Methods, devices, equipment, media and program products for determining welding process parameters

Abstract

The present invention relates to the field of welding technology and provides a method, apparatus, device, medium, and program product for determining welding process parameters. The method comprises: obtaining welding material information; determining at least one reference variation of the characteristic parameter based on characteristic parameters in the welding material information; the reference variation being the deviation between the characteristic parameter and a preset reference variation; using the at least one reference variation as a target reference variation, and determining a first welding process parameter of the welding material based on the target reference variation and preset logical association information; the logical association information being used to represent the logical relationship between the reference variation and the welding process parameter. The present invention optimizes welding process parameters based on the welding material information and the logical relationship between the reference variation and the welding process parameter, thereby improving welding quality stability, production efficiency, and reducing costs.

Description

Technical Field

[0001] The present invention relates to the field of welding technology, and in particular to a method, device, equipment, medium and program product for determining welding process parameters. Background Art

[0002] While automated welding technology has significantly improved production efficiency in the production of thick-plate cylindrical container shells, it still faces numerous challenges in practical application. In particular, when incoming materials from previous processes are inconsistent, automated welding stations often experience welding defects and quality fluctuations. These issues not only affect weld quality but also result in additional labor (such as gouging, repair welding, and grinding), increasing production costs and impacting production schedules. Consequently, optimizing welding process parameters has become a pressing issue. Summary of the Invention

[0003] The present invention aims to address at least one of the technical problems existing in the prior art. To this end, the present invention proposes a method for determining welding process parameters. By using welding material information and the logical relationship between reference variation and welding process parameters, the method optimizes welding process parameters, thereby improving welding quality stability, production efficiency, and reducing costs.

[0004] The present invention also provides a welding process parameter determination device, an electronic device, a non-transitory computer-readable storage medium, and a computer program product.

[0005] A method for determining welding process parameters according to an embodiment of the first aspect of the present invention includes:

[0006] Obtain welding material information;

[0007] Determining at least one reference variation of the characteristic parameter according to the characteristic parameter in the welding material information; the reference variation is a deviation of the characteristic parameter from a preset reference variation;

[0008] At least one of the reference changes is used as a target reference change, and a first welding process parameter of the welding material is determined according to the target reference change and preset logical association information; the preset logical association information is used to represent the logical relationship between the reference change and the welding process parameter.

[0009] According to one embodiment of the present invention, determining the first welding process parameter of the welding material according to the target reference variation and preset logical association information includes:

[0010] In a case where one of the reference variations is used as the target reference variation, the target reference variation is input as an input parameter into a first relational model in the logical association information to obtain a first welding process parameter of the welding material output by the first relational model;

[0011] In a case where the at least two reference variations are used as the target reference variations, the at least two target reference variations are input into a second relationship model in the logical association information to obtain a first welding process parameter of the welding material output by the second relationship model;

[0012] The input parameter of the first relationship model is a single target baseline variation, and the input parameter of the second relationship model is at least two target baseline variations.

[0013] According to one embodiment of the present invention, the first relationship model is determined based on the following method:

[0014] Acquiring a sample characteristic parameter, and measuring a change value of a sample welding process parameter while changing a sample baseline change amount of the sample characteristic parameter;

[0015] Recording the change values ​​of the sample welding process parameters under multiple sample reference value change ranges to form multiple groups of first data points;

[0016] Performing curve fitting on the plurality of groups of the first data points to obtain first curve information for describing the relationship between the sample baseline variation and the sample welding process parameters;

[0017] The first relationship model is determined according to the first curve information.

[0018] According to one embodiment of the present invention, the second relationship model is determined based on the following method:

[0019] Acquiring sample characteristic parameters, and determining a reference adjustment range of the sample characteristic parameters according to weights of the sample characteristic parameters;

[0020] Adjusting the amplitude of the reference value of the sample characteristic parameter and simultaneously changing the sample reference variation of a plurality of the sample characteristic parameters to measure the variation value of the sample welding process parameter;

[0021] Recording the change values ​​of the sample welding process parameters under multiple sample reference value change ranges to form multiple groups of second data points;

[0022] Performing curve fitting on the plurality of groups of the second data points to obtain second curve information for describing the relationship between the sample baseline variation and the sample welding process parameters;

[0023] The second relationship model is determined according to the second curve information.

[0024] According to one embodiment of the present invention, the weights of the sample characteristic parameters are determined based on the following method:

[0025] Analyze historical welding data to determine the influence of the sample characteristic parameters on the welding results;

[0026] Determining a first weight of the sample feature parameter according to the influence information;

[0027] Analyzing a plurality of welding scenarios to determine a second weight of the sample characteristic parameter in each of the welding scenarios;

[0028] The weight of the sample feature parameter is determined according to the first weight and the second weight.

[0029] According to an embodiment of the present invention, determining the first relationship model according to the first curve information includes:

[0030] Optimizing the first curve information according to a first process parameter range in which a deviation exists in the first curve information, or a second process parameter range that affects welding quality;

[0031] determining the first relationship model according to the optimized first curve information;

[0032] The determining the second relationship model according to the second curve information includes:

[0033] Optimizing the second curve information according to a third process parameter range in which a deviation exists in the second curve information, or a fourth process parameter range that affects welding quality;

[0034] The second relationship model is determined according to the optimized second curve information.

[0035] According to one embodiment of the present invention, after determining the first welding process parameter of the welding material according to the target reference variation and the preset logical association information, the method further includes:

[0036] Performing a welding operation according to the first welding process parameters;

[0037] During the welding process, collect welding pictures and / or welding waveforms;

[0038] Performing image analysis on the welding image and / or the welding waveform to determine a welding result;

[0039] determining a second welding process parameter of the welding material according to the welding result;

[0040] A welding operation is performed according to the second welding process parameters.

[0041] According to a second embodiment of the present invention, a device for determining welding process parameters includes:

[0042] Acquisition module, used to obtain welding material information;

[0043] a deviation determination module, configured to determine at least one reference variation of the characteristic parameter according to the characteristic parameter in the welding material information; the reference variation being a deviation of the characteristic parameter from a preset reference variation;

[0044] The welding process parameter determination module is used to use at least one of the reference changes as a target reference change, and determine the first welding process parameter of the welding material according to the target reference change and preset logical association information; the preset logical association information is used to represent the logical relationship between the reference change and the welding process parameter.

[0045] According to an embodiment of the third aspect of the present invention, an electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, a method for determining welding process parameters as described above is implemented.

[0046] According to a non-transitory computer-readable storage medium of an embodiment of the fourth aspect of the present invention, a computer program is stored thereon, and when the computer program is executed by a processor, the method for determining welding process parameters as described in any one of the above is implemented.

[0047] A computer program product according to an embodiment of the fifth aspect of the present invention includes a computer program, which, when executed by a processor, implements any of the above-mentioned methods for determining welding process parameters.

[0048] The above one or more technical solutions in the embodiments of the present invention have at least one of the following technical effects:

[0049] Based on the welding material information and the logical relationship between the reference variation and the welding process parameters, the welding process parameters are optimized, which improves the stability of welding quality, production efficiency, and reduces costs.

[0050] By deploying a real-time online monitoring system during the welding process, continuously tracking various key parameters of the welding process and making dynamic adjustments based on the real-time collected data, this closed-loop control can not only solve the quality fluctuations caused by dimensional inconsistencies (such as groove angle, gap, misalignment, etc.) during the welding process, but also improve the quality and consistency of the welded joints by optimizing the welding process parameters in real time.

[0051] Additional aspects and advantages of the present invention will be set forth in part in the description which follows and, in part, will be obvious from the description which follows, or may be learned by practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0053] Figure 1 It is a flow chart of a method for determining welding process parameters provided by an embodiment of the present invention.

[0054] Figure 2 3 is a schematic diagram of a curve showing a change in swing amplitude versus groove width provided by an embodiment of the present invention.

[0055] Figure 3 1 is a schematic diagram of a curve showing a change in welding current versus groove width provided by an embodiment of the present invention.

[0056] Figure 4 It is a structural schematic diagram of a welding process parameter determination device provided by an embodiment of the present invention.

[0057] Figure 5 It is a structural diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0058] The following embodiments of the present invention are described in further detail with reference to the accompanying drawings and examples. The following examples are used to illustrate the present invention but are not intended to limit the scope of the present invention.

[0059] In the description of the embodiments of the present invention, it should be noted that the terms “first”, “second” and “third” are used for descriptive purposes only and should not be understood as indicating or implying relative importance.

[0060] In the embodiments of the present invention, unless otherwise expressly specified or limited, a first feature being "above" or "below" a second feature may mean that the first and second features are in direct contact, or that the first and second features are in indirect contact through an intermediate medium. Furthermore, a first feature being "above," "above," or "above" a second feature may mean that the first feature is directly above or diagonally above the second feature, or simply means that the first feature is at a higher level than the second feature. A first feature being "below," "below," or "below" a second feature may mean that the first feature is directly below or diagonally below the second feature, or simply means that the first feature is at a lower level than the second feature.

[0061] In the description of this specification, the reference terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" mean that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the embodiment of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art can combine and combine different embodiments or examples described in this specification and features of different embodiments or examples without contradiction.

[0062] In the welding production process of thick plate cylindrical container shells, the following problems exist:

[0063] 1) The dimensional consistency of the incoming material, such as groove angle, misalignment, and gap, is poor. Automatic welding cannot cover the current errors, resulting in problems such as weld penetration and molten pool collapse.

[0064] 2) There will be weld spots inside the groove. Some weld spots fill the weld groove, making it difficult for the automatic welding current to penetrate the weld spot position, resulting in defects such as lack of fusion;

[0065] 3) After rolling, the straightness of the cylinder is poor, which will form bulges or depressions, resulting in uncontrollable back penetration and defects.

[0066] In related technologies, welding automation equipment mainly relies on preset standard parameters and cannot be dynamically adjusted and optimized according to variables in the actual production process. The lack of optimization means of intelligent systems makes it difficult for production lines to achieve refined control of the welding process, resulting in frequent quality fluctuations.

[0067] Based on the above problems, the present invention proposes a method for determining welding process parameters, specifically a welding process optimization technology based on an online welding quality monitoring system.

[0068] Figure 1 Schematic diagram of the flow of the method for determining welding process parameters provided by an embodiment of the present invention. Figure 1 , an embodiment of the present invention provides a method for determining welding process parameters, comprising:

[0069] Step 101: Obtain welding material information.

[0070] The executor of the present invention may be welding equipment, welding device, etc., wherein the welding equipment, welding device, etc. are provided with an arc welding online monitoring system, which includes a closed-loop optimization function for welding process parameters.

[0071] Welding material information can be obtained through external sources before welding, or detected and identified by the system during the welding process. Welding material can be understood as the entire pre-welding process, where various parts undergo processes such as cutting, sandblasting, and curling, and are finally assembled together through the assembly process to form a complete structural unit ready for welding. This assembled material is the "incoming material" that the arc welding online monitoring system focuses on. It includes all previously processed components, and these components have been preliminarily configured in position and shape according to welding requirements.

[0072] Step 102: Determine at least one reference variation of the characteristic parameter according to the characteristic parameter in the welding material information.

[0073] Before welding, the arc welding online monitoring system will extract characteristic parameters from the welding material information, among which the characteristic parameters may include: plate thickness, which indicates the thickness of the welding workpiece; length, which indicates the length of the workpiece; diameter, if it is a welding workpiece with a circular cross-section such as a pipe, its diameter needs to be obtained; groove angle, which indicates the groove angle of the welding joint, which usually affects the heat input and molten pool shape during welding; gap, which indicates the size of the gap in the joint part of the welding workpiece; misalignment, which indicates the degree of misalignment of the joint part of the welding workpiece.

[0074] After extracting the characteristic parameters, each characteristic parameter is compared with its corresponding preset reference value to calculate the deviation between the two, namely the reference variation (δ m). For example, if the groove angle measured during welding is 21 degrees, while the design requires a groove angle of 20 degrees, the reference variation (δ m) is 21-20 = 1 degree. The reference variation (δ m) can be positive or negative: a positive deviation, meaning the actual measurement value is greater than the reference value, indicates an oversize; a negative deviation, meaning the actual measurement value is less than the reference value, indicates an undersize.

[0075] Step 103: Using at least one of the reference variations as a target reference variation, and determining a first welding process parameter of the welding material according to the target reference variation and preset logical association information.

[0076] It is understood that the target baseline variation includes at least one baseline variation; the logical association information is used to represent the logical relationship between the baseline variation and the welding process parameters, and the logical association information can be represented by a function. The welding process parameters may include welding current, voltage, oscillation amplitude, welding speed, and other parameters.

[0077] At least one baseline variation is used as a target baseline variation. Based on the target baseline variation and pre-set logical association information, a first welding process parameter for the welding material is determined. For example, a logical relationship is pre-established between the baseline variations corresponding to groove angle, gap, misalignment, and other factors and welding process parameters (such as welding current, voltage, oscillation amplitude, and welding speed). Then, combined with the target baseline variation, an optimized welding process parameter recommendation is made. The first welding process parameter can be understood as the optimal welding process parameter, which is a combination of process parameters that maximizes welding efficiency, saves energy and materials, and reduces defects while ensuring welding quality. Optionally, the optimal process parameter is dynamically adjusted, enabling real-time feedback and adjustment, particularly in a volatile production environment.

[0078] In one embodiment, when one baseline change is used as a target baseline change, the target baseline change is input as an input parameter into a first relational model in the logical association information to obtain the first welding process parameter of the welding material output by the first relational model; when at least two baseline changes are used as target baseline changes, at least two target baseline changes are input into a second relational model in the logical association information to obtain the first welding process parameter of the welding material output by the second relational model; wherein the input parameter of the first relational model is a single target baseline change, and the input parameter of the second relational model is at least two target baseline changes.

[0079] It is understood that both the first and second relationship models are used to describe the logical relationship between the baseline variation and the welding process parameters. Specifically, the first function expression corresponding to the first relationship model can be a univariate multivariate function including one independent variable, which is used to describe the logical relationship between a baseline variation and a welding process parameter. By adjusting the baseline variation, the welding process parameter can be optimized. The second function expression corresponding to the second relationship model can be a multivariate linear function including two or more independent variables, which is used to describe the logical relationship between multiple baseline variations and a welding process parameter. By adjusting multiple baseline variations, a welding process parameter can be jointly optimized. The independent variable is the sample baseline variation of the sample characteristic parameter.

[0080] For example, suppose is the passive variable (i.e. welding process parameter), is the baseline change, where Indicates the Baseline changes, Indicates the The first benchmark change corresponds to welding process parameters.

[0081] if and There is a one-to-one corresponding change relationship, and the first function expression can be expressed as:

[0082] ; (1)

[0083] in, 、 、 、 、 are all constants and can be obtained by curve fitting; and In the logical relationship of , the corresponding constants are different, for example, Figure 2 , the logical relationship between the swing amplitude and the groove width is:

[0084] ;

[0085] refer to Figure 3 , the logical relationship between welding current and groove width is:

[0086] .

[0087] if and There is a one-to-many change relationship, and the second function expression can be expressed as:

[0088] ; (2)

[0089] in, 、 、 、 It is obtained based on formula (1). By expanding formula (2) with reference to formula (1), we can obtain:

[0090] ; (3)

[0091] in, Welding process parameters determined by multiple reference variations; 、 、 、 、 are all constants and can be obtained by curve fitting; and In the logical relationship, the corresponding constants are different.

[0092] Optionally, the second function expression may also be a multivariate function including multiple independent variables, as shown in formula (3).

[0093] After determining the logical relationship between the reference variation and the welding process parameters, the welding process parameters can be optimized based on the logical relationship. For example, assuming that the reference variation includes the reference variation of the groove angle and the gap, and the welding process parameter to be determined is the swing amplitude. In this case, the reference variation of the groove angle can be used as an input parameter and input into the first relationship model. The first relationship model outputs the swing amplitude corresponding to the reference variation of the groove angle through formula (1). The reference variation of the gap can also be used as an input parameter and input into the first relationship model. The first relationship model outputs the swing amplitude corresponding to the reference variation of the gap through formula (1). Alternatively, the reference variation of the groove angle and the gap can be used as input parameters and input into the second relationship model. The second relationship model outputs the swing amplitude corresponding to the reference variation of the groove angle and the gap through formula (2).

[0094] The welding process parameter determination method provided by an embodiment of the present invention obtains welding material information; determines at least one reference variation of the characteristic parameter based on characteristic parameters in the welding material information; the reference variation is the deviation between the characteristic parameter and a preset reference variation; uses the at least one reference variation as a target reference variation, and determines a first welding process parameter of the welding material based on the target reference variation and preset logical association information; the logical association information is used to represent the logical relationship between the reference variation and the welding process parameter. The present invention optimizes the welding process parameters based on the welding material information and the logical relationship between the reference variation and the welding process parameter, thereby improving the stability of welding quality, production efficiency, and reducing costs.

[0095] Based on the above embodiment, the first function expression is determined based on the following method:

[0096] Step 110, obtaining a sample characteristic parameter, and measuring a change value of a sample welding process parameter while changing a sample baseline change amount of the sample characteristic parameter;

[0097] Step 111, recording the change values ​​of the sample welding process parameters under multiple sample reference value change ranges respectively to form multiple groups of first data points;

[0098] Step 112, performing curve fitting on the plurality of groups of the first data points to obtain first curve information for describing the relationship between the sample baseline variation and the sample welding process parameters;

[0099] Step 113: Determine the first relationship model according to the first curve information.

[0100] Specifically, each time a sample benchmark change (such as the groove angle) is changed, the corresponding change values ​​of other sample welding process parameters (such as welding current, filling amount, swing amplitude, etc.) are measured; under different sample benchmark change amplitudes (such as different degrees of forward and reverse changes in the groove angle), the corresponding change values ​​of the sample welding process parameters are recorded respectively to form multiple groups of first data points. Finally, the collected multiple groups of first data points are curve fitted to obtain a function used to describe the relationship between the sample benchmark change and the sample welding process parameters, and this function is used as the first function expression.

[0101] Taking the relationship between welding current and groove angle as an example, when the groove angle changes relative to the standard groove, the welding current and other welding process parameters (such as filler weight and swing amplitude) also need to be adjusted accordingly. For example, if the groove increases by 10 degrees, the original current will be too high to prevent weld penetration. In this case, it is necessary to determine the appropriate welding current and filler weight parameters. This set of data (such as the corresponding current and filler weight changes for a 10-degree groove change) is a data point. Similarly, for groove changes of 5 and 15 degrees, there will be corresponding appropriate values ​​for parameters such as current and swing amplitude. Furthermore, it is necessary to consider both the forward and reverse directions when determining data points. That is, not only the case of a larger groove (forward direction) but also the case of a smaller groove (reverse direction). By determining the appropriate values ​​for parameters such as welding current and swing amplitude for different groove changes (such as ±5 degrees, ±10 degrees, and ±15 degrees), multiple data points are obtained. After obtaining these data points, express them in a coordinate system (the X-axis can be the change in the groove angle, and the Y-axis can be the change in the corresponding welding process parameters, such as current, filling amount, swing, etc.). Use mathematical methods (curve fitting) to connect these data points into a curve (such as Figure 2-Figure 3 (As shown in Figure 2), the mathematical expression of this curve is used to describe the logical relationship between welding process parameters and groove angle changes. Based on the curve information obtained by fitting, a first relationship model is established.

[0102] In one embodiment, the first curve information is optimized based on a first process parameter range having a deviation in the first curve information or a second process parameter range affecting welding quality; and a first relationship model is determined based on the optimized first curve information.

[0103] It is understandable that, since the actual welding situation is very complex and is affected by many factors, the preliminary fitting curve may deviate greatly from the actual situation in some intervals. For example, when welding workpieces with different material combinations and a large range of groove angle variations, it is possible that within a certain groove angle interval, the welding current corresponding to the preliminary fitting curve is used, but the welding effect is not good, and problems such as unsightly weld formation and insufficient penetration occur. This indicates that there is a large deviation between the fitting situation in this interval and the actual situation, that is, the first process parameter interval. In addition, some intervals involve key control points for welding quality, and the precise matching of parameters is very demanding. For example, when welding some pressure vessel welds that have extremely high requirements for strength and sealing, the matching accuracy requirements between parameters such as groove size and welding current are far greater than those in ordinary welding. If there are deviations in these intervals, it will lead to serious quality risks, so it is necessary to accurately determine the key interval of parameters, that is, the second process parameter interval that affects welding quality.

[0104] For intervals with large deviations in the first curve information (i.e., the first process parameter interval) or critical intervals (i.e., the second process parameter interval), more refined measurements and experiments can be conducted to re-determine the process parameters at each point within these intervals to improve the accuracy of the fitted curve in these intervals. For example, in intervals with large deviations, the density of experimental data can be increased, and as much experimental data as possible can be collected, especially where key parameters vary significantly. More accurate experimental results ensure that the fitted curve better reflects the dynamic changes in the actual welding process. After obtaining more accurate parameter values, the nodes in the fitted curve are adjusted and reconnected to make the final fitted curve more accurate within these critical intervals. Finally, a first relationship model is established based on the optimized fitted curve. By optimizing the fitted curve to better reflect the relationship between actual welding process parameters, especially in intervals with large variations or deviations during the welding process, precise adjustment of parameter values ​​in these intervals can improve the accuracy of the fitted curve, providing more reliable guidance for actual welding operations and ensuring stable and consistent welding quality.

[0105] The embodiment of the present invention determines the appropriate values ​​of the welding process parameters under different changing conditions through actual measurement and adjustment, and then uses mathematical curve fitting to obtain a first function expression that describes the relationship between the sample baseline change and the sample welding process parameters, so as to achieve the optimization of the welding process parameters based on the first function expression.

[0106] Based on the above embodiment, the second function expression is determined based on the following method:

[0107] Step 120: Acquire sample characteristic parameters, and determine a reference adjustment range of the sample characteristic parameters according to the weights of the sample characteristic parameters;

[0108] Step 121, adjusting the amplitude according to the reference value of the sample characteristic parameter, and simultaneously changing the sample reference variation of a plurality of the sample characteristic parameters to measure the variation value of the sample welding process parameter;

[0109] Step 122, recording the change values ​​of the sample welding process parameters under multiple sample reference value change ranges respectively to form multiple groups of second data points;

[0110] Step 123, performing curve fitting on the plurality of groups of the second data points to obtain second curve information for describing the relationship between the sample baseline variation and the sample welding process parameters;

[0111] Step 124: Determine the second relationship model according to the second curve information.

[0112] Each characteristic parameter (such as plate thickness, groove angle, and current) has an uneven impact on welding process parameters. Weights are introduced to quantify these differences in influence, enabling more accurate optimization of welding process parameters. Weights can be determined based on historical welding data, welding scenarios, experience, or experimentation to quantify the importance of each characteristic parameter.

[0113] In one embodiment, the weight of the sample characteristic parameter is determined based on the following method: analyzing historical welding data to determine the influence information of the sample characteristic parameter on the welding result; determining the first weight of the sample characteristic parameter based on the influence information; analyzing multiple welding scenes to determine the second weight of the sample characteristic parameter in each welding scene; and determining the weight of the sample characteristic parameter based on the first weight and the second weight.

[0114] Specifically, historical welding data is analyzed to assess the impact of each sample characteristic parameter (such as current, voltage, and welding speed) on welding results (such as weld quality and weld strength). Based on this analysis, the relative importance of each sample characteristic parameter within the entire dataset is determined, resulting in a first weight. For example, statistical methods (such as regression analysis and correlation analysis) can be used to evaluate the relationship between sample characteristic parameters and welding results.

[0115] Welding scenarios can involve different factors, such as materials, plate thicknesses, and welding methods. The same sample characteristic parameters may have different impacts on the welding results in different scenarios. Therefore, multiple welding scenarios are analyzed, and a second weight for each sample characteristic parameter is determined based on the degree of influence of each sample characteristic parameter in each scenario. For example, suppose the welding scenarios include: Scenario A: Thin plate welding (e.g., 2mm steel plate); Scenario B: Thick plate welding (e.g., 10mm steel plate). In Scenario A, welding speed may have a greater impact on weld quality because thin plates are more susceptible to welding speed. Welding too fast may result in incomplete penetration, while welding too slowly may cause overheating. In contrast, in Scenario B, current may have a greater impact on weld quality because thicker plates require higher currents to ensure good fusion. By analyzing the welding results in different scenarios, a second weight for each sample characteristic parameter can be derived. Assume that the following conclusions are drawn through analysis: Scenario A (thin plate welding): the second weight of current (I) is 0.3, and the second weight of welding speed (V) is 0.7; Scenario B (thick plate welding): the second weight of current (I) is 0.8, and the second weight of welding speed (V) is 0.2.

[0116] Finally, the first and second weights are combined to obtain a comprehensive weight for each sample's characteristic parameter. For example, the first and second weights are weighted and summed to obtain the comprehensive weight. This comprehensive weight more accurately reflects the relative importance of each feature in different scenarios, providing more precise guidance for optimizing the welding process.

[0117] By combining the first weight obtained from historical data analysis with the second weight obtained in different scenarios, a more accurate and flexible feature weight can be obtained. Based on this, it is possible to cope with the different effects of parameters on the results in different welding scenarios, thereby providing a scientific basis for the optimization of the actual welding process.

[0118] Based on the weights of the sample characteristic parameters, the adjustment range of the baseline quantity of the sample characteristic parameters is determined. For example, if the weight of the plate thickness is large, the adjustment range of the plate thickness is large; if the weight of the groove angle is small, the adjustment range of the groove angle is small. Based on the adjustment range of the baseline quantity of the sample characteristic parameters, multiple sample baseline changes (such as the groove angle) are changed each time, and the corresponding changes in other sample welding process parameters (such as welding current, fill amount, swing amplitude, etc.) are measured. Under different sample baseline change ranges (such as different degrees of forward and reverse changes in the groove angle), the corresponding changes in the sample welding process parameters are recorded respectively to form multiple sets of second data points. Finally, the multiple sets of collected second data points are curve fitted to obtain a function used to describe the relationship between the sample baseline change and the sample welding process parameters, and this function is used as the second function expression.

[0119] Taking the relationship between welding current, groove angle, and plate thickness as an example, we set the initial standard groove angle to 30 degrees and the standard plate thickness to 6 mm, corresponding to a standard welding current of 100 A. This initial value serves as the baseline for subsequent comparisons. The corresponding welding current is recorded as the groove angle and plate thickness are varied. When the groove angle is increased to 40 degrees and the plate thickness remains unchanged at 6 mm, multiple welding tests reveal that, to ensure weld quality, the welding current needs to be adjusted to 85 A. This yields data points (10 degrees, 6, 15 A). The change in groove angle serves as the first independent variable on the X-axis, the plate thickness as the second independent variable on the X-axis, and the change in welding current as the dependent variable on the Y-axis. When the groove angle is reduced to 25 degrees and the plate thickness remains at 6 mm, the welding current should be adjusted to 110 A, resulting in data points (-5 degrees, 6, 10 A). When the groove angle is reduced to 35 degrees and the plate thickness increases to 8 mm, the welding current is adjusted to 90 A, and data points (5 degrees, 8, 10 A) are recorded. When the groove angle is maintained at 30 degrees, the plate thickness is reduced to 4mm, and the welding current becomes 120A, corresponding to the data point (0 degrees, 4, 20A). The combination of groove angle and plate thickness is further changed. For example, when the groove angle is changed to 45 degrees and the plate thickness is 7mm, the welding current is adjusted to 75A, forming the data point (15 degrees, 7, 25A). Considering that the influence of groove angle and plate thickness on welding current may be more complex and not a simple linear relationship, a multivariate linear or multivariate multivariate function is selected for fitting. The collected data points are substituted into the fitting function to obtain the influence function of groove angle and plate thickness on welding current. Based on this influence function, a second relationship model is established.

[0120] In one embodiment, the second curve information is optimized according to the third process parameter range in which there is a deviation in the second curve information, or the fourth process parameter range that affects the welding quality; and the second relationship model is determined according to the optimized second curve information.

[0121] It should be understood that the third process parameter interval has the same meaning as the first process parameter interval, both indicating intervals with significant deviations in the preliminary fitting curve. For intervals with significant deviations in the second curve information (i.e., the third process parameter interval) or the fourth process parameter interval that affects welding quality, more refined measurements and experiments can be conducted to re-determine the process parameters for each point within these intervals to improve the accuracy of the fitting curve in these intervals. For example, in intervals with significant deviations, the density of experimental data can be increased, collecting as much experimental data as possible, especially in areas where key parameters vary significantly. More accurate experimental results ensure that the fitting curve better reflects the dynamic changes in the actual welding process. After obtaining more accurate parameter values, the nodes in the fitting curve are adjusted and reconnected to make the final fitting curve more accurate within these key intervals. Finally, a second relationship model is established based on the optimized fitting curve. By optimizing the fitting curve to better reflect the relationship between actual welding process parameters, especially in intervals with significant variations or deviations during the welding process, precise adjustment of parameter values ​​in these intervals can improve the accuracy of the fitting curve, providing more reliable guidance for actual welding operations and ensuring stable and consistent welding quality.

[0122] The embodiment of the present invention determines the appropriate values ​​of the welding process parameters under different changing conditions through actual measurement and adjustment, and then uses mathematical curve fitting to obtain a second function expression that describes the relationship between the sample baseline change and the sample welding process parameters, so as to achieve the optimization of the welding process parameters based on the second function expression.

[0123] Based on the above embodiment, after determining the first welding process parameter of the welding material according to the target reference variation and the preset logical association information, the method further includes:

[0124] Step 130, performing a welding operation according to the first welding process parameters;

[0125] Step 131, during the welding process, collecting welding pictures and / or welding waveforms;

[0126] Step 132: performing image analysis on the welding image and / or the welding waveform to determine a welding result;

[0127] Step 133, determining a second welding process parameter of the welding material according to the welding result;

[0128] Step 134: Perform a welding operation according to the second welding process parameters.

[0129] After determining the first welding process parameters (i.e., the optimal welding process parameters), the system performs the welding operation based on the first welding process parameters. During the actual welding process, the welding equipment collects real-time data through sensors, cameras, or other detection devices. This data can include: welding images, which are captured by high-resolution cameras during the welding process, such as the welding arc, weld pool, and weld seam; welding waveforms, which are generated by real-time acquisition of signals such as current and voltage to reflect changes in welding current and voltage fluctuations.

[0130] The images of the welding process are then analyzed using image processing techniques (such as edge detection, image segmentation, and morphological analysis). Image analysis can help detect welding defects such as uneven welds, weld spatter, weld porosity, and weld defects. By analyzing the welding waveform, it is possible to determine whether the current and voltage during the welding process are within the predetermined range and whether they fluctuate smoothly. For example, excessive current fluctuations may indicate a problem in the welding process, resulting in unstable weld quality. Based on the analysis of the images and waveforms, the system can automatically assess the welding quality and determine whether it meets the predetermined standards.

[0131] Based on the results of the first welding process parameter analysis (i.e., weld quality assessment), the system adjusts the welding process parameters. If a weld defect or substandard weld quality is detected, the system automatically adjusts the welding process parameters (such as current, voltage, and welding speed) based on the analysis results to obtain the second welding process parameters. This process is a key component of closed-loop control. By adjusting welding process parameters in real time, it ensures that subsequent welding operations will improve quality and reduce defects.

[0132] The welding operation is performed according to the second welding process parameters. These parameters have been optimized based on feedback from the welding process, with the goal of improving weld quality through adjustments. This closed-loop control approach allows the system to dynamically adjust process parameters under varying welding conditions, thereby continuously improving weld quality.

[0133] In one embodiment, welding problems in the welding system are identified based on welding results. Welding alarms are then generated based on these problems. Evaluation of welding results can reveal system problems or defects (e.g., weak welds, uneven welds, excessive heat-affected zones, etc.). These problems can be caused by improper process parameter settings, equipment failure, improper operation, and other factors. The system needs to be able to identify potential problems based on feedback from the welding process (images, waveforms, etc.). If the system detects certain welding problems (e.g., defects or abnormal parameters during welding), a welding alarm is generated. This alarm can include the type of welding defect (e.g., porosity, cracks, cold welds), abnormal process parameters (e.g., excessive current, excessive welding speed), equipment failure, or operational issues. This welding alarm can promptly notify the operator or automatically adjust equipment parameters to prevent further deterioration of quality issues.

[0134] The embodiments of the present invention deploy a real-time online monitoring system during the welding process to continuously track various key parameters of the welding process and make dynamic adjustments based on the real-time collected data. This closed-loop control not only solves quality fluctuations caused by dimensional inconsistencies (such as groove angle, gap, misalignment, etc.) during the welding process, but also improves the quality and consistency of the welded joints by optimizing the welding process parameters in real time.

[0135] The welding process parameter determination device provided by the present invention is described below. The welding process parameter determination device described below and the welding process parameter determination method described above can refer to each other.

[0136] refer to Figure 4 The welding process parameter determination device provided by the present invention includes an acquisition module 401, a deviation determination module 402 and a welding process parameter determination module 403.

[0137] An acquisition module 401 is used to acquire welding material information;

[0138] The deviation determination module 402 is configured to determine at least one reference variation of the characteristic parameter according to the characteristic parameter in the welding material information; the reference variation is a deviation of the characteristic parameter from a preset reference variation;

[0139] The welding process parameter determination module 403 is used to use at least one of the reference changes as a target reference change, and determine the first welding process parameter of the welding material according to the target reference change and preset logical association information; the preset logical association information is used to represent the logical relationship between the reference change and the welding process parameter.

[0140] The welding process parameter determination device provided in an embodiment of the present invention obtains welding material information; determines at least one reference variation of the characteristic parameter based on characteristic parameters in the welding material information; the reference variation is the deviation between the characteristic parameter and a preset reference variation; uses the at least one reference variation as a target reference variation, and determines a first welding process parameter of the welding material based on the target reference variation and preset logical association information; the logical association information is used to represent the logical relationship between the reference variation and the welding process parameter. Based on the welding material information and the logical relationship between the reference variation and the welding process parameter, the present invention optimizes the welding process parameters, improves welding quality stability, production efficiency, and reduces costs.

[0141] In one embodiment, the welding process parameter determination module 403 is specifically used to: when one of the benchmark changes is used as the target benchmark change, the target benchmark change is input as an input parameter into the first relationship model in the logical association information to obtain the first welding process parameter of the welding material output by the first relationship model; when at least two of the benchmark changes are used as the target benchmark changes, the at least two target benchmark changes are input into the second relationship model in the logical association information to obtain the first welding process parameter of the welding material output by the second relationship model; wherein the input parameter of the first relationship model is a single target benchmark change, and the input parameter of the second relationship model is at least two target benchmark changes.

[0142] In one embodiment, the welding process parameter determination module 403 is further used to: obtain sample characteristic parameters, and measure the change value of the sample welding process parameters when changing the sample baseline change amount of the sample characteristic parameters; record the change value of the sample welding process parameters under multiple sample baseline change amplitudes to form multiple groups of first data points; perform curve fitting on the multiple groups of first data points to obtain first curve information for describing the relationship between the sample baseline change amount and the sample welding process parameters; and determine the first relationship model based on the first curve information.

[0143] In one embodiment, the welding process parameter determination module 403 is also used to: obtain sample characteristic parameters, and determine the baseline adjustment amplitude of the sample characteristic parameters according to the weight of the sample characteristic parameters; according to the baseline adjustment amplitude of the sample characteristic parameters, simultaneously change the sample baseline variation of multiple sample characteristic parameters to measure the change value of the sample welding process parameters; under multiple sample baseline variation amplitudes, respectively record the change value of the sample welding process parameters to form multiple groups of second data points; perform curve fitting on the multiple groups of second data points to obtain second curve information for describing the relationship between the sample baseline variation and the sample welding process parameters; and determine the second relationship model based on the second curve information.

[0144] In one embodiment, the welding process parameter determination module 403 is also used to: analyze historical welding data to determine the influence information of the sample characteristic parameters on the welding results; determine the first weight of the sample characteristic parameters based on the influence information; analyze multiple welding scenes to determine the second weight of the sample characteristic parameters in each welding scene; and determine the weight of the sample characteristic parameters based on the first weight and the second weight.

[0145] In one embodiment, the welding process parameter determination module 403 is also used to: optimize the first curve information according to the first process parameter interval in which there is a deviation in the first curve information, or the second process parameter interval that affects the welding quality; determine the first relationship model according to the optimized first curve information; optimize the second curve information according to the third process parameter interval in which there is a deviation in the second curve information, or the fourth process parameter interval that affects the welding quality; and determine the second relationship model according to the optimized second curve information.

[0146] In one embodiment, the welding process parameter determination module 403 is also used to: perform a welding operation according to the first welding process parameters; during the welding process, collect welding pictures and / or welding waveforms; perform image analysis on the welding pictures and / or the welding waveforms to determine the welding results; determine the second welding process parameters of the welding material according to the welding results; and perform a welding operation according to the second welding process parameters.

[0147] Figure 5 An example of a physical structure diagram of an electronic device is shown below. Figure 5As shown, the electronic device may include: a processor 510, a communication interface 520, a memory 530, and a communication bus 540, wherein the processor 510, the communication interface 520, and the memory 530 communicate with each other via the communication bus 540. The processor 510 may call logic instructions in the memory 530 to execute the following method: obtaining welding material information; determining at least one baseline variation of the characteristic parameter based on characteristic parameters in the welding material information; the baseline variation is the deviation between the characteristic parameter and a preset baseline; using at least one of the baseline variations as a target baseline variation, and determining a first welding process parameter of the welding material based on the target baseline variation and preset logical association information; the preset logical association information is used to represent the logical relationship between the baseline variation and the welding process parameter.

[0148] Furthermore, the logic instructions in the aforementioned memory 530 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as a USB flash drive, a mobile hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0149] On the other hand, an embodiment of the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, it is implemented to execute the welding process parameter determination method provided in the above-mentioned embodiments, for example, including: obtaining welding material information; determining at least one baseline change of the characteristic parameter based on the characteristic parameters in the welding material information; the baseline change is the deviation between the characteristic parameter and the preset baseline; using at least one of the baseline changes as a target baseline change, and determining the first welding process parameter of the welding material based on the target baseline change and preset logical association information; the preset logical association information is used to represent the logical relationship between the baseline change and the welding process parameter.

[0150] On the other hand, an embodiment of the present invention discloses a computer program product, which includes a computer program stored on a non-transitory computer-readable storage medium, and the computer program includes program instructions. When the program instructions are executed by a computer, the computer can execute the welding process parameter determination method provided by the above-mentioned method embodiments, for example, including: obtaining welding material information; determining at least one baseline change of the characteristic parameter based on the characteristic parameters in the welding material information; the baseline change is the deviation of the characteristic parameter from a preset baseline; using at least one of the baseline changes as a target baseline change, and determining the first welding process parameter of the welding material based on the target baseline change and preset logical association information; the preset logical association information is used to represent the logical relationship between the baseline change and the welding process parameter.

[0151] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, i.e., they may be located in one location or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of the present embodiment. Persons of ordinary skill in the art will be able to understand and implement the present invention without inventive effort.

[0152] Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented using software plus a necessary general-purpose hardware platform, or of course, hardware. Based on this understanding, the essence of the above technical solution, or the portion that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, or an optical disk, and includes a number of instructions for causing a computer device (such as a personal computer, server, or network device) to execute the methods described in each embodiment or certain portions of the embodiments.

[0153] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

[0154] The above embodiments are intended to illustrate the present invention only and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the embodiments, it should be understood by those skilled in the art that various combinations, modifications, or equivalent substitutions of the technical solutions of the present invention do not depart from the spirit and scope of the technical solutions of the present invention and should be encompassed by the scope of the present invention.

Claims

1. A method for determining welding process parameters, characterized in that: include: Obtain welding material information; Determining at least one baseline variation of the characteristic parameter according to the characteristic parameter in the welding material information; The reference variation is the deviation between the characteristic parameter and the preset reference value; using at least one of the reference variations as a target reference variation, and determining a first welding process parameter of the welding material according to the target reference variation and preset logical association information; The preset logical association information is used to represent the logical relationship between the reference variation and the welding process parameters; The step of determining a first welding process parameter of the welding material according to the target reference variation and preset logical association information includes: In a case where one of the reference variations is used as the target reference variation, the target reference variation is input as an input parameter into a first relational model in the logical association information to obtain a first welding process parameter of the welding material output by the first relational model; In a case where the at least two reference variations are used as the target reference variations, the at least two target reference variations are input into a second relationship model in the logical association information to obtain a first welding process parameter of the welding material output by the second relationship model; The input parameter of the first relationship model is a single target baseline variation, and the input parameter of the second relationship model is at least two target baseline variations; The first relationship model is determined based on the following method: Acquiring a sample characteristic parameter, and measuring a change value of a sample welding process parameter while changing a sample baseline change amount of the sample characteristic parameter; Recording the change values ​​of the sample welding process parameters under multiple sample reference value change ranges to form multiple groups of first data points; Performing curve fitting on the plurality of groups of the first data points to obtain first curve information for describing the relationship between the sample baseline variation and the sample welding process parameters; determining the first relationship model according to the first curve information; Determining the first relationship model according to the first curve information includes: Optimizing the first curve information according to a first process parameter range in which a deviation exists in the first curve information, or a second process parameter range that affects welding quality; The first relationship model is determined according to the optimized first curve information.

2. The method for determining welding process parameters according to claim 1, wherein: The second relational model is determined based on the following method: Acquiring sample characteristic parameters, and determining a reference adjustment range of the sample characteristic parameters according to weights of the sample characteristic parameters; Adjusting the amplitude of the reference value of the sample characteristic parameter and simultaneously changing the sample reference variation of a plurality of the sample characteristic parameters to measure the variation value of the sample welding process parameter; Recording the change values ​​of the sample welding process parameters under multiple sample reference value change ranges to form multiple groups of second data points; Performing curve fitting on the plurality of groups of the second data points to obtain second curve information for describing the relationship between the sample baseline variation and the sample welding process parameters; The second relationship model is determined according to the second curve information.

3. The method for determining welding process parameters according to claim 2, wherein: The weights of the sample characteristic parameters are determined based on the following method: Analyze historical welding data to determine the influence of the sample characteristic parameters on the welding results; Determining a first weight of the sample feature parameter according to the influence information; Analyzing a plurality of welding scenarios to determine a second weight of the sample characteristic parameter in each of the welding scenarios; The weight of the sample feature parameter is determined according to the first weight and the second weight.

4. The method for determining welding process parameters according to claim 2, wherein: The determining the second relationship model according to the second curve information includes: Optimizing the second curve information according to a third process parameter range in which a deviation exists in the second curve information, or a fourth process parameter range that affects welding quality; The second relationship model is determined according to the optimized second curve information.

5. The method for determining welding process parameters according to any one of claims 1 to 4, characterized in that: After determining the first welding process parameter of the welding material according to the target reference variation and the preset logical association information, the method further includes: Performing a welding operation according to the first welding process parameters; During the welding process, collect welding pictures and / or welding waveforms; Performing image analysis on the welding image and / or the welding waveform to determine a welding result; determining a second welding process parameter of the welding material according to the welding result; A welding operation is performed according to the second welding process parameters.

6. A welding process parameter determination device for implementing the welding process parameter determination method according to any one of claims 1 to 5, characterized in that: include: Acquisition module, used to obtain welding material information; a deviation determination module, configured to determine at least one reference variation of the characteristic parameter according to the characteristic parameter in the welding material information; the reference variation being a deviation of the characteristic parameter from a preset reference variation; a welding process parameter determination module, configured to use at least one of the reference variation amounts as a target reference variation amount, and determine a first welding process parameter of the welding material according to the target reference variation amount and preset logical association information; The preset logical association information is used to represent the logical relationship between the reference variation and the welding process parameters.

7. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the method for determining welding process parameters according to any one of claims 1 to 5 is implemented.

8. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for determining welding process parameters according to any one of claims 1 to 5 is implemented.

9. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the method for determining welding process parameters according to any one of claims 1 to 5 is implemented.

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