067] This invention can be a method, apparatus, system, and / or computer program product. The computer program product may include a computer-readable storage medium having computer-readable program instructions loaded thereon for performing various aspects of the invention.

[0068] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for intelligent cooling control of metal stamping dies based on temperature monitoring, characterized in that, include: Temperature values ​​at multiple measuring points of the metal stamping die during the stamping cycle are collected to form a temperature data sequence; Based on the temperature values ​​and locations of each measuring point in the temperature data sequence, the temperature gradient vector between the measuring points is calculated. The temperature gradient vector is then spatially integrated along the inside of the mold to reconstruct the temperature distribution field at locations where no measuring points are arranged inside the mold, thereby identifying the spatial location and heat accumulation intensity of abnormal temperature areas. Extract the temperature evolution trajectory of each measuring point in the temperature data sequence during the continuous stamping cycle, calculate the rate of change of the temperature evolution trajectory, and predict the heat accumulation intensity of the temperature anomaly area at the end of the next stamping cycle. Based on the spatial location of the temperature anomaly area and the spatial layout of each cooling channel, calculate the cooling influence coefficient of each cooling channel on the temperature anomaly area. Based on the cooling influence coefficient, the predicted value of heat accumulation intensity, and the overall temperature uniformity constraint of the mold, an optimization objective for flow distribution is constructed and the flow distribution value of each cooling channel is obtained by solving the problem. The flow rate of the cooling medium in each cooling channel is adjusted according to the flow distribution value, and the metal stamping die is cooled during the stamping cycle interval.

2. The method according to claim 1, characterized in that, Based on the temperature values ​​and locations of each measuring point in the temperature data sequence, the temperature gradient vector between the measuring points is calculated. This temperature gradient vector is then spatially integrated along the interior of the mold to reconstruct the temperature distribution field at locations where no measuring points are located within the mold. This process identifies the spatial location and heat accumulation intensity of abnormal temperature areas, including: Based on the temperature values ​​and locations of each measuring point in the temperature data sequence, a mold space grid cell is constructed, the center coordinates and boundary coordinates of each grid cell are calculated, and the set of boundary measuring points of each grid cell is determined according to the distance relationship between the boundary coordinates and the measuring point locations. Extract the temperature values ​​of each measuring point in the boundary measuring point set, calculate the temperature gradient vector between measuring points in the boundary measuring point set, and extend the temperature gradient vector from the boundary coordinates to the center coordinates to obtain the internal temperature gradient field of each grid cell. The internal temperature gradient field is spatially integrated along the path from the boundary coordinates to the center coordinates inside the mold. The temperature value at the center coordinates of each grid cell is calculated. The temperature value and the center coordinates are combined to reconstruct the temperature distribution field at the location inside the mold where no measuring points are arranged. Extract grid cells in the temperature distribution field whose temperature values ​​exceed a preset temperature reference threshold, mark the center coordinates of the grid cells as the spatial location of the temperature anomaly region, and calculate the difference between the temperature value and the preset temperature reference threshold as the heat accumulation intensity.

3. The method according to claim 1, characterized in that, The temperature evolution trajectory of each measuring point in the temperature data sequence during continuous stamping cycles is extracted, the rate of change of the temperature evolution trajectory is calculated, and the predicted heat accumulation intensity of the temperature anomaly region at the end of the next stamping cycle is calculated. The temperature values ​​of each measuring point in the temperature data sequence at the end of each stamping cycle in the continuous stamping cycle are obtained, and the temperature evolution trajectory of each measuring point is constructed in chronological order. Calculate the cross-correlation function between the temperature evolution trajectories of each measuring point within the temperature anomaly area, extract the time offset corresponding to the peak value of the cross-correlation function, identify heat source measuring points and conduction measuring points based on the time offset, and record the heat conduction delay time between heat source measuring points and conduction measuring points; Calculate the rate of change of the temperature evolution trajectory at the heat source measuring point, and based on the rate of change and the heat conduction delay time, extrapolate the predicted temperature value of the conduction measuring point at the end of the next stamping cycle; The location of conduction measurement points where the predicted temperature value exceeds the preset temperature reference threshold is identified. The spatial distribution density of conduction measurement points exceeding the threshold in the temperature anomaly area is statistically analyzed. The heat accumulation state in the temperature anomaly area is evaluated based on the deviation between the spatial distribution density and the predicted temperature value, and the predicted value of the heat accumulation intensity in the temperature anomaly area at the end of the next stamping cycle is obtained.

4. The method according to claim 1, characterized in that, The rate of change of the temperature evolution trajectory at the heat source measuring point is calculated. Based on the rate of change and the heat conduction delay time, the predicted temperature value of the conducting measuring point at the end of the next stamping cycle is deduced, including: Extract the temperature value at the end of the continuous ramming cycle from the temperature evolution trajectory of the heat source measuring point, and calculate the temperature difference at the end of adjacent ramming cycles; The change in temperature difference is obtained by performing a second difference on the continuous temperature difference. When the change in temperature difference is positive, it is determined that the heat source measuring point is in an accelerated heating state. When the change in temperature difference is negative, it is determined that the heat source measuring point is in a decelerated heating state. For heat source measuring points in the accelerated heating state, the temperature values ​​of multiple ram cycles in the temperature evolution trajectory are extracted to calculate the rate of change; for heat source measuring points in the decelerated heating state, the temperature values ​​of a small number of ram cycles in the temperature evolution trajectory are extracted to calculate the rate of change. The rate of change of the heat source measuring point is applied to the time period corresponding to the heat conduction delay time. The temperature state of the heat source measuring point after the heat conduction delay time is deduced, and the deduced temperature state is assigned to the conduction measuring point as the predicted temperature value of the conduction measuring point at the end of the next stamping cycle.

5. The method according to claim 1, characterized in that, Based on the spatial location of the temperature anomaly area and the spatial layout of each cooling channel, the cooling influence coefficient of each cooling channel on the temperature anomaly area is calculated, including: The spatial location and cooling medium flow direction of each cooling channel are obtained, the temperature gradient field of the temperature anomaly area is extracted, and the direction with the largest gradient value in the temperature gradient field is identified as the dominant heat diffusion direction. Calculate the spatial distance between each cooling channel and the temperature anomaly area based on the spatial location of each cooling channel and the spatial location of the temperature anomaly area. Calculate the spatial angle between the cooling medium flow direction and the dominant heat diffusion direction based on the cooling medium flow direction and the dominant heat diffusion direction of each cooling channel. Based on the spatial angle range, the cooling channels are divided into opposing cooling channels, lateral cooling channels, and forward cooling channels. The forward cooling channels are excluded to obtain the effective cooling channels. The opposing cooling intensity and the lateral cooling intensity are calculated based on the spatial distance and spatial angle of the effective cooling channels, and the coverage range of each effective cooling channel for the temperature anomaly area is determined. When the coverage areas of multiple effective cooling channels overlap, the opposing cooling intensity and lateral cooling intensity of each effective cooling channel within the overlapping area are superimposed to obtain the cooling influence coefficient of each effective cooling channel on the temperature anomaly area.

6. The method according to claim 1, characterized in that, Based on the cooling influence coefficient, the predicted value of heat accumulation intensity, and the constraint of overall mold temperature uniformity, an optimization objective for flow distribution is constructed and the flow distribution values ​​for each cooling channel are obtained by solving for the following: Extract the cooling influence coefficient and predicted heat accumulation intensity of each cooling channel, calculate the target cooling capacity of each cooling channel and convert it into flow demand value; Extract the temperature fluctuation range and temperature gradient distribution of non-abnormal areas of the mold in the temperature distribution field, and determine the overall temperature uniformity constraint of the mold. Extract the heat conduction delay time of heat source measurement points and conduction measurement points in the temperature anomaly area, identify the dominant cooling channel corresponding to the heat source measurement point and the cooperative cooling channel corresponding to the conduction measurement point, and calculate the flow distribution timing offset between the dominant cooling channel and the cooperative cooling channel. Calculate the temperature disturbance range of each cooling channel to the non-abnormal area of ​​the mold based on the flow demand value. When the temperature disturbance range violates the overall temperature uniformity constraint of the mold, calculate the constraint correction coefficient. The flow demand value is used as the optimization variable, the constraint correction coefficient is used as the constraint condition, and the flow allocation time offset is used as the time allocation rule. The flow allocation optimization objective that minimizes the temperature disturbance range while satisfying the target cooling amount is constructed and solved iteratively. The iteration stops when the temperature disturbance range meets the overall temperature uniformity constraint of the mold. The flow distribution value of the dominant cooling channel in the current stamping cycle gap and the flow distribution value of the cooperative cooling channel in the delayed stamping cycle gap are obtained. The flow distribution value of each cooling channel is then obtained by summing them up.

7. The method according to claim 1, characterized in that, Adjusting the cooling medium flow rate of each cooling channel according to the flow distribution value, and cooling the metal stamping die during the stamping cycle interval includes: Monitor the stamping cycle status, and mark the start time of the stamping cycle gap when the current stamping cycle ends; At the start of the stamping cycle gap, the opening of the flow control valve of the main cooling channel is adjusted according to the flow distribution value of the main cooling channel in the current stamping cycle gap, and the main cooling channel is started to deliver cooling medium to the metal stamping die. The start-up time of the co-cooling channel is calculated based on the flow distribution timing offset between the main cooling channel and the co-cooling channel and the start time of the stamping cycle gap. When the start-up time of the collaborative cooling channel is reached, the opening of the flow control valve of the collaborative cooling channel is adjusted according to the flow distribution value of the collaborative cooling channel in the delayed stamping cycle gap, and the collaborative cooling channel is started to deliver cooling medium to the metal stamping die. Monitor the stamping cycle status, and when the start of the next stamping cycle is detected, close the flow control valves of each cooling channel to stop cooling the metal stamping die.

8. A temperature-monitored intelligent cooling control system for metal stamping dies, used to implement the method described in any one of claims 1-7, characterized in that, include: The temperature acquisition unit is used to acquire the temperature values ​​of multiple measuring points of the metal stamping die during the stamping cycle, forming a temperature data sequence. The temperature field re-unit is used to calculate the temperature gradient vector between measuring points based on the temperature value and location of each measuring point in the temperature data sequence. The temperature gradient vector is then spatially integrated along the inside of the mold to reconstruct the temperature distribution field at locations where no measuring points are arranged inside the mold, and to identify the spatial location and heat accumulation intensity of temperature anomaly areas. The temperature prediction unit is used to extract the temperature evolution trajectory of each measuring point in the temperature data sequence during the continuous stamping cycle, calculate the rate of change of the temperature evolution trajectory, and predict the heat accumulation intensity of the temperature anomaly area at the end of the next stamping cycle. The cooling influence unit is used to calculate the cooling influence coefficient of each cooling channel on the temperature anomaly area based on the spatial location of the temperature anomaly area and the spatial layout of each cooling channel. The flow optimization unit is used to construct the flow distribution optimization objective and solve for the flow distribution value of each cooling channel based on the cooling influence coefficient, the predicted value of heat accumulation intensity and the overall temperature uniformity constraint of the mold. The cooling control unit is used to adjust the flow rate of the cooling medium in each cooling channel according to the flow distribution value, and to cool the metal stamping die during the stamping cycle interval.

9. An electronic device, characterized in that, include: processor; Memory used to store processor-executable instructions; The processor is configured to invoke instructions stored in the memory to execute the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having computer program instructions stored thereon, characterized in that, When the computer program instructions are executed by the processor, they implement the method described in any one of claims 1 to 7.

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