Intelligent scheduling method for die stamping production line

Abstract

The application discloses a mould stamping production line intelligent scheduling method, comprising the steps of data acquisition, analysis, calculation, judgment, scheduling I, scheduling II, load reduction, migration and optimization. The application cooperatively monitors the pressure data and temperature data in real time, reduces the probability of stress evaluation deviation caused by pure dependence on pressure data, optimizes the scheduling strategy in time, reduces the probability of energy waste or mold scheduling delay, improves the adaptive adjustment capability of mold scheduling, reduces the waiting time waste by reducing the stamping speed and prolonging the pressure holding time, improves the comprehensive efficiency of the equipment, reduces the production capacity loss, and thus improves the production efficiency of the material.

Description

Technical Field

[0001] This invention relates to the technical field of data analysis, specifically to an intelligent scheduling method for mold stamping production lines. Background Technology

[0002] Stamping dies are a commonly used type of production and processing equipment in industry, with wide applications in fields such as construction engineering, biochemistry, and food. During use, stamping dies need to be maintained and scheduled according to their operating status and various maintenance parameters to achieve efficient energy allocation and utilization, reducing the probability of energy waste or insufficient equipment supply.

[0003] Traditional methods for scheduling stamping dies on production lines typically rely on temperature sensors at fixed locations to display the die temperature and on production line workers to conduct regular inspections. During these inspections, workers observe the die temperature and refer to maintenance parameters, using their experience to judge the die's condition and fatigue status, thereby performing die maintenance or scheduling.

[0004] Currently, existing technologies for monitoring and controlling molds typically involve deploying a certain number of point monitoring devices to detect various data such as temperature and pressure of the mold, and using fixed parameter control. When one or more parameters are detected to be close to the fixed upper limit, the current mold is scheduled and maintained according to a predetermined scheduling route or with manual intervention, and the backup mold is moved or 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 fluctuations in material quality, energy waste, or untimely mold scheduling, thereby reducing the production efficiency of materials. Summary of the Invention

[0006] The purpose of this invention is to provide an intelligent scheduling method for die stamping production lines to solve the problems mentioned in the background art.

[0007] The intelligent scheduling method for a die stamping production line provided by this invention achieves its objective by employing the following technical solution:

[0008] The intelligent scheduling method for a die stamping production line includes the following steps:

[0009] Data acquisition: N sensors are distributed on the stamping die to acquire pressure and temperature data detected by the N sensors during the stamping process;

[0010] Analysis: Identify the variation characteristics of pressure and temperature data from N sensors, calculate the equivalent stress values ​​at the sensor locations, analyze whether each sensor location is a stress concentration point, and record the stress concentration points.

[0011] Calculation: Establish a stress concentration region model based on the stress concentration location, match the stress concentration region model with the preset stress distribution data model, and calculate the load mismatch degree of the stamping die based on the matching result;

[0012] Judgment: Obtain the life decay curve of the stamping die material. If the load mismatch is greater than the preset mismatch, it is determined that the wear of the stamping die has increased. Obtain the die life loss value and the upper limit of the life loss value R according to the life decay curve. Calculate the cumulative value of the die life loss value r during the stamping process. If r > αR, where α∈(0,1], execute the scheduling I step.

[0013] Scheduling I: Reduce the current stamping speed of the die and extend the holding time for a single stamping, then execute Scheduling II.

[0014] Dispatch II: Dispatch and maintain the current mold, and replace the spare mold online.

[0015] By adopting the above technical solution, pressure and temperature data detected by N sensors are acquired during the stamping process. The temporal variation characteristics of the pressure data from the N sensors are identified. That is, by calculating the sensor pressure data and converting it into equivalent stress value data at the corresponding sensor location, the stress concentration location is obtained. Real-time collaborative monitoring of pressure and temperature data helps reduce the probability of large deviations in stress assessment caused by relying solely on pressure data. Temperature data can correct for 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 and consistency of stress assessment results, and reduces subjective errors from relying on human experience. When the load mismatch is high, it indicates that there is a serious stress concentration in one or more areas inside the stamping die, thus triggering subsequent maintenance scheduling. Timely optimization of scheduling strategies 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, through progressive control, reduces stamping speed to delay damage while providing preparation time for online die replacement, significantly shortening production downtime. Extending the holding time in conjunction with speed reduction helps reduce residual stress peaks, extending die life to a certain extent, reducing waiting time waste, improving overall equipment efficiency, and effectively linking maintenance preparation with production frequency, reducing capacity loss, and thus improving material production efficiency.

[0016] Optionally, the scheduling step I is updated as follows: reduce the current die's stamping speed to β% of the rated speed and extend the holding time of a single stamping, where β∈(0,100], obtain the backup die scheduling path length D and the die working temperature T, preheat the backup die to γT, where γ∈(0,1], and maintain the temperature difference within the specified range, and when the backup die moves to the minimum distance between it and the current die according to the scheduling path to the specified scheduling distance, the scheduling step II is executed.

[0017] By adopting the above technical solutions, reducing the stamping speed and extending the holding time, followed by preheating the spare die, the current stamping die can be scheduled for maintenance and replacement. This graded response mechanism, through progressive control, reduces stamping speed to delay damage while providing preparation time for online die replacement, significantly shortening production interruption time. Extending the holding time in conjunction with speed reduction helps reduce residual stress peaks, extending die life to a certain extent. By preheating the spare die and maintaining a temperature difference range, the temperature of the spare die is guaranteed to meet the standard when it is in place, reducing assembly stress caused by differences in thermal expansion coefficients. This improves the first stamping pass rate after die switching, reduces wasted waiting time, improves overall equipment efficiency, and effectively connects maintenance preparation with production frequency, reducing capacity loss and thus improving material production efficiency.

[0018] 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 as follows: Where v is the operating speed of the mold changing equipment, and τ is the temperature rise time constant of the mold material. This is the initial temperature of the spare mold.

[0019] By adopting the above technical solution, the preheating time is dynamically adjusted based on the path length D, reducing the matching error between the stamping speed adjustment and the mold preheating time. This improves the synchronization of temperature compliance when the spare mold arrives, reduces preheating energy consumption, effectively increases the first 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 can shorten the preheating time and increase preheating power to promote temperature compliance. This adapts to different mold materials, enhancing adaptability and reliability for different production scenarios, reducing manual intervention, and thus improving material production efficiency.

[0020] 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 as follows: .

[0021] By adopting the above technical solution, 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 springback 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 a fixed speed reduction strategy, this extends the effective life of the mold while controlling the total production capacity loss within a certain range. It maximizes production efficiency while maintaining mold safety, reduces waiting time waste, improves the overall efficiency of the equipment, and thus improves the production efficiency of materials.

[0022] Optionally, the calculation steps are updated as follows: obtain the internal space data of the preset mold, organize the stress concentration locations obtained from the analysis according to their spatial coordinate sequence, establish a three-dimensional stress concentration region model, match the stress concentration region model with the preset stress distribution data model, and calculate the load mismatch degree of the stamping mold based on the matching results.

[0023] By adopting the above technical solution, the stress concentration area model is located based on the coordinate mapping of the internal spatial data of the mold. This significantly reduces the positioning error of the two-dimensional projection of the stress concentration area, minimizes the loss of thickness direction information in the two-dimensional projection, improves the accuracy of calculating the projected area, reduces the probability of ineffective maintenance due to misjudgment, and facilitates the accurate identification of multi-region stress concentration under asymmetric loads, which can lead to eccentric wear. Compared with traditional monitoring methods, this method can effectively improve the anomaly detection rate, thereby improving the spatiotemporal accuracy of stress assessment, accurately identifying the mold's usage status, reducing the probability of energy waste or untimely mold scheduling, and thus 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.

[0024] Optionally, in the calculation step, the stress concentration region model is matched with a preset stress distribution data model, and the matching result is analyzed to obtain the load mismatch degree of the stamping die. Specifically, the projected area of ​​the stress concentration region is calculated based on the three-dimensional model and recorded as follows: Calculate the average stress in the stress concentration region and record it as... The projected area of ​​the preset stress distribution data model is obtained as follows: The allowable stress of the material is The calculated load mismatch of the stamping die is: .

[0025] By adopting the above technical solution, the load mismatch degree is quantified by calculating the projected area and stress. Compared with the traditional fixed threshold alarm mechanism, this helps to improve the objectivity of stress assessment and the consistency of assessment results, and reduces the subjective error of relying on human experience. When the load mismatch degree is high, it indicates that there is a serious stress concentration in one or more areas inside the stamping die, thereby triggering subsequent maintenance scheduling in advance, optimizing the scheduling strategy in a timely manner, reducing the probability of energy waste or untimely die scheduling, improving the adaptive adjustment capability of die scheduling, and thus improving the production efficiency of materials.

[0026] Optionally, the analysis steps are updated as follows: identify the variation characteristics of pressure data from N sensors, calculate the equivalent stress value of the sensor pressure data, record the sensor locations with equivalent stress values ​​greater than a preset stress value as the first location set, record the sensor locations with temperatures greater than a preset temperature as the second location set, merge the first location set and the second location set into a plastic strain location set, calculate the overlap coefficient corresponding to each sensor location in the plastic strain location set, record the sensor locations with overlap coefficients greater than the overlap threshold as stress concentration locations, and record the remaining sensor locations as stress release locations.

[0027] By adopting the above technical solution, the sensor locations with equivalent stress values ​​greater than preset stress values ​​are recorded as the first location set, the sensor locations with temperatures greater than preset temperatures are recorded as the second location set, and the first location set and the second location set are merged into a plastic strain location set. The overlap coefficient corresponding to each sensor location in the plastic strain location set is calculated, and the sensor locations with overlap coefficients greater than the overlap threshold are recorded as stress concentration locations, and the remaining sensor locations are recorded as stress release locations. The overlap threshold is selected based on the value of the overlap coefficient calculated from historical monitoring data.

[0028] Optionally, a de-loading step is also included: the design threshold for the number of stamping times of the stamping die is L. When the cumulative number of stamping times of the stamping die is detected to reach T=0.8L, the associated equipment of the stamping die is de-loaded, and the stamping pressure of the stamping die is reduced by 5%-15%.

[0029] By adopting the above technical solutions, early intervention when the design threshold of 80% is reached avoids the vicious cycle of accelerated damage due to overload, extending the remaining life of the mold. The 5%-15% load reduction range 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 graded control. The load of related equipment such as punch presses and feeders is reduced simultaneously, eliminating the bottleneck effect, significantly extending the service life of the mold, reducing the risk of sudden failures, maximizing production efficiency while maintaining mold safety, improving the overall efficiency of equipment, and thus improving the production efficiency of materials.

[0030] 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 of the stamping die of the new production line through a feature space alignment algorithm, and the preset stress distribution data model of the stamping die of the new production line is adapted.

[0031] By adopting the above technical solution, this method can effectively eliminate the impact of equipment heterogeneity, improve the availability of historical data, reduce the training data requirements for new production line models, shorten the debugging cycle of new production lines, and retain core mapping relationships, such as stress-temperature coupling laws, through model parameter migration. After data migration, only some geometric parameters, such as mold dimensions, need to be adjusted, which greatly improves the adaptation efficiency. When the new production line is running, the model can be continuously optimized, gradually eliminating initial projection errors, improving the anomaly recognition rate, improving the overall efficiency of equipment, and thus improving the production efficiency of materials.

[0032] Optionally, optimization steps may also be included: optimizing the matching between mold maintenance windows and production tasks using integer programming algorithms, and optimizing the insertion time slots for mold maintenance in the production line scheduling scheme.

[0033] By adopting the above technical solutions, data such as production task priorities, equipment maintenance time windows, and maintenance resources are incorporated into the integer programming algorithm model. Compared with traditional manual scheduling methods, this significantly improves the executability of maintenance plans and enhances production continuity. In the production line scheduling scheme, conflict resolution algorithms, such as using the Lagrange relaxation method to handle complex constraints, generate near-optimal solutions, optimize the insertion time slots for mold maintenance, meet real-time scheduling requirements, effectively avoid critical production periods, improve on-time delivery rate and equipment utilization, minimize capacity loss, improve overall equipment efficiency, reduce waiting time waste, and thus improve material production efficiency.

[0034] Compared with the prior art, the beneficial effects of the present invention are:

[0035] 1. By coordinating real-time monitoring of pressure and temperature data, the probability of significant deviations in stress assessment caused by relying solely on pressure data is reduced. Temperature data can correct for 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 to traditional fixed threshold alarm mechanisms, this approach improves the objectivity and consistency of stress assessment results, reducing subjective errors arising from reliance on human experience. When the load mismatch is high, it indicates severe stress concentration in one or more areas within the stamping die, triggering subsequent maintenance scheduling. Timely optimization of scheduling strategies reduces the probability of energy waste or untimely mold scheduling, improves the adaptive adjustment capability of mold scheduling, and ultimately increases 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, through progressive control, reduces stamping speed to delay damage while providing preparation time for online die replacement, significantly shortening production downtime. Extending the holding time in conjunction with speed reduction helps reduce residual stress peaks, extending die life to a certain extent, reducing waiting time waste, improving overall equipment efficiency, and effectively linking maintenance preparation with production frequency, reducing capacity loss, and thus improving material production efficiency.

[0036] 2. By reducing the stamping speed and extending the holding time, followed by preheating the spare die, the current stamping die can be scheduled for maintenance and replacement. This graded response mechanism, through progressive control, reduces stamping speed to delay damage while providing preparation time for online die replacement, significantly shortening production interruption time. Extending the holding time in conjunction with speed reduction helps reduce residual stress peaks, extending die life to a certain extent. By preheating the spare die and maintaining a temperature difference range, the temperature of the spare die is guaranteed to meet the standard when it is in place, reducing assembly stress caused by differences in thermal expansion coefficients. This improves the first stamping pass rate after die switching, reduces wasted waiting time, improves overall equipment efficiency, and effectively connects maintenance preparation with production frequency, reducing capacity loss and thus improving material production efficiency.

[0037] 3. By dynamically adjusting the preheating time through the path length D, the matching error between the stamping speed adjustment and the mold preheating time is reduced, improving the synchronization of temperature attainment when the spare mold arrives, reducing preheating energy consumption, effectively improving the first 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 material's thermal inertia; high thermal conductivity materials shorten the preheating time and increase preheating power to promote temperature attainment, thus adapting to different mold materials. This enhances adaptability and reliability to different production scenarios, reduces manual intervention, and thereby improves material production efficiency.

[0038] 4. By dynamically adjusting β based on the cumulative loss value r and the upper limit R, when r approaches αR, β linearly decreases to a safe range, reducing the probability of equipment vibration caused by sudden speed changes, reducing sheet metal springback defects caused by sudden speed drops, and reducing material waste. Maintaining a high β value in the early stages of loss and gradually reducing speed in the later stages, compared to a fixed speed reduction strategy, extends the effective life of the mold while controlling the total production capacity loss within a certain range. Maximizing production efficiency while maintaining mold safety, reducing waiting time waste, improving overall equipment efficiency, and thus improving material production efficiency.

[0039] 5. By quantifying the load mismatch degree through the calculation of 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 relying on human experience judgment. When the load mismatch degree is high, it indicates that there is a serious stress concentration in one or more areas inside the stamping die, thereby triggering subsequent maintenance scheduling in advance, optimizing the scheduling strategy in a timely manner, reducing the probability of energy waste or untimely die scheduling, improving the adaptive adjustment capability of die scheduling, and thus improving the production efficiency of materials. Attached Figure Description

[0040] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. Obviously, the drawings described below are merely some embodiments of the present invention, and those skilled in the art can obtain other drawings based on these drawings without creative effort. In the drawings:

[0041] Figure 1 This is a flowchart of an intelligent scheduling method for a die stamping production line according to an embodiment of the present invention. Detailed Implementation

[0042] The following will be based on embodiments of the present invention. Figure 1 The technical solutions in the embodiments of the present invention are clearly and completely described herein. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0043] This embodiment discloses an intelligent scheduling method for a die stamping production line, referring to... Figure 1 The process includes data acquisition step S1, analysis step S2, calculation step S3, judgment step S4, scheduling step I S5, scheduling step II S6, load reduction step S7, migration step S8, and optimization step S9.

[0044] S1. Data acquisition steps: N sensors are distributed on the stamping die to acquire the pressure and temperature data detected by the N sensors during the stamping process.

[0045] S2. Analysis steps: Identify the pressure data change characteristics 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 the plastic strain position set. Calculate the overlap coefficient corresponding to each sensor position in the plastic strain position set, record the sensor positions with overlap coefficients greater than the overlap threshold as stress concentration positions, and record the remaining sensor positions as stress release positions.

[0046] S3. Calculation Steps: Obtain the internal spatial data of the preset mold; organize the stress concentration locations obtained from the analysis according to their spatial coordinate sequence; establish a three-dimensional stress concentration region model; match the stress concentration region model with the preset stress distribution data model; calculate the projected area of ​​the stress concentration region based on the three-dimensional model and record it. Calculate the average stress in the stress concentration region and record it as... The projected area of ​​the preset stress distribution data model is obtained as follows: The allowable stress of the material is The calculated load mismatch of the stamping die is: .

[0047] S4. Judgment Step: Obtain the life decay curve of the stamping die material. If the load mismatch is greater than the preset mismatch, it is determined that the stamping die wear has increased. Obtain the die life loss value and the upper limit of the life loss value R according to the life decay 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.

[0048] S5, Scheduling Step I: Reduce the current die stamping speed to β% of the rated speed and extend the holding time for a single stamping, where β∈(0,100], and the specific calculation model for the value of β is as follows: Obtain the backup mold scheduling path length D and the mold working temperature T, and preheat the backup mold to γT, where γ∈(0,1]. The specific calculation model for the value of γ is as follows: Where v is the operating speed of the mold changing equipment, and τ is the temperature rise time constant of the mold material. The initial temperature of the backup mold is set, and the temperature difference is maintained within a specified range. When the backup mold moves according to the scheduling path to a point where the minimum distance between it and the current mold is the specified scheduling distance, scheduling step II is executed.

[0049] S6, Scheduling II Step: Schedule and maintain the current mold, and replace the spare mold online.

[0050] S7. Load Reduction Step: The design threshold for the number of stamping times of the stamping die is L. When the cumulative number of stamping times of the stamping die is detected to reach T=0.8L, the associated equipment of the stamping die is deloaded, and the stamping pressure of the stamping die is reduced by 5%-15%.

[0051] S8. Migration Steps: 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 of 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.

[0052] S9. Optimization steps: Optimize the matching between mold maintenance window and production task through integer programming algorithm, and optimize the insertion time slot of mold maintenance in the production line scheduling scheme.

[0053] The implementation principle of the intelligent scheduling method for the die stamping production line in this embodiment is as follows:

[0054] N sensors are connected to the stamping die. During the stamping process, the pressure and temperature data detected by the N sensors are acquired. The temporal variation 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.

[0055] Sensor locations with equivalent stress values ​​greater than preset stress values ​​are recorded as the first set of locations, and sensor locations with temperatures greater than preset temperatures are recorded as the second set of locations. The first set of locations and the second set of locations are merged into a plastic strain location set. The overlap coefficient corresponding to each sensor location in the plastic strain location set is calculated. Sensor locations with overlap coefficients greater than the overlap threshold are recorded as stress concentration locations, and the remaining sensor locations are recorded as stress release locations. The overlap threshold is selected based on the overlap coefficient value calculated from historical monitoring data.

[0056] By coordinating real-time monitoring of pressure and temperature data, the probability of significant deviations in stress assessment caused by relying solely on pressure data is reduced. Temperature data can correct for the attenuation effect of material yield strength at high temperatures. By fusing data from multiple dimensions to select the plastic strain location of the stamping die, and then calculating the stress concentration location through the overlap coefficient, the limitations of detecting a single physical quantity are overcome. By merging and calculating the plastic strain set, interference from abnormal sensor data is effectively suppressed, the die's usage status is accurately identified, and the accuracy of feature extraction is improved. By distinguishing between stress concentration and release areas, targeted cooling or structural reinforcement can be implemented, improving the utilization rate of maintenance resources and reducing the probability of energy waste or untimely die scheduling, thereby improving material production efficiency.

[0057] Obtain the three-dimensional data of the internal space of the preset mold, organize the stress concentration locations obtained from the analysis according to their spatial coordinate sequence, and build a three-dimensional stress concentration region model based on the spatial three-dimensional data. Match the stress concentration region model with the preset stress distribution data model, that is, calculate the projected area of ​​the stress concentration region through the three-dimensional model and record it. Calculate the average stress in the stress concentration region and record it as... The projected area of ​​the preset stress distribution data model is obtained as follows: The preset stress distribution data model is selected based on the stress distribution data model calculated from historical monitoring data, and the allowable stress of the stamping die material is... The calculated load mismatch of the stamping die is: This refers to the degree of unevenness of the stress concentration area within the ideal distribution area, which is used to characterize the degree of local wear of the stamping die.

[0058] Based on coordinate mapping of internal mold spatial data, the model for locating stress concentration areas significantly reduces the positioning error of two-dimensional projection of stress concentration areas, minimizes the loss of thickness direction information in two-dimensional projection, improves the accuracy of calculating the projected area, and reduces the probability of ineffective maintenance due to misjudgment. It also facilitates the accurate identification of multi-region stress concentration under asymmetric loads, which can lead to eccentric wear. Compared to traditional monitoring methods, it effectively improves the anomaly detection rate, thereby enhancing the spatiotemporal accuracy of stress assessment, accurately identifying the mold's usage status, reducing the probability of energy waste or untimely mold scheduling, and ultimately improving material production efficiency. The preset stress distribution model can be updated periodically to adapt to stress field evolution caused by mold wear and process adjustments, providing a high-fidelity data foundation for load assessment and effectively reducing the probability of misjudgment.

[0059] Quantifying load mismatch by calculating the projected area and stress improves the objectivity and consistency of stress assessment compared to traditional fixed threshold alarm mechanisms. It also reduces subjective errors caused by relying on human experience. When the load mismatch is high, it indicates that there is severe stress concentration in one or more areas inside the stamping die, thus triggering subsequent maintenance scheduling in advance. This allows for timely optimization of scheduling strategies, reducing the probability of energy waste or untimely die scheduling, and improving the adaptive adjustment capability of die scheduling, thereby increasing material production efficiency.

[0060] Obtain the life decay curve of the stamping die material. The life decay curve is usually obtained through experimental calibration or factory data. If the load mismatch is greater than the preset mismatch, it is determined that the wear of the stamping die has increased. The preset mismatch is selected based on the load mismatch calculated from historical monitoring data. Obtain the die life loss value and the upper limit of the life loss value R corresponding to the load mismatch according to the life decay 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 I step.

[0061] Quantitative evaluation of load mismatch helps reduce the probability of over-maintenance or under-maintenance, identifies potential failure risks in advance, effectively reduces the total life cycle cost of stamping dies, significantly improves the production stability and continuity of the production line, reduces the prediction error of the remaining life of the dies, and compared with traditional periodic maintenance, can greatly reduce the frequency of unplanned downtime, reduce energy waste, and thus improve the production efficiency of materials.

[0062] When it is determined that r > αR, the current stamping speed of the die is reduced to β% of the rated speed, and the holding time of a single stamping is extended, where β ∈ (0, 100]. The specific calculation model for the value of β is as follows: Obtain the backup mold scheduling path length D and the mold working temperature T, and preheat the backup mold to γT, where γ∈(0,1]. The specific calculation model for the value of γ is as follows: Where v is the operating speed of the mold changing equipment, and τ is the temperature rise time constant of the mold material. The initial temperature of the backup mold is set, and the temperature difference is maintained within a specified range. When the backup mold moves to the specified scheduling distance from the current mold according to the scheduling path, scheduling step II is executed to schedule and maintain the current mold and replace the backup mold online.

[0063] By reducing the stamping speed and extending the holding time, followed by preheating the spare die, the current stamping die can be scheduled for maintenance and replacement. This graded response mechanism, through progressive control, reduces stamping speed to delay damage while providing preparation time for online die changing, significantly shortening production interruption time. Extending the holding time in conjunction with speed reduction helps reduce residual stress peaks, extending die life to a certain extent. By preheating the spare die and maintaining a temperature difference range, the temperature of the spare die is guaranteed to meet the standard when it is in place, reducing assembly stress caused by differences in thermal expansion coefficients. This improves the first stamping pass rate after die switching, reduces wasted waiting time, improves overall equipment efficiency, and effectively connects maintenance preparation with production frequency, reducing capacity loss and thus improving material production efficiency.

[0064] By dynamically adjusting the preheating time based on the path length D, the matching error between the stamping speed adjustment and the mold preheating time is reduced, improving the synchronization of temperature compliance when the spare mold arrives, reducing preheating energy consumption, effectively improving the first stamping pass rate after mold switching, and reducing the probability of energy waste caused by secondary heating. By reflecting the material's thermal inertia through the temperature rise time constant τ, high thermal conductivity materials can shorten the preheating time and increase preheating power to promote temperature compliance, thus adapting to different mold materials. This enhances adaptability and reliability to different production scenarios, reduces manual intervention, and thereby improves material production efficiency.

[0065] β is dynamically adjusted based on 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 springback 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 a fixed speed reduction strategy, this extends the effective life of the mold while controlling the total production capacity loss within a certain range. It maximizes production efficiency while maintaining mold safety, reduces waiting time waste, improves the overall efficiency of the equipment, and thus improves the production efficiency of materials.

[0066] The design threshold for the number of stamping cycles of a stamping die is L. When the cumulative number of stamping cycles of the stamping die reaches T=0.8L, the associated equipment of the stamping die is deloaded, and the stamping force of the stamping die is reduced by 5%-15%. This setting intervenes in advance when the design threshold of 80% is reached, avoiding the vicious cycle of overload accelerating damage, extending the remaining life of the die. The 5%-15% deload range is suitable for different damage stages. While improving production continuity, it controls the local strain amplitude below the fatigue limit, improves the practicality of force-level control, and simultaneously deloads associated equipment such as the punch press and feeder, eliminating the bottleneck 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.

[0067] When a new production line is added, a feature space alignment algorithm is used to project the historical preset stress distribution data model of the stamping dies from the existing production line to the corresponding preset stress distribution data model of the stamping dies in the new production line, and then adapts the preset stress distribution data model of the stamping dies in the new production line. This approach effectively eliminates the impact of equipment heterogeneity, improves the availability of historical data, reduces the need for training data for the new production line model, shortens the debugging cycle of the new production line, and preserves core mapping relationships, such as stress-temperature coupling laws, through model parameter migration. After data migration, only some geometric parameters, such as die dimensions, need to be adjusted, significantly improving adaptation efficiency. During operation on the new production line, the model can be continuously optimized, gradually eliminating initial projection errors, improving anomaly detection rate, and enhancing overall equipment efficiency, thereby increasing material production efficiency.

[0068] The matching between mold maintenance windows and production tasks is optimized using integer programming algorithms, thus improving the insertion time slots for mold maintenance in the production line scheduling scheme. This approach, by incorporating production task priorities, equipment maintenance time windows, and maintenance resources into the integer programming algorithm model, significantly enhances the executability of maintenance plans and improves production continuity compared to traditional manual scheduling methods. In the production line scheduling scheme, conflict resolution algorithms, such as the Lagrange relaxation method, are used to handle complex constraints and generate near-optimal solutions to optimize the insertion time slots for mold maintenance. This meets real-time scheduling requirements, effectively avoids critical production periods, improves on-time delivery rate and equipment utilization, minimizes capacity loss, enhances overall equipment efficiency, reduces wasted waiting time, and ultimately improves material production efficiency.

[0069] 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, all equivalent changes made in accordance with the structure, shape and principle of the present invention should be covered within the scope of protection of the present invention.

Claims

1. An intelligent scheduling method for a die stamping production line, characterized in that, Includes the following steps: Data acquisition: N sensors are distributed on the stamping die to acquire pressure and temperature data detected by the N sensors during the stamping process; Analysis: Identify the pressure data change characteristics of N sensors, calculate the equivalent stress value of the sensor pressure data, record the sensor locations with equivalent stress values ​​greater than the preset stress value as the first location set, record the sensor locations with temperatures greater than the preset temperature as the second location set, and merge the first location set and the second location set into a plastic strain location set. Calculate the overlap coefficient corresponding to each sensor location in the plastic strain location set, record the sensor locations with overlap coefficients greater than the overlap threshold as stress concentration locations, and record the remaining sensor locations as stress release locations; Calculation: Establish a stress concentration region model based on the stress concentration location, match the stress concentration region model with the preset stress distribution data model, and calculate the load mismatch degree of the stamping die based on the matching result; Judgment: Obtain the life decay curve of the stamping die material. If the load mismatch is greater than the preset mismatch, it is determined that the wear of the stamping die has increased. Obtain the die life loss value and the upper limit of the life loss value R according to the life decay curve. Calculate the cumulative value of the die life loss value r during the stamping process. If r > αR, where α∈(0,1], execute the scheduling I step. Scheduling I: Reduce the current stamping speed of the die and extend the holding time for a single stamping, then execute Scheduling II. Dispatch II: Dispatch and maintain the current mold, and replace the spare mold online.

2. The intelligent scheduling method for a die stamping production line according to claim 1, characterized in that: The scheduling step I is updated as follows: reduce the current die's stamping speed to β% of the rated speed and extend the holding time of a single stamping, where β∈(0,100], obtain the backup die scheduling path length D and the die working temperature T, preheat the backup die to γT, where γ∈(0,1], and maintain the temperature difference within the specified range, and when the backup die moves to the minimum distance between it and the current die according to the scheduling path to the specified scheduling distance, the scheduling step II is executed.

3. The intelligent scheduling method for a die 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 as follows: Where v is the operating speed of the mold changing equipment, and τ is the temperature rise time constant of the mold material. This is the initial temperature of the spare mold.

4. The intelligent scheduling method for a die 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 as follows: .

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

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

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

8. The intelligent scheduling method for a die 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 of 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.

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

Images