Intelligent scheduling method for die stamping production line

Abstract

The invention discloses an intelligent scheduling method for a die stamping production line. The intelligent scheduling method comprises a data acquisition step, an analysis step, a calculation step, a judgment step, a scheduling I step, a scheduling II step, a load reduction step, a migration step and an optimization step. According to the method, the pressure data and the temperature data are cooperatively monitored in real time, so that the probability of stress evaluation deviation caused by pure dependence on the pressure data is reduced, the scheduling strategy is optimized in time, the probability of occurrence of conditions such as energy waste or untimely mold scheduling is reduced, the adaptive adjustment capability of mold scheduling is improved, and the service life of the mold is prolonged. The stamping speed is reduced, the pressure maintaining time is prolonged, wait time waste is reduced, the comprehensive efficiency of equipment is improved, productivity loss is reduced, and therefore the production efficiency of materials is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of data analysis, and in particular to an intelligent scheduling method for a mold stamping production line. Background Art

[0002] Stamping dies are a common type of industrial production and processing equipment, widely used in construction, biochemistry, food, and other fields. During use, stamping dies require maintenance and scheduling based on data such as their usage status and various maintenance parameters to achieve efficient energy allocation and utilization, reducing the possibility of energy waste and equipment shortages.

[0003] The traditional scheduling method for stamping dies on production lines usually relies on fixed-point temperature sensors to display the die temperature and relies on production line workers to conduct regular inspections. During the inspections, workers observe the die temperature and refer to maintenance parameters. Based on experience, they judge the die's usage status and fatigue condition, and then perform die maintenance or die scheduling.

[0004] At present, when monitoring and scheduling molds, existing technologies usually deploy a certain number of point monitoring devices to detect various data such as the temperature and pressure of the mold, and adopt fixed parameter control. When one or more parameters are detected to be close to the fixed parameter upper limit, the current mold is scheduled for maintenance according to a predetermined scheduling route or manual intervention is added, and the spare mold is moved and replaced.

[0005] Regarding the above technical solution, this method only analyzes the mold usage status through point detection data at limited locations, which makes it difficult to accurately identify the mold usage status. The mold scheduling lacks adaptive adjustment capabilities, and it is difficult to optimize the scheduling strategy in a timely manner, resulting in material quality fluctuations, energy waste or untimely mold scheduling, thereby reducing material production efficiency. Summary of the Invention

[0006] The purpose of the present invention is to provide an intelligent scheduling method for a mold stamping production line to solve the problems raised in the above background technology.

[0007] The intelligent scheduling method for a mold stamping production line provided by the present invention adopts the following technical solutions to achieve the invention objectives: The intelligent scheduling method of the die stamping production line includes the following steps: Data acquisition: N sensors are distributed on the stamping die, and the pressure and temperature data detected by the N sensors during the stamping process are acquired; Analysis: Identify the changing characteristics of the pressure and temperature data of N sensors, calculate the equivalent stress value data at the sensor location, analyze whether the location of each sensor is a stress concentration location, and record the stress concentration location; Calculation: A stress concentration area model is established based on the stress concentration location, and the stress concentration area model is matched with the preset stress distribution data model. The matching result is calculated to obtain the load mismatch degree of the stamping die; Judgment: Obtain the life attenuation curve of the stamping die material. If the load mismatch is greater than the preset mismatch, it is determined that the loss of the stamping die has increased. The die life loss value and the upper limit of the life loss value R are obtained according to the life attenuation curve. The cumulative value of the die life loss value during the stamping process is calculated as r. If r>αR, where α∈(0,1], execute the scheduling step I; Scheduling I: Reduce the stamping speed of the current mold and extend the holding time of a single stamping, and execute Scheduling II step; Scheduling II: Schedule and maintain the current mold and replace the spare mold online.

[0008] By employing the above technical solution, during the stamping process, pressure and temperature data from N sensors are acquired, and the temporal variation characteristics of the pressure data from the N sensors are identified. Specifically, the sensor pressure data is calculated and converted into equivalent stress values ​​at the corresponding sensor locations, thereby determining the locations of stress concentrations. Real-time, coordinated monitoring of pressure and temperature data helps reduce the probability of significant deviations in stress assessments resulting from relying solely on pressure data. Temperature data can correct for the attenuation effect of material yield strength at high temperatures, effectively suppressing interference from abnormal sensor data, accurately identifying the die's operating status, and improving the accuracy of feature extraction. Compared to traditional fixed-threshold alarm mechanisms, this approach improves the objectivity and consistency of stress assessment results, while reducing subjective errors often associated with manual judgment. A high load mismatch indicates severe stress concentration in one or more areas within the stamping die, triggering subsequent maintenance scheduling and timely optimization of scheduling strategies. This reduces the probability of energy waste and untimely die scheduling, enhances the adaptive adjustment capabilities of die scheduling, and ultimately improves material production efficiency. By reducing the stamping speed and extending the holding time, the current stamping die can be scheduled for maintenance and replacement. This graded response mechanism uses progressive control to reduce the stamping speed to delay damage while gaining preparation time for online die change, greatly shortening the production interruption time. Extending the holding time and reducing the speed are conducive to reducing the residual stress peak, extending the die life to a certain extent, reducing waiting time waste, and improving the overall efficiency of the equipment. It effectively connects maintenance preparation with production frequency, reduces production capacity loss, and thus improves material production efficiency.

[0009] Optionally, the scheduling step I is updated as follows: reduce the stamping speed of the current mold to β% of the rated speed and extend the holding time of a single stamping, where β∈(0,100]; obtain the scheduling path length D of the spare mold and the mold working temperature T; preheat the temperature of the spare mold to γT, γ∈(0,1]; and maintain the temperature difference within the specified range; when the spare mold moves along the scheduling path to a minimum spacing with the current mold that is the specified scheduling spacing, execute the scheduling step II.

[0010] By adopting the above technical solution, by reducing the stamping speed and extending the holding time, and then preheating the spare mold, the current stamping mold can be scheduled for maintenance and mold replacement. This graded response mechanism uses progressive control to reduce the stamping speed and delay damage while gaining preparation time for online mold change, which greatly shortens the production interruption time. Extending the holding time and reducing the speed is conducive to reducing the residual stress peak and extending the mold life to a certain extent. By preheating the spare mold in advance and maintaining the temperature difference range, it is guaranteed that the temperature of the spare mold meets the standard when it is in place, reducing the assembly stress caused by the difference in thermal expansion coefficient, and improving the first stamping pass rate after the mold is switched, reducing waiting time waste, and improving the overall efficiency of the equipment. It effectively connects maintenance preparation with production frequency, reduces production capacity loss, and thus improves material production efficiency.

[0011] Optionally, in the scheduling step I, the value of γ is determined based on D and T. The specific calculation model for the value of γ is: , where v is the operating speed of the mold changing equipment, τ is the temperature rise time constant of the mold material, is the initial temperature of the spare mold.

[0012] By adopting this technical solution, the preheat time is dynamically adjusted based on the path length D, minimizing the error in matching stamping speed adjustment with mold preheat time. This improves the synchronization of the standby mold's arrival temperature compliance, reduces preheating energy consumption, effectively improves the first-time stamping pass rate after mold switching, and reduces the probability of energy waste caused by secondary heating. By using the temperature rise time constant τ to reflect the material's thermal inertia, high thermal conductivity materials shorten preheat time, and increasing preheating power to promote temperature compliance, thus adapting to different mold materials. This enhances adaptability and reliability in different production scenarios, reduces manual intervention, and thus improves material production efficiency.

[0013] Optionally, in the scheduling step I, the value of β is determined based on R and r. The specific calculation model for the value of β is: .

[0014] By adopting the above technical solution, β is dynamically adjusted according to the cumulative loss value r and the upper limit R. When r is close to αR, β decreases linearly to a safe range, reducing the probability of equipment vibration caused by sudden speed changes, reducing sheet metal rebound defects caused by sudden speed drops, and reducing material waste. A higher β value is maintained in the early stage of loss, and the speed is gradually reduced in the later stage. Compared with the fixed speed reduction strategy, the effective life of the mold is extended while the total production capacity loss is controlled within a certain range. While maintaining mold safety, production efficiency is maximized, waiting time waste is reduced, and the overall efficiency of the equipment is improved, thereby improving material production efficiency.

[0015] Optionally, the calculation steps are updated as follows: obtaining the preset internal space data of the mold, organizing the stress concentration positions obtained by analysis according to their spatial coordinate sequence, establishing a three-dimensional stress concentration area model, matching the stress concentration area model with the preset stress distribution data model, and calculating the matching results to obtain the load mismatch of the stamping mold.

[0016] By adopting the above technical solution, the stress concentration area model is located based on the coordinate mapping of the mold's internal spatial data. This helps significantly reduce the positioning error of the two-dimensional projection of the stress concentration area, mitigates the defect of two-dimensional projection losing thickness direction information, improves the accuracy of calculating the projected area, and reduces the probability of ineffective maintenance caused by misjudgment. It is conducive to accurately identifying the occurrence of multi-region stress concentration under asymmetric loads, which may lead to eccentric wear. Compared with traditional monitoring methods, it can effectively improve the anomaly detection rate, thereby improving the spatiotemporal accuracy of stress assessment, accurately identifying the usage status of the mold, and reducing the probability of energy waste or untimely mold scheduling, thereby improving material production efficiency. The preset stress distribution model can be updated regularly to adapt to the stress field evolution caused by mold wear, process adjustments, etc., providing a high-fidelity data foundation for load assessment and effectively reducing the probability of misjudgment.

[0017] Optionally, in the calculation step, the stress concentration area model is matched with the preset stress distribution data model, and the matching results are analyzed to obtain the load mismatch of the stamping die. Specifically, the projected area of ​​the stress concentration area is calculated according to the three-dimensional model and recorded as , calculate the average stress in the stress concentration area and record it as , the projection area of ​​the preset stress distribution data model is obtained as , the allowable stress of the material is , the load mismatch of the stamping die is calculated as: .

[0018] By adopting the above technical solution, the load mismatch is quantified by calculating the projected area and stress. Compared with the traditional fixed threshold alarm mechanism, it is beneficial to improve the objectivity of stress assessment and the consistency of assessment results, and reduce the subjective error of manual experience judgment. When the load mismatch is high, it means that there is a serious stress concentration in a single area or multiple areas inside the stamping die, thereby triggering subsequent maintenance scheduling in advance, optimizing the scheduling strategy in time, reducing the probability of energy waste or untimely mold scheduling, and improving the adaptive adjustment capability of mold scheduling, thereby improving material production efficiency.

[0019] Optionally, the analysis step is updated to: identify the change characteristics of N sensor pressure data, calculate the equivalent stress value of the sensor pressure data, record the sensor positions where the equivalent stress value is greater than the preset stress value as the first position set, record the sensor positions where the temperature is greater than the preset temperature as the second position set, and merge the first position set and the second position set into a plastic strain position set, calculate the coincidence coefficient corresponding to each sensor position in the plastic strain position set, the coincidence coefficient is the ratio of the equivalent stress value to the temperature, record the sensor positions where the coincidence coefficient is greater than the coincidence threshold as stress concentration positions, and record the remaining sensor positions as stress release positions.

[0020] By adopting the above technical solution, the sensor positions where the equivalent stress value is greater than the preset stress value are recorded as the first position set, the sensor positions where the temperature is greater than the preset temperature are recorded as the second position set, and the first position set and the second position set are merged into the plastic strain position set. The coincidence coefficient corresponding to each sensor position in the plastic strain position set is calculated, wherein the coincidence coefficient is measured as the ratio of the equivalent stress value to the temperature, the sensor positions where the coincidence coefficient is greater than the coincidence threshold are recorded as stress concentration positions, and the remaining sensor positions are recorded as stress release positions, wherein the coincidence threshold is selected based on the value of the coincidence coefficient calculated based on historical monitoring data.

[0021] Optionally, a load reduction step is also included: the design threshold of the number of stamping times of the stamping die is L. When it is detected that the cumulative number of stamping times of the stamping die reaches T=0.8L, the associated equipment of the stamping die is operated at a reduced load, and the stamping strength of the stamping die is reduced by 5%-15%.

[0022] By adopting the above technical solution, early intervention is carried out when the design threshold reaches 80%, avoiding the vicious cycle of overload-accelerated damage and extending the remaining life of the mold. The load reduction range of 5%-15% is adapted to different damage stages. While improving production continuity, the local strain amplitude is controlled below the fatigue limit, improving the practicality of force classification regulation, and synchronously reducing the load on related equipment such as punching machines and feeders to eliminate the weak link effect, significantly extend the service life of the mold, reduce the risk of sudden failure, maximize production efficiency while maintaining mold safety, improve the overall efficiency of equipment, and thus improve the production efficiency of materials.

[0023] Optionally, a migration step is also included: when a new production line is added, the historical preset stress distribution data model of the stamping die of the original production line is projected to the preset stress distribution data model corresponding to the stamping die of the new production line through the feature space alignment algorithm, and the preset stress distribution data model of the stamping die of the new production line is adapted.

[0024] By adopting the above technical solution, this method can effectively eliminate the impact of equipment heterogeneity, improve the availability of historical data, reduce the demand for new production line model training data, shorten the new production line debugging cycle, and retain the core mapping relationship through model parameter migration, such as the stress-temperature coupling law. After migrating the data, only some geometric parameters need to be adjusted, such as mold size, which greatly improves the adaptation efficiency. When the new production line is running, the model can be continuously optimized, the initial projection error can be gradually eliminated, the abnormality recognition rate can be improved, and the overall efficiency of the equipment can be improved, thereby improving the production efficiency of materials.

[0025] Optionally, an optimization step is also included: optimizing the matching between the mold maintenance window and the production task through an integer programming algorithm, and optimizing the insertion time slot of the mold maintenance in the scheduling plan of the production line.

[0026] By incorporating data such as production task priorities, equipment maintenance time windows, and maintenance resources into an integer programming algorithm model, the aforementioned technical solution significantly improves the feasibility of maintenance plans and enhances production continuity compared to traditional manual scheduling methods. In the production line scheduling solution, conflict resolution algorithms, such as Lagrangian relaxation, are used to handle complex constraints and generate near-optimal solutions. This optimizes the insertion time slots for mold maintenance to meet real-time scheduling requirements, effectively avoiding critical production periods, improving on-time delivery rates and equipment utilization, minimizing capacity losses, enhancing overall equipment efficiency, and reducing wasted waiting time, thereby increasing material production efficiency.

[0027] Compared with the prior art, the present invention has the following beneficial effects: 1. Real-time coordinated monitoring of pressure and temperature data helps reduce the probability of large deviations in stress assessments caused by relying solely on pressure data. Temperature data can correct the attenuation effect of material yield strength at high temperatures, effectively suppress interference from abnormal sensor data, accurately identify the mold's usage status, and improve the accuracy of feature extraction. Compared with traditional fixed threshold alarm mechanisms, this helps improve the objectivity of stress assessments and the consistency of assessment results, while reducing subjective errors based on human experience. When the load mismatch is high, it indicates that stress concentration is severe in a single or multiple areas within the stamping die, triggering subsequent maintenance scheduling and timely optimization of scheduling strategies. This reduces the probability of energy waste or untimely mold scheduling, improves the adaptive adjustment capability of mold scheduling, and thus improves material production efficiency. By reducing the stamping speed and extending the holding time, the current stamping die can be scheduled for maintenance and replacement. This graded response mechanism uses progressive control to reduce the stamping speed to delay damage while gaining preparation time for online die change, greatly shortening the production interruption time. Extending the holding time and reducing the speed are conducive to reducing the residual stress peak, extending the die life to a certain extent, reducing waiting time waste, and improving the overall efficiency of the equipment. It effectively connects maintenance preparation with production frequency, reduces production capacity loss, and thus improves material production efficiency.

[0028] 2. By reducing the stamping speed and extending the holding time, and then preheating the spare mold, the current stamping mold can be scheduled for maintenance and mold replacement. This hierarchical response mechanism uses progressive control to reduce the stamping speed to delay damage while gaining preparation time for online mold change, greatly shortening the production interruption time. Extending the holding time and reducing the speed are conducive to reducing the residual stress peak and extending the mold life to a certain extent. By preheating the spare mold in advance and maintaining the temperature difference range, it is guaranteed that the temperature of the spare mold meets the standard when it is in place, reducing the assembly stress caused by the difference in thermal expansion coefficient, and improving the first stamping pass rate after the mold is switched, reducing waiting time waste, and improving the overall efficiency of the equipment. It effectively connects maintenance preparation with production frequency, reduces production capacity loss, and thus improves material production efficiency.

[0029] 3. Dynamically adjust the preheat time based on the path length D, minimizing the error between stamping speed adjustment and mold preheat time. This improves the synchronization of the standby mold's arrival temperature compliance, reduces preheating energy consumption, effectively improves the first-time stamping pass rate after mold switching, and reduces the probability of energy waste caused by secondary heating. The temperature rise time constant τ reflects the material's thermal inertia. High thermal conductivity materials shorten preheat time, and increasing preheating power promotes temperature compliance. This adaptability to different mold materials enhances adaptability and reliability in different production scenarios, reduces manual intervention, and improves material production efficiency.

[0030] 4. β is dynamically adjusted through the cumulative loss value r and the upper limit R. When r approaches αR, β decreases linearly to a safe range, reducing the probability of equipment vibration caused by sudden speed changes, reducing sheet metal rebound defects caused by sudden speed drops, and reducing material waste. A high β value is maintained in the early stage of loss, and the speed is gradually reduced in the later stage. Compared with the fixed speed reduction strategy, this extends the effective life of the mold while controlling the total production capacity loss within a certain range. While maintaining mold safety, it maximizes production efficiency, reduces waiting time waste, improves the overall efficiency of the equipment, and thus improves material production efficiency.

[0031] 5. Quantifying the load mismatch by calculating the projected area and stress is beneficial to improving the objectivity of stress assessment and the consistency of assessment results compared to the traditional fixed threshold alarm mechanism, and reducing the subjective error of manual judgment. When the load mismatch is high, it indicates that there is a serious stress concentration in a single area or multiple areas inside the stamping die, thereby triggering subsequent maintenance scheduling in advance, optimizing the scheduling strategy in time, reducing the probability of energy waste or untimely mold scheduling, and improving the adaptive adjustment capability of mold scheduling, thereby improving material production efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] The drawings described herein are used to provide a further understanding of this application and constitute a part of this application. The illustrative embodiments of this application and their descriptions are used to explain this application and do not constitute an undue limitation on this application. Obviously, the drawings described below are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without inventive effort. In the drawings: Figure 1 This is a flow chart of an intelligent scheduling method for a mold stamping production line according to an embodiment of the present invention. DETAILED DESCRIPTION

[0033] The following is a combination of the embodiments of the present invention Figure 1 The technical solutions in the embodiments of the present invention are clearly and completely described. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. All other embodiments derived by persons of ordinary skill in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0034] This embodiment discloses an intelligent scheduling method for a die stamping production line, referring to Figure 1 , including data acquisition step S1, analysis step S2, calculation step S3, judgment step S4, scheduling I step S5, scheduling II step S6, load reduction step S7, migration step S8 and optimization step S9.

[0035] S1. Data acquisition step: N sensors are distributed on the stamping die, and pressure data and temperature data detected by the N sensors during the stamping process are acquired.

[0036] S2. Analysis steps: Identify the change characteristics of the pressure data of N sensors, calculate the equivalent stress value of the sensor pressure data, record the sensor positions with equivalent stress values ​​greater than the preset stress value as the first position set, record the sensor positions with temperatures greater than the preset temperature as the second position set, and merge the first position set and the second position set into a plastic strain position set, calculate the coincidence coefficient corresponding to each sensor position in the plastic strain position set, where the coincidence coefficient is the ratio of the equivalent stress value to the temperature, record the sensor positions with coincidence coefficients greater than the coincidence threshold as stress concentration positions, and record the remaining sensor positions as stress release positions.

[0037] S3. Calculation steps: Obtain the preset mold internal space data, organize the analyzed stress concentration positions according to their spatial coordinate sequence, establish a three-dimensional stress concentration area model, match the stress concentration area model with the preset stress distribution data model, calculate the projected area of ​​the stress concentration area according to the three-dimensional model and record it as , calculate the average stress in the stress concentration area and record it as , the projection area of ​​the preset stress distribution data model is obtained as , the allowable stress of the material is , the load mismatch of the stamping die is calculated as: .

[0038] S4. Judgment step: Obtain the life attenuation curve of the stamping die material. If the load mismatch is greater than the preset mismatch, determine that the loss of the stamping die has increased. Obtain the die life loss value and the upper limit R of the life loss value according to the life attenuation curve. Calculate the cumulative value of the die life loss value during the stamping process as r. If r>αR, where α∈(0,1], execute the scheduling step I.

[0039] S5, Scheduling step I: Reduce the current die stamping speed to β% of the rated speed and extend the holding time of a single stamping, where β∈(0,100], and the specific calculation model for β is: , obtain the spare mold scheduling path length D and the mold working temperature T, and preheat the temperature of the spare mold to γT, γ∈(0,1], the value calculation model of γ is specifically as follows: , where v is the operating speed of the mold changing equipment, τ is the temperature rise time constant of the mold material, The initial temperature of the spare mold is maintained within the specified range. When the spare mold moves along the scheduling path to a minimum distance from the current mold equal to the specified scheduling distance, the scheduling step II is executed.

[0040] S6, Scheduling II step: schedule and maintain the current mold and replace the spare mold online.

[0041] S7. Load reduction step: The design threshold of the number of stamping times of the stamping die is L. When it is detected that the cumulative number of stamping times of the stamping die reaches T=0.8L, the associated equipment of the stamping die is operated at a reduced load, and the stamping strength of the stamping die is reduced by 5%-15%.

[0042] S8. Migration step: When a new production line is added, the historical preset stress distribution data model of the stamping die of the original production line is projected to the preset stress distribution data model corresponding to the stamping die of the new production line through the feature space alignment algorithm, and the preset stress distribution data model of the stamping die of the new production line is adapted.

[0043] S9. Optimization step: Optimize the matching between the mold maintenance window and the production task through the integer programming algorithm, and optimize the insertion time slot of mold maintenance in the production line scheduling plan.

[0044] The implementation principle of the intelligent scheduling method for the mold stamping production line of this embodiment is as follows: N sensors are distributed and connected to the stamping die. During the stamping process of the material, the pressure data and temperature data detected by the N sensors are obtained, and the time series change characteristics of the pressure data of the N sensors are identified. That is, the sensor pressure data is calculated and converted into equivalent stress value data at the corresponding sensor location.

[0045] The sensor positions with equivalent stress values ​​greater than the preset stress value are recorded as the first position set, and the sensor positions with temperatures greater than the preset temperature are recorded as the second position set. The first position set and the second position set are merged into the plastic strain position set, and the coincidence coefficient corresponding to each sensor position in the plastic strain position set is calculated, where the coincidence coefficient is measured as the ratio of the equivalent stress value to the temperature. The sensor positions with coincidence coefficients greater than the coincidence threshold are recorded as stress concentration positions, and the remaining sensor positions are recorded as stress release positions. The coincidence threshold is selected based on the value of the coincidence coefficient calculated based on historical monitoring data.

[0046] Real-time coordinated monitoring of pressure data and temperature data can help reduce the probability of large deviations in stress assessment caused by relying solely on pressure data. Temperature data can correct the attenuation effect of material yield strength at high temperatures. The plastic strain position of the stamping die is selected through data fusion in multiple dimensions, and the stress concentration position is obtained by calculating the coincidence coefficient. This is conducive to breaking through the limitations of single physical quantity detection. Through the combined calculation of the plastic strain set, the interference of abnormal sensor data can be effectively suppressed, the use status of the die can be accurately identified, and the accuracy of feature extraction can be improved. By distinguishing between stress concentration and release areas, targeted cooling or structural strengthening can be implemented, the utilization rate of maintenance resources can be improved, and the probability of energy waste or untimely die scheduling can be reduced, thereby improving material production efficiency.

[0047] Obtain the preset three-dimensional data of the internal space of the mold, organize the stress concentration positions obtained by analysis according to their spatial coordinate sequence, and establish a three-dimensional stress concentration area model based on the spatial three-dimensional data. Match the stress concentration area model with the preset stress distribution data model, that is, calculate the projected area of ​​the stress concentration area through the three-dimensional model and record it as , calculate the average stress in the stress concentration area and record it as , the projection area of ​​the preset stress distribution data model is obtained as , where the preset stress distribution data model is selected based on the stress distribution data model calculated based on historical monitoring data, and the allowable stress of the stamping die material is , the load mismatch of the stamping die is calculated as: , which is the imbalance of the stress concentration area in the ideal distribution area, which is used to characterize the local loss degree of the stamping die.

[0048] Using coordinate mapping of the mold's internal spatial data to locate stress concentration area models significantly reduces positioning errors in the two-dimensional projection of stress concentration areas, mitigates the loss of thickness information in two-dimensional projections, improves the accuracy of projected area calculations, and reduces the probability of ineffective maintenance due to misjudgments. This helps accurately identify multi-region stress concentrations under asymmetric loads, which can lead to eccentric wear and other conditions. Compared to traditional monitoring methods, this method can effectively improve the anomaly detection rate, thereby enhancing the spatiotemporal accuracy of stress assessment, accurately identifying the mold's usage status, and reducing the probability of energy waste or untimely mold scheduling, thereby improving material production efficiency. The preset stress distribution model can be regularly updated to adapt to stress field evolution caused by mold wear, process adjustments, and other factors, providing a high-fidelity data foundation for load assessment and effectively reducing the probability of misjudgments.

[0049] Quantifying the load mismatch by calculating the projected area and stress is beneficial to improving the objectivity of stress assessment and the consistency of assessment results compared to the traditional fixed threshold alarm mechanism, and reducing the subjective error of manual judgment. When the load mismatch is high, it indicates that there is a serious stress concentration in a single area or multiple areas inside the stamping die, thereby triggering subsequent maintenance scheduling in advance, optimizing the scheduling strategy in time, reducing the probability of energy waste or untimely mold scheduling, and improving the adaptive adjustment capability of mold scheduling, thereby improving material production efficiency.

[0050] Obtain the life attenuation curve of the stamping die material, wherein the life attenuation curve is usually obtained through experimental calibration or factory data. If the load mismatch is greater than the preset mismatch, it is judged that the loss of the stamping die has increased. The preset mismatch is selected based on the load mismatch calculated based on the historical monitoring data. The die life loss value and the upper limit R of the life loss value corresponding to the load mismatch are obtained according to the life attenuation curve. The cumulative value of the die life loss value during the stamping process is calculated as r. If r>αR, where α∈(0,1], the scheduling step I is executed.

[0051] Quantifying the evaluation results of load mismatch metrics can help reduce the probability of over-maintenance or under-maintenance, identify potential failure risks in advance, effectively reduce the full life cycle cost of stamping dies, significantly improve the production stability and continuity of the production line, and reduce the prediction error of the remaining life of the die. Compared with traditional scheduled maintenance, it can greatly reduce the frequency of unplanned downtime, reduce energy waste, and thus improve material production efficiency.

[0052] When it is judged that r>αR, the punching speed of the current mold is reduced to β% of the rated speed, and the holding time of a single punch is extended, where β∈(0,100], and the calculation model of β is as follows: , obtain the spare mold scheduling path length D and the mold working temperature T, and preheat the temperature of the spare mold to γT, γ∈(0,1], the value calculation model of γ is specifically as follows: , where v is the operating speed of the mold changing equipment, τ is the temperature rise time constant of the mold material, The initial temperature of the spare mold is maintained within the specified range. When the spare mold moves along the scheduling path to a minimum spacing of the specified scheduling spacing from the current mold, the scheduling step II is executed to schedule and maintain the current mold and replace the spare mold online.

[0053] By reducing the stamping speed and extending the holding time, and then preheating the spare mold, the current stamping mold can be scheduled for maintenance and mold replacement. This graded response mechanism uses progressive control to reduce the stamping speed and delay damage while gaining preparation time for online mold change, greatly shortening the production interruption time. Extending the holding time and reducing the speed are conducive to reducing the residual stress peak and extending the mold life to a certain extent. By preheating the spare mold in advance and maintaining the temperature difference range, it is guaranteed that the temperature of the spare mold meets the standard when it is in place, reducing the assembly stress caused by the difference in thermal expansion coefficient, and improving the first stamping pass rate after the mold is switched, reducing waiting time waste, and improving the overall efficiency of the equipment. It effectively connects maintenance preparation with production frequency, reduces production capacity loss, and thus improves material production efficiency.

[0054] Dynamically adjusting the preheat time based on the path length D minimizes the error between stamping speed adjustment and mold preheat time, improving the synchronization of temperature compliance upon arrival of the backup mold, reducing preheating energy consumption, effectively improving the first-time stamping pass rate after mold switching, and reducing the probability of energy waste caused by secondary heating. The temperature rise time constant τ reflects the thermal inertia of the material. High thermal conductivity materials shorten preheat time, and increasing preheating power promotes temperature compliance. This adapts to different mold materials, enhances adaptability and reliability in different production scenarios, reduces manual intervention, and improves material production efficiency.

[0055] β is dynamically adjusted according to the cumulative loss value r and the upper limit R. When r approaches αR, β decreases linearly to a safe range, reducing the probability of equipment vibration caused by sudden speed changes, reducing sheet metal rebound defects caused by sudden speed drops, and reducing material waste. A high β value is maintained in the early stage of loss, and the speed is gradually reduced in the later stage. Compared with the fixed speed reduction strategy, this extends the effective life of the mold while controlling the total production capacity loss within a certain range. While maintaining mold safety, it maximizes production efficiency, reduces waiting time waste, improves the overall efficiency of the equipment, and thus improves material production efficiency.

[0056] The design threshold for the number of punching times of a stamping die is L. When it is detected that the cumulative number of punching times of the stamping die reaches T=0.8L, the associated equipment of the stamping die is deloaded and the punching force of the stamping die is reduced by 5%-15%. This setting allows early intervention when the design threshold reaches 80%, avoiding the vicious cycle of overload-accelerated damage and extending the remaining life of the die. The 5%-15% deload amplitude is adapted to different damage stages. While improving production continuity, it controls the local strain amplitude below the fatigue limit, improves the practicality of force grading control, and simultaneously deloads associated equipment such as punch presses and feeders, eliminating the short-board effect, significantly extending the service life of the die, reducing the risk of sudden failures, maximizing production efficiency while maintaining die safety, improving the overall efficiency of the equipment, and thus improving the production efficiency of materials.

[0057] When a new production line is added, the historical preset stress distribution data model of the stamping die of the original production line is projected onto the preset stress distribution data model corresponding to the stamping die of the new production line through the feature space alignment algorithm, and the preset stress distribution data model of the stamping die of the new production line is adapted. With this setting, the method can effectively eliminate the impact of equipment heterogeneity, improve the availability of historical data, reduce the demand for training data for the new production line model, shorten the debugging cycle of the new production line, retain the core mapping relationship, such as the stress-temperature coupling law, through model parameter migration, and only need to adjust some geometric parameters, such as mold size, after migrating the data, greatly improving the adaptation efficiency. When the new production line is running, the model can be continuously optimized, gradually eliminating the initial projection error, improving the abnormality recognition rate, and improving the overall efficiency of the equipment, thereby improving the production efficiency of materials.

[0058] By optimizing the matching between mold maintenance windows and production tasks through integer programming algorithms, the insertion time slots for mold maintenance are optimized in the production line scheduling plan. This setting, by incorporating data such as production task priority, equipment maintenance time windows, and maintenance resources into the integer programming algorithm model, significantly improves the feasibility of maintenance plans and enhances production continuity compared to traditional manual scheduling methods. In the production line scheduling plan, conflict resolution algorithms, such as the Lagrangian relaxation method, are used to handle complex constraints and generate near-optimal solutions. The insertion time slots for mold maintenance are optimized to meet real-time scheduling requirements, effectively avoid critical production periods, improve delivery punctuality and equipment utilization, minimize production capacity losses, improve the overall efficiency of equipment, reduce waiting time waste, and thus improve material production efficiency.

[0059] The above are all preferred embodiments of the present invention, and are not intended to limit the scope of protection of the present invention. Therefore, any equivalent changes made based on the structure, shape, and principle of the present invention should be included in the scope of protection of the present invention.

Claims

1. An intelligent scheduling method for a die stamping production line, characterized in that: The following steps are involved: Data acquisition: N sensors are distributed on the stamping die, and the pressure and temperature data detected by the N sensors during the stamping process are acquired; Analysis: Identify the changing characteristics of the pressure and temperature data of N sensors, calculate the equivalent stress value data at the sensor location, analyze whether the location of each sensor is a stress concentration location, and record the stress concentration location; Calculation: A stress concentration area model is established based on the stress concentration location, and the stress concentration area model is matched with the preset stress distribution data model. The matching result is calculated to obtain the load mismatch degree of the stamping die; Judgment: Obtain the life attenuation curve of the stamping die material. If the load mismatch is greater than the preset mismatch, it is determined that the loss of the stamping die has increased. The die life loss value and the upper limit of the life loss value R are obtained according to the life attenuation curve. The cumulative value of the die life loss value during the stamping process is calculated as r. If r>αR, where α∈(0,1], execute the scheduling step I; Scheduling I: Reduce the stamping speed of the current mold and extend the holding time of a single stamping, and execute Scheduling II step; Scheduling II: Schedule and maintain the current mold and replace the spare mold online.

2. The intelligent scheduling method for a mold stamping production line according to claim 1, characterized in that: The scheduling step I is updated as follows: reduce the stamping speed of the current mold to β% of the rated speed and extend the holding time of a single stamping, where β∈(0,100]; obtain the scheduling path length D and the mold working temperature T of the spare mold; preheat the temperature of the spare mold to γT, γ∈(0,1]; and maintain the temperature difference within the specified range; when the spare mold moves along the scheduling path to a minimum spacing with the current mold that is the specified scheduling spacing, execute the scheduling step II.

3. The intelligent scheduling method for a mold stamping production line according to claim 2, characterized in that: In the scheduling step I, the value of γ is determined based on D and T. The specific calculation model for the value of γ is: , where v is the operating speed of the mold changing equipment, τ is the temperature rise time constant of the mold material, is the initial temperature of the spare mold.

4. The intelligent scheduling method for a mold stamping production line according to claim 2, characterized in that: In the scheduling step I, the value of β is determined based on R and r. The specific calculation model for the value of β is: .

5. The intelligent scheduling method for a mold stamping production line according to claim 1, characterized in that: The calculation steps are updated as follows: obtain the preset internal space data of the mold, organize the stress concentration positions obtained by analysis according to their spatial coordinate sequence, establish a three-dimensional stress concentration area model, match the stress concentration area model with the preset stress distribution data model, and calculate the matching results to obtain the load mismatch of the stamping die.

6. The intelligent scheduling method for a mold stamping production line according to claim 5, characterized in that: In the calculation step, the stress concentration area model is matched with the preset stress distribution data model, and the matching results are analyzed to obtain the load mismatch of the stamping die. Specifically, the projected area of ​​the stress concentration area is calculated according to the three-dimensional model and recorded as , calculate the average stress in the stress concentration area and record it as , the projection area of ​​the preset stress distribution data model is obtained as , the allowable stress of the material is , the load mismatch of the stamping die is calculated as: .

7. The intelligent scheduling method for a mold stamping production line according to claim 1, characterized in that: The analysis steps are updated as follows: identifying the change characteristics of the pressure data of N sensors, calculating the equivalent stress value of the sensor pressure data, recording the sensor positions with equivalent stress values ​​greater than the preset stress value as the first position set, recording the sensor positions with temperatures greater than the preset temperature as the second position set, and merging the first position set and the second position set into a plastic strain position set, calculating the coincidence coefficient corresponding to each sensor position in the plastic strain position set, where the coincidence coefficient is the ratio of the equivalent stress value to the temperature, recording the sensor positions with coincidence coefficients greater than the coincidence threshold as stress concentration positions, and recording the remaining sensor positions as stress release positions.

8. The intelligent scheduling method for a mold stamping production line according to claim 1, characterized in that: It also includes a load reduction step: the design threshold of the number of stamping times of the stamping die is L. When it is detected that the cumulative number of stamping times of the stamping die reaches T=0.8L, the associated equipment of the stamping die is operated at a reduced load, and the stamping strength of the stamping die is reduced by 5%-15%.

9. The intelligent scheduling method for a mold stamping production line according to claim 1, characterized in that: It also includes a migration step: when a new production line is added, the historical preset stress distribution data model of the stamping die of the original production line is projected to the preset stress distribution data model corresponding to the stamping die of the new production line through the feature space alignment algorithm, and the preset stress distribution data model of the stamping die of the new production line is adapted.

10. The intelligent scheduling method for a mold stamping production line according to claim 1, characterized in that: It also includes an optimization step: optimizing the matching of mold maintenance windows and production tasks through integer programming algorithms, and optimizing the insertion time slots for mold maintenance in the production line scheduling plan.

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