Welding data intelligent management system and method based on automatic production

Abstract

The invention relates to the technical field of welding data management, and particularly discloses an intelligent welding data management system and method based on automatic production, and the system comprises an image collection module, a welding seam detection module, a workpiece prediction module, a workpiece early warning module and a welding correction module. Further detecting to obtain geometric features and texture features of corresponding welding seams of the welding workpiece; performing fatigue life analysis on the corresponding welding seam of the welding workpiece, and calculating the fatigue life of the corresponding welding seam of the welding workpiece to obtain the predicted service life of the welding workpiece; early warning screening is conducted on the welding workpieces according to the predicted service life of the welding workpieces; therefore, the welding seam of the welding workpiece is subjected to welding correction, comprehensive quality control and management are carried out on the welding workpiece, the service life of the welding workpiece can be prolonged, the reliability of the welding workpiece can be improved, the maintenance cost can be reduced, and the accident risk caused by welding seam defects is reduced.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of welding data management, and relates to a welding data intelligent management system and method based on automatic production. BACKGROUND

[0002] In the field of modern industrial manufacturing, welding technology is widely used in the connection of various metal structures and mechanical parts. However, the complexity of the welding process and the quality of the welded joints directly affect the overall performance and service life of the product. Traditional welding quality detection methods mainly rely on visual inspection and simple non-destructive testing techniques such as ultrasonic testing, radiographic testing, etc. Although these methods can find defects in the weld to some extent, they have low efficiency, the detection results depend on the experience of the operator, and the detection accuracy is not high.

[0003] Weld joint life prediction of welded workpieces is a key issue in industrial manufacturing and engineering applications, and still has some defects and drawbacks. The current technology still has some significant defects and drawbacks in the weld joint life prediction of welded workpieces. First, the weld joint life prediction model usually relies on idealized assumptions and simplified mathematical formulas, and these models often cannot fully reflect the complex parameter influence in the actual working condition, which will significantly affect the fatigue life of the weld.

[0004] Secondly, the weld joint life prediction also faces the problems of data shortage and data quality. High-quality fatigue test data is essential for establishing and verifying the life prediction model, but due to the high cost and long cycle of fatigue testing, the actual obtained data is often limited and scattered. In addition, the accuracy and consistency of the data directly affect the reliability of the model, and small differences in experimental conditions may cause significant deviations in the data. SUMMARY

[0005] In view of the problems existing in the prior art, the present application provides a welding data intelligent management system and method based on automatic production, which solves the above technical problems.

[0006] In order to achieve the above-mentioned purposes and other purposes, the technical scheme adopted by the present application is as follows:

[0007] The present application provides a welding data intelligent management system based on automatic production, which comprises an image acquisition module, a weld detection module, a workpiece prediction module, a workpiece warning module and a welding correction module. The above-mentioned modules are connected by wired and / or wireless connection to realize data transmission between the modules.

[0008] The image acquisition module is used to acquire the weld image of the welded workpiece.

[0009] The weld detection module is connected with the image acquisition module, and is configured to perform image extraction on the weld image of the welding workpiece, and then detect the geometric feature and the texture feature of the weld corresponding to the welding workpiece.

[0010] The workpiece prediction module is configured to perform fatigue life analysis on the weld corresponding to the welding workpiece, calculate the fatigue life of the weld corresponding to the welding workpiece, and thus obtain the predicted service life of the welding workpiece.

[0011] The workpiece early warning module is configured to perform early warning screening on the welding workpiece according to the predicted service life of the welding workpiece.

[0012] The welding correction module is connected with the workpiece early warning module, and is configured to perform welding correction on the weld of the welding workpiece according to the early warning screening result.

[0013] The geometric feature of the weld corresponding to the welding workpiece specifically includes the weld height, the radius of the weld fillet, and the geometric shape.

[0014] The texture feature of the weld corresponding to the welding workpiece specifically includes the roughness and the uniformity.

[0015] The specific calculation logic of the stress amplitude σ of the weld corresponding to the welding workpiece is as follows.

[0016] The stress amplitude σ of the weld corresponding to the welding workpiece is calculated based on the weld height h and the radius r of the weld fillet of the weld corresponding to the welding workpiece. P is an externally applied load, and MJ is the effective cross-sectional area of the weld corresponding to the welding workpiece. The specific calculation process is as follows.

[0017] The geometric shape of the weld corresponding to the welding workpiece is obtained. If the geometric shape of the weld corresponding to the welding workpiece is identified as a butt weld, the effective cross-sectional area MJ1 of the weld corresponding to the welding workpiece is HD·KD, where HD is the thickness of the weld corresponding to the welding workpiece, and KD is the width of the weld corresponding to the welding workpiece.

[0018] If the geometric shape of the weld corresponding to the welding workpiece is identified as a fillet weld, the effective cross-sectional area MJ2 of the weld corresponding to the welding workpiece is a·HD·KD, where a is the leg length of the weld corresponding to the welding workpiece. a is the leg length of the weld corresponding to the welding workpiece.

[0019] If the geometric shape of the weld corresponding to the welding workpiece is identified as a T-shaped weld, the effective cross-sectional area MJ3 of the weld corresponding to the welding workpiece is a×HD.

[0020] In summary, the effective cross-sectional area MJ of the weld corresponding to the welding workpiece is obtained, where the value range of MJ is MJ1, MJ2, or MJ3.

[0021] The specific calculation logic of the fatigue strength index b of the weld corresponding to the welding workpiece is as follows.

[0022] determine the weld shape coefficient k1 of the welding workpiece corresponding to the weld based on the geometry of the weld corresponding to the welding workpiece;

[0023] determine the weld size coefficient of the welding workpiece corresponding to the weld

[0024] determine the surface roughness coefficient k3 of the welding workpiece corresponding to the weld = 1-0.01·ZC, ZC is the roughness of the weld corresponding to the welding workpiece;

[0025] based on the uniformity of the weld corresponding to the welding workpiece, the weld uniformity coefficient k4 of the welding workpiece is obtained;

[0026] Thus, the fatigue strength index b of the weld corresponding to the welding workpiece is calculated = k0·k1·k2·k3·k4, k0 is the reference fatigue strength value of the base material of the welding workpiece.

[0027] Exemplary, the fatigue life of the weld corresponding to the welding workpiece is calculated, the specific operation logic is:

[0028] based on the geometric features and texture features of the weld corresponding to the welding workpiece, the stress amplitude σ and the fatigue strength index b of the weld corresponding to the welding workpiece are calculated respectively;

[0029] Thus, the fatigue life of the weld corresponding to the welding workpiece is calculated F is the fatigue strength life of the material corresponding to the welding workpiece.

[0030] Exemplary, the specific calculation logic of the fatigue strength life F of the material corresponding to the welding workpiece is:

[0031] compare the base material of the welding workpiece with the welding material of the weld;

[0032] if the base material of the welding workpiece is inconsistent with the welding material of the weld, the reference fatigue strength value k0 corresponding to the welding material of the weld and the stress concentration factor A2 at the weld joint are obtained; the corrected fatigue strength at the weld joint of the welding workpiece is calculated by using the correction formula η is the pre-defined material inconsistency correction coefficient, and its specific calculation formula is ML' is the elastic modulus of the base material corresponding to the welding workpiece, ML" is the elastic modulus of the welding material corresponding to the weld; finally, the fatigue strength coefficient f1 of the material corresponding to the welding workpiece when the base material of the welding workpiece is inconsistent with the welding material of the weld is obtained by calculation formula f1 = A3-A4, A4 is the residual stress;

[0033] if the base material of the welding workpiece is consistent with the welding material of the weld, the reference fatigue strength value B1 corresponding to the base material of the welding workpiece is obtained; the corrected fatigue strength value B2 of the welding workpiece is obtained by calculation formula obtaining the fatigue strength coefficient f2 of the welding workpiece corresponding to the material when the welding material ratio of the base material of the welding workpiece to the weld is consistent, and B2 is the geometric correction coefficient of the weld joint;

[0034] In summary, the fatigue strength coefficient f of the welding workpiece corresponding to the material is obtained, wherein f takes the value of f1 or f2;

[0035] Thus, the fatigue strength life of the welding workpiece corresponding to the material is obtained

[0036] For example, the specific calculation logic of the expected service life of the welding workpiece is:

[0037] Obtain the historical test data of the welding workpiece, and then fit the SN curve of the welding workpiece;

[0038] According to the historical test data of the welding workpiece, the data of the stress changing with time in the historical test of the welding workpiece is obtained, all peak and valley values are extracted from the stress time sequence, and the rainflow counting method is used to process the extracted peak and valley data, and then the cycle times under different stress levels are counted;

[0039] According to the SN curve of the welding workpiece, the material constants m and n of the welding workpiece are determined, and thus the fatigue life G of the welding workpiece corresponding to each stress level is calculated i = m·(XH i ) -n , XH i is the cycle time under the i-th stress level, i is the number of each stress level, and i takes the value range of 1 to S;

[0040] Comprehensive analysis of the cumulative damage of the welding workpiece

[0041] and then calculate the expected service life of the welding workpiece

[0042] For example, the specific operation logic of the workpiece warning module is:

[0043] Compare the expected service life of the welding workpiece with the predefined welding workpiece service life threshold 1. If the former is less than the latter, mark the welding workpiece for rework, and execute the welding correction module on the welding workpiece. Otherwise, it is determined that the welding workpiece meets the production standard, and the corresponding factory processing is performed.

[0044] For example, the welding seam of the welding workpiece is welded, and the specific operation logic is:

[0045] Determine the thickness, width, length and area of the repair area of the welding seam of the welding workpiece by non-destructive testing method;

[0046] And the welding current I, welding voltage V and welding speed υ of the automatic welding machine are acquired;

[0047] The machine welding current of the automatic welding machine corresponding to the to-be-repaired area of the welding workpiece is calculated φ is a set empirical coefficient, usually between 30-40, and is set to 35 here, The to-be-repaired electrode diameter of the automatic welding machine corresponding to the to-be-repaired area of the welding workpiece is calculated

[0048] The machine welding heat input of the automatic welding machine corresponding to the to-be-repaired area of the welding workpiece is calculated

[0049]

[0050] The machine welding seam filling metal amount JS of the automatic welding machine corresponding to the to-be-repaired area of the welding workpiece is calculated, and MJ' is the area cross section of the to-be-repaired area of the welding workpiece corresponding to the welding seam, and L is the length of the to-be-repaired area of the welding workpiece corresponding to the welding seam;

[0051] The machine welding preheating temperature of the automatic welding machine corresponding to the to-be-repaired area of the welding workpiece is calculated γ1 and γ2 are the thickness and width of the to-be-repaired area of the welding workpiece corresponding to the welding seam respectively, and C is a set welding material constant, The heat conduction coefficient of the automatic welding machine is calculated

[0052] According to the machine welding current, the machine welding seam filling metal amount, the machine welding heat input and the machine welding preheating temperature of the automatic welding machine corresponding to the to-be-repaired area of the welding workpiece, welding control is performed on the automatic welding machine, so that welding correction is realized on the to-be-repaired area of the welding workpiece corresponding to the welding seam.

[0053] Another aspect of the present application provides a welding data intelligent management method based on automatic production, comprising:

[0054] The welding seam image of the welding workpiece is collected, and the welding seam image of the welding workpiece is extracted, and then the geometric features and texture features of the welding seam of the welding workpiece are detected;

[0055] The fatigue life of the welding seam of the welding workpiece is analyzed, and the fatigue life of the welding seam of the welding workpiece is calculated, so that the expected service life of the welding workpiece is obtained;

[0056] According to the expected service life of the welding workpiece, the welding workpiece is pre-warned and screened, and according to the pre-warning screening result, the welding seam of the welding workpiece is welded and corrected.

[0057] As described above, the welding data intelligent management system and method based on automatic production provided by the present application have at least the following beneficial effects:

[0058] (1)The welding data intelligent management system and method based on automatic production provided by the application has the advantages and necessity that the geometric features and texture features of the weld corresponding to the welding workpiece are detected by image extraction of the weld image of the welding workpiece. The geometric features and texture features of the weld directly reflect the welding quality, and the extraction of these features can help identify possible defects in the welding process. These defects are the main reason for the failure of the welding workpiece, so early identification and processing can significantly improve the reliability and safety of the welding workpiece.

[0059] (2)The embodiment of the application can calculate the fatigue life of the weld corresponding to the welding workpiece by analyzing the fatigue life of the weld corresponding to the welding workpiece. Fatigue life analysis can predict the life of the welding workpiece under actual use conditions and avoid sudden accidents caused by weld failure. This prediction can not only prolong the service life of the welding workpiece, but also reduce maintenance costs and improve production efficiency.

[0060] (3)The embodiment of the application can perform early warning screening on the welding workpiece according to the predicted service life of the welding workpiece. Potential problems can be found in time before the welding workpiece enters the use stage, and preventive maintenance or correction can be performed. This early warning mechanism can greatly reduce sudden failures caused by weld defects and improve the overall reliability of the equipment and system.

[0061] (4)The embodiment of the application can perform welding correction on the weld of the welding workpiece to ensure that the weld quality meets the design requirements. This correction measure can not only prolong the service life of the welding workpiece, but also improve its carrying capacity and fatigue resistance, ensuring its safety and reliability in actual use. BRIEF DESCRIPTION OF DRAWINGS

[0062] In order to more clearly illustrate the technical solutions of the embodiments of the application, the following will briefly introduce the drawings needed for the embodiment description. Obviously, the drawings in the following description are only some embodiments of the application, and other drawings can be obtained by those skilled in the art without creative labor.

[0063] Figure 1 It is a schematic diagram of the connection of the modules of the system of the application.

[0064] Figure 2 It is a schematic diagram of the connection of the steps of the method of the application. DETAILED DESCRIPTION

[0065] The above content will be combined with the embodiments of the present application, which are only examples and descriptions of the concept of the present application. Those skilled in the art can make various modifications or supplements to the described specific embodiments or use similar ways to replace, as long as they do not deviate from the concept of the present application or exceed the scope defined by the present claims, which should belong to the protection scope of the present application.

[0066] Embodiment 1

[0067] Please refer to Figure 1 As shown in the figure, an intelligent welding data management system based on automatic production includes an image acquisition module, a weld detection module, a workpiece prediction module, a workpiece early warning module, and a welding correction module. The above modules are connected by wired and / or wireless connection to realize data transmission between modules.

[0068] The image acquisition module is used to acquire the weld image of the welding workpiece.

[0069] The weld detection module is connected with the image acquisition module, extracts the image of the weld image of the welding workpiece, and then detects the geometric features and texture features of the corresponding weld of the welding workpiece.

[0070] On the basis of the above embodiment, the geometric features of the corresponding weld of the welding workpiece specifically include weld height, radius of weld fillet, and geometric shape.

[0071] The texture features of the corresponding weld of the welding workpiece specifically include roughness and uniformity.

[0072] Use the surface roughness measuring instrument to place the probe of the measuring instrument smoothly on the weld surface. Start the instrument and record the measurement data. Measure multiple times at different positions of the weld to obtain representative data; analyze the roughness curve obtained by measurement, calculate parameters such as Ra (arithmetic average roughness) and Rz (ten-point average roughness); statistically analyze the data of multiple measurement points to obtain the average roughness of the weld.

[0073] Use a caliper, vernier caliper or other precision measuring tool to measure the width and height of the weld; measure at multiple positions such as the beginning, middle and end of the weld, record all the measured data, compare the size data of different positions, and evaluate the uniformity of the weld.

[0074] The workpiece prediction module performs fatigue life analysis on the corresponding weld of the welding workpiece, calculates the fatigue life of the corresponding weld of the welding workpiece, and thus obtains the predicted service life of the welding workpiece.

[0075] On the basis of the above embodiment, the specific calculation logic of the stress amplitude σ of the corresponding weld of the welding workpiece is as follows:

[0076] Based on the weld height h and the radius r of the weld fillet of the weld corresponding to the welded workpiece, the stress amplitude of the weld corresponding to the welded workpiece is calculated P is an externally applied load, MJ is the effective cross-sectional area of the weld corresponding to the welded workpiece, and the specific calculation process is as follows:

[0077] The geometry of the weld corresponding to the welded workpiece is obtained, and if the geometry of the weld corresponding to the welded workpiece is identified as a butt weld, the effective cross-sectional area MJ1 of the weld corresponding to the welded workpiece is HD·KD, HD is the thickness of the weld corresponding to the welded workpiece, and KD is the width of the weld corresponding to the welded workpiece.

[0078] If the geometry of the weld corresponding to the welded workpiece is identified as a fillet weld, the effective cross-sectional area of the weld corresponding to the welded workpiece is a is the leg length of the weld corresponding to the welded workpiece.

[0079] If the geometry of the weld corresponding to the welded workpiece is identified as a T-shaped weld, the effective cross-sectional area MJ3 of the weld corresponding to the welded workpiece is a×HD.

[0080] In summary, the effective cross-sectional area MJ of the weld corresponding to the welded workpiece is obtained, wherein the value range of MJ is MJ1, MJ2 or MJ3.

[0081] By measuring the strain near the weld corresponding to the welded workpiece, and combining the elastic modulus of the material, the externally applied load is calculated.

[0082] On the basis of the above embodiment, the specific calculation logic of the fatigue strength index b of the weld corresponding to the welded workpiece is:

[0083] Based on the geometry of the weld corresponding to the welded workpiece, the weld shape coefficient k1 corresponding to the welded workpiece is determined.

[0084] If the geometry of the weld corresponding to the welded workpiece is a fillet weld, k1 takes a value of 0.8, if the geometry of the weld corresponding to the welded workpiece is a T-shaped weld, k1 takes a value of 0.7, and if the geometry of the weld corresponding to the welded workpiece is a butt weld, k1 takes a value of 1.0.

[0085] The weld size coefficient k2 corresponding to the welded workpiece is determined

[0086] The surface roughness coefficient k3 of the weld corresponding to the welded workpiece is determined as k3 = 1-0.01·ZC, ZC is the roughness of the weld corresponding to the welded workpiece.

[0087] Based on the uniformity of the weld corresponding to the welded workpiece, the weld uniformity coefficient k4 corresponding to the welded workpiece is obtained.

[0088] According to the uniformity of the weld corresponding to the welded workpiece, the welded workpiece is classified into high uniformity, medium uniformity and low uniformity, if the uniformity of the weld corresponding to the welded workpiece is identified as high uniformity, k4 is 1.0, if the uniformity of the weld corresponding to the welded workpiece is identified as medium uniformity, k4 is 0.9, and if the uniformity of the weld corresponding to the welded workpiece is identified as low uniformity, k4 is 0.8.

[0089] The fatigue strength index b of the weld corresponding to the welded workpiece is calculated as b=k0·k1·k2·k3·k4, wherein k0 is the reference fatigue strength value of the base material of the welded workpiece.

[0090] On the basis of the above embodiment, the fatigue life of the weld corresponding to the welded workpiece is calculated, and the specific operation logic is as follows:

[0091] Based on the geometric characteristics and texture characteristics of the weld corresponding to the welded workpiece, the stress amplitude σ and the fatigue strength index b of the weld corresponding to the welded workpiece are calculated respectively.

[0092] The fatigue life of the weld corresponding to the welded workpiece is calculated as F is the fatigue strength life of the material corresponding to the welded workpiece.

[0093] On the basis of the above embodiment, the specific calculation logic of the fatigue strength life F of the material corresponding to the welded workpiece is as follows:

[0094] The base material of the welded workpiece is compared with the welding material of the weld.

[0095] If the base material of the welded workpiece is inconsistent with the welding material of the weld, the reference fatigue strength value k0 corresponding to the welding material of the weld and the stress concentration factor A2 at the weld joint are obtained, and the corrected fatigue strength at the weld joint of the welded workpiece is calculated by using the correction formula η is a predefined material inconsistency correction coefficient, and its specific calculation formula is ML' is the elastic modulus of the base material corresponding to the welded workpiece, and ML'' is the elastic modulus of the welding material corresponding to the weld; finally, the fatigue strength coefficient f1 of the material corresponding to the welded workpiece when the base material of the welded workpiece is inconsistent with the welding material of the weld is obtained by the calculation formula f1=A3-A4, and A4 is the residual stress.

[0096] The stress concentration at the weld is a key factor affecting the fatigue performance. The stress concentration factor can be obtained by finite element analysis (FEA) or empirical formula.

[0097] The residual stress generated in the welding process also affects the fatigue strength. It is usually measured by experiment or simulated by calculation.

[0098] If the base material of the welded workpiece is consistent with the welding material ratio of the weld, the fatigue strength coefficient B1 corresponding to the base material of the welded workpiece is obtained; the fatigue strength coefficient f2 of the welded workpiece corresponding material when the base material of the welded workpiece is consistent with the welding material ratio of the weld is obtained through the calculation formula B2 is the geometric correction coefficient of the weld joint;

[0099] The fatigue strength coefficient and the fatigue strength index can be obtained by fitting the fatigue stress-life curve (S-N curve) of the material.

[0100] The geometry of the weld, such as the weld angle and the weld width, also affects the fatigue strength. Geometric correction coefficients can be used to consider these effects.

[0101] In summary, the fatigue strength coefficient f of the welded workpiece corresponding material is obtained, where f takes the value of f1 or f2;

[0102] Thus, the fatigue strength life of the welded workpiece corresponding material is obtained

[0103] Linear regression fitting is performed using the numpy and scipy libraries in Python. The specific code is shown below:

[0104] import numpy as np

[0105] from scipy.stats import linregress

[0106] # Example data: stress amplitude and fatigue life

[0107] stress = np.array([100, 150, 200, 250, 300])

[0108] life = np.array([1e6, 5e5, 2e5, 1e5, 5e4])

[0109] # Log transformation

[0110] log_stress = np.log10(stress)

[0111] log_life = np.log10(life)

[0112] # Linear regression

[0113] slope, intercept, r_value, p_value, std_err = linregress(log_life, log_stress)

[0114] Fitting parameters

[0115] m = 10**intercept

[0116] n = slope

[0117] print(f"Fitting parameters: m={m:.4f}, n={n:.4f}")

[0118] print(f"Correlation coefficient: r={r_value:.4f}")

[0119] According to the historical test data of the welded workpiece, the stress data corresponding to the historical test of the welded workpiece with time is obtained, all peak and valley values are extracted from the stress time sequence, and the extracted peak and valley data are processed using the rainflow counting method, and then the cycle number under different stress levels is counted.

[0120] Suppose there is a stress time sequence as follows:

[0121] σ(t) = [0, 5, 3, 7, 2, 6, 1, 8, 4, 0]

[0122] Process using rainflow counting method:

[0123] Extract peak and valley values: peak values: 5, 7, 6, 8; valley values: 0, 3, 2, 1;

[0124] Pair peak and valley: pair (5, 0), (7, 3), (6, 2), (8, 1);

[0125] Count cycle number:

[0126] Stress range: 5, 4, 4, 7;

[0127] Corresponding cycle number: 1, 1, 1, 1;

[0128] Through the above method, the cycle number under different stress levels can be obtained.

[0129] According to the SN curve of the welded workpiece, the material constants m and n of the welded workpiece are determined, and the fatigue life G of the welded workpiece under each stress level is calculated i = m·(XH i ) -n , XH i is the cycle number under the i-th stress level, i is the number of each stress level, i takes the value range of 1 to S;

[0130] Comprehensive analysis of the cumulative damage of the welded workpiece

[0131] and calculate the predicted service life of the welded workpiece

[0132] The workpiece early warning module: according to the expected service life of the welded workpiece, the welded workpiece is pre-warned and screened;

[0133] On the basis of the above embodiment, the specific operation logic of the workpiece early warning module is:

[0134] The expected service life of the welded workpiece is compared with the predefined welded workpiece service life threshold 1, if the former is less than the latter, the welded workpiece is marked for rework, and the welding correction module is executed on the welded workpiece, otherwise it is determined that the welded workpiece meets the production standard, and the corresponding factory processing is carried out.

[0135] The welding correction module: connected with the workpiece early warning module, used for welding correction of the weld of the welded workpiece according to the pre-warning screening result.

[0136] On the basis of the above embodiment, the welding correction of the weld of the welded workpiece is carried out, and the specific operation logic is:

[0137] The thickness, width, length and area section of the repair area of the corresponding weld of the welded workpiece are determined by non-destructive testing method;

[0138] And the welding current I, welding voltage V and welding speed υ of the automatic welding machine are obtained;

[0139] The machine welding current of the automatic welding machine corresponding to the repair area of the welded workpiece is calculated φ is a set empirical coefficient, usually taking a value between 30-40, here set to 35, The diameter of the repair electrode of the automatic welding machine corresponding to the repair area of the welded workpiece is calculated

[0140] The machine welding heat input of the automatic welding machine corresponding to the repair area of the welded workpiece is calculated

[0141]

[0142] The machine weld metal filling amount JS of the automatic welding machine corresponding to the repair area of the welded workpiece is calculated ， MJ' is the area section of the repair area of the corresponding weld of the welded workpiece, and L is the length of the repair area of the corresponding weld of the welded workpiece;

[0143] The machine welding preheating temperature of the automatic welding machine corresponding to the repair area of the welded workpiece is calculated γ1, γ2 are the thickness and width of the repair area of the corresponding weld of the welded workpiece respectively, and C is a set welding material constant, The thermal conductivity coefficient of the automatic welding machine is

[0144] According to the machine welding current, the machine weld filling metal amount, the machine welding heat input and the machine welding preheating temperature of the automatic welding machine corresponding to the to-be-repaired area of the welded workpiece, the welding control of the automatic welding machine is performed, and the welding correction of the to-be-repaired area of the corresponding weld of the welded workpiece is realized.

[0145] Embodiment 2

[0146] Please refer to Figure 2 As shown in the figure, a welding data intelligent management method based on automatic production, the method comprises the following steps:

[0147] The weld image of the welded workpiece is collected, and the weld image of the welded workpiece is extracted, and then the geometric features and texture features of the corresponding weld of the welded workpiece are detected;

[0148] The fatigue life of the corresponding weld of the welded workpiece is analyzed, and the fatigue life of the corresponding weld of the welded workpiece is calculated, and thus the expected service life of the welded workpiece is obtained;

[0149] According to the expected service life of the welded workpiece, the welded workpiece is prewarned and screened, and according to the prewarning and screening result, the weld of the welded workpiece is welded and corrected.

[0150] It should be understood that in various embodiments of the present application, the size of the sequence number of the above-mentioned processes does not mean the order of execution, and the execution order of the processes should be determined according to its function and inherent logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0151] It should be understood that according to A, B is determined, which means that B is determined only according to A, but also B can be determined according to A and / or other information.

[0152] The above is only a specific embodiment of the present application, but the protection scope of the present application is not limited to this, any skilled person in the art can easily think of changes or replacements within the technical range disclosed in the present application, which should be covered in the protection scope of the present application. Therefore, the protection scope of the present application should be limited by the protection scope of the claims.

[0153] Finally: the above is only the preferred embodiment of the present application, and is not used to limit the present application, any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the present application should be included in the protection scope of the present application.

Claims

1. An intelligent management system for welding data based on automated production, characterized in that: include: Image acquisition module: used to acquire weld seam images of welding workpieces; Weld seam detection module: connected to the image acquisition module, extracts the weld seam image of the welded workpiece, and then detects the geometric features and texture features of the weld seam corresponding to the welded workpiece; Workpiece prediction module: performs fatigue life analysis on the corresponding welds of the welding workpiece, calculates the fatigue life of the corresponding welds of the welding workpiece, and thus obtains the expected service life of the welding workpiece; Workpiece early warning module: performs early warning screening of welding workpieces based on their expected service life; Welding correction module: connected to the workpiece early warning module, used to perform welding correction on the weld of the welding workpiece according to the early warning screening results.

2. The intelligent management system for welding data based on automated production according to claim 1, characterized in that: The geometric characteristics of the weld corresponding to the welding workpiece specifically include the weld height, the radius of the weld fillet and the geometric shape; The texture characteristics of the weld seam of the welding workpiece specifically include roughness and uniformity.

3. The intelligent management system for welding data based on automated production according to claim 1 is characterized in that: Calculate the fatigue life of the corresponding weld of the welded workpiece. The specific operation logic is: Based on the geometric characteristics and texture characteristics of the weld seam corresponding to the welding workpiece, the stress amplitude σ and fatigue strength index b of the weld seam corresponding to the welding workpiece are calculated respectively; The fatigue life of the corresponding weld of the welded workpiece is calculated from this F is the fatigue strength life of the corresponding material of the welding workpiece.

4. The intelligent management system for welding data based on automated production according to claim 3 is characterized in that: The specific calculation logic of the stress amplitude σ of the welding workpiece corresponding to the weld is: Calculate the stress amplitude of the weld corresponding to the weld of the welded workpiece based on the weld height h and the radius r of the weld fillet P is the externally applied load, MJ is the effective cross-sectional area of ​​the weld corresponding to the welded workpiece, and the specific calculation process is as follows: Obtain the geometric shape of the weld corresponding to the welding workpiece. If the geometric shape of the weld corresponding to the welding workpiece is identified as a butt weld, then the effective cross-sectional area MJ1 of the weld corresponding to the welding workpiece = HD·KD, where HD is the thickness of the weld corresponding to the welding workpiece, and KD is the width of the weld corresponding to the welding workpiece. If the geometry of the weld corresponding to the welding workpiece is identified as a fillet weld, the effective cross-sectional area of ​​the weld corresponding to the welding workpiece is a is the leg length of the welding workpiece corresponding to the weld; If the geometric shape of the weld corresponding to the welding workpiece is identified as a T-shaped weld, then the effective cross-sectional area of ​​the weld corresponding to the welding workpiece MJ3 = a × HD; In summary, the effective cross-sectional area MJ of the weld corresponding to the welding workpiece is obtained, where the value range of MJ is MJ1, MJ2 or MJ3.

5. The intelligent management system for welding data based on automated production according to claim 4 is characterized in that: The specific calculation logic of the fatigue strength index b of the weld corresponding to the welding workpiece is: Determine the weld shape coefficient k1 corresponding to the weld workpiece based on the geometric shape of the weld corresponding to the weld workpiece; Determine the weld size factor corresponding to the welding workpiece Determine the surface roughness coefficient k3 = 1-0.01·ZC of the weld seam corresponding to the weld workpiece, where ZC is the roughness of the weld seam corresponding to the weld workpiece; Based on the uniformity of the weld corresponding to the welded workpiece, the weld uniformity coefficient k4 corresponding to the welded workpiece is obtained; The fatigue strength index of the corresponding weld of the welded workpiece is calculated as b=k0·k1·k2·k3·k4, where k0 is the reference fatigue strength value of the base material of the welded workpiece.

6. The intelligent management system for welding data based on automated production according to claim 5, characterized in that: The specific calculation logic of the fatigue strength life F of the corresponding material of the welding workpiece is: Compare the base material of the welded workpiece with the welding material of the weld; If the base material of the welded workpiece is inconsistent with the welding material of the weld, obtain the reference fatigue strength value k0 corresponding to the welding material of the weld and the stress concentration factor A2 at the weld joint; Calculate the corrected fatigue strength of the corresponding weld joint of the welded workpiece using the corrected formula η is the predefined material inconsistency correction coefficient, and its specific calculation formula is: ML′ is the elastic modulus of the welded workpiece corresponding to the parent material, and ML″ is the elastic modulus of the weld corresponding to the welding material. Finally, the fatigue strength coefficient f1 of the welded workpiece corresponding to the material when the parent material of the welded workpiece and the welding material of the weld are inconsistent is obtained by calculating the formula f1=A3-A4. A4 is the residual stress. If the base material of the welded workpiece is consistent with the welding material of the weld, the reference fatigue strength value B1 corresponding to the base material of the welded workpiece is obtained; the calculation formula is: The fatigue strength coefficient f2 of the corresponding material of the welded workpiece is obtained when the base material of the welded workpiece is consistent with the welding material of the weld, and B2 is the geometric correction coefficient at the weld joint; In summary, the fatigue strength coefficient f of the corresponding material of the welding workpiece is obtained, where f is f1 or f2; The fatigue strength life of the corresponding material of the welding workpiece is obtained 7. The intelligent management system for welding data based on automated production according to claim 3 is characterized in that: The specific calculation logic of the expected service life of the welding workpiece is: Obtain historical test data of the welding workpiece and then fit the SN curve of the welding workpiece; Based on the historical test data of the welded workpiece, the stress variation data of the welded workpiece in the corresponding historical test is obtained. All peaks and valleys are extracted from the stress time series. The extracted peak and valley data are processed using the rain flow counting method, and the number of cycles under different stress levels is then counted. According to the SN curve of the welding workpiece, the material constants m and n of the welding workpiece are determined, and the fatigue life G of the welding workpiece corresponding to each stress level is calculated. i =m·(XH i ) -n , XH i is the number of cycles under the i-th stress level, i is the number under each stress level, and the value of i ranges from 1 to S; Comprehensive analysis of the cumulative damage of welding workpieces Then calculate the expected service life of the welded workpiece 8. The intelligent management system for welding data based on automated production according to claim 1 is characterized in that: The specific operation logic of the workpiece early warning module is: The expected service life of the welding workpiece is compared with the predefined welding workpiece service life threshold. If the former is less than the latter, the welding workpiece is marked for rework and the welding correction module is executed on the welding workpiece. Otherwise, it is determined that the welding workpiece meets the production standards and is shipped accordingly.

9. The intelligent management system for welding data based on automated production according to claim 8, characterized in that: Perform welding correction on the weld seam of the welded workpiece. The specific operation logic is as follows: Determine the thickness, width, length and cross-sectional area of ​​the area to be repaired corresponding to the weld of the welding workpiece by non-destructive testing methods; And obtain the welding current I, welding voltage V and welding speed υ of the automated welding machine; Calculate the welding current of the automated welding machine corresponding to the area of ​​the welded workpiece to be repaired φ is the set empirical coefficient, The diameter of the welding rod to be repaired in the automated welding machine corresponding to the area to be repaired on the welding workpiece; Calculate the welding heat input of the automated welding machine corresponding to the area of ​​the workpiece to be repaired Calculate the filler metal volume of the weld seam of the automated welding machine corresponding to the area to be repaired: JS = MJ'·L ， MJ′ is the cross-sectional area of ​​the weld to be repaired area of ​​the welding workpiece corresponding to the weld, and L is the length of the weld to be repaired area of ​​the welding workpiece corresponding to the weld; Calculate the welding preheat temperature of the automated welding machine corresponding to the area of ​​the welded workpiece to be repaired γ1 and γ2 are the thickness and width of the weld area to be repaired, respectively. C is the set welding material constant. The thermal conductivity of the automated welding machine; The automated welding machine is controlled based on the machine welding current, machine weld filler metal amount, machine welding heat input and machine welding preheating temperature of the automated welding machine corresponding to the area to be repaired of the welding workpiece, so as to realize welding correction of the area to be repaired corresponding to the weld of the welding workpiece.

10. A welding data intelligent management method based on automated production, characterized in that: It is implemented based on an intelligent management system for welding data based on automated production according to any one of claims 1 to 9, comprising: Collect the weld seam image of the welded workpiece, perform image extraction on the weld seam image of the welded workpiece, and then detect the geometric features and texture features of the corresponding weld seam of the welded workpiece; Perform fatigue life analysis on the corresponding welds of the welding workpiece, calculate the fatigue life of the corresponding welds of the welding workpiece, and thus obtain the expected service life of the welding workpiece; The welding workpiece is pre-screened according to its expected service life, and the weld seam of the welding workpiece is corrected according to the pre-screening result.

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