Lightweight aluminum alloy cold stamping data verification method based on MES

Abstract

The invention relates to the technical field of data processing and verification, in particular to a lightweight aluminum alloy cold stamping data verification method based on MES. The method comprises the following steps that die manufacturing parameters, stamping strength parameters, stamping speed parameters and stamping environment parameters of the lightweight aluminum alloy are collected through an MES system; pressure and speed reference data measurement is conducted on the stamping strength parameters and the stamping speed parameters, plate pressure degree layering is conducted on the pressure and speed reference data according to the die manufacturing parameters, and plate pressure degree layer data are formed. Through a data processing technology, an anomaly detection technology and a data verification technology, deformation structure change real-time monitoring and verification are carried out on aluminum alloy cold stamping production, so that verification accuracy is improved; abnormal traceability of an abnormal deformation structure is achieved, so that the quality stability of a stamping part is improved, and the production efficiency of light-weight aluminum alloy cold stamping is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing and verification, and in particular to a lightweight aluminum alloy cold stamping data verification method based on MES. Background Art

[0002] Cold stamping technology is one of the important processes in aluminum alloy processing. Its development history includes the application from traditional manual stamping to automated, high-precision cold stamping equipment. With the application of high-strength aluminum alloys, the production of high-strength components is achieved through pre-hardening treatment and controlled microstructure phase change. In modern manufacturing, the manufacturing execution system (MES) optimizes the production process and controls quality by real-time monitoring of data in the production process. In the field of aluminum alloy cold stamping, the MES system is used to collect process data, including material type, size, equipment parameters, environmental conditions, etc. With the gradual improvement of production efficiency and product quality requirements, data verification technology has developed from simple independent monitoring to comprehensive data multi-level verification. In the existing aluminum alloy cold stamping production, the detection of stamping structure deformation usually relies on post-sampling, and it is impossible to monitor the dynamic changes of deformation during the stamping process in real time. Once an abnormality occurs in the stamping process, it is difficult to quickly locate the root cause of the problem, resulting in low efficiency in lightweight aluminum alloy cold stamping production. Summary of the invention

[0003] Based on this, it is necessary to provide a lightweight aluminum alloy cold stamping data verification method based on MES to solve at least one of the above technical problems.

[0004] To achieve the above purpose, a lightweight aluminum alloy cold stamping data verification method based on MES is provided, the method comprising the following steps: Step S1: collecting mold manufacturing parameters, stamping strength parameters, stamping speed parameters and stamping environment parameters of lightweight aluminum alloy through the MES system; Step S2: Determine the pressure-speed benchmark data of the stamping strength parameters and the stamping speed parameters, and perform plate pressure degree stratification on the pressure-speed benchmark data according to the mold manufacturing parameters to form plate pressure degree layer data; perform stamping environment change detection on the plate pressure degree layer data based on the stamping environment parameters, and obtain the aluminum alloy shape parameters in real time through the MES system; perform stamping structure deformation detection on the aluminum alloy shape parameters according to the stamping environment change to generate stamping structure deformation data; Step S3: comparing the stamping structure deformation data with the preset standard structure deformation data. If the stamping structure deformation data does not conform to the preset standard structure deformation data, it is marked as abnormal stamping structure deformation data. Step S4: Perform stamping traceability analysis on the abnormal deformation data of the stamping structure, identify the abnormal characteristics of the stamping deformation data, and obtain the stamping deformation abnormal traceability data; feed back the stamping deformation abnormal traceability data to the MES system in real time, and dynamically adjust the lightweight aluminum alloy cold stamping based on the real-time feedback results.

[0005] The present invention collects the mold manufacturing parameters, stamping strength parameters, stamping speed parameters and stamping environment parameters of lightweight aluminum alloy through the MES system, and can obtain the key data in the stamping process comprehensively and in real time. The stamping strength parameters and the stamping speed parameters are measured as pressure speed benchmark data, and the pressure speed benchmark data is layered according to the mold manufacturing parameters to form accurate plate pressure degree layer data; the coordinated optimization of various parameters in the stamping process is ensured to avoid quality problems caused by parameter deviation, thereby significantly improving the quality stability of stamping parts; the stamping environment change amount of the plate pressure degree layer data is detected based on the stamping environment parameters, and the aluminum alloy shape parameters are obtained in real time through the MES system. The aluminum alloy shape parameters are subjected to stamping structure deformation detection according to the stamping environment change amount, and the stamping structure deformation data is generated. This dynamic monitoring mechanism can reflect the influence of stamping environment changes on the deformation of stamping parts in real time, adjust the stamping process parameters in time, and ensure the stability of the stamping process in a complex environment; the stamping structure deformation data is compared with the preset standard structure deformation data, and the abnormal stamping structure deformation data that does not meet the standards can be quickly identified. This quality inspection method based on data comparison avoids the subjectivity and inefficiency of traditional manual inspection, and improves the accuracy and efficiency of inspection; the stamping structure deformation abnormal data is subjected to stamping traceability analysis, abnormal features are identified, and stamping deformation abnormal traceability data is obtained. The traceability data is fed back to the MES system in real time, and the lightweight aluminum alloy cold stamping process is dynamically adjusted based on the feedback results. This process not only realizes the precise traceability of stamping quality problems, but also optimizes the stamping process parameters and reduces the recurrence of quality problems through the dynamic adjustment function of the MES system; through precise data collection, analysis and feedback mechanism, the scrap rate caused by stamping quality problems is reduced. At the same time, the real-time monitoring and dynamic adjustment functions of the MES system optimize the production process, improve equipment utilization and production efficiency, and thus reduce production costs. Therefore, the present invention realizes real-time monitoring and verification of deformation structure changes in aluminum alloy cold stamping production through data processing technology, abnormal detection technology and data verification technology to improve verification accuracy; realizes abnormal traceability of abnormal deformation structure to improve the quality stability of stamping parts, thereby improving the production efficiency of lightweight aluminum alloy cold stamping.

[0006] Preferably, step S1 comprises the following steps: Step S11: Setting the MES system to collect data of lightweight aluminum alloy every 10 to 30 seconds, setting the collection range of dimensional accuracy to ±0.05 mm to ±0.20 mm, and setting the collection range of surface roughness to Ra0.8-Ra2.0, so as to generate mold manufacturing parameters; Step S12: Data is collected at the front end, middle end and back end of the stamping stroke respectively through the MES system, with each collection interval being 5-15 seconds, the stamping force range being 20-50 kN, and the stamping stroke range being 50-200 mm, so as to generate stamping strength parameters; Step S13: The speed data is collected in sections during the stamping process through the MES system, with each section collecting interval being 3-8 seconds and the stamping speed collection range being 50-200 mm / s, so as to generate stamping speed parameters; Step S14: The temperature, humidity and air pressure of the stamping workshop are monitored in real time through the MES system. The temperature collection range is 15-30°C, the humidity collection range is 30%-70%, and the air pressure collection range is 0.1-0.12MPa to generate stamping environment parameters.

[0007] The present invention realizes the standardization and dynamic association of stamping parameters by normalizing the stamping strength parameters, converting the stamping speed parameters into a time linear function, and calculating the stamping speed change rate, thereby providing a unified quantitative basis for the calculation of subsequent benchmark values; weighted correlation calculation is performed on the stamping pressure value mapping data and the stamping speed change rate, and the stamping process is segmentedly fitted, wherein the segmented fitting takes into account the starting, stable and ending stages of the stamping process, thereby ensuring the accuracy of the benchmark data; the pressure speed benchmark data is layered according to the pressure degree of the plate according to the mold manufacturing parameters to form the plate pressure degree layer data, thereby realizing refined layered monitoring of the stamping process; the stamping environment change amount is detected for the plate pressure degree layer data based on the stamping environment parameters, thereby further ensuring the stability of the stamping process; the aluminum alloy shape parameters are acquired in real time through the MES system, and the stamping structure deformation detection is performed on the aluminum alloy shape parameters according to the stamping environment change amount, thereby realizing real-time monitoring of the stamping structure deformation, and being able to timely discover deformation anomalies during the stamping process; Preferably, step S2 comprises the following steps: Step S21: Map the punching strength parameter to the punching interval range of 0.2-0.8 for normalization processing to obtain punching force value mapping data; convert the punching speed parameter into a time linear function, and calculate the punching speed change parameter over time to obtain the punching speed change rate; Step S22: performing weighted correlation calculation on the punching force value mapping data and the punching speed change rate to obtain a preliminary pressure speed reference value; Step S23: performing segmented fitting on the preliminary pressure-speed reference value, dividing the stamping process into a starting segment, a stable segment and an ending segment, setting different fitting curve parameters respectively, with the fitting slope range of the starting segment being 0.2-0.5, the fitting slope range of the stable segment being 0.05-0.1, and the fitting slope range of the ending segment being 0.1-0.3, so as to generate pressure-speed reference data; Step S24: stratifying the pressure degree of the plate according to the pressure speed reference data according to the mold manufacturing parameters to form plate pressure degree layer data; Step S25: performing stamping environment change detection on the sheet metal pressure level layer data based on the stamping environment parameters to obtain the stamping environment change; Step S26: acquiring aluminum alloy shape parameters in real time through the MES system; performing stamping structure deformation detection on the aluminum alloy shape parameters according to the stamping environment change amount, and generating stamping structure deformation data.

[0008] The present invention realizes the standardization and dynamic association of stamping strength and speed parameters by normalizing the stamping strength parameters into stamping force value mapping data, and converting the stamping speed parameters into a time linear function to calculate the stamping speed change rate; weighted association calculation is performed on the stamping force value mapping data and the stamping speed change rate, and the preliminary pressure speed reference value is segmented to fit and generate pressure speed reference data; the segmented fitting considers the starting section, stable section and ending section of the stamping process, and sets the fitting slope range respectively to ensure the accuracy of the reference data, and provide a reliable basis for subsequent monitoring and adjustment. The pressure speed reference data is layered according to the mold manufacturing parameters to form the plate pressure degree layer data, and the refined layered monitoring of the stamping process is realized; the stamping environment change amount is detected for the plate pressure degree layer data based on the stamping environment parameters, and the stability of the stamping process is further ensured. The aluminum alloy shape parameters are obtained in real time through the MES system, and the stamping structure deformation detection is performed on the aluminum alloy shape parameters according to the stamping environment change amount, so as to realize the real-time monitoring of the stamping structure deformation and provide data support for subsequent quality control and process adjustment.

[0009] Preferably, step S24 includes the following steps: Step S241: Divide the cavity size accuracy of the mold manufacturing parameters into ranges. If the cavity size accuracy range is from ±0.05mm to ±0.10mm, it is a high-precision cavity size range; if the cavity size accuracy range is from ±0.10mm to ±0.15mm, it is a medium-precision cavity size range; if the cavity size accuracy range is from ±0.15mm to ±0.20mm, it is a low-precision cavity size range; Step S242: merging and marking the high-precision cavity size range, the medium-precision cavity size range, and the low-precision cavity size range to generate cavity size precision division data; Step S243: Divide the surface roughness of the mold manufacturing parameters into ranges. If the surface roughness is Ra0.8-Ra1.2, it is a low surface roughness range; if the surface roughness is Ra1.2-Ra1.6, it is a medium surface roughness range; if the surface roughness is Ra1.6-Ra2.0, it is a high surface roughness range; Step S244: merging and marking the low surface roughness range, the medium surface roughness range, and the high surface roughness range to generate surface roughness division data; Step S245: Based on the cavity size accuracy division data and the surface roughness division data, the pressure speed reference data is layered into pressure levels of the aluminum alloy plate to form plate pressure level layer data.

[0010] The present invention realizes a refined classification of mold manufacturing accuracy by dividing the cavity size accuracy of mold manufacturing parameters into a high-precision cavity size range (±0.05mm to ±0.10mm), a medium-precision cavity size range (±0.10mm to ±0.15mm) and a low-precision cavity size range (±0.15mm to ±0.20mm); the high-, medium- and low-precision cavity size ranges are combined and marked to ensure the standardization and traceability of cavity size accuracy information, which is convenient for adjustment and optimization of subsequent process parameters. By dividing the surface roughness of the mold manufacturing parameters into low surface roughness range (Ra0.8-Ra1.2), medium surface roughness range (Ra1.2-Ra1.6) and high surface roughness range (Ra1.6-Ra2.0), the hierarchical management of the mold surface roughness is realized, providing a reference for the subsequent adjustment of process parameters; the low, medium and high surface roughness ranges are merged and marked to generate surface roughness division data, ensuring the standardization and traceability of surface roughness information; based on the cavity size accuracy division data and the surface roughness division data, the pressure degree of aluminum alloy plates is layered on the pressure speed benchmark data to form the plate pressure degree layer data. By comprehensively considering the accuracy and roughness of the mold manufacturing parameters, the accurate stratification of the plate pressure degree during the stamping process is achieved.

[0011] Preferably, step S25 comprises the following steps: Step S251: using a moving average method to smooth the temperature data of the stamping environment parameters, setting the window size to 10-20 data points to obtain environmental temperature processing data; Step S252: using exponential smoothing method to process humidity data of stamping environment parameters, with the smoothing coefficient range set to 0.1-0.3, to obtain environmental humidity processing data; Step S253: using the median smoothing method to process the air pressure data of the stamping environment parameters, setting the window size to 5-10 data points to obtain the environmental air pressure processing data; Step S254: Calculate the temperature difference of the ambient temperature processed data, and set the temperature difference change to ±2°C to ±5°C; Calculate the humidity difference of the ambient temperature processed data, and set the humidity difference change to ±5% to ±15%; Calculate the air pressure difference of the ambient temperature processed data, and set the air pressure difference change to ±0.01MPa to ±0.03MPa; Step S255: If the temperature variation is within ±2°C, the humidity variation is within ±5%, and the air pressure variation is within ±0.01MPa, it is marked as a low environmental variation level; if the temperature variation is ±3°C to ±5°C, the humidity variation is ±10% to ±15%, and the air pressure variation is ±0.02MPa to ±0.03MPa, it is marked as a medium environmental variation level; if the temperature variation exceeds ±5°C, the humidity variation exceeds ±15%, and the air pressure variation exceeds ±0.03MPa, it is marked as a high environmental variation level; Step S256: Map the stamping environment variation to the sheet pressure level layer data according to the low environment variation level, the medium environment variation level and the high environment variation level to obtain the stamping environment variation.

[0012] The present invention uses the moving average method to smooth the temperature data of the stamping environment parameters, sets the window size to 10-20 data points, and obtains the environmental temperature processing data; uses the exponential smoothing method to process the humidity data, and the smoothing coefficient range is 0.1-0.3 to obtain the environmental humidity processing data; uses the median smoothing method to process the air pressure data, and the window size is 5-10 data points to obtain the environmental air pressure processing data; these smoothing methods effectively reduce the noise of the environmental parameter data and improve the stability and availability of the data. The difference calculation is performed on the environmental temperature, humidity and air pressure processing data respectively, and the temperature difference change is set to ±2°C to ±5°C, the humidity difference change is ±5% to ±15%, and the air pressure difference change is ±0.01MPa to ±0.03MPa; the change of the stamping environment parameters is quantified into a specific range, which provides a clear basis for the subsequent hierarchical division. According to the range of changes in temperature, humidity and air pressure, the stamping environment changes are divided into three levels: low, medium and high. The low variation level corresponds to temperature variation within ±2°C, humidity variation within ±5%, and air pressure variation within ±0.01MPa; the medium variation level corresponds to temperature variation from ±3°C to ±5°C, humidity variation from ±10% to ±15%, and air pressure variation from ±0.02MPa to ±0.03MPa; the high variation level corresponds to temperature variation exceeding ±5°C, humidity variation exceeding ±15%, and air pressure variation exceeding ±0.03MPa, which can clearly reflect the degree of change in the stamping environment. According to the low, medium, and high variation levels of the environment, the stamping environment variation is mapped to the sheet metal pressure level layer data, and the stamping environment variation is combined with the sheet metal pressure level layer data to achieve accurate quantification of the impact of stamping environment changes on stamping quality.

[0013] Preferably, step S26 includes the following steps: Step S261: Setting collection points at the front end, middle end, rear end and edge of the aluminum alloy through the MES system, and collecting the cross-sectional dimensions, thickness parameters and surface flatness of the aluminum alloy respectively, so as to obtain the shape parameters of the aluminum alloy; Step S262: performing an environmental impact detection on the aluminum alloy shape parameters according to the stamping environment change amount to obtain the affected area of ​​the aluminum alloy; performing cold stamping stress position determination on the affected area of ​​the aluminum alloy to generate the affected stress position; Step S263: performing stress uniformity detection on the affected stress position, and determining the degree of structural impact according to the stress uniformity, and generating the degree of impact on the stamping structure; Step S264: Record the structural deformation parameters of the impact degree of the stamping structure and generate stamping structure deformation data.

[0014] The present invention sets collection points at the front end, middle end, rear end and edge of the aluminum alloy to respectively collect the cross-sectional dimensions, thickness parameters and surface flatness of the aluminum alloy, thereby comprehensively covering the key parts of the aluminum alloy, ensuring the integrity and representativeness of the collected data, and providing a basis for subsequent detection and analysis; the shape parameters of the aluminum alloy are detected according to the change in the stamping environment, the affected area of ​​the aluminum alloy is determined, and the cold stamping stress position of the affected area is determined, thereby realizing accurate positioning of the environmentally affected area of ​​the aluminum alloy, avoiding indiscriminate detection of the entire plate, and improving detection efficiency and accuracy; stress uniformity detection is performed on the affected stress position, and the degree of structural influence is determined according to the stress uniformity. By quantifying the stress uniformity, the structural changes of the aluminum alloy in the stamping process can be clearly reflected, providing a scientific basis for subsequent process adjustment and quality control; the structural deformation parameters are recorded for the degree of influence of the stamping structure, thereby realizing dynamic monitoring of the deformation of the aluminum alloy during the stamping process, and being able to discover potential quality problems in a timely manner.

[0015] Preferably, step S3 comprises the following steps: Step S31: dividing the stamping structural deformation data into a stamping stretching area, a stamping bending area and a stamping flanging area to generate structural deformation position data; Step S32: setting deformation indexes for the structural deformation position data, specifically setting the cross-sectional dimension standard range to ±0.05mm to ±0.10mm; setting the thickness standard range to ±0.02mm to ±0.03mm; and setting the surface flatness standard range to Ra0.5 to Ra1.0; Step S33: merging the standard range of cross-sectional dimensions, the standard range of thickness, and the standard range of surface flatness to obtain preset standard structural deformation data; Step S34: Compare the stamping structure deformation data with the preset standard structure deformation data. If the cross-sectional dimension changes within ±0.05mm, the thickness changes within ±0.02mm, and the surface flatness changes within Ra0.5, it is marked as a slight deformation data of the stamping structure; if the cross-sectional dimension changes between ±0.05mm and ±0.10mm, the thickness changes between ±0.02mm and ±0.03mm, and the surface flatness changes between Ra0.5 and Ra1.0, it is marked as a moderate deformation of the stamping structure; if the cross-sectional dimension changes more than ±0.10mm, the thickness changes more than ±0.03mm, and the surface flatness changes more than Ra1.0, it is marked as a severe deformation of the stamping structure; Step S35: Based on the slight deformation of the stamping structure, the medium deformation of the stamping structure and the severe deformation of the stamping structure, the stamping structure deformation abnormality level mapping is performed to obtain the stamping structure deformation abnormality data.

[0016] The present invention divides the position of the stamping structure deformation data, clearly distinguishes the stamping stretching area, stamping bending area and stamping flanging area, can accurately locate the specific area where the deformation occurs, and provide a clear regional basis for subsequent deformation analysis and quality control; sets the standard range of cross-sectional dimensions (±0.05mm to ±0.10mm), the standard range of thickness (±0.02mm to ±0.03mm) and the standard range of surface flatness (Ra0.5 to Ra1.0) for the structural deformation position data; these standardized deformation indicators provide clear quantitative standards for subsequent deformation data comparison, ensuring the objectivity and consistency of deformation evaluation. The standard ranges of cross-sectional dimensions, thickness and surface flatness are combined to generate preset standard structural deformation data; this process establishes a unified comparison benchmark, provides a standardized reference for the classification and evaluation of stamping structure deformation data, and ensures the accuracy and reliability of deformation evaluation. By comparing the stamping structure deformation data with the preset standard structure deformation data, the deformation data is divided into three levels: slight deformation, medium deformation and severe deformation, which can clearly reflect the degree of deformation and provide clear guidance for subsequent quality control and process adjustment. Based on the three levels of stamping structure deformation, the abnormal level mapping is carried out to achieve accurate identification and classification of stamping structure deformation abnormalities, providing data support for real-time feedback and dynamic adjustment of the MES system, which helps to optimize the stamping process and improve product quality.

[0017] Preferably, step S4 comprises the following steps: Step S41: performing deformation abnormality type detection on the deformation abnormality data of the stamping structure to obtain deformation abnormality type data; performing abnormal parameter quantification on the deformation abnormality type data to generate abnormal parameter quantification data; Step S42: performing deformation feature recognition on the stamping structure deformation abnormality data according to the abnormal parameter quantification data to obtain the structural deformation abnormality features; Step S43: performing stamping deformation abnormality tracing analysis on the structural deformation abnormality characteristics to obtain stamping deformation abnormality tracing data; Step S44: Feedback the abnormal stamping deformation traceability data to the MES system in real time, and dynamically adjust the lightweight aluminum alloy cold stamping based on the real-time feedback results.

[0018] The present invention detects the type of deformation anomaly of the stamping structure deformation anomaly data, and quantifies the abnormal parameters of the deformation anomaly type data, thereby realizing the classification and quantification of deformation anomalies, and providing an accurate data basis for subsequent feature identification and traceability analysis; the deformation feature of the stamping structure deformation anomaly data is identified according to the abnormal parameter quantification data, and the specific features of the deformation anomaly can be accurately located through the feature identification driven by the quantitative data; the stamping deformation anomaly traceability analysis is performed on the structural deformation anomaly features, and the root cause of the deformation anomaly can be clarified. The stamping deformation anomaly traceability data is fed back to the MES system in real time, and the lightweight aluminum alloy cold stamping process is dynamically adjusted based on the real-time feedback results, thereby realizing a rapid response to quality issues and a dynamic optimization of process parameters, and ensuring the stability of the stamping process and the reliability of product quality.

[0019] Preferably, step S43 includes the following steps: Step S431: dividing the structural deformation abnormality characteristics into stamping process time series segments, and setting the time range of each segment to 10-30 seconds; Step S432: identifying the abnormal trend change of stamping deformation in the stamping process time series segment to obtain the abnormal trend data of stamping deformation; Step S433: determining the source of the abnormal trend of the stamping deformation abnormal trend data to obtain the abnormal trend source data; performing stamping deformation abnormality verification on the abnormal trend source data to generate a stamping deformation abnormality verification result; Step S434: Mark the stamping deformation anomaly verification result for traceability to obtain stamping deformation anomaly traceability data.

[0020] The present invention divides the structural deformation abnormality characteristics into stamping process time series segments, sets the time range of each segment to 10-30 seconds, can subdivide the stamping process into multiple time periods, and perform segmented analysis on the deformation abnormality characteristics. The stamping deformation abnormality trend change is identified for the stamping process time series segment. By segmented identification of the deformation abnormality trend, the dynamic changes of the deformation abnormality in different time periods can be clearly captured, providing key information for determining the source of the abnormality. The source of the abnormal trend is determined for the stamping deformation abnormality trend data, and the stamping deformation abnormality is verified for the abnormal trend source data, and the stamping deformation abnormality verification result is generated. By verifying the accuracy of the abnormal source, the reliability of the traceability analysis is ensured, and a clear basis is provided for subsequent process adjustments. The stamping deformation abnormality verification result is traced and marked, and the traceability mark can clearly record the source and verification result of the deformation abnormality, providing accurate data support for the real-time feedback and dynamic adjustment of the MES system, thereby achieving efficient optimization and quality control of the stamping process.

[0021] Preferably, step S44 includes the following steps: Step S441: converting the stamping deformation abnormality traceability data into a format to obtain traceability format conversion data; Step S442: Set the data transmission frequency to once every 10-30 seconds, and feed back the traceability format conversion data to the MES system in real time to obtain real-time feedback results; Step S443: determining the cold stamping operation type based on the real-time feedback result to obtain cold stamping operation type data; performing real-time monitoring of operation parameters on the cold stamping operation type data to generate real-time cold stamping operation parameters; Step S444: Dynamically adjust the lightweight aluminum alloy cold stamping parameters according to the real-time cold stamping operation parameters.

[0022] The present invention converts the format of the abnormal traceability data of stamping deformation, ensures the standardization and compatibility of the traceability data, provides a unified data structure for subsequent data transmission and system integration, and ensures the efficient processing of data in the MES system. The data transmission frequency is set to be transmitted once every 10-30 seconds, and the traceability format conversion data is fed back to the MES system in real time. By setting a reasonable transmission frequency, the timely update and stable transmission of the data are ensured, and the MES system is provided with continuous and accurate traceability information, supporting rapid response and dynamic adjustment; the cold stamping operation type is determined based on the real-time feedback results, and the operation parameters of the cold stamping operation type data are monitored in real time, which realizes the accurate identification of the cold stamping operation type and the real-time monitoring of the operation parameters, and provides an accurate basis for the subsequent parameter adjustment, ensuring the pertinence and effectiveness of the adjustment. The lightweight aluminum alloy cold stamping parameters are dynamically adjusted according to the real-time cold stamping operation parameters. The dynamic adjustment based on the real-time monitoring data can quickly respond to abnormal changes in the stamping process, optimize the stamping parameters, and ensure the stability of the stamping process and the reliability of product quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 It is a schematic diagram of the steps of a MES-based lightweight aluminum alloy cold stamping data verification method; Figure 2 for Figure 1 Detailed implementation steps of step S2 in the flowchart; Figure 3 for Figure 2 Detailed implementation steps of step S24; The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0024] The technical method of the present invention is described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within the scope of protection of the present invention.

[0025] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0026] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0027] To achieve this, please refer to Figures 1 to 3 , a lightweight aluminum alloy cold stamping data verification method based on MES, the method comprising the following steps: Step S1: collecting mold manufacturing parameters, stamping strength parameters, stamping speed parameters and stamping environment parameters of lightweight aluminum alloy through the MES system; Step S2: Determine the pressure-speed benchmark data of the stamping strength parameters and the stamping speed parameters, and perform plate pressure degree stratification on the pressure-speed benchmark data according to the mold manufacturing parameters to form plate pressure degree layer data; perform stamping environment change detection on the plate pressure degree layer data based on the stamping environment parameters, and obtain the aluminum alloy shape parameters in real time through the MES system; perform stamping structure deformation detection on the aluminum alloy shape parameters according to the stamping environment change to generate stamping structure deformation data; Step S3: comparing the stamping structure deformation data with the preset standard structure deformation data. If the stamping structure deformation data does not conform to the preset standard structure deformation data, it is marked as abnormal stamping structure deformation data. Step S4: Perform stamping traceability analysis on the abnormal deformation data of the stamping structure, identify the abnormal characteristics of the stamping deformation data, and obtain the stamping deformation abnormal traceability data; feed back the stamping deformation abnormal traceability data to the MES system in real time, and dynamically adjust the lightweight aluminum alloy cold stamping based on the real-time feedback results.

[0028] The present invention collects the mold manufacturing parameters, stamping strength parameters, stamping speed parameters and stamping environment parameters of lightweight aluminum alloy through the MES system, and can obtain the key data in the stamping process comprehensively and in real time. The stamping strength parameters and the stamping speed parameters are measured as pressure speed benchmark data, and the pressure speed benchmark data is layered according to the mold manufacturing parameters to form accurate plate pressure degree layer data; the coordinated optimization of various parameters in the stamping process is ensured to avoid quality problems caused by parameter deviation, thereby significantly improving the quality stability of stamping parts; the stamping environment change amount of the plate pressure degree layer data is detected based on the stamping environment parameters, and the aluminum alloy shape parameters are obtained in real time through the MES system. The aluminum alloy shape parameters are subjected to stamping structure deformation detection according to the stamping environment change amount, and the stamping structure deformation data is generated. This dynamic monitoring mechanism can reflect the influence of stamping environment changes on the deformation of stamping parts in real time, adjust the stamping process parameters in time, and ensure the stability of the stamping process in a complex environment; the stamping structure deformation data is compared with the preset standard structure deformation data, and the abnormal stamping structure deformation data that does not meet the standards can be quickly identified. This quality inspection method based on data comparison avoids the subjectivity and inefficiency of traditional manual inspection, and improves the accuracy and efficiency of inspection; the stamping structure deformation abnormal data is subjected to stamping traceability analysis, abnormal features are identified, and stamping deformation abnormal traceability data is obtained. The traceability data is fed back to the MES system in real time, and the lightweight aluminum alloy cold stamping process is dynamically adjusted based on the feedback results. This process not only realizes the precise traceability of stamping quality problems, but also optimizes the stamping process parameters and reduces the recurrence of quality problems through the dynamic adjustment function of the MES system; through precise data collection, analysis and feedback mechanism, the scrap rate caused by stamping quality problems is reduced. At the same time, the real-time monitoring and dynamic adjustment functions of the MES system optimize the production process, improve equipment utilization and production efficiency, and thus reduce production costs. Therefore, the present invention realizes real-time monitoring and verification of deformation structure changes in aluminum alloy cold stamping production through data processing technology, abnormal detection technology and data verification technology to improve verification accuracy; realizes abnormal traceability of abnormal deformation structure to improve the quality stability of stamping parts, thereby improving the production efficiency of lightweight aluminum alloy cold stamping.

[0029] In the embodiment of the present invention, reference Figure 1 As shown, the MES-based lightweight aluminum alloy cold stamping data verification method includes the following steps: Step S1: collecting mold manufacturing parameters, stamping strength parameters, stamping speed parameters and stamping environment parameters of lightweight aluminum alloy through the MES system; In an embodiment of the present invention, during the mold manufacturing process, a material composition analyzer installed on the mold processing equipment is used to detect the alloy composition of the mold material in real time using a spectral analysis technology. The analyzer converts the detected spectral signal into an electrical signal, and transmits it to the MES system via industrial Ethernet, which is stored as mold material attribute data. After the mold processing is completed, a three-coordinate measuring machine (CMM) is used to measure the key dimensions of the mold with high precision. The measuring machine obtains the mold dimension data through laser scanning or contact detection, and transmits it to the MES system in numerical form through the RS232 interface, which is stored as mold dimension accuracy data. The mold surface is scanned using an optical profiler, and the surface profile data is collected by an optical sensor, and the surface roughness value is calculated. The data is transmitted to the MES system via a USB interface and stored as mold surface roughness data. During the mold heat treatment process, the quenching temperature, tempering temperature and corresponding holding time are collected in real time by a temperature sensor and a time recorder installed in the heat treatment furnace. The sensor collects the temperature data at a millisecond frequency and transmits it to the MES system via the Modbus protocol, which is stored as mold heat treatment parameter data. A high-precision pressure sensor is installed at the contact position between the punch and the die of the stamping equipment. The sensor adopts a strain gauge pressure sensor, which can convert the stamping force into an electrical signal. The sensor collects the stamping force data in real time at a sampling frequency of 1000Hz, and transmits it to the MES system through the Profibus industrial bus and stores it as stamping force data. A linear displacement sensor is installed on the movement path of the punch. The sensor adopts a magnetostrictive displacement sensor, which can monitor the up and down movement distance of the punch in real time. The sensor collects the stamping stroke data at a sampling frequency of 100Hz, and transmits it to the MES system through the Modbus TCP protocol and stores it as stamping stroke data. A uniformly distributed speckle pattern is sprayed on the surface of the aluminum alloy sheet, and two high-speed industrial cameras are used to take images of the deformation of the sheet during the stamping process from different angles. The collected image data is processed by the DIC (digital image correlation) analysis system to calculate the strain distribution on the sheet surface. The DIC system transmits the strain distribution data to the MES system in the form of a two-dimensional array through Ethernet and stores it as stress distribution data. A speed sensor is installed on the punch drive device of the stamping equipment. The sensor adopts a photoelectric speed sensor, which can monitor the movement speed of the punch in different stages (fast approach, slow stamping, and return) in real time. The sensor collects speed data at a sampling frequency of 500Hz, and transmits it to the MES system through the Canopen protocol and stores it as punch speed data. A temperature and humidity sensor is installed in the stamping workshop. The sensor adopts a semiconductor temperature and humidity sensor, which can monitor the ambient temperature in the workshop in real time. The sensor collects temperature data at a sampling frequency of 1Hz, and transmits it to the MES system through the wireless ZigBee protocol and stores it as ambient temperature data. The ambient humidity in the workshop is monitored in real time through a semiconductor temperature and humidity sensor, just like the sensor for ambient temperature collection.The sensor collects humidity data at a sampling frequency of 1 Hz and transmits it to the MES system via the wireless ZigBee protocol and stores it as ambient humidity data.

[0030] Step S2: Determine the pressure-speed benchmark data of the stamping strength parameters and the stamping speed parameters, and perform plate pressure degree stratification on the pressure-speed benchmark data according to the mold manufacturing parameters to form plate pressure degree layer data; perform stamping environment change detection on the plate pressure degree layer data based on the stamping environment parameters, and obtain the aluminum alloy shape parameters in real time through the MES system; perform stamping structure deformation detection on the aluminum alloy shape parameters according to the stamping environment change to generate stamping structure deformation data; In an embodiment of the present invention, the MES system is used to measure the pressure speed reference data of the stamping strength parameters (including stamping force, stamping stroke and stress distribution) and the stamping speed parameters (punch movement speed). By analyzing the relationship between the stamping force and the stamping speed, the pressure speed reference curve of different stamping stages is determined in combination with the stamping stroke data. The curve is generated by a mathematical fitting method with the stamping force as the vertical axis and the stamping speed as the horizontal axis, and is stored in the process database of the MES system. The pressure speed reference data is layered according to the pressure degree of the plate according to the mold manufacturing parameters (such as mold material properties, dimensional accuracy, surface roughness and heat treatment parameters). Through the data analysis module in the MES system, the mold manufacturing parameters are correlated with the pressure speed reference data, and the pressure speed reference data is divided into different plate pressure degree layers according to the material characteristics and processing accuracy of the mold. For example, for high-precision molds, a lower pressure threshold is set; for ordinary precision molds, a higher pressure threshold is set, thereby forming plate pressure degree layer data. The stamping environment change amount detection is performed on the plate pressure degree layer data based on the stamping environment parameters (ambient temperature and humidity). The MES system monitors the changes in ambient temperature and humidity in the stamping workshop in real time, and compares and analyzes these data with the pressure level layer data of the plate. The environmental change detection algorithm is used to calculate the impact of environmental parameter changes on the pressure level of the plate. For example, when the ambient temperature rises, the yield strength of the material decreases, so it is necessary to adjust the pressure-speed reference curve. The MES system obtains the shape parameters of the aluminum alloy in real time, including the initial shape, size and deformation of the plate after stamping. The digital image correlation (DIC) technology is used to monitor the aluminum alloy plate in the stamping process in real time to obtain its shape change data. These data are collected by high-speed cameras and transmitted to the MES system and stored as aluminum alloy shape parameters. The aluminum alloy shape parameters are used to detect the stamping structure deformation according to the stamping environment change. The deformation detection module in the MES system combines the environmental change and the aluminum alloy shape parameters to calculate the structural deformation during the stamping process. The finite element analysis method is used to simulate the stress and strain distribution of the plate during the stamping process to generate stamping structure deformation data. The data is stored in the MES system in the form of a three-dimensional model or a two-dimensional curve for subsequent quality analysis and process optimization.

[0031] Step S3: comparing the stamping structure deformation data with the preset standard structure deformation data. If the stamping structure deformation data does not conform to the preset standard structure deformation data, it is marked as abnormal stamping structure deformation data. In an embodiment of the present invention, the stamping structure deformation data is first retrieved from the process comprehensive database. These data are obtained by simulating the stress and strain distribution in the stamping process by the finite element analysis method and stored in the form of a three-dimensional model or a two-dimensional curve. At the same time, the corresponding standard structure deformation data is obtained from the standard structure deformation database preset by the system. These standard data are pre-set based on historical process data and material performance characteristics, and are verified to meet the process requirements. Through the data comparison module in the MES system, the stamping structure deformation data and the standard structure deformation data are compared point by point. The comparison process includes one-by-one verification of key parameters such as deformation degree, stress distribution, and strain rate. The specific operation is: the deformation degree in the stamping structure deformation data is calculated with the corresponding value in the standard data. If the difference exceeds the preset allowable error range (for example, the deformation degree deviation exceeds 5%), the data point is determined to be abnormal. For the stress distribution parameters, the similarity analysis method is used to compare the stress distribution diagram in the stamping structure deformation data with the standard stress distribution diagram, and the similarity coefficient between the two is calculated. If the similarity coefficient is lower than the set threshold (for example, lower than 0.95), the data is marked as abnormal stamping structure deformation data. Finally, the MES system summarizes all comparison results and stores the data marked as abnormal in the abnormal database, while generating a detailed abnormal report for subsequent process adjustment and quality analysis.

[0032] Step S4: Perform stamping traceability analysis on the abnormal deformation data of the stamping structure, identify the abnormal characteristics of the stamping deformation data, and obtain the stamping deformation abnormal traceability data; feed back the stamping deformation abnormal traceability data to the MES system in real time, and dynamically adjust the lightweight aluminum alloy cold stamping based on the real-time feedback results.

[0033] In an embodiment of the present invention, data marked as abnormal deformation of stamping structure are extracted from the MES system, and these data include stress distribution, strain degree, stamping speed, stamping force and stamping environment parameters in the stamping process. The data analysis module is used to identify the features of abnormal data, and the stamping process is simulated and analyzed in combination with the simulation function of the stamping analysis software JSTAMP. The whole process of sheet stamping is simulated by JSTAMP software, including the self-weight deflection of the sheet, deep drawing, blanking, flanging and other links, and the position and degree of sheet rupture, wrinkling, agitation and rebound are accurately predicted. According to the simulation results, abnormal features are identified, such as stress concentration areas, deformation tolerance parts or the influence of stamping environment changes on deformation. The identified stamping deformation abnormal traceability data is fed back to the MES system in real time through the data transmission module in the MES system. Based on the real-time feedback results, the MES system optimizes the stamping process parameters using a dynamic adjustment algorithm. The dynamic adjustment algorithm is based on the support vector machine regression model and the improved particle swarm algorithm, and can be optimized according to the multivariate nonlinear relationship between stamping process parameters and forming quality. For example, when it is detected that the abnormal deformation of the stamping structure is due to stress concentration caused by excessive stamping speed, the MES system will automatically reduce the stamping speed; if the abnormality is due to fluctuations in material properties caused by changes in ambient temperature, compensation is made by adjusting the stamping pressure or mold temperature. The MES system transmits the adjusted process parameters to the stamping equipment control system in real time to achieve dynamic adjustment of the lightweight aluminum alloy cold stamping process. In this way, the MES system can ensure the stability of the stamping process and product quality, while reducing dependence on manual experience and improving production efficiency.

[0034] Preferably, step S1 comprises the following steps: Step S11: Setting the MES system to collect data of lightweight aluminum alloy every 10 to 30 seconds, setting the collection range of dimensional accuracy to ±0.05 mm to ±0.20 mm, and setting the collection range of surface roughness to Ra0.8-Ra2.0, so as to generate mold manufacturing parameters; Step S12: Data is collected at the front end, middle end and back end of the stamping stroke respectively through the MES system, with each collection interval being 5-15 seconds, the stamping force range being 20-50 kN, and the stamping stroke range being 50-200 mm, so as to generate stamping strength parameters; Step S13: The speed data is collected in sections during the stamping process through the MES system, with each section collecting interval being 3-8 seconds and the stamping speed collection range being 50-200 mm / s, so as to generate stamping speed parameters; Step S14: The temperature, humidity and air pressure of the stamping workshop are monitored in real time through the MES system. The temperature collection range is 15-30°C, the humidity collection range is 30%-70%, and the air pressure collection range is 0.1-0.12MPa to generate stamping environment parameters.

[0035] In an embodiment of the present invention, the acquisition frequency of lightweight aluminum alloy is set in the MES system to be collected once every 10 seconds to 30 seconds. For the dimensional accuracy of the mold, a high-precision coordinate measuring machine (CMM) is used for measurement, and the acquisition range is set to ±0.05mm to ±0.20mm. The surface roughness is collected by an optical profiler, and the collection range is set to Ra0.8 to Ra2.0. The collected dimensional accuracy and surface roughness data are integrated through the MES system to generate mold manufacturing parameters and store them in the process database of the MES system. The MES system collects data at the front end, middle end and back end of the stamping stroke, and each collection interval is set to 5 seconds to 15 seconds. The collection range of the punching force is set to 20kN to 50kN, and the collection range of the stamping stroke is set to 50mm to 200mm. The punching force and stamping stroke data are collected respectively using high-precision pressure sensors and displacement sensors installed on the stamping equipment. The collected data is transmitted and stored in real time through the MES system to generate stamping strength parameters. In the MES system, speed data during the stamping process is collected in sections, and the collection interval of each section is set to 3 seconds to 8 seconds. The collection range of stamping speed is set to 50mm / s to 200mm / s. The speed sensor installed on the stamping equipment monitors the movement speed of the punch in different stages (fast approach, slow stamping and return) in real time. The collected speed data is transmitted and stored through the MES system to generate stamping speed parameters. The temperature, humidity and air pressure of the stamping workshop are monitored in real time through the MES system. The temperature collection range is set to 15°C to 30°C, the humidity collection range is set to 30% to 70%, and the air pressure collection range is set to 0.1MPa to 0.12MPa. Temperature and humidity sensors and air pressure sensors are installed in the workshop to collect environmental parameters in real time and transmit the data to the MES system. The collected environmental parameters are stored in association with the stamping process parameters to generate stamping environmental parameters.

[0036] As an example of the present invention, refer to Figure 2 As shown, in this example, step S2 includes: Step S21: Map the punching strength parameter to the punching interval range of 0.2-0.8 for normalization processing to obtain punching force value mapping data; convert the punching speed parameter into a time linear function, and calculate the punching speed change parameter over time to obtain the punching speed change rate; Step S22: performing weighted correlation calculation on the punching force value mapping data and the punching speed change rate to obtain a preliminary pressure speed reference value; Step S23: performing segmented fitting on the preliminary pressure-speed reference value, dividing the stamping process into a starting segment, a stable segment and an ending segment, setting different fitting curve parameters respectively, with the fitting slope range of the starting segment being 0.2-0.5, the fitting slope range of the stable segment being 0.05-0.1, and the fitting slope range of the ending segment being 0.1-0.3, so as to generate pressure-speed reference data; Step S24: stratifying the pressure degree of the plate according to the pressure speed reference data according to the mold manufacturing parameters to form plate pressure degree layer data; Step S25: performing stamping environment change detection on the sheet metal pressure level layer data based on the stamping environment parameters to obtain the stamping environment change; Step S26: acquiring aluminum alloy shape parameters in real time through the MES system; performing stamping structure deformation detection on the aluminum alloy shape parameters according to the stamping environment change amount, and generating stamping structure deformation data.

[0037] In an embodiment of the present invention, the collected punching force values ​​are mapped to a punching interval range of 0.2-0.8 for normalization. The specific operation is: through the data processing module in the MES system, the punching force value is scaled to the specified interval in proportion. Assuming that the original punching force value range is 20kN to 50kN, the normalization formula is: normalized punching force value = (original punching force value-minimum value) / (maximum value-minimum value)×(0.8-0.2)+0.2; for example, for a punching force value of 30kN, the normalized value is 0.4. The punching speed parameter is converted into a time linear function, and the rate of change of the punching speed over time is calculated. According to the punching speed calculation formula: punching speed = punching stroke / time; the rate of change of the punching speed over time is calculated by the MES system, that is: the rate of change of the punching speed = (current speed-speed at the previous moment) / time interval, and the processed data is stored as the rate of change of the punching speed. The punching force value mapping data and the rate of change of the punching speed are weightedly associated with each other. Through the data analysis module in the MES system, the weight of the punching force value mapping data is set to 0.6, and the weight of the punching speed change rate is set to 0.4. The weighted sum of the two is calculated to obtain the preliminary pressure speed reference value. The calculation formula is: preliminary pressure speed reference value = punching force value mapping data × 0.6 + punching speed change rate × 0.4; the preliminary pressure speed reference value is segmented and fitted, and the stamping process is divided into the starting segment, the stable segment and the ending segment; in the starting segment, the fitting slope range is set to 0.2-0.5; in the stable segment, the fitting slope range is set to 0.05-0.1; in the ending segment, the fitting slope range is set to 0.1-0.3. Through the fitting algorithm in the MES system, curve fitting is performed on each segment of data to generate pressure speed reference data. The pressure speed benchmark data is processed in layers according to the mold manufacturing parameters (including dimensional accuracy and surface roughness). Through the data classification module in the MES system, combined with the acquisition range of mold manufacturing parameters (dimensional accuracy ±0.05mm to ±0.20mm, surface roughness Ra0.8 to Ra2.0), the pressure speed benchmark data is divided into different pressure levels to form the plate pressure level layer data. The stamping environment change amount of the plate pressure level layer data is detected based on the stamping environment parameters (temperature, humidity, air pressure). The MES system monitors the environmental parameters of the stamping workshop in real time (temperature 15-30°C, humidity 30%-70%, air pressure 0.1-0.12MPa), and calculates the impact of environmental parameter changes on the plate pressure level. The calculation formula is: stamping environment change amount = (current environmental parameter value-standard environmental parameter value) / standard environmental parameter value×100%; the aluminum alloy shape parameters are obtained in real time through the MES system, including the initial shape and size of the plate and the deformation after stamping.The digital image correlation (DIC) technology is used to monitor the aluminum alloy sheets in the stamping process in real time. The DIC technology sprays a speckle pattern on the surface of the sheet and uses a high-speed camera to capture images before and after deformation. The image before deformation is gridded and each sub-area is treated as a rigid motion. In the image after deformation, the area with the largest mutual correlation coefficient with the sub-area is found through correlation calculation to determine the displacement of the sub-area. All sub-areas are calculated to obtain full-field deformation information. The shape parameters of the aluminum alloy are adjusted according to the change in the stamping environment to generate stamping structure deformation data.

[0038] As an example of the present invention, refer to Figure 3 As shown, in this example, step S24 includes: Step S241: Divide the cavity size accuracy of the mold manufacturing parameters into ranges. If the cavity size accuracy range is from ±0.05mm to ±0.10mm, it is a high-precision cavity size range; if the cavity size accuracy range is from ±0.10mm to ±0.15mm, it is a medium-precision cavity size range; if the cavity size accuracy range is from ±0.15mm to ±0.20mm, it is a low-precision cavity size range; Step S242: merging and marking the high-precision cavity size range, the medium-precision cavity size range, and the low-precision cavity size range to generate cavity size precision division data; Step S243: Divide the surface roughness of the mold manufacturing parameters into ranges. If the surface roughness is Ra0.8-Ra1.2, it is a low surface roughness range; if the surface roughness is Ra1.2-Ra1.6, it is a medium surface roughness range; if the surface roughness is Ra1.6-Ra2.0, it is a high surface roughness range; Step S244: merging and marking the low surface roughness range, the medium surface roughness range, and the high surface roughness range to generate surface roughness division data; Step S245: Based on the cavity size accuracy division data and the surface roughness division data, the pressure speed reference data is layered into pressure levels of the aluminum alloy plate to form plate pressure level layer data.

[0039] In an embodiment of the present invention, the cavity size accuracy in the mold manufacturing parameters is divided into ranges through the data processing module in the MES system; according to the specific value of the cavity size accuracy: if the cavity size accuracy range is ±0.05mm to ±0.10mm, it is marked as a high-precision cavity size range; if the cavity size accuracy range is ±0.10mm to ±0.15mm, it is marked as a medium-precision cavity size range; if the cavity size accuracy range is ±0.15mm to ±0.20mm, it is marked as a low-precision cavity size range. The high-precision, medium-precision and low-precision cavity size ranges are combined and marked. Through the data classification module in the MES system, the above-divided cavity size accuracy ranges are integrated into cavity size accuracy division data and stored in the process database of the MES system; the surface roughness in the mold manufacturing parameters is divided into ranges through the MES system, according to the specific value of the surface roughness: if the surface roughness is Ra0.8 to Ra1.2, it is marked as a low surface roughness range; if the surface roughness is Ra1.2 to Ra1.6, it is marked as a medium surface roughness range; if the surface roughness is Ra1.6 to Ra2.0, it is marked as a high surface roughness range; the low surface roughness range, medium surface roughness range and high surface roughness range are divided into Merge marking is performed, and the surface roughness ranges divided above are integrated into surface roughness division data through the data classification module in the MES system, and stored in the process database of the MES system; based on the cavity size accuracy division data and the surface roughness division data, the pressure degree of aluminum alloy plates is stratified for the pressure speed benchmark data; through the data analysis module in the MES system: for the high-precision cavity size range and the low surface roughness range, a lower pressure threshold is set to divide it into a high-precision pressure layer; for the medium-precision cavity size range and the medium surface roughness range, a medium pressure threshold is set to divide it into a medium-precision pressure layer; for the low-precision cavity size range and the high surface roughness range, a higher pressure threshold is set to divide it into a low-precision pressure layer.

[0040] It is particularly important that step S245 includes the following steps: Step S2451: extracting features from the cavity size accuracy division data in terms of size deviation, shape tolerance and position accuracy to obtain cavity size accuracy feature data; Step S2452: Calculate the cavity curvature change rate for the cavity size accuracy feature data to obtain the cavity curvature change rate; map the cavity curvature change rate to the cavity size accuracy feature data to obtain the size deviation distribution feature; Step S2453: Divide the surface roughness data into multiple small areas, and perform local contrast enhancement on each area to obtain a surface roughness enhancement feature; extract texture features of the surface roughness enhancement feature to obtain a surface roughness texture feature; Step S2454: Correlate the size deviation distribution features with the surface roughness texture features to obtain deviation-roughness correlation data; Step S2455: detecting the aluminum alloy pressure position on the pressure velocity reference data to obtain the aluminum alloy pressure position; determining the plate pressure parameter on the aluminum alloy pressure position based on the deviation-roughness correlation data to generate the plate pressure parameter; Step S2456: converting the pressure parameters of the plate into pressure values, and dividing the pressure values ​​into pressure levels to generate pressure level data; Step S2457: Layer the pressure level data of the aluminum alloy plate according to the pressure degree, and obtain the plate pressure degree layer data.

[0041] In an embodiment of the present invention, a multi-dimensional feature extraction is performed on the cavity size accuracy division data through a data analysis module in an MES system. The specific operation is as follows: a high-precision three-dimensional coordinate measuring machine (CMM) is used to collect the size data of the cavity, and its deviation from the design value is calculated to obtain the size deviation feature; the shape of the cavity is scanned by an optical measuring device, and its shape deviation (such as roundness, flatness, etc.) is calculated to obtain the shape tolerance feature. A laser tracker is used to measure the key position points of the cavity, and its position deviation is calculated to obtain the position accuracy feature. The mathematical calculation module in the MES system is used to perform differential processing on the geometric data of the cavity surface to calculate the curvature change rate. The calculated cavity curvature change rate is associated with the size deviation feature to generate a size deviation distribution feature map. The cavity surface is divided into multiple small areas, and the area of ​​each area is about 10mm×10mm. The surface roughness data of each small area is locally contrast enhanced by using histogram equalization technology. The enhanced surface roughness texture feature is extracted by the gray level co-occurrence matrix (GLCM) method. The size deviation distribution feature is fused with the surface roughness texture feature. Through the data association module in the MES system, the two are associated based on the position information of the cavity to generate deviation-roughness association data. The position detection module in the MES system is used in combination with the pressure sensor data to determine the pressure position of the aluminum alloy plate. Based on the deviation-roughness association data, the pressure parameters of the pressure position are analyzed to generate the pressure parameters of the plate. The pressure parameters of the plate are converted into standardized pressure values ​​to eliminate the differences between different sensors. The pressure values ​​are graded according to the preset pressure range (such as 0-10kN for low level, 10-20kN for medium level, and 20kN and above for high level). According to the pressure level data, the aluminum alloy plates are layered for pressure degree; through the data stratification module in the MES system, the plates are divided into high, medium and low pressure level levels to generate plate pressure level layer data.

[0042] Preferably, step S25 includes the following steps: Step S251: using a moving average method to smooth the temperature data of the stamping environment parameters, setting the window size to 10-20 data points to obtain environmental temperature processing data; Step S252: using exponential smoothing method to process humidity data of stamping environment parameters, with the smoothing coefficient range set to 0.1-0.3, to obtain environmental humidity processing data; Step S253: using the median smoothing method to process the air pressure data of the stamping environment parameters, setting the window size to 5-10 data points to obtain the environmental air pressure processing data; Step S254: Calculate the temperature difference of the ambient temperature processed data, and set the temperature difference change to ±2°C to ±5°C; Calculate the humidity difference of the ambient temperature processed data, and set the humidity difference change to ±5% to ±15%; Calculate the air pressure difference of the ambient temperature processed data, and set the air pressure difference change to ±0.01MPa to ±0.03MPa; Step S255: If the temperature variation is within ±2°C, the humidity variation is within ±5%, and the air pressure variation is within ±0.01MPa, it is marked as a low environmental variation level; if the temperature variation is ±3°C to ±5°C, the humidity variation is ±10% to ±15%, and the air pressure variation is ±0.02MPa to ±0.03MPa, it is marked as a medium environmental variation level; if the temperature variation exceeds ±5°C, the humidity variation exceeds ±15%, and the air pressure variation exceeds ±0.03MPa, it is marked as a high environmental variation level; Step S256: Map the stamping environment variation to the sheet pressure level layer data according to the low environment variation level, the medium environment variation level and the high environment variation level to obtain the stamping environment variation.

[0043] In an embodiment of the present invention, a moving average method is used to smooth the temperature data in the stamping environment parameters. The window size is set to 10-20 data points. The specific operation is to perform a sliding window average calculation on the continuously collected temperature data through the data processing module in the MES system; for example, if the window size is 15 data points, each smoothed temperature value is the arithmetic mean of 15 continuous temperature data in the window. The processed data is stored as ambient temperature processing data. The humidity data in the stamping environment parameters is processed using the exponential smoothing method, and the smoothing coefficient range is set to 0.1-0.3. Through the data analysis module in the MES system, the exponential smoothing formula is applied to the humidity data, and the calculation formula is: smoothed humidity value = smoothing coefficient × current humidity value + (1-smoothing coefficient) × smoothing value at the previous moment; the median smoothing method is used to process the air pressure data in the stamping environment parameters, and the window size is set to 5-10 data points. The data processing module in the MES system calculates the median of the sliding window for the continuously collected air pressure data; for example, if the window size is 8 data points, each smoothed air pressure value is the median of the 8 consecutive air pressure data in the window, and the processed data is stored as the ambient air pressure processing data. The temperature difference calculation is performed on the ambient temperature processing data, and the temperature difference change range is set to ±2°C to ±5°C; the humidity difference calculation is performed on the ambient humidity processing data, and the humidity difference change range is set to ±5% to ±15%; the pressure difference calculation is performed on the ambient air pressure processing data, and the pressure difference change range is set to ±0.01MPa to ±0.03MPa. The data difference module in the MES system calculates the difference between adjacent time points and determines whether it is within the set range. If the temperature change is within ±2°C, the humidity change is within ±5%, and the air pressure change is within ±0.01MPa, it is marked as the low environmental change level; if the temperature change is between ±3°C and ±5°C, the humidity change is between ±10% and ±15%, and the air pressure change is between ±0.02MPa and ±0.03MPa, it is marked as the medium environmental change level; if the temperature change exceeds ±5°C, the humidity change exceeds ±15%, and the air pressure change exceeds ±0.03MPa, it is marked as the high environmental change level. The marking results are stored in the environmental change database of the MES system. According to the low environmental change level, the environmental change level, and the high environmental change level, the stamping environmental change level is mapped to the sheet metal pressure level data. Through the data mapping module in the MES system, the environmental variation level is associated with the sheet metal pressure level data to generate the stamping environmental variation; for example, for the low environmental variation level, the sheet metal pressure level data is kept unchanged; for the environmental variation level, the pressure threshold is adjusted appropriately; for the high environmental variation level, the pressure and speed parameters are further adjusted.

[0044] Preferably, step S26 includes the following steps: Step S261: Setting collection points at the front end, middle end, rear end and edge of the aluminum alloy through the MES system, and collecting the cross-sectional dimensions, thickness parameters and surface flatness of the aluminum alloy respectively, so as to obtain the shape parameters of the aluminum alloy; Step S262: performing an environmental impact detection on the aluminum alloy shape parameters according to the stamping environment change amount to obtain the affected area of ​​the aluminum alloy; performing cold stamping stress position determination on the affected area of ​​the aluminum alloy to generate the affected stress position; Step S263: performing stress uniformity detection on the affected stress position, and determining the degree of structural impact according to the stress uniformity, and generating the degree of impact on the stamping structure; Step S264: Record the structural deformation parameters of the impact degree of the stamping structure and generate stamping structure deformation data.

[0045] In the embodiment of the present invention, the MES system sets collection points at the front end, middle end, rear end and edge of the aluminum alloy plate, and collects the cross-sectional dimensions, thickness parameters and surface flatness of the aluminum alloy respectively to obtain the shape parameters of the aluminum alloy; the specific operation is: installing a high-precision laser measuring instrument and an ultrasonic thickness gauge at the designated position of the aluminum alloy plate, respectively used to measure the cross-sectional dimensions and thickness parameters; using an optical profiler or a contact probe to measure the surface flatness, collect data and transmit it to the MES system; the collected cross-sectional dimensions, thickness and surface flatness data are stored in the process database of the MES system to form the shape parameters of the aluminum alloy. According to the change in the stamping environment, the aluminum alloy shape parameters are detected for the aluminum alloy environmental impact part to obtain the affected area of ​​the aluminum alloy, and the cold stamping stress position of the affected area is determined to generate the affected stress position. The MES system analyzes the aluminum alloy shape parameters according to the stamping environment change (temperature, humidity, air pressure change), identifies the area of ​​dimensional deviation or flatness change caused by environmental changes; uses finite element analysis software (such as Dynaform) to simulate the stress distribution of the affected area to determine the stress concentration position. The affected areas and their stress concentration positions are marked and stored in the MES system to generate the affected stress position data; stress uniformity detection is performed on the affected stress positions, and the degree of structural impact is determined based on the stress uniformity to generate the degree of impact on the stamping structure. The specific operation is as follows: the MES system calls the stress uniformity detection algorithm, analyzes the stress distribution of the affected stress positions, and calculates the stress deviation rate. According to the stress deviation rate, the degree of structural impact in the affected area is divided into three levels: slight, medium and severe, and the results are stored in the MES system. The structural deformation parameters of the impact on the stamping structure are recorded to generate the stamping structure deformation data. The specific operation is as follows: the MES system records the deformation parameters of the affected area according to the degree of structural impact, including the maximum deformation, deformation direction and deformation range. The deformation parameters are combined with the aluminum alloy shape parameters to generate complete stamping structure deformation data and store them in the process database of the MES system.

[0046] Preferably, step S3 comprises the following steps: Step S31: dividing the stamping structural deformation data into a stamping stretching area, a stamping bending area and a stamping flanging area to generate structural deformation position data; Step S32: setting deformation indexes for the structural deformation position data, specifically setting the cross-sectional dimension standard range to ±0.05mm to ±0.10mm; setting the thickness standard range to ±0.02mm to ±0.03mm; and setting the surface flatness standard range to Ra0.5 to Ra1.0; Step S33: merging the standard range of cross-sectional dimensions, the standard range of thickness, and the standard range of surface flatness to obtain preset standard structural deformation data; Step S34: Compare the stamping structure deformation data with the preset standard structure deformation data. If the cross-sectional dimension changes within ±0.05mm, the thickness changes within ±0.02mm, and the surface flatness changes within Ra0.5, it is marked as a slight deformation data of the stamping structure; if the cross-sectional dimension changes between ±0.05mm and ±0.10mm, the thickness changes between ±0.02mm and ±0.03mm, and the surface flatness changes between Ra0.5 and Ra1.0, it is marked as a moderate deformation of the stamping structure; if the cross-sectional dimension changes more than ±0.10mm, the thickness changes more than ±0.03mm, and the surface flatness changes more than Ra1.0, it is marked as a severe deformation of the stamping structure; Step S35: Based on the slight deformation of the stamping structure, the medium deformation of the stamping structure and the severe deformation of the stamping structure, the stamping structure deformation abnormality level mapping is performed to obtain the stamping structure deformation abnormality data.

[0047] In an embodiment of the present invention, the structural deformation position of the stamping structural deformation data is divided by the MES system, and the deformation area of ​​the aluminum alloy plate is divided into a stamping stretching area, a stamping bending area and a stamping flanging area to generate structural deformation position data. The specific operation is: using the data analysis module in the MES system, combined with the stamping process parameters and deformation data, to identify the different deformation characteristic areas of the plate during the stamping process; according to the deformation characteristics, the plate is divided into a stretching area (mainly manifested as the longitudinal extension of the plate), a bending area (mainly manifested as the bending deformation of the plate) and a flanging area (mainly manifested as the flanging deformation of the edge of the plate), and the division results are stored as structural deformation position data. The deformation index of the structural deformation position data is set, specifically: the standard range of the cross-sectional size is set to ±0.05mm to ±0.10mm; the standard range of the thickness is set to ±0.02mm to ±0.03mm; the standard range of the surface flatness is set to Ra0.5 to Ra1.0. The standard range of cross-sectional dimensions, the standard range of thickness, and the standard range of surface flatness are combined to obtain the preset standard structural deformation data. The specific operation is as follows: the MES system integrates the above-set deformation indicators into a standard data template, which contains the standard range of deformation indicators for the stretching area, bending area, and flanging area. The standard structural deformation data is stored in the process database of the MES system for subsequent deformation data comparison. The stamping structure deformation data is compared with the preset standard structure deformation data, and marked according to the comparison results: if the cross-sectional size changes within ±0.05mm, the thickness changes within ±0.02mm, and the surface flatness changes within Ra0.5, it is marked as a slight deformation data of the stamping structure; if the cross-sectional size changes between ±0.05mm and ±0.10mm, the thickness changes between ±0.02mm and ±0.03mm, and the surface flatness changes between Ra0.5 and Ra1.0, it is marked as a medium deformation of the stamping structure; if the cross-sectional size changes exceed ±0.10mm, the thickness changes exceed ±0.03mm, and the surface flatness changes exceed Ra1.0, it is marked as a severe deformation of the stamping structure; the comparison results are stored in the MES system for subsequent abnormal level mapping. Based on the slight deformation of the stamping structure, the medium deformation of the stamping structure, and the severe deformation of the stamping structure, the stamping structure deformation abnormal level mapping is performed to obtain the stamping structure deformation abnormal data. The specific operation is as follows: the MES system maps slight deformation, medium deformation and severe deformation to different abnormality levels according to the marking results; the abnormality level mapping results are stored as stamping structure deformation abnormality data for subsequent process adjustment and quality control.

[0048] Preferably, step S4 comprises the following steps: Step S41: performing deformation abnormality type detection on the deformation abnormality data of the stamping structure to obtain deformation abnormality type data; performing abnormal parameter quantification on the deformation abnormality type data to generate abnormal parameter quantification data; Step S42: performing deformation feature recognition on the stamping structure deformation abnormality data according to the abnormal parameter quantification data to obtain the structural deformation abnormality features; Step S43: performing stamping deformation abnormality tracing analysis on the structural deformation abnormality characteristics to obtain stamping deformation abnormality tracing data; Step S44: Feedback the abnormal stamping deformation traceability data to the MES system in real time, and dynamically adjust the lightweight aluminum alloy cold stamping based on the real-time feedback results.

[0049] In the embodiment of the present invention, the data analysis module in the MES system is used to detect the abnormal deformation type of the stamping structure deformation abnormal data; the abnormal type in the deformation data is automatically identified by combining the characteristics of high amplitude change, high frequency change, etc. through a unified recognition method of multiple abnormal forms; the specific operation is: the deformation data is segmented according to the time series, and the data is automatically segmented by information entropy to avoid the interference of high amplitude and high frequency changes. The segmented data is feature extracted by the angle anomaly factor (ABOF) and the local density anomaly factor (LOF), the deformation abnormal type is identified, and the deformation abnormal type data is generated. The abnormal parameters of the deformation abnormal type data are quantified, and the key parameters of each abnormal type (such as the cross-sectional size change, thickness change, and surface flatness change) are quantified by the statistical analysis method to generate abnormal parameter quantification data. For example, for a certain abnormal type, the cross-sectional size change is quantified to be 0.08mm, the thickness change is 0.025mm, and the surface flatness change is Ra0.8; according to the abnormal parameter quantification data, the deformation feature of the stamping structure deformation abnormal data is identified. Through the feature recognition module in the MES system, combined with the preset deformation feature standards (such as the cross-sectional dimension standard range of ±0.05mm to ±0.10mm, the thickness standard range of ±0.02mm to ±0.03mm, and the surface flatness standard range of Ra0.5 to Ra1.0), the characteristics of abnormal data are identified. For example, if the cross-sectional dimension change in a certain area is 0.12mm, which exceeds the standard range, it will be marked as a severe deformation feature, and finally the structural deformation abnormality feature data will be generated. The structural deformation abnormality features are traced for stamping deformation abnormality analysis. Through the traceability analysis module in the MES system, combined with the stamping process parameters (such as stamping speed, stamping force, mold state, etc.), the root cause of the deformation abnormality is analyzed. The specific operation is: using multi-task learning (MTL) and convolutional neural network (CNN), the relationship between the deformation abnormality features and the stamping process parameters is modeled, and the key process parameters that cause the deformation abnormality are identified. For example, if the deformation abnormality in a certain area is related to the stamping speed being too fast, the traceability result is "stamping speed is too high"; the traceability data of the stamping deformation abnormality is fed back to the MES system in real time, and the lightweight aluminum alloy cold stamping process is dynamically adjusted based on the real-time feedback results. Through the dynamic adjustment module in the MES system, the stamping process parameters are adjusted according to the traceability results. If the traceability result shows that the stamping speed is too high, the stamping speed is automatically reduced; if the deformation abnormality is related to mold wear, it is prompted to replace the mold. The adjusted process parameters are updated to the stamping equipment control system in real time to ensure the stability and quality control of the subsequent stamping process.

[0050] Preferably, step S43 includes the following steps: Step S431: dividing the structural deformation abnormality characteristics into stamping process time series segments, and setting the time range of each segment to 10-30 seconds; Step S432: identifying the abnormal trend change of stamping deformation in the stamping process time series segment to obtain the abnormal trend data of stamping deformation; Step S433: determining the source of the abnormal trend of the stamping deformation abnormal trend data to obtain the abnormal trend source data; performing stamping deformation abnormality verification on the abnormal trend source data to generate a stamping deformation abnormality verification result; Step S434: Mark the stamping deformation anomaly verification result for traceability to obtain stamping deformation anomaly traceability data.

[0051] In an embodiment of the present invention, the structural deformation abnormality characteristic data is divided into stamping process time series segments through the MES system. The stamping process is divided into multiple time series segments, and the time range of each segment is set to 10-30 seconds. The specific operation is: using the time series analysis module in the MES system, according to the timestamp information of the stamping process, the deformation abnormality characteristic data is divided into multiple subsequences. For example, if the total duration of the stamping process is 120 seconds, it can be divided into 4 time series segments, each segment lasting 30 seconds; the stamping process time series segment is used to identify the trend change of stamping deformation abnormality. Through the trend analysis module in the MES system, the deformation data of each time series segment is analyzed to identify the trend change of deformation abnormality. The specific operation is: using the maximum likelihood estimation (MLE) or least squares estimation (LSE) method, the deformation data of each time series segment is fitted to detect the change point of the deformation trend; if the deformation data in a certain time series segment shows an obvious upward trend, it is marked as "upward trend change" and the stamping deformation abnormality trend data is generated. Determine the source of abnormal trend of stamping deformation abnormal trend data. Through the traceability analysis module in the MES system, combined with stamping process parameters (such as stamping force, stamping speed, mold state, etc.), analyze the source of abnormal deformation trend. The specific operation is: use the angle anomaly factor (ABOF) and local anomaly factor (LOF) to extract features of abnormal trend data and identify the key process parameters that cause abnormal deformation. For example, if an abnormal trend is related to a sudden change in stamping speed, its source is marked as "stamping speed change". Verify the stamping deformation abnormality of the source data of the abnormal trend. Through the verification module in the MES system, combined with the preset deformation standard range (such as cross-sectional size, thickness, surface flatness, etc.), verify the source data of the abnormal trend. For example, if the cross-sectional size change corresponding to a certain abnormal trend source exceeds the standard range of ±0.10mm, the verification result is "abnormal" and the stamping deformation abnormality verification result is generated. Mark the stamping deformation abnormality verification result for traceability to obtain the stamping deformation abnormality traceability data. Through the traceability marking module in the MES system, the abnormal verification results are associated with the corresponding stamping process parameters, time series segments and deformation characteristics. For example, if an abnormal verification result is marked as "abnormal cross-sectional dimensions caused by stamping speed changes", its traceability mark is "stamping speed changes"; finally, the traceability data is stored in the abnormality database of the MES system.

[0052] It is particularly important that step S433 includes the following steps: Step S4331: extracting spatiotemporal features of the stamping deformation abnormal trend data, by continuously monitoring the displacement and stress of the abnormal deformation area during the stamping process, recording the evolution characteristics of the abnormal trend in time and space, and obtaining the spatiotemporal feature data of the abnormal trend; Step S4332: Enhance the abnormal trend spatiotemporal feature data, identify the small feature changes in the abnormal deformation area, and enhance the key features to obtain abnormal trend enhanced features; Step S4333: Screening the abnormal trend enhancement features and removing the interference information irrelevant to the abnormal trend to obtain the abnormal trend source feature data; Step S4334: analyzing the propagation path of the abnormal trend source characteristic data during the stamping process to obtain the abnormal trend data of the propagation path; determining the source position of the abnormal trend data of the propagation path to obtain the abnormal trend source data; tracing the evolution path of the abnormal trend source data during the stamping process to obtain the abnormal trend source location information; Step S4335: Perform multi-factor verification processing on the abnormal trend source location information, specifically perform multi-factor verification on material deformation, mold wear and process parameter settings, and generate stamping deformation abnormality verification results.

[0053] In the embodiment of the present invention, the MES system is used to extract the spatiotemporal features of the stamping deformation abnormal trend data. During the stamping process, the abnormal deformation area is continuously monitored by high-precision displacement sensors and strain gauges, and the evolution characteristics of the abnormal trend in time and space are recorded. The specific operation is: a laser displacement sensor is used to record the displacement changes of the abnormal area in real time at a sampling frequency of 10Hz. The stress data is collected by strain gauges, and the dynamic changes of stress during the stamping process are analyzed in combination with the timestamp information. The displacement and stress data are aligned in time series to generate abnormal trend spatiotemporal feature data. The abnormal trend spatiotemporal feature data is enhanced to identify small feature changes and enhance key features. The specific operation is: wavelet transform is used to perform multi-scale analysis on the spatiotemporal feature data to extract small deformation features. The principal component analysis (PCA) method is used to reduce the dimension of the extracted small features, and the significance of the key features is enhanced at the same time, and the enhanced feature data is stored as abnormal trend enhancement features. The abnormal trend enhancement features are screened to eliminate interference information that is not related to the abnormal trend. The specific operation is: correlation analysis is used to screen out features that are highly correlated with the abnormal trend. The threshold method is used to eliminate interference features below the set threshold to ensure the purity of feature data. The filtered feature data is stored as abnormal trend source feature data. The propagation path of the abnormal trend source feature data is analyzed. The dynamic time warping (DTW) algorithm is used to analyze the propagation path of abnormal features during the stamping process. The source position of the propagation path is determined by combining the geometric model and sensor layout of the stamping equipment. The evolution path of the abnormal trend is tracked backward from the source position through the reverse tracking algorithm to generate the abnormal trend source location information. The abnormal trend source location information is verified by multiple factors, including: simulating the deformation of the material during the stamping process through finite element analysis (FEA) to verify whether the abnormal trend is related to the material properties. Check the wear of the mold surface and verify whether the abnormal trend is related to the mold wear in combination with the mold usage record. Compare the current stamping process parameters (such as stamping force and stamping speed) with the standard process parameters to verify whether the abnormal trend is related to the process parameter settings; and generate the stamping deformation abnormality verification results based on the above verification information.

[0054] Preferably, step S44 includes the following steps: Step S441: converting the stamping deformation abnormality traceability data into a format to obtain traceability format conversion data; Step S442: Set the data transmission frequency to once every 10-30 seconds, and feed back the traceability format conversion data to the MES system in real time to obtain real-time feedback results; Step S443: determining the cold stamping operation type based on the real-time feedback result to obtain cold stamping operation type data; performing real-time monitoring of operation parameters on the cold stamping operation type data to generate real-time cold stamping operation parameters; Step S444: Dynamically adjust the lightweight aluminum alloy cold stamping parameters according to the real-time cold stamping operation parameters.

[0055] In an embodiment of the present invention, the data processing module in the MES system is used to convert the format of the abnormal traceability data of stamping deformation; the specific operation is: converting the traceability data from the original format (such as the binary or text format collected by the equipment) to a standardized format (such as JSON or XML format) that the MES system can recognize and process; such as reorganizing the data field containing the abnormal source information to ensure that it conforms to the data structure preset by the MES system. The converted data is stored as traceability format conversion data. The data transmission frequency is set to be transmitted once every 10-30 seconds, and the traceability format conversion data is fed back to the MES system in real time. The specific operation is: using the data transmission module in the MES system, combined with the middleware (such as Apache Kafka or RabbitMQ) to achieve efficient data transmission. The middleware is responsible for transmitting the traceability format conversion data to the real-time data processing unit of the MES system at a set frequency (every 10-30 seconds) to ensure the real-time and accuracy of the data. After the transmission is completed, the real-time feedback result is obtained. The real-time feedback result is analyzed to determine the current cold stamping operation type. Through the process identification module in the MES system, combined with the preset process parameter template (such as stamping speed, stamping force, mold state, etc.), it is identified whether the current operation belongs to the operation type of stretching, bending or flanging. For example, if the real-time feedback result shows that the stamping force is large and the stamping speed is slow, it is determined to be a stretching operation type. The identification result is stored as cold stamping operation type data. The operation parameters of the cold stamping operation type data are monitored in real time. Through the real-time monitoring module in the MES system, combined with the sensor data of the stamping equipment (such as pressure sensor, displacement sensor, speed sensor, etc.), the key parameters of the current operation are obtained in real time. For example, for the stretching operation, the parameters such as stamping force, stamping speed and mold stroke are monitored in real time. The monitoring results generate real-time cold stamping operation parameters and are stored in the real-time database of the MES system. According to the real-time cold stamping operation parameters, the lightweight aluminum alloy cold stamping parameters are dynamically adjusted. The specific operation is: through the dynamic adjustment module in the MES system, combined with the preset process adjustment rules (such as the mapping relationship between stamping force and deformation degree), the stamping process parameters are adjusted according to the real-time operation parameters. For example, if the real-time monitoring shows that the punching force is too high and causes abnormal deformation, the punching force will be automatically reduced; if the punching speed is too fast and affects the uniformity of deformation, the punching speed will be adjusted. The adjusted parameters are updated to the punching equipment control system in real time to ensure the stability and quality control of the subsequent punching process.

[0056] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0057] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A lightweight aluminum alloy cold stamping data verification method based on MES, characterized in that: The following steps are involved: Step S1: collecting mold manufacturing parameters, stamping strength parameters, stamping speed parameters and stamping environment parameters of lightweight aluminum alloy through the MES system; Step S2: Determine the pressure-speed benchmark data of the stamping strength parameters and the stamping speed parameters, and perform plate pressure degree stratification on the pressure-speed benchmark data according to the mold manufacturing parameters to form plate pressure degree layer data; perform stamping environment change detection on the plate pressure degree layer data based on the stamping environment parameters, and obtain the aluminum alloy shape parameters in real time through the MES system; perform stamping structure deformation detection on the aluminum alloy shape parameters according to the stamping environment change to generate stamping structure deformation data; Step S3: comparing the stamping structure deformation data with the preset standard structure deformation data. If the stamping structure deformation data does not conform to the preset standard structure deformation data, it is marked as abnormal stamping structure deformation data. Step S4: Perform stamping traceability analysis on the abnormal deformation data of the stamping structure, identify the abnormal characteristics of the stamping deformation data, and obtain the stamping deformation abnormal traceability data; feed back the stamping deformation abnormal traceability data to the MES system in real time, and dynamically adjust the lightweight aluminum alloy cold stamping based on the real-time feedback results.

2. The MES-based lightweight aluminum alloy cold stamping data verification method according to claim 1 is characterized in that: Step S1 includes the following steps: Step S11: Setting the MES system to collect data of lightweight aluminum alloy every 10 to 30 seconds, setting the collection range of dimensional accuracy to ±0.05 mm to ±0.20 mm, and setting the collection range of surface roughness to Ra0.8-Ra2.0, so as to generate mold manufacturing parameters; Step S12: Data is collected at the front end, middle end and back end of the stamping stroke respectively through the MES system, with each collection interval being 5-15 seconds, the stamping force range being 20-50 kN, and the stamping stroke range being 50-200 mm, so as to generate stamping strength parameters; Step S13: The speed data is collected in sections during the stamping process through the MES system, with each section collecting interval being 3-8 seconds and the stamping speed collection range being 50-200 mm / s, so as to generate stamping speed parameters; Step S14: The temperature, humidity and air pressure of the stamping workshop are monitored in real time through the MES system. The temperature collection range is 15-30°C, the humidity collection range is 30%-70%, and the air pressure collection range is 0.1-0.12MPa to generate stamping environment parameters.

3. The MES-based lightweight aluminum alloy cold stamping data verification method according to claim 1 is characterized in that: Step S2 includes the following steps: Step S21: Map the punching strength parameter to the punching interval range of 0.2-0.8 for normalization processing to obtain punching force value mapping data; convert the punching speed parameter into a time linear function, and calculate the punching speed change parameter over time to obtain the punching speed change rate; Step S22: performing weighted correlation calculation on the punching force value mapping data and the punching speed change rate to obtain a preliminary pressure speed reference value; Step S23: performing segmented fitting on the preliminary pressure-speed reference value, dividing the stamping process into a starting segment, a stable segment and an ending segment, setting different fitting curve parameters respectively, with the fitting slope range of the starting segment being 0.2-0.5, the fitting slope range of the stable segment being 0.05-0.1, and the fitting slope range of the ending segment being 0.1-0.3, so as to generate pressure-speed reference data; Step S24: stratifying the pressure degree of the plate according to the pressure speed reference data according to the mold manufacturing parameters to form plate pressure degree layer data; Step S25: performing stamping environment change detection on the sheet metal pressure level layer data based on the stamping environment parameters to obtain the stamping environment change; Step S26: acquiring aluminum alloy shape parameters in real time through the MES system; performing stamping structure deformation detection on the aluminum alloy shape parameters according to the stamping environment change amount, and generating stamping structure deformation data.

4. The MES-based lightweight aluminum alloy cold stamping data verification method according to claim 3 is characterized in that: Step S24 includes the following steps: Step S241: Divide the cavity size accuracy of the mold manufacturing parameters into ranges. If the cavity size accuracy range is from ±0.05mm to ±0.10mm, it is a high-precision cavity size range; if the cavity size accuracy range is from ±0.10mm to ±0.15mm, it is a medium-precision cavity size range; if the cavity size accuracy range is from ±0.15mm to ±0.20mm, it is a low-precision cavity size range; Step S242: merging and marking the high-precision cavity size range, the medium-precision cavity size range, and the low-precision cavity size range to generate cavity size precision division data; Step S243: Divide the surface roughness of the mold manufacturing parameters into ranges. If the surface roughness is Ra0.8-Ra1.2, it is a low surface roughness range; if the surface roughness is Ra1.2-Ra1.6, it is a medium surface roughness range; if the surface roughness is Ra1.6-Ra2.0, it is a high surface roughness range; Step S244: merging and marking the low surface roughness range, the medium surface roughness range, and the high surface roughness range to generate surface roughness division data; Step S245: Based on the cavity size accuracy division data and the surface roughness division data, the pressure speed reference data is layered into pressure levels of the aluminum alloy plate to form plate pressure level layer data.

5. The MES-based lightweight aluminum alloy cold stamping data verification method according to claim 3 is characterized in that: Step S25 includes the following steps: Step S251: using a moving average method to smooth the temperature data of the stamping environment parameters, setting the window size to 10-20 data points to obtain environmental temperature processing data; Step S252: using exponential smoothing method to process humidity data of stamping environment parameters, with the smoothing coefficient range set to 0.1-0.3, to obtain environmental humidity processing data; Step S253: using the median smoothing method to process the air pressure data of the stamping environment parameters, setting the window size to 5-10 data points to obtain the environmental air pressure processing data; Step S254: Calculate the temperature difference of the ambient temperature processed data, and set the temperature difference change to ±2°C to ±5°C; Calculate the humidity difference of the ambient temperature processed data, and set the humidity difference change to ±5% to ±15%; Calculate the air pressure difference of the ambient temperature processed data, and set the air pressure difference change to ±0.01MPa to ±0.03MPa; Step S255: If the temperature variation is within ±2°C, the humidity variation is within ±5%, and the air pressure variation is within ±0.01MPa, it is marked as a low environmental variation level; if the temperature variation is ±3°C to ±5°C, the humidity variation is ±10% to ±15%, and the air pressure variation is ±0.02MPa to ±0.03MPa, it is marked as a medium environmental variation level; if the temperature variation exceeds ±5°C, the humidity variation exceeds ±15%, and the air pressure variation exceeds ±0.03MPa, it is marked as a high environmental variation level; Step S256: Map the stamping environment variation to the sheet pressure level layer data according to the low environment variation level, the medium environment variation level and the high environment variation level to obtain the stamping environment variation.

6. The MES-based lightweight aluminum alloy cold stamping data verification method according to claim 3 is characterized in that: Step S26 includes the following steps: Step S261: Setting collection points at the front end, middle end, rear end and edge of the aluminum alloy through the MES system, and collecting the cross-sectional dimensions, thickness parameters and surface flatness of the aluminum alloy respectively, so as to obtain the shape parameters of the aluminum alloy; Step S262: performing an environmental impact detection on the aluminum alloy shape parameters according to the stamping environment change amount to obtain the affected area of ​​the aluminum alloy; performing cold stamping stress position determination on the affected area of ​​the aluminum alloy to generate the affected stress position; Step S263: performing stress uniformity detection on the affected stress position, and determining the degree of structural impact according to the stress uniformity, and generating the degree of impact on the stamping structure; Step S264: Record the structural deformation parameters of the impact degree of the stamping structure and generate stamping structure deformation data.

7. The MES-based lightweight aluminum alloy cold stamping data verification method according to claim 6 is characterized in that: Step S3 includes the following steps: Step S31: dividing the stamping structural deformation data into a stamping stretching area, a stamping bending area and a stamping flanging area to generate structural deformation position data; Step S32: setting deformation indexes for the structural deformation position data, specifically setting the cross-sectional dimension standard range to ±0.05mm to ±0.10mm; setting the thickness standard range to ±0.02mm to ±0.03mm; and setting the surface flatness standard range to Ra0.5 to Ra1.0; Step S33: merging the standard range of cross-sectional dimensions, the standard range of thickness, and the standard range of surface flatness to obtain preset standard structural deformation data; Step S34: Compare the stamping structure deformation data with the preset standard structure deformation data. If the cross-sectional dimension changes within ±0.05mm, the thickness changes within ±0.02mm, and the surface flatness changes within Ra0.5, it is marked as a slight deformation data of the stamping structure; if the cross-sectional dimension changes between ±0.05mm and ±0.10mm, the thickness changes between ±0.02mm and ±0.03mm, and the surface flatness changes between Ra0.5 and Ra1.0, it is marked as a moderate deformation of the stamping structure; if the cross-sectional dimension changes more than ±0.10mm, the thickness changes more than ±0.03mm, and the surface flatness changes more than Ra1.0, it is marked as a severe deformation of the stamping structure; Step S35: Based on the slight deformation of the stamping structure, the medium deformation of the stamping structure and the severe deformation of the stamping structure, the stamping structure deformation abnormality level mapping is performed to obtain the stamping structure deformation abnormality data.

8. The MES-based lightweight aluminum alloy cold stamping data verification method according to claim 1, characterized in that: Step S4 includes the following steps: Step S41: performing deformation abnormality type detection on the deformation abnormality data of the stamping structure to obtain deformation abnormality type data; performing abnormal parameter quantification on the deformation abnormality type data to generate abnormal parameter quantification data; Step S42: performing deformation feature recognition on the stamping structure deformation abnormality data according to the abnormal parameter quantification data to obtain the structural deformation abnormality features; Step S43: performing stamping deformation abnormality tracing analysis on the structural deformation abnormality characteristics to obtain stamping deformation abnormality tracing data; Step S44: Feedback the abnormal stamping deformation traceability data to the MES system in real time, and dynamically adjust the lightweight aluminum alloy cold stamping based on the real-time feedback results.

9. The MES-based lightweight aluminum alloy cold stamping data verification method according to claim 8, characterized in that: Step S43 includes the following steps: Step S431: dividing the structural deformation abnormality characteristics into stamping process time series segments, and setting the time range of each segment to 10-30 seconds; Step S432: identifying the abnormal trend change of stamping deformation in the stamping process time series segment to obtain the abnormal trend data of stamping deformation; Step S433: determining the source of the abnormal trend of the stamping deformation abnormal trend data to obtain the abnormal trend source data; performing stamping deformation abnormality verification on the abnormal trend source data to generate a stamping deformation abnormality verification result; Step S434: Mark the stamping deformation anomaly verification result for traceability to obtain stamping deformation anomaly traceability data.

10. The MES-based lightweight aluminum alloy cold stamping data verification method according to claim 8, characterized in that: Step S44 includes the following steps: Step S441: converting the format of the stamping deformation abnormality traceability data to obtain traceability format conversion data; Step S442: Set the data transmission frequency to once every 10-30 seconds, and feed back the traceability format conversion data to the MES system in real time to obtain real-time feedback results; Step S443: determining the cold stamping operation type based on the real-time feedback result to obtain cold stamping operation type data; performing real-time monitoring of operation parameters on the cold stamping operation type data to generate real-time cold stamping operation parameters; Step S444: Dynamically adjust the lightweight aluminum alloy cold stamping parameters according to the real-time cold stamping operation parameters.

Images