Lightweight aluminum alloy cold stamping data verification method based on mes

Abstract

The present application relates to the technical field of data processing and verification, and particularly relates to a lightweight aluminum alloy cold stamping data verification method based on MES. The method comprises the following steps: collecting die manufacturing parameters, stamping strength parameters, stamping speed parameters and stamping environment parameters of lightweight aluminum alloy through an MES system; determining pressure speed benchmark data of the stamping strength parameters and the stamping speed parameters, and layering the pressure speed benchmark data according to the die manufacturing parameters to form plate pressure degree layer data. Through data processing technology, abnormality detection technology and data verification technology, the present application realizes real-time monitoring and verification of deformation structure changes of aluminum alloy cold stamping production, so as to improve verification accuracy; realizes abnormality tracing of abnormal deformation structures, so as to improve stamping quality stability, thereby improving the production efficiency of lightweight aluminum alloy cold stamping.

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 development from traditional manual stamping to the application of 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 transformation. In modern manufacturing, the manufacturing execution system (MES) optimizes the production process and controls quality by monitoring data in the production process in real time. 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. As the requirements for production efficiency and product quality gradually increase, 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-inspection, 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 object, a lightweight aluminum alloy cold stamping data verification method based on MES is provided, the method comprising the following steps:

[0005] Step S1: Collecting lightweight aluminum alloy mold manufacturing parameters, stamping strength parameters, stamping speed parameters, and stamping environment parameters through the MES system;

[0006] 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;

[0007] 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.

[0008] Step S4: stamping traceability analysis is performed on the stamping structure deformation abnormal data to identify the abnormal features of the stamping deformation abnormal data, so as to obtain stamping deformation abnormal traceability data; the stamping deformation abnormal traceability data is fed back to the MES system in real time, and the cold stamping of the lightweight aluminum alloy is dynamically adjusted based on the real-time feedback result.

[0009] The mold manufacturing parameters, stamping strength parameters, stamping speed parameters and stamping environment parameters of the lightweight aluminum alloy are collected by the MES system, so that the key data in the stamping process can be comprehensively and real-timely obtained. The stamping strength parameters and the stamping speed parameters are subjected to pressure speed benchmark data measurement, and the pressure speed benchmark data is subjected to sheet pressure degree layering according to the mold manufacturing parameters, so as to form accurate sheet pressure degree layer data; the collaborative optimization of the parameters in the stamping process is ensured, and the quality problems caused by parameter deviation are avoided, so that the quality stability of the stamped parts is significantly improved; the sheet pressure degree layer data is subjected to stamping environment change amount detection based on the stamping environment parameters, and the aluminum alloy shape parameters are real-timely obtained by the MES system. The aluminum alloy shape parameters are subjected to stamping structure deformation detection according to the stamping environment change amount, so as to generate stamping structure deformation data. This dynamic monitoring mechanism can real-timely reflect the influence of the stamping environment change on the deformation of the stamped parts, timely adjust the stamping process parameters, and ensure the stability of the stamping process under complex environment; the stamping structure deformation data is compared with the preset standard structure deformation data, so that the stamping structure deformation abnormal data that does not conform to the standard can be quickly identified. This quality detection method based on data comparison avoids the subjectivity and low efficiency of traditional manual detection, and improves the accuracy and efficiency of detection; the stamping structure deformation abnormal data is subjected to stamping traceability analysis, and the abnormal features are identified, so as to obtain stamping deformation abnormal traceability data. The traceability data is real-timely fed back to the MES system, and the cold stamping process of the lightweight aluminum alloy is dynamically adjusted based on the feedback result. This process not only realizes the accurate traceability of the stamping quality problems, but also optimizes the stamping process parameters through the dynamic adjustment function of the MES system, so as to reduce the repeated occurrence of quality problems; through the accurate data collection, analysis and feedback mechanism, the waste rate caused by stamping quality problems is reduced. At the same time, the real-time monitoring and dynamic adjustment function of the MES system optimizes the production process, improves the equipment utilization rate and production efficiency, and thus reduces the production cost. Therefore, through the data processing technology, abnormal detection technology and data verification technology, the present application realizes the real-time monitoring and verification of the deformation structure change of the aluminum alloy cold stamping production, so as to improve the verification accuracy; realizes the abnormal traceability of the abnormal deformation structure, so as to improve the quality stability of the stamped parts, and thus improves the production efficiency of the lightweight aluminum alloy cold stamping.

[0010] Preferably, step S1 comprises the following steps:

[0011] Step S11: Set the acquisition frequency of the MES system for lightweight aluminum alloy to be collected once every 10-30 seconds, set the acquisition range of dimensional accuracy to be ±0.05mm to ±0.20mm, and set the acquisition range of surface roughness to be Ra0.8-Ra2.0, to generate mold manufacturing parameters;

[0012] Step S12: Collect data at the front, middle and rear ends of the stamping stroke through the MES system, with an interval of 5-15 seconds each time, and collect the stamping force in the range of 20-50kN and the stamping stroke in the range of 50-200mm, to generate stamping strength parameters;

[0013] Step S13: Collect speed data in sections during stamping through the MES system, with an interval of 3-8 seconds each section, and collect the stamping speed in the range of 50-200mm / s, to generate stamping speed parameters;

[0014] Step S14: Real-time monitor the temperature, humidity and air pressure of the stamping workshop through the MES system, with a temperature acquisition range of 15-30°C, a humidity acquisition range of 30%-70%, and an air pressure acquisition range of 0.1-0.12MPa, to generate stamping environment parameters.

[0015] The present application realizes the standardization and dynamic correlation of stamping parameters by normalizing the stamping strength parameters and converting the stamping speed parameters into a time linear function to calculate the stamping speed change rate, providing a unified quantitative basis for the calculation of subsequent reference values; the stamping force value mapping data and the stamping speed change rate are calculated by weighted correlation, and the stamping process is fitted in sections, wherein the fitting in sections considers the start, stable and end stages of the stamping process, ensuring the accuracy of the reference data; the sheet metal pressure degree layer data is formed by layering the sheet metal pressure degree according to the mold manufacturing parameters, realizing the fine layered monitoring of the stamping process; the sheet metal pressure degree layer data is detected for stamping environment change based on the stamping environment parameters, further ensuring the stability of the stamping process; the aluminum alloy shape parameters are obtained in real time through the MES system, and the stamping structure deformation is detected based on the stamping environment change, realizing the real-time monitoring of the stamping structure deformation, and the deformation abnormality in the stamping process can be found in time;

[0016] Preferably, step S2 comprises the following steps:

[0017] Step S21: Normalize the stamping strength parameters to the range of 0.2-0.8 stamping interval to obtain stamping force value mapping data; convert the stamping speed parameters into a time linear function, and calculate the stamping speed change rate with time to obtain the stamping speed change rate;

[0018] Step S22: the stamping force value mapping data is calculated by weighted correlation with the stamping speed change rate to obtain a preliminary pressure speed reference value;

[0019] Step S23: the preliminary pressure speed reference value is segmented and fitted to divide the stamping process into a starting segment, a stable segment and an ending segment, and different fitting curve parameters are set for the segments, the fitting slope range of the starting segment is 0.2-0.5, the fitting slope range of the stable segment is 0.05-0.1, and the fitting slope range of the ending segment is 0.1-0.3, to generate pressure speed reference data;

[0020] Step S24: the pressure speed reference data is layered according to the die manufacturing parameters to form sheet pressure degree layer data;

[0021] Step S25: the sheet pressure degree layer data is detected for stamping environment change amount based on the stamping environment parameters to obtain the stamping environment change amount;

[0022] Step S26: the aluminum alloy shape parameters are obtained in real time through the MES system, and the stamping structure deformation data is generated by detecting the stamping structure deformation of the aluminum alloy shape parameters according to the stamping environment change amount.

[0023] The stamping strength parameters are normalized to stamping force value mapping data, and the stamping speed parameters are converted to a time linear function to calculate the stamping speed change rate, so that the standardization and dynamic correlation of the stamping strength and speed parameters are realized; the stamping force value mapping data is calculated by weighted correlation with the stamping speed change rate, and the preliminary pressure speed reference value is segmented and fitted to generate pressure speed reference data; the starting segment, the stable segment and the ending segment of the stamping process are considered in the segmented fitting, and the fitting slope range is set 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 die manufacturing parameters to form sheet pressure degree layer data, so that the fine layered monitoring of the stamping process is realized; the sheet pressure degree layer data is detected for stamping environment change amount based on the stamping environment parameters, so that 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 data is generated by detecting the stamping structure deformation of the aluminum alloy shape parameters according to the stamping environment change amount, so that the real-time monitoring of the stamping structure deformation is realized to provide data support for subsequent quality control and process adjustment.

[0024] Preferably, step S24 comprises the following steps:

[0025] 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;

[0026] Step S242: Merge and mark 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;

[0027] Step S243: Dividing 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;

[0028] 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;

[0029] Step S245: Based on the cavity size accuracy division data and the surface roughness division data, the pressure velocity reference data is layered into pressure levels of the aluminum alloy plate to form plate pressure level layer data.

[0030] The mold manufacturing precision is finely classified by dividing the cavity size precision of the mold manufacturing parameter 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-precision, medium-precision and low-precision cavity size ranges are combined and marked to ensure the standardization and traceability of the cavity size precision information, facilitating the adjustment and optimization of subsequent process parameters. The mold surface roughness is divided into a low-surface roughness range (Ra0.8-Ra1.2), a medium-surface roughness range (Ra1.2-Ra1.6) and a high-surface roughness range (Ra1.6-Ra2.0) to realize the hierarchical management of the mold surface roughness and provide a reference for the adjustment of subsequent process parameters; the low-surface roughness, medium-surface roughness and high-surface roughness ranges are combined and marked to generate surface roughness division data, ensuring the standardization and traceability of the surface roughness information; the aluminum alloy plate pressure degree is layered based on the cavity size precision division data and the surface roughness division data, forming plate pressure degree layer data, and the precision of the mold manufacturing parameter and the roughness are comprehensively considered to realize the accurate layering of the plate pressure degree in the stamping process.

[0031] Preferably, the step S25 comprises the following steps:

[0032] Step S251: smoothing the temperature data of the stamping environment parameter by using the moving average method, setting the window size to 10-20 data points to obtain the environment temperature processing data;

[0033] Step S252: processing the humidity data of the stamping environment parameter by using the exponential smoothing method, setting the smoothing coefficient range to 0.1-0.3 to obtain the environment humidity processing data;

[0034] Step S253: processing the air pressure data of the stamping environment parameter by using the median smoothing method, setting the window size to 5-10 data points to obtain the environment air pressure processing data;

[0035] Step S254: calculating the temperature difference value of the environment temperature processing data, and setting the temperature difference change amount to ±2°C to ±5°C; calculating the humidity difference value of the environment temperature processing data, and setting the humidity difference change amount to ±5% to ±15%; calculating the air pressure difference value of the environment temperature processing data, and setting the air pressure difference change amount to ±0.01MPa to ±0.03MPa;

[0036] Step S255: If the temperature variation is within ±2°C, the humidity variation is within ±5%, and the air pressure variation is within ±0.01 MPa, mark as the environment low 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 ±0.02 MPa to ±0.03 MPa, mark as the environment medium variation level; if the temperature variation exceeds ±5°C, the humidity variation exceeds ±15%, and the air pressure variation exceeds ±0.03 MPa, mark as the environment high variation level.

[0037] Step S256: According to the environment low variation level, the environment medium variation level, and the environment high variation level, stamping environment variation mapping is performed on the panel pressure level layer data to obtain the stamping environment variation.

[0038] The present application adopts the moving average method to smooth the temperature data of the stamping environment parameters, sets the window size to 10-20 data points to obtain the environment 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 environment humidity processing data; adopts the median smoothing method to process the air pressure data, and the window size is 5-10 data points to obtain the environment air pressure processing data; these smoothing processing methods effectively reduce the noise of the environment parameter data, and improve the stability and availability of the data. The difference value of the environment temperature, humidity and air pressure processing data is calculated respectively, and the temperature difference value variation is set to ±2°C to ±5°C, the humidity difference value variation is set to ±5% to ±15%, and the air pressure difference value variation is set to ±0.01 MPa to ±0.03 MPa; the variation of the stamping environment parameters is quantified to a specific range, which provides a clear basis for subsequent level division. According to the variation range of the temperature, humidity and air pressure, the stamping environment variation is divided into low, medium and high three levels. The low variation level corresponds to the temperature variation within ±2°C, the humidity variation within ±5%, and the air pressure variation within ±0.01 MPa; the medium variation level corresponds to the temperature variation of ±3°C to ±5°C, the humidity variation of ±10% to ±15%, and the air pressure variation of ±0.02 MPa to ±0.03 MPa; the high variation level corresponds to the temperature variation exceeding ±5°C, the humidity variation exceeding ±15%, and the air pressure variation exceeding ±0.03 MPa, which can clearly reflect the variation degree of the stamping environment. According to the environment low, medium and high variation levels, the stamping environment variation mapping is performed on the panel pressure level layer data, the stamping environment variation is combined with the panel pressure level layer data, and the precise quantification of the influence of the stamping environment variation on the stamping quality is realized.

[0039] Preferably, step S26 comprises the following steps:

[0040] Step S261: Set acquisition points at the front end, middle end, rear end and edge part of the aluminum alloy through the MES system, and acquire the cross-sectional size, thickness parameter and surface flatness of the aluminum alloy respectively to obtain the shape parameter of the aluminum alloy;

[0041] Step S262: Detect the aluminum alloy environment-affected part according to the stamping environment change amount, obtain the affected area of the aluminum alloy, determine the cold stamping stress position of the affected area, and generate the affected stress position;

[0042] Step S263: Detect the stress uniformity of the affected stress position, determine the structure affected degree according to the stress uniformity, and generate the stamping structure affected degree;

[0043] Step S264: Record the structure deformation parameter of the stamping structure affected degree, and generate the stamping structure deformation data.

[0044] The present application sets acquisition points at the front end, middle end, rear end and edge part of the aluminum alloy, respectively acquires the cross-sectional size, thickness parameter and surface flatness, can fully cover the key parts of the aluminum alloy, ensures the completeness and representativeness of the collected data, and provides a basis for subsequent detection and analysis; according to the stamping environment change amount, the shape parameter of the aluminum alloy is detected, the affected area of the aluminum alloy is determined, the cold stamping stress position of the affected area is determined, the precise positioning of the environment-affected area of the aluminum alloy is realized, the indiscriminate detection of the entire plate is avoided, and the detection efficiency and accuracy are improved; the stress uniformity of the affected stress position is detected, the structure affected degree is determined according to the stress uniformity, the stress uniformity is quantified, the structure change of the aluminum alloy in the stamping process can be clearly reflected, and a scientific basis is provided for subsequent process adjustment and quality control; the structure deformation parameter of the stamping structure affected degree is recorded, the dynamic monitoring of the deformation of the aluminum alloy in the stamping process is realized, and potential quality problems can be found in time.

[0045] Preferably, step S3 comprises the following steps:

[0046] Step S31: Divide the structure deformation position data of the stamping structure deformation data into a stamping stretching area, a stamping bending area and a stamping flanging area to generate structure deformation position data;

[0047] Step S32: Set the deformation index of the structure deformation position data, specifically set the cross-sectional size standard range to ±0.05mm to ±0.10mm; set the thickness standard range to ±0.02mm to ±0.03mm; set the surface flatness standard range to Ra0.5 to Ra1.0;

[0048] Step S33: merging the cross-sectional size standard range, the thickness standard range, and the surface flatness standard range to obtain preset standard structure deformation data;

[0049] Step S34: comparing the stamping structure deformation data with the preset standard structure deformation data, if the cross-sectional size change is within ±0.05mm, the thickness change is within ±0.02mm, and the surface flatness change is within Ra0.5, then it is marked as stamping structure slight deformation data; if the cross-sectional size change is between ±0.05mm and ±0.10mm, the thickness change is between ±0.02mm and ±0.03mm, and the surface flatness change is between Ra0.5 and Ra1.0, then it is marked as stamping structure moderate deformation; if the cross-sectional size change exceeds ±0.10mm, the thickness change exceeds ±0.03mm, and the surface flatness change exceeds Ra1.0, then it is marked as stamping structure severe deformation.

[0050] Step S35: mapping the stamping structure deformation anomaly level based on the stamping structure slight deformation, the stamping structure moderate deformation, and the stamping structure severe deformation to obtain stamping structure deformation anomaly data.

[0051] The present application divides the stamping structure deformation data by position, clearly distinguishes the stamping stretching area, the stamping bending area, and the stamping flanging area, can accurately locate the specific area where deformation occurs, provides clear regional basis for subsequent deformation analysis and quality control; sets the cross-sectional size standard range (±0.05mm to ±0.10mm), the thickness standard range (±0.02mm to ±0.03mm), and the surface flatness standard range (Ra0.5 to Ra1.0) for the structure deformation position data; these standardized deformation indicators provide clear quantitative standards for subsequent deformation data comparison, ensure the objectivity and consistency of deformation evaluation. The standard ranges of cross-sectional size, thickness, and surface flatness are merged to generate preset standard structure deformation data; this process establishes a unified comparison benchmark, provides standardized reference for the classification and evaluation of stamping structure deformation data, 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 of slight deformation, moderate 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 realized, which realizes the accurate identification and classification of stamping structure deformation anomaly, provides data support for real-time feedback and dynamic adjustment of MES system, helps to optimize the stamping process and improve product quality.

[0052] Preferably, step S4 comprises the following steps:

[0053] Step S41: performing deformation abnormality type detection on the stamping structure deformation abnormality data to obtain deformation abnormality type data; performing abnormality parameter quantification on the deformation abnormality type data to generate abnormal parameter quantification data;

[0054] 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 feature;

[0055] Step S43: performing stamping deformation anomaly tracing analysis on the structural deformation anomaly characteristics to obtain stamping deformation anomaly tracing data;

[0056] Step S44: Feedback 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.

[0057] The present invention detects the type of deformation anomaly on the data of abnormal deformation of stamped structures 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 present invention identifies the deformation characteristics of the abnormal deformation data of stamped structures based on the quantified data of abnormal parameters. Through the feature identification driven by quantitative data, the specific characteristics of the deformation anomaly can be accurately located. The source analysis of the stamping deformation anomaly on the structural deformation anomaly characteristics can clarify the root cause of the deformation anomaly. 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, realizing a rapid response to quality issues and dynamic optimization of process parameters, ensuring the stability of the stamping process and the reliability of product quality.

[0058] Preferably, step S43 includes the following steps:

[0059] Step S431: Divide the structural deformation abnormality characteristics into stamping process time series segments, and set the time range of each segment to 10-30 seconds;

[0060] Step S432: identifying abnormal stamping deformation trend changes in the stamping process time series segment to obtain abnormal stamping deformation trend data;

[0061] 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;

[0062] Step S434: Mark the stamping deformation anomaly verification result for traceability to obtain stamping deformation anomaly traceability data.

[0063] The present invention divides the structural deformation anomaly characteristics into stamping process time series segments, sets the time range of each segment to 10-30 seconds, and can subdivide the stamping process into multiple time periods, and perform segmented analysis on the deformation anomaly characteristics. The stamping deformation anomaly trend change is identified for the stamping process time series segment. By segmented identification of the deformation anomaly trend, the dynamic changes of the deformation anomaly in different time periods can be clearly captured, providing key information for determining the source of the anomaly. The source of the stamping deformation anomaly trend data is determined, and the stamping deformation anomaly is verified for the abnormal trend source data to generate the stamping deformation anomaly verification result. 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 anomaly verification result is traceably marked. The traceability mark can clearly record the source of the deformation anomaly and the verification result, 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.

[0064] Preferably, step S44 includes the following steps:

[0065] Step S441: converting the stamping deformation abnormality traceability data into a format to obtain traceability format converted data;

[0066] Step S442: Set the data transmission frequency to once every 10-30 seconds, and feed back the traceability format converted data to the MES system in real time to obtain real-time feedback results;

[0067] 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;

[0068] Step S444: Dynamically adjust the lightweight aluminum alloy cold stamping parameters according to the real-time cold stamping operation parameters.

[0069] 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 operating parameters of the cold stamping operation type data are monitored in real time, realizing the accurate identification of the cold stamping operation type and the real-time monitoring of the operating parameters, providing an accurate basis for subsequent parameter adjustment, and 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

[0070] Figure 1 The figure is a flowchart of a lightweight aluminum alloy cold stamping data verification method based on MES;

[0071] Figure 2 for Figure 1 Detailed implementation steps of step S2 in FIG.

[0072] Figure 3 for Figure 2 Detailed implementation steps of step S24 are shown in the flowchart;

[0073] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings and in conjunction with the embodiments. DETAILED DESCRIPTION

[0074] The following is a clear and complete description of the technical method of the present invention in conjunction with the accompanying drawings. It is obvious that the embodiments described are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making any creative efforts are within the scope of protection of the present invention.

[0075] Further, the accompanying drawings are included to provide a further understanding of the present application, and are incorporated in and constitute a part of this specification. The drawings illustrate embodiments of the present application and, together with the description, serve to explain the principles of the present application. In the drawings:

[0076] It is to be understood that, although terms such as "first", "second", and so on can be used herein to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another. For example, without departing from the scope of the exemplary embodiments, a first element can be referred to as a second element, and similarly a second element can be referred to as a first element. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0077] To achieve the above object, there is provided Figures 1 to 3 A method for verifying cold stamping data of lightweight aluminum alloy based on MES, the method comprising the following steps:

[0078] Step S1: Collecting mold manufacturing parameters, stamping strength parameters, stamping speed parameters and stamping environment parameters of lightweight aluminum alloy through an MES system;

[0079] Step S2: Determining pressure speed reference data of the stamping strength parameters and the stamping speed parameters, and layering the pressure speed reference data according to the mold manufacturing parameters to form sheet pressure degree layer data; detecting stamping environment change quantity of the sheet pressure degree layer data based on the stamping environment parameters, and acquiring aluminum alloy shape parameters in real time through the MES system; detecting stamping structure deformation of the aluminum alloy shape parameters according to the stamping environment change quantity to generate stamping structure deformation data;

[0080] Step S3: Comparing the stamping structure deformation data with preset standard structure deformation data, and if the stamping structure deformation data does not conform to the preset standard structure deformation data, marking it as stamping structure deformation abnormal data;

[0081] Step S4: Performing stamping traceability analysis on the stamping structure deformation abnormal data, identifying abnormal features of the stamping deformation abnormal data to obtain stamping deformation abnormal traceability data; feeding back the stamping deformation abnormal traceability data to the MES system in real time, and dynamically adjusting the cold stamping of the lightweight aluminum alloy based on the real-time feedback result.

[0082] The application can comprehensively and real-timely acquire key data in the stamping process by collecting the mold manufacturing parameters, the stamping strength parameters, the stamping speed parameters and the stamping environment parameters of the lightweight aluminum alloy through the MES system. The stamping strength parameters and the stamping speed parameters are subjected to pressure speed benchmark data measurement, and the pressure speed benchmark data is subjected to plate pressure degree layering according to the mold manufacturing parameters, so as to form accurate plate pressure degree layer data. The collaborative optimization of various parameters in the stamping process is ensured, and quality problems caused by parameter deviation are avoided, so as to significantly improve the quality stability of the stamped part. The plate pressure degree layer data is subjected to stamping environment change amount detection based on the stamping environment parameters, and the aluminum alloy shape parameters are real-timely acquired through the MES system. The aluminum alloy shape parameters are subjected to stamping structure deformation detection according to the stamping environment change amount, so as to generate stamping structure deformation data. This dynamic monitoring mechanism can real-timely reflect the influence of stamping environment change on the deformation of the stamped part, timely adjust the stamping process parameters, and ensure the stability of the stamping process under complex environment. The stamping structure deformation data is compared with the preset standard structure deformation data, so as to quickly identify abnormal stamping structure deformation data that does not conform to the standard. This quality detection mode based on data comparison avoids the subjectivity and low efficiency of traditional manual detection, and improves the accuracy and efficiency of detection. The abnormal stamping structure deformation data is subjected to stamping traceability analysis, abnormal features are identified, and stamping deformation abnormal traceability data is obtained. The traceability data is real-timely fed back to the MES system, and the lightweight aluminum alloy cold stamping process is dynamically adjusted based on the feedback result. This process not only realizes the accurate traceability of stamping quality problems, but also optimizes the stamping process parameters through the dynamic adjustment function of the MES system, so as to reduce the repeated occurrence of quality problems. Through the accurate data acquisition, analysis and feedback mechanism, the waste rate caused by stamping quality problems is reduced. At the same time, the real-time monitoring and dynamic adjustment function of the MES system optimizes the production process, improves the equipment utilization rate and production efficiency, and thus reduces the production cost. Therefore, the application realizes the real-time monitoring and verification of the deformation structure change of the aluminum alloy cold stamping production through data processing technology, abnormal detection technology and data verification technology, so as to improve the verification accuracy, realizes the abnormal traceability of the abnormal deformation structure, so as to improve the quality stability of the stamped part, and thus improves the production efficiency of the lightweight aluminum alloy cold stamping.

[0083] In the embodiment of the application, as shown in FIG. 1, Figure 1 The MES-based lightweight aluminum alloy cold stamping data verification method includes the following steps:

[0084] Step S1: collecting the mold manufacturing parameters, the stamping strength parameters, the stamping speed parameters and the stamping environment parameters of the lightweight aluminum alloy through the MES system;

[0085] In the mold manufacturing process, the material composition analyzer installed on the mold processing equipment is used to detect the alloy composition of the mold material in real time by using spectral analysis technology. The analyzer converts the detected spectral signal into an electrical signal and transmits it to the MES system through an industrial Ethernet, and stores it as mold material attribute data. After the mold processing is completed, the key dimensions of the mold are measured with high precision by using a three-coordinate measuring machine (CMM). The measuring machine obtains the mold size data by laser scanning or contact detection, and transmits the data in numerical form to the MES system through an RS232 interface, and stores it as mold size precision data. The mold surface is scanned using an optical profilometer, 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 through a USB interface and stored as mold surface roughness data. During the mold heat treatment process, the temperature sensor and time recorder installed in the heat treatment furnace are used to collect the quenching temperature, tempering temperature and corresponding holding time in real time. The sensor collects temperature data at a millisecond level and transmits it to the MES system through the Modbus protocol, and stores it as mold heat treatment parameter data. A high-precision pressure sensor is installed at the contact position of the punch and the mold of the stamping equipment. The sensor uses a strain gauge pressure sensor, which can convert the stamping force into an electrical signal. The sensor collects 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 uses a magnetostrictive displacement sensor, which can monitor the up and down movement distance of the punch in real time. The sensor collects 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 uniform distribution of speckle patterns is sprayed on the surface of the aluminum alloy plate, and two high-speed industrial cameras are used to shoot the deformation images of the plate during stamping from different angles. Through the DIC (digital image correlation method) analysis system, the collected image data is processed to calculate the strain distribution on the surface of the plate. 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 driving device of the stamping equipment. The sensor uses an optical speed sensor, which can monitor the movement speed of the punch at different stages (fast approach, slow stamping, and return). 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 uses a semiconductor temperature and humidity sensor, which can monitor the environmental 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 environmental temperature data. The same as the environmental temperature collection sensor, the environmental humidity in the workshop is monitored in real time by a semiconductor temperature and humidity sensor.The sensor collects humidity data at a sampling frequency of 1 Hz and transmits the data to the MES system through a wireless ZigBee protocol and stores the data as environmental humidity data.

[0086] In step S2, the stamping strength parameter and the stamping speed parameter are subjected to pressure speed reference data measurement, the plate pressure degree is layered according to the die manufacturing parameter, the plate pressure degree layer data is formed, the stamping environment change quantity of the plate pressure degree layer data is detected based on the stamping environment parameter, the aluminum alloy shape parameter is acquired in real time through the MES system, the stamping structure deformation of the aluminum alloy shape parameter is detected according to the stamping environment change quantity, and the stamping structure deformation data is generated.

[0087] In the embodiment of the present application, the MES system is used to determine the pressure-velocity 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, and combining the stamping stroke data, the pressure-velocity reference curve of different stamping stages is determined. The curve takes the stamping force as the vertical axis and the stamping speed as the horizontal axis, is generated by a mathematical fitting method, and is stored in the process database of the MES system. According to the mold manufacturing parameters (such as mold material properties, dimensional accuracy, surface roughness and heat treatment parameters), the pressure-velocity reference data is layered according to the sheet pressure level. Through the data analysis module in the MES system, the mold manufacturing parameters are associated with the pressure-velocity reference data for correlation analysis, and according to the material properties and machining accuracy of the mold, the pressure-velocity reference data is divided into different sheet pressure level layers. For example, for high-precision molds, a lower pressure threshold is set; for ordinary precision molds, a higher pressure threshold is set, thereby forming sheet pressure level data. Based on the stamping environmental parameters (environmental temperature and humidity), the sheet pressure level data is detected for stamping environmental change. Through the MES system, the changes in environmental temperature and humidity in the stamping workshop are monitored in real time, and these data are compared and analyzed with the sheet pressure level data. Using the environmental change detection algorithm, the influence of environmental parameter changes on the sheet pressure level is calculated. For example, when the environmental temperature rises, the yield strength of the material decreases, so the pressure-velocity reference curve needs to be adjusted. The MES system acquires aluminum alloy shape parameters in real time, including the initial shape, size of the sheet, and deformation after stamping. Using the digital image correlation (DIC) technology, the aluminum alloy sheet during stamping is monitored in real time to obtain its shape change data. These data are collected by a high-speed camera and transmitted to the MES system, and stored as aluminum alloy shape parameters. According to the stamping environmental change, the aluminum alloy shape parameters are detected for stamping structural deformation. Through the deformation detection module in the MES system, the environmental change and the aluminum alloy shape parameters are combined to calculate the structural deformation during stamping. Using the finite element analysis method, the stress and strain distribution of the sheet during stamping is simulated to generate stamping structural deformation data. The data is stored in the MES system in the form of a three-dimensional model or a two-dimensional curve, which is used for subsequent quality analysis and process optimization.

[0088] Step S3: comparing the stamping structural deformation data with the preset standard structural deformation data, if the stamping structural deformation data does not conform to the preset standard structural deformation data, marking it as stamping structural deformation abnormal data;

[0089] In the embodiments of the present application, first, stamping structure deformation data is called from the process synthesis database. These data are obtained by simulating the stress and strain distribution in the stamping process through finite element analysis method and stored in the form of three-dimensional model or two-dimensional curve. At the same time, corresponding standard structure deformation data is obtained from the system preset standard structure deformation database. These standard data are preset based on historical process data and material performance characteristics and 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 checking key parameters such as deformation degree, stress distribution, strain rate one by one. The specific operation is as follows: the deformation degree in the stamping structure deformation data and the corresponding value in the standard data are calculated by difference. If the difference exceeds the preset allowable error range (for example, the deformation degree deviation exceeds 5%), it is determined that the data point is abnormal. For stress distribution parameters, similarity analysis method is adopted to compare the stress distribution graph in the stamping structure deformation data with the standard stress distribution graph and calculate the similarity coefficient of the two. If the similarity coefficient is lower than the set threshold (for example, lower than 0.95), the data is marked as stamping structure deformation abnormal data. Finally, the MES system summarizes all the comparison results and stores the data marked as abnormal in the abnormal database, and generates a detailed abnormal report for subsequent process adjustment and quality analysis.

[0090] Step S4: stamping traceability analysis is performed on the stamping structure deformation abnormal data to identify the abnormal characteristics of the stamping deformation abnormal data to obtain stamping deformation abnormal traceability data; the stamping deformation abnormal traceability data is fed back to the MES system in real time, and the cold stamping of lightweight aluminum alloy is dynamically adjusted based on the real-time feedback result.

[0091] In the embodiment of the present application, data marked as stamping structure deformation anomaly is extracted from the MES system, including 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 characteristics of the abnormal data, and the simulation function of the stamping analysis software JSTAMP is combined to simulate and analyze the stamping process. The whole process of sheet metal stamping is simulated by JSTAMP software, including self-weight deflection of sheet metal, deep drawing forming, blanking, flanging and other links, and the positions and degrees of sheet metal rupture, wrinkling, bulging and springback are accurately predicted. According to the simulation results, abnormal characteristics such as stress concentration area, deformation out-of-tolerance position or the influence of stamping environment change on deformation are identified. The traced data of the identified stamping deformation anomaly 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 by using a dynamic adjustment algorithm. The dynamic adjustment algorithm is based on a support vector machine regression model and an improved particle swarm optimization algorithm, and can optimize the multivariate nonlinear relationship between stamping process parameters and forming quality. For example, when it is detected that the stamping structure deformation anomaly is caused by stress concentration due to too fast stamping speed, the MES system will automatically reduce the stamping speed; if the anomaly is caused by material performance fluctuation due to environmental temperature change, the stamping pressure or die temperature is adjusted for compensation. The MES system transmits the adjusted process parameters to the stamping equipment control system in real time, realizes the dynamic adjustment of the cold stamping process of lightweight aluminum alloy, and ensures the stability of the stamping process and the product quality, reduces the dependence on manual experience, and improves the production efficiency.

[0092] Preferably, step S1 comprises the following steps:

[0093] Step S11: setting the acquisition frequency of the MES system for lightweight aluminum alloy to be collected once every 10-30 seconds, setting the acquisition range of dimensional accuracy to be ±0.05mm to ±0.20mm, and setting the acquisition range of surface roughness to be Ra0.8-Ra2.0, to generate the mold manufacturing parameters;

[0094] Step S12: collecting data at the front end, middle end and rear end of the stamping stroke by the MES system, with an interval of 5-15 seconds each time, collecting the stamping force in the range of 20-50kN, and collecting the stamping stroke in the range of 50-200mm, to generate the stamping strength parameters;

[0095] Step S13: collecting speed data in sections during the stamping process by the MES system, with an interval of 3-8 seconds for each section, and collecting the stamping speed in the range of 50-200mm / s, to generate the stamping speed parameters;

[0096] Step S14: Real-time monitoring of the temperature, humidity and air pressure of the stamping workshop through the MES system, with the temperature collection range being 15-30°C, the humidity collection range being 30%-70%, and the air pressure collection range being 0.1-0.12 MPa, to generate the stamping environment parameters.

[0097] In the embodiment of the present application, the collection frequency of the lightweight aluminum alloy in the MES system is set to be collected once every 10-30 seconds. For the dimensional accuracy of the mold, a high-precision three-coordinate measuring machine (CMM) is used for measurement, and the collection range is set to be ±0.05 mm to ±0.20 mm. The collection of surface roughness is carried out by an optical profilometer, and the collection range is set to be Ra0.8 to Ra2.0. The collected dimensional accuracy and surface roughness data are integrated by the MES system to generate mold manufacturing parameters and stored in the process database of the MES system. The data collection is carried out at the front end, middle end and rear end of the stamping stroke through the MES system, and the collection interval is set to be 5-15 seconds each time. The collection range of the stamping force is set to be 20-50 kN, and the collection range of the stamping stroke is set to be 50-200 mm. The high-precision pressure sensor and displacement sensor installed on the stamping equipment are used to collect the stamping force and stamping stroke data, respectively. The collected data is transmitted and stored in real time by the MES system to generate the stamping strength parameters. In the MES system, the speed data in the stamping process is collected in sections, and the collection interval is set to be 3-8 seconds per section. The collection range of the stamping speed is set to be 50-200 mm / s. The speed sensor installed on the stamping equipment is used to monitor the movement speed of the punch in different stages (fast approach, slow stamping and return). The collected speed data is transmitted and stored by the MES system to generate the stamping speed parameters. The temperature, humidity and air pressure of the stamping workshop are monitored in real time by the MES system. The temperature collection range is set to be 15-30°C, the humidity collection range is set to be 30%-70%, and the air pressure collection range is set to be 0.1-0.12 MPa. The temperature, humidity and air pressure sensors are installed in the workshop to collect the environmental parameters in real time, and the data is transmitted to the MES system. The collected environmental parameters are stored in association with the stamping process parameters to generate the stamping environment parameters.

[0098] As an example of the present application, reference is made to Fig. 1, which shows a schematic diagram of a system for monitoring the stamping process according to the present application. In this example, the step S2 comprises: Figure 2

[0099] Step S21: mapping the stamping strength parameters to the 0.2-0.8 stamping interval range for normalization to obtain stamping force value mapping data; converting the stamping speed parameters into a time linear function and calculating the stamping speed variation parameter with time to obtain the stamping speed change rate;

[0100] ​Step S22: The stamping force value mapping data is calculated by weighting correlation with the stamping speed change rate to obtain a preliminary pressure speed reference value;

[0101] Step S23: The preliminary pressure speed reference value is segmented and fitted to divide the stamping process into a starting segment, a stable segment and an ending segment, and different fitting curve parameters are set for each segment. The fitting slope of the starting segment ranges from 0.2 to 0.5, the fitting slope of the stable segment ranges from 0.05 to 0.1, and the fitting slope of the ending segment ranges from 0.1 to 0.3 to generate pressure speed reference data;

[0102] Step S24: The pressure speed reference data is layered according to the die manufacturing parameters to form sheet pressure level layer data;

[0103] Step S25: The sheet pressure level layer data is detected for stamping environment change amount based on the stamping environment parameters to obtain the stamping environment change amount;

[0104] Step S26: The aluminum alloy shape parameters are obtained in real time through the MES system, and the stamping structure deformation data is generated by detecting the stamping structure deformation of the aluminum alloy shape parameters according to the stamping environment change amount.

[0105] 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: the punching force value is scaled to the specified interval by the data processing module in the MES system. 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 punching speed change rate = (current speed - previous moment speed) / time interval, and the processed data is stored as the punching speed change rate. The punching force value mapping data and the punching speed change rate are weightedly associated with each other. Using the data analysis module in the MES system, the weight of the punching force mapping data was set to 0.6, and the weight of the punching speed change rate was set to 0.4. The weighted sum of the two was calculated to obtain the preliminary pressure-speed benchmark value. The calculation formula is: preliminary pressure-speed benchmark value = punching force mapping data × 0.6 + punching speed change rate × 0.4. The preliminary pressure-speed benchmark value was segmented and fitted, dividing the stamping process into the starting segment, the stable segment, and the ending segment. In the starting segment, the fitting slope range was set to 0.2-0.5; in the stable segment, the fitting slope range was set to 0.05-0.1; in the ending segment, the fitting slope range was set to 0.1-0.3. Using the fitting algorithm in the MES system, curve fitting was performed on each segment of data to generate the pressure-speed benchmark data. The pressure-velocity benchmark data is layered based on mold manufacturing parameters (including dimensional accuracy and surface roughness). Using the data classification module in the MES system, combined with the mold manufacturing parameter acquisition range (dimensional accuracy ±0.05mm to ±0.20mm, surface roughness Ra0.8 to Ra2.0), the pressure-velocity benchmark data is divided into different pressure levels, generating sheet metal pressure level layer data. The sheet metal pressure level layer data is then tested for stamping environmental changes based on stamping environmental parameters (temperature, humidity, and air pressure). The MES system monitors the press shop's environmental parameters in real time (temperature 15-30°C, humidity 30%-70%, and air pressure 0.1-0.12MPa) and calculates the impact of these changes on sheet metal pressure levels. The calculation formula is: stamping environmental change = (current environmental parameter value - standard environmental parameter value) / standard environmental parameter value × 100%. The MES system also acquires aluminum alloy shape parameters in real time, including the sheet metal's initial shape and dimensions, as well as post-stamping deformation.Digital image correlation (DIC) technology is used to monitor aluminum alloy sheets during the stamping process in real time. 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, correlation calculations are performed to find the area with the largest correlation coefficient with the sub-area and determine the displacement of the sub-area. Calculations are performed on all sub-areas to obtain full-field deformation information. The shape parameters of the aluminum alloy are adjusted according to the changes in the stamping environment to generate stamping structure deformation data.

[0106] As an example of the present invention, refer to Figure 3 As shown, in this example, step S24 includes:

[0107] 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;

[0108] Step S242: Merge and mark 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;

[0109] Step S243: Dividing 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;

[0110] 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;

[0111] Step S245: Based on the cavity size accuracy division data and the surface roughness division data, the pressure velocity reference data is layered into pressure levels of the aluminum alloy plate to form plate pressure level layer data.

[0112] In the embodiment of the present application, the data processing module in the MES system divides the cavity size precision in the mold manufacturing parameters into ranges; according to the specific value of the cavity size precision, if the cavity size precision range is ±0.05mm to ±0.10mm, it is marked as a high-precision cavity size range; if the cavity size precision range is ±0.10mm to ±0.15mm, it is marked as a medium-precision cavity size range; if the cavity size precision 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, the data classification module in the MES system integrates the above-mentioned divided cavity size precision ranges into cavity size precision division data, and stores them in the process database of the MES system; the MES system divides the surface roughness in the mold manufacturing parameters into ranges, 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, the medium-surface roughness range and the high-surface roughness range are combined and marked, the data classification module in the MES system integrates the above-mentioned divided surface roughness ranges into surface roughness division data, and stores them in the process database of the MES system; based on the cavity size precision division data and the surface roughness division data, the aluminum alloy plate part pressure degree is layered according to the pressure speed reference 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, which is divided 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, which is divided 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, which is divided into a low-precision pressure layer.

[0113] Especially important is that step S245 comprises the following steps:

[0114] Step S2451: The cavity size precision division data is respectively extracted from the size deviation, the shape tolerance and the position precision three directions to obtain the cavity size precision feature data;

[0115] Step S2452: The cavity size precision feature data is calculated to obtain the cavity curvature change rate; the cavity curvature change rate is mapped to the cavity size precision feature data to obtain the size deviation distribution feature;

[0116] 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;

[0117] Step S2454: Correlating the size deviation distribution features with the surface roughness texture features to obtain deviation-roughness correlation data;

[0118] Step S2455: detecting the aluminum alloy pressure position based on the pressure-velocity reference data to obtain the aluminum alloy pressure position; determining the plate pressure parameter based on the deviation-roughness correlation data to generate the plate pressure parameter;

[0119] 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;

[0120] Step S2457: Layer the pressure level data according to the pressure degree of the aluminum alloy plate to obtain plate pressure degree layer data.

[0121] In the embodiment of the present application, the data analysis module in the MES system is used to perform multi-dimensional feature extraction on the cavity size precision division data. The specific operation is as follows: a high-precision three-coordinate measuring instrument (CMM) is used to collect the size data of the cavity, and the deviation of the size data from the design value is calculated to obtain the size deviation feature; an optical measuring device is used to scan the shape of the cavity, and the 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 the position deviation is calculated to obtain the position precision feature. The mathematical calculation module in the MES system is used to perform differential processing on the geometric data of the cavity surface, and the curvature change rate is calculated. 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 a plurality of small regions, and the area of each region is about 10mm*10mm. The histogram equalization technique is used to enhance the local contrast of the surface roughness data of each small region. The enhanced surface roughness texture feature is extracted by the gray level co-occurrence matrix (GLCM) method. The size deviation distribution feature and the surface roughness texture feature are fused. The data association module in the MES system is used to associate the two features based on the position information of the cavity to generate deviation-roughness association data. The position detection module in the MES system is used to determine the pressure position of the aluminum alloy plate in combination with the pressure sensor data. Based on the deviation-roughness association data, the pressure parameters of the pressure position are analyzed to generate plate pressure parameters. The plate pressure parameters are converted into standardized pressure values to eliminate the differences between different sensors. According to the preset pressure range (such as 0-10kN for low level, 10-20kN for medium level, and 20kN or more for high level), the pressure values are classified into different levels. According to the pressure level data, the aluminum alloy plate is divided into different pressure levels; the data layering module in the MES system is used to divide the plate into high, medium and low pressure levels to generate plate pressure level data.

[0122] Preferably, step S25 comprises the following steps:

[0123] Step S251: using the moving average method to smooth the temperature data of the stamping environment parameters, setting the window size to 10-20 data points to obtain the environment temperature processing data;

[0124] Step S252: using the exponential smoothing method to process the humidity data of the stamping environment parameters, setting the smoothing coefficient range to 0.1-0.3 to obtain the environment humidity processing data;

[0125] 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 environment air pressure processing data;

[0126] Step S254: Calculate the temperature difference of the processed ambient temperature data, and set the temperature difference change to ±2°C to ±5°C; calculate the humidity difference of the processed ambient temperature data, and set the humidity difference change to ±5% to ±15%; calculate the air pressure difference of the processed ambient temperature data, and set the air pressure difference change to ±0.01MPa to ±0.03MPa;

[0127] 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;

[0128] Step S256: mapping the stamping environment variation to the sheet metal 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.

[0129] In the embodiment of the present application, the 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 calculate the moving window average of the continuously collected temperature data through the data processing module in the MES system. For example, if the window size is 15 data points, then each smoothed temperature value is the arithmetic mean of the 15 consecutive temperature data in the window. The processed data is stored as environment 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. The data analysis module in the MES system is used to apply the exponential smoothing formula to the humidity data, and the calculation formula is: smoothed humidity value = smoothing coefficient x current humidity value + (1-smoothing coefficient) x last time smoothing value. 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 is used to calculate the moving window median of the continuously collected air pressure data. For example, if the window size is 8 data points, then each smoothed air pressure value is the median of the 8 consecutive air pressure data in the window. The processed data is stored as environment air pressure processing data. The temperature difference value of the environment temperature processing data is calculated, and the temperature difference change range is set to ±2°C to ±5°C. The humidity difference value of the environment humidity processing data is calculated, and the humidity difference change range is set to ±5% to ±15%. The air pressure difference value of the environment air pressure processing data is calculated, and the air pressure difference change range is set to ±0.01MPa to ±0.03MPa. The data difference module in the MES system is used to calculate the difference between adjacent time points and determine 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, then it is marked as the environment low 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, then it is marked as the environment medium change level. If the temperature change exceeds ±5°C, the humidity change exceeds ±15%, and the air pressure change exceeds ±0.03MPa, then it is marked as the environment high change level. The marking result is stored in the environment change database in the MES system. According to the environment low change level, the environment medium change level, and the environment high change level, the stamping environment change value is mapped to the sheet pressure level data. The data mapping module in the MES system is used to associate the environment change level with the sheet pressure level data to generate the stamping environment change value. For example, for the environment low change level, the sheet pressure level data remains unchanged. For the environment medium change level, the pressure threshold is adjusted appropriately. For the environment high change level, the pressure and speed parameters are further adjusted.

[0130] Preferably, step S26 comprises the following steps:

[0131] Step S261: using the MES system to set collection points at the front end, middle end, rear end, and edge of the aluminum alloy, and respectively collect the aluminum alloy cross-sectional dimensions, aluminum alloy thickness parameters, and aluminum alloy surface flatness to obtain the aluminum alloy shape parameters;

[0132] Step S262: detecting the aluminum alloy environment-affected area based on the aluminum alloy shape parameters according to the stamping environment change to obtain the aluminum alloy affected area; determining the cold stamping stress position of the aluminum alloy affected area to generate the affected stress position;

[0133] Step S263: performing stress uniformity detection on the affected stress position, and determining the degree of structural impact based on the stress uniformity, thereby generating the degree of impact on the stamping structure;

[0134] Step S264: Record the structural deformation parameters of the impact degree of the stamping structure to generate stamping structure deformation data.

[0135] In the embodiment of the present application, the MES system is used to set acquisition points at the front end, middle end, rear end and edge of the aluminum alloy plate to acquire the cross-sectional size, thickness parameter and surface flatness of the aluminum alloy to obtain the aluminum alloy shape parameter. Specifically, a high-precision laser measuring instrument and an ultrasonic thickness gauge are installed at the specified position of the aluminum alloy plate to measure the cross-sectional size and thickness parameter, respectively. An optical profiler or a contact probe is used to measure the surface flatness, and the data is collected and transmitted to the MES system. The acquired cross-sectional size, thickness and surface flatness data are stored in the process database of the MES system to form the aluminum alloy shape parameter. The aluminum alloy shape parameter is detected at the aluminum alloy environmental influence position according to the stamping environmental change amount 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 parameter according to the stamping environmental change amount (temperature, humidity, and pressure change amount) to identify the area where the size deviation or flatness change is caused by environmental change. The stress distribution of the affected area is simulated by using a finite element analysis software (such as Dynaform) to determine the stress concentration position. The affected area and the stress concentration position are marked and stored in the MES system to generate the affected stress position data. The stress uniformity of the affected stress position is detected, and the structure affected degree is determined according to the stress uniformity to generate the stamping structure affected degree. Specifically, the MES system calls the stress uniformity detection algorithm to analyze the stress distribution of the affected stress position and calculate the stress deviation rate. According to the stress deviation rate, the structure affected degree of the affected area is divided into three levels of slight, moderate and severe, and the results are stored in the MES system. The structure deformation parameter of the stamping structure affected degree is recorded to generate the stamping structure deformation data. Specifically, the MES system records the deformation parameters of the affected area according to the structure affected degree, including the maximum deformation amount, deformation direction and deformation range. The deformation parameters and the aluminum alloy shape parameter are combined to generate complete stamping structure deformation data, which are stored in the process database of the MES system.

[0136] Preferably, step S3 comprises the following steps:

[0137] Step S31: The stamping structure deformation data is divided into a stamping stretching area, a stamping bending area and a stamping flanging area to generate structure deformation position data;

[0138] Step S32: The deformation index of the structure deformation position data is set. Specifically, the cross-sectional size standard range is set to ±0.05mm to ±0.10mm; the thickness standard range is set to ±0.02mm to ±0.03mm; and the surface flatness standard range is set to Ra0.5 to Ra1.0;

[0139] Step S33: merging the cross-sectional size standard range, the thickness standard range, and the surface flatness standard range to obtain preset standard structure deformation data;

[0140] Step S34: comparing the stamping structure deformation data with the preset standard structure deformation data. If the cross-sectional size change is within ±0.05 mm, the thickness change is within ±0.02 mm, and the surface flatness change is within Ra 0.5, it is marked as stamping structure slight deformation data. If the cross-sectional size change is between ±0.05 mm and ±0.10 mm, the thickness change is between ±0.02 mm and ±0.03 mm, and the surface flatness change is between Ra 0.5 and Ra 1.0, it is marked as stamping structure moderate deformation. If the cross-sectional size change exceeds ±0.10 mm, the thickness change exceeds ±0.03 mm, and the surface flatness change exceeds Ra 1.0, it is marked as stamping structure severe deformation.

[0141] Step S35: mapping the stamping structure deformation anomaly level based on the stamping structure slight deformation, the stamping structure moderate deformation, and the stamping structure severe deformation to obtain stamping structure deformation anomaly data.

[0142] 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 as follows: 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 cross-sectional dimensions is set to ±0.05mm to ±0.10mm; the standard range of thickness is set to ±0.02mm to ±0.03mm; and the standard range of surface flatness is set to Ra0.5 to Ra1.0. The standard ranges for cross-sectional dimensions, thickness, and surface flatness are combined to generate pre-set standard structural deformation data. Specifically, the MES system integrates these defined deformation indicators into a standard data template, which includes standard deformation ranges for the stretching, bending, and flanging zones. This standard structural deformation data is stored in the MES system's process database 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 dimension change is within ±0.05mm, the thickness change is within ±0.02mm, and the surface flatness change is within Ra0.5, it is marked as slight stamping structure deformation data; if the cross-sectional dimension change is between ±0.05mm and ±0.10mm, the thickness change is between ±0.02mm and ±0.03mm, and the surface flatness change is between Ra0.5 and Ra1.0, it is marked as medium stamping structure deformation; if the cross-sectional dimension change exceeds ±0.10mm, the thickness change exceeds ±0.03mm, and the surface flatness change exceeds Ra1.0, it is marked as severe stamping structure deformation. The comparison results are stored in the MES system for subsequent abnormality level mapping. Based on the slight stamping structure deformation, medium stamping structure deformation, and severe stamping structure deformation, the stamping structure deformation abnormality level mapping is performed to obtain the stamping structure deformation abnormality data. The specific operation is as follows: the MES system maps slight deformation, moderate deformation and severe deformation to different abnormality levels based on the marking results; the abnormality level mapping results are stored as stamping structure deformation abnormality data for subsequent process adjustments and quality control.

[0143] Preferably, step S4 includes the following steps:

[0144] Step S41: performing deformation abnormality type detection on the stamping structure deformation abnormality data to obtain deformation abnormality type data; performing abnormality parameter quantification on the deformation abnormality type data to generate abnormal parameter quantification data;

[0145] 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 feature;

[0146] Step S43: performing stamping deformation anomaly tracing analysis on the structural deformation anomaly characteristics to obtain stamping deformation anomaly tracing data;

[0147] Step S44: Feedback 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.

[0148] In this embodiment of the present invention, the data analysis module within the MES system is used to detect deformation anomaly types in stamping structure deformation data. A unified recognition method for multiple anomaly morphologies, combined with features such as high-amplitude and high-frequency variations, automatically identifies anomaly types within the deformation data. Specifically, the deformation data is segmented into time series and automatically segmented using information entropy to avoid interference from high-amplitude and high-frequency variations. Feature extraction is performed on the segmented data using the Angular Outlier Factor (ABOF) and Local Outlier Factor (LOF) to identify the deformation anomaly type and generate deformation anomaly type data. Anomaly parameter quantification is performed on the deformation anomaly type data. Key parameters for each anomaly type (such as cross-sectional dimension change, thickness change, and surface flatness change) are quantified using statistical analysis methods to generate quantified anomaly parameter data. For example, for a particular anomaly type, the quantified cross-sectional dimension change is 0.08 mm, the thickness change is 0.025 mm, and the surface flatness change is Ra0.8. Based on the quantified anomaly parameter data, deformation characteristics of the stamping structure deformation anomaly data are 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 is generated. The structural deformation abnormality feature is subjected to stamping deformation abnormality traceability 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 feature and the stamping process parameters is modeled, and the key process parameters that cause the deformation abnormality are identified. For example, if an abnormal deformation in a certain area is related to excessive stamping speed, the traceability result will be "stamping speed too high"; the traceability data of the stamping deformation anomaly 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. The dynamic adjustment module in the MES system adjusts the stamping process parameters 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 anomaly is related to mold wear, a prompt to replace the mold is displayed. The adjusted process parameters are updated in real time to the stamping equipment control system to ensure the stability and quality control of the subsequent stamping process.

[0149] Preferably, step S43 includes the following steps:

[0150] Step S431: The structural deformation anomaly feature is divided into a stamping process time sequence segment, and the time range of each segment is set to 10-30 seconds;

[0151] Step S432: The stamping process time sequence segment is subjected to stamping deformation anomaly trend change identification to obtain stamping deformation anomaly trend data;

[0152] Step S433: The stamping deformation anomaly trend data is subjected to anomaly trend source determination to obtain anomaly trend source data; the anomaly trend source data is subjected to stamping deformation anomaly verification to generate a stamping deformation anomaly verification result;

[0153] Step S434: The stamping deformation anomaly verification result is subjected to traceability marking to obtain stamping deformation anomaly traceability data.

[0154] In this embodiment of the present invention, the MES system divides the structural deformation anomaly feature data into time series segments during the stamping process. The stamping process is divided into multiple time series segments, with each segment having a time range of 10-30 seconds. Specifically, the MES system's time series analysis module segments the deformation anomaly feature data into multiple subsequences based on the stamping process's timestamp information. For example, if the total stamping process duration is 120 seconds, it can be divided into four time series segments, each lasting 30 seconds. Stamping deformation anomaly trend changes are then identified within these time series segments. The MES system's trend analysis module analyzes the deformation data within each time series segment to identify the trend change of the deformation anomaly. Specifically, the deformation data within each time series segment is fitted using the maximum likelihood estimation (MLE) or least squares estimation (LSE) method to detect the change points in the deformation trend. If the deformation data within a time series segment exhibits a clear upward trend, it is marked as "upward trend change," and stamping deformation anomaly trend data is generated. Determine the source of abnormal stamping deformation trend data. The traceability analysis module within the MES system, combined with stamping process parameters (such as stamping force, stamping speed, and die condition), analyzes the source of abnormal deformation trends. Specifically, the angular outlier factor (ABOF) and local outlier factor (LOF) are used to extract features from the abnormal trend data and identify the key process parameters that cause the deformation anomaly. For example, if an abnormal trend is associated with a sudden change in stamping speed, its source is marked as "stamping speed change." The source data of the abnormal trend is verified as a stamping deformation anomaly. The verification module within the MES system verifies the source data of the abnormal trend based on preset deformation standard ranges (such as cross-sectional dimensions, thickness, and surface flatness). For example, if the cross-sectional dimension change corresponding to a source of an abnormal trend exceeds the standard range of ±0.10mm, the verification result is "abnormal," and a stamping deformation anomaly verification result is generated. The stamping deformation anomaly verification result is marked as traceable, generating stamping deformation anomaly traceability data. The traceability tagging module in the MES system associates and tags abnormal verification results with corresponding stamping process parameters, time series segments, and deformation characteristics. For example, if an abnormal verification result is marked as "cross-sectional dimension abnormality caused by stamping speed variation," its traceability tag is then changed to "stamping speed variation." Ultimately, the traceability data is stored in the MES system's abnormality database.

[0155] It is particularly important that step S433 includes the following steps:

[0156] Step S4331: Extracting spatiotemporal features of the stamping deformation anomaly trend data. By continuously monitoring the displacement and stress of the abnormal deformation area during the stamping process, the evolution characteristics of the abnormal trend in time and space are recorded to obtain the spatiotemporal feature data of the abnormal trend.

[0157] Step S4332: Enhance the abnormal trend spatiotemporal feature data to identify small feature changes in the abnormal deformation area and enhance the key features to obtain abnormal trend enhanced features;

[0158] Step S4333: Screening the abnormal trend enhancement features and removing interference information irrelevant to the abnormal trend to obtain abnormal trend source feature data;

[0159] Step S4334: Analyze the propagation path of the abnormal trend source characteristic data during the stamping process to obtain the abnormal trend data of the propagation path; determine the source location of the abnormal trend data of the propagation path to obtain the abnormal trend source data; track the evolution path of the abnormal trend source data during the stamping process to obtain the abnormal trend source location information;

[0160] 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.

[0161] In this embodiment of the present invention, the MES system extracts spatiotemporal features from stamping deformation anomaly trend data. During the stamping process, high-precision displacement sensors and strain gauges are used to continuously monitor the abnormal deformation area, recording the temporal and spatial evolution of the abnormal trend. Specifically, a laser displacement sensor is used to record the displacement changes of the abnormal area in real time at a sampling frequency of 10 Hz. Stress data is collected using strain gauges and, combined with timestamp information, analyzed to analyze the dynamic changes in stress during the stamping process. The displacement and stress data are aligned in time series to generate spatiotemporal feature data of the abnormal trend. Feature enhancement is performed on the spatiotemporal feature data of the abnormal trend to identify subtle feature changes and enhance key features. Specifically, a wavelet transform is used to perform multi-scale analysis on the spatiotemporal feature data to extract subtle deformation features. Principal component analysis (PCA) is used to reduce the dimensionality of the extracted subtle features while enhancing the significance of key features. The enhanced feature data is stored as enhanced abnormal trend features. The enhanced abnormal trend features are filtered to eliminate interference information unrelated to the abnormal trend. Specifically, correlation analysis is used to select features highly correlated with the abnormal trend. A threshold method is used to eliminate interfering features below a 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 the abnormal feature during the stamping process. The source location of the propagation path is determined by combining the geometric model of the stamping equipment and the sensor layout. A reverse tracing algorithm is used to trace the evolution path of the abnormal trend from the source location in reverse order to generate abnormal trend source location information. This abnormal trend source location information is verified through multiple factors, including: Finite element analysis (FEA) simulation of material deformation during the stamping process to verify whether the abnormal trend is related to material properties. The wear of the mold surface is examined and, based on mold usage records, the abnormal trend is verified to be related to mold wear. The current stamping process parameters (such as stamping force and stamping speed) are compared with the standard process parameters to verify whether the abnormal trend is related to the process parameter settings. Combining this verification information, a stamping deformation anomaly verification result is generated.

[0162] Preferably, step S44 includes the following steps:

[0163] Step S441: converting the stamping deformation abnormality traceability data into a format to obtain traceability format converted data;

[0164] Step S442: Set the data transmission frequency to once every 10-30 seconds, and feed back the traceability format converted data to the MES system in real time to obtain real-time feedback results;

[0165] 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;

[0166] Step S444: Dynamically adjust the lightweight aluminum alloy cold stamping parameters according to the real-time cold stamping operation parameters.

[0167] In one embodiment of the present invention, the data processing module in the MES system converts the format of traceability data related to stamping deformation anomalies. Specifically, the data processing module converts the traceability data from its original format (e.g., binary or text format acquired by the equipment) into a standardized format (e.g., JSON or XML) that the MES system can recognize and process. For example, the data fields containing the source of the anomaly are reorganized to ensure they conform to the MES system's predefined data structure. The converted data is stored as traceability format-converted data. The data transmission frequency is set to every 10-30 seconds, and the traceability format-converted data is fed back to the MES system in real time. Specifically, the data transmission module in the MES system is combined with middleware (e.g., Apache Kafka or RabbitMQ) to achieve efficient data transmission. The middleware is responsible for transmitting the traceability format-converted data at a set frequency (every 10-30 seconds) to the MES system's real-time data processing unit, ensuring real-time and accuracy. After the transmission is complete, real-time feedback is generated. This real-time feedback is analyzed to determine the current cold stamping operation type. The process identification module in the MES system, combined with pre-set process parameter templates (such as stamping speed, stamping force, and die state), identifies the current operation type as stretching, bending, or flanging. For example, if real-time feedback indicates a high stamping force and a slow stamping speed, the operation is determined to be a stretching operation. The identification result is stored as cold stamping operation type data. The cold stamping operation type data is then used to monitor operational parameters in real time. The real-time monitoring module in the MES system, combined with sensor data from the stamping equipment (such as pressure sensors, displacement sensors, and speed sensors), obtains key parameters of the current operation in real time. For example, for a stretching operation, parameters such as stamping force, stamping speed, and die stroke are monitored in real time. The monitoring results generate real-time cold stamping operation parameters and are stored in the MES system's real-time database. Based on the real-time cold stamping operation parameters, dynamic adjustment of lightweight aluminum alloy cold stamping parameters is performed. Specifically, the dynamic adjustment module in the MES system, combined with pre-set process adjustment rules (such as the mapping between stamping force and deformation degree), adjusts the stamping process parameters based on the real-time operating parameters. For example, if real-time monitoring detects that excessive stamping force is causing abnormal deformation, the force is automatically reduced. If the stamping speed is too fast, affecting deformation uniformity, the speed is adjusted. The adjusted parameters are updated in real time to the stamping equipment control system, ensuring stability and quality control of the subsequent stamping process.

[0168] The present invention is therefore intended to be illustrative and non-restrictive in all respects, with the scope of the invention being defined by the appended claims rather than the foregoing description, and all changes that come within the meaning and range of equivalents of the application documents are intended to be embraced therein.

[0169] The foregoing description is intended only to provide specific embodiments of the present invention, which will enable those skilled in the art to understand and implement the present invention. Various modifications to these embodiments will be readily 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 is not intended to be limited to the embodiments shown herein, but is to be construed in the widest possible manner consistent with the principles and novel features disclosed 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 lightweight aluminum alloy mold manufacturing parameters, stamping strength parameters, stamping speed parameters, and stamping environment parameters 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 level stratification on the pressure-speed benchmark data according to the mold manufacturing parameters to form plate pressure level layer data; perform stamping environment change detection on the plate pressure level 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; wherein, step S2 includes the following steps: Step S21: Mapping the punching strength parameter to the punching interval range of 0.2-0.8 for normalization processing to obtain punching force value mapping data; converting the punching speed parameter into a time linear function, and calculating 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: Perform segmented fitting on the preliminary pressure-speed reference value, dividing the stamping process into a starting segment, a stable segment, and an ending segment, and setting different fitting curve parameters for each segment. The fitting slope range of the starting segment is 0.2-0.5, the fitting slope range of the stable segment is 0.05-0.1, and the fitting slope range of the ending segment is 0.1-0.3, so as to generate pressure-speed reference data; Step S24: stratifying the pressure level of the plate according to the pressure velocity reference data based on the mold manufacturing parameters to form plate pressure level layer data. Specifically, 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: Merge and mark 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: Dividing 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: stratifying the pressure degree of the aluminum alloy plate based on the cavity size accuracy classification data and the surface roughness classification data 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, and generating 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 abnormal 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, 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 dimensional accuracy collection range to ±0.05 mm to ±0.20 mm, and setting the surface roughness collection range to Ra0.8 to Ra2.0, to generate mold manufacturing parameters; Step S12: Data is collected at the front, middle, and back ends of the stamping stroke through the MES system, with each collection interval being 5-15 seconds. The stamping force range is 20-50 kN, and the stamping stroke range is 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 the collection interval of each section being 3-8 seconds and the stamping speed collection range being 50-200 mm / s, 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, 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 the processed environment temperature data; Step S252: using exponential smoothing to process humidity data of stamping environment parameters, with the smoothing coefficient range set to 0.1-0.3, to obtain processed environmental humidity 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 processed environmental air pressure data; Step S254: Calculate the temperature difference of the processed ambient temperature data, and set the temperature difference change to ±2°C to ±5°C; calculate the humidity difference of the processed ambient temperature data, and set the humidity difference change to ±5% to ±15%; calculate the air pressure difference of the processed ambient temperature 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: mapping the stamping environment variation to the sheet metal 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.

4. The MES-based lightweight aluminum alloy cold stamping data verification method according to claim 1, characterized in that: Step S26 includes the following steps: Step S261: using the MES system to set collection points at the front end, middle end, rear end, and edge of the aluminum alloy, and respectively collect the aluminum alloy cross-sectional dimensions, aluminum alloy thickness parameters, and aluminum alloy surface flatness to obtain the aluminum alloy shape parameters; Step S262: detecting the aluminum alloy environment-affected area based on the aluminum alloy shape parameters according to the stamping environment change to obtain the aluminum alloy affected area; determining the cold stamping stress position of the aluminum alloy affected area to generate the affected stress position; Step S263: performing stress uniformity detection on the affected stress position, and determining the degree of structural impact based on the stress uniformity, thereby generating the degree of impact on the stamping structure; Step S264: Record the structural deformation parameters of the impact degree of the stamping structure to generate stamping structure deformation data.

5. The MES-based lightweight aluminum alloy cold stamping data verification method according to claim 4, 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 change is within ±0.05mm, the thickness change is within ±0.02mm, and the surface flatness change is within Ra0.5, it is marked as a slight deformation data of the stamping structure; if the cross-sectional dimension change is between ±0.05mm and ±0.10mm, the thickness change is between ±0.02mm and ±0.03mm, and the surface flatness change is between Ra0.5 and Ra1.0, it is marked as a moderate deformation of the stamping structure; if the cross-sectional dimension change exceeds ±0.10mm, the thickness change exceeds ±0.03mm, and the surface flatness change exceeds Ra1.0, it is marked as a severe deformation of the stamping structure; Step S35: performing stamping structure deformation abnormality level mapping based on the stamping structure slight deformation, the stamping structure medium deformation, and the stamping structure severe deformation to obtain stamping structure deformation abnormality data.

6. 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 stamping structure deformation abnormality data to obtain deformation abnormality type data; performing abnormality 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 feature; Step S43: performing stamping deformation anomaly tracing analysis on the structural deformation anomaly characteristics to obtain stamping deformation anomaly tracing data; Step S44: Feedback 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.

7. The MES-based lightweight aluminum alloy cold stamping data verification method according to claim 6, characterized in that: Step S43 includes the following steps: Step S431: Divide the structural deformation abnormality characteristics into stamping process time series segments, and set the time range of each segment to 10-30 seconds; Step S432: identifying abnormal stamping deformation trend changes in the stamping process time series segment to obtain abnormal stamping deformation trend data; 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.

8. The MES-based lightweight aluminum alloy cold stamping data verification method according to claim 6, characterized in that: Step S44 includes the following steps: Step S441: converting the stamping deformation abnormality traceability data into a format to obtain traceability format converted data; Step S442: Set the data transmission frequency to once every 10-30 seconds, and feed back the traceability format converted 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.

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