A welding control method, a welding control system, an electronic device, and a medium

Abstract

This application provides a welding control method, a welding control system, an electronic device, and a medium. The method includes: acquiring welding conditions of a workpiece to be welded; acquiring a preset welding quality prediction model; inputting the welding conditions and multiple initial welding process parameters into the welding quality prediction model, and receiving welding quality prediction data of each initial welding process parameter output by the welding quality prediction model; determining a target welding process parameter from among the multiple initial welding process parameters based on the welding quality prediction data; and performing welding processing on the workpiece using the target welding process parameter. This application's embodiments use a welding quality prediction model to predict the welding quality under specified welding conditions and initial welding process parameters, and then use the welding quality prediction data to select a target welding process parameter, making the target welding process parameter applicable to the specified welding conditions, thereby improving welding efficiency.

Description

Technical Field

[0001] This application relates to the field of welding technology, specifically to a welding control method, a welding control system, electronic equipment, and a medium. Background Technology

[0002] In industries such as shipbuilding and processing, the development of welding technology directly impacts ship production efficiency. In shipbuilding, sub-assemblies are a basic and widely used structural form for the hull. Modern shipbuilding methods first manufacture various sub-assemblies in workshops and open-air factories using welding technology, and then assemble them into ship sections. To improve ship production efficiency, industrial robots can be used for automated welding operations, thus promoting the automation and intelligent development of shipbuilding.

[0003] However, industrial robots can usually only operate according to pre-set welding process parameters. When faced with new welding conditions, the pre-set welding process parameters are often not applicable, which greatly affects welding efficiency. Summary of the Invention

[0004] This application provides a welding control method, a welding control system, an electronic device, and a medium, aimed at improving welding efficiency.

[0005] On the one hand, this application provides a welding control method, the welding control method comprising:

[0006] Obtain the welding conditions for the workpiece to be welded, wherein the welding conditions include at least one of the following: base material type, welding material type, plate thickness, butt joint type, and bevel type;

[0007] Obtain the preset welding quality prediction model;

[0008] The welding conditions and multiple initial welding process parameters are input into the welding quality prediction model, and the welding quality prediction data of each initial welding process parameter output by the welding quality prediction model are received.

[0009] Based on the welding quality prediction data, the target welding process parameters are determined from among the multiple initial welding process parameters;

[0010] The workpiece to be welded is then subjected to welding using the target welding process parameters.

[0011] In one possible implementation of this application, after performing welding treatment on the workpiece using the target welding process parameters, the method further includes:

[0012] Obtain the actual welding quality data of the workpiece to be welded;

[0013] The welding quality prediction model is adjusted based on the welding conditions, the target welding process parameters, and the actual welding quality data.

[0014] In one possible implementation of this application, before obtaining the welding conditions of the workpiece to be welded, the method further includes:

[0015] Obtain historical welding conditions, historical welding process parameters, and actual historical welding quality data for historical welded workpieces;

[0016] Based on the historical welding conditions, the historical welding process parameters, and the actual historical welding quality data, a model training dataset is generated.

[0017] The welding quality prediction model is obtained by training the model using the model training dataset.

[0018] In one possible implementation of this application, after obtaining the welding conditions of the workpiece to be welded, the method further includes:

[0019] In a preset welding process database, it is checked whether the welding conditions are stored. The welding process database stores multiple welding process card information, and each welding process card information includes associated preset welding conditions and preset welding process parameters.

[0020] If the welding conditions are not stored in the welding process database, the step of obtaining the preset welding quality prediction model is executed.

[0021] In one possible implementation of this application, after performing welding treatment on the workpiece using the target welding process parameters, the method further includes:

[0022] Obtain the actual welding quality data of the workpiece to be welded;

[0023] Based on the actual welding quality data, determine whether the workpiece to be welded is qualified.

[0024] When the workpiece to be welded passes the welding test, a welding process card is generated based on the welding conditions and the target welding process parameters, and stored in the welding process database.

[0025] In one possible implementation of this application, after detecting whether the welding conditions are stored in a preset welding process database, the method further includes:

[0026] If the welding conditions are stored in the welding process database, a target welding process card information including the welding conditions is determined from among the multiple welding process card information in the welding process database.

[0027] Based on the preset welding process parameters in the target welding process card information, the workpiece to be welded is subjected to welding processing.

[0028] In one possible implementation of this application, the step of welding the workpiece to be welded based on the preset welding process parameters in the target welding process card information includes:

[0029] The preset welding process parameters in the target welding process card information are adjusted to obtain the adjusted welding process parameters.

[0030] The workpiece to be welded is then welded using the adjusted welding process parameters.

[0031] After welding the workpiece using adjusted welding process parameters, the process further includes:

[0032] Obtain the actual welding quality data of the workpiece to be welded after adjustment;

[0033] The actual welding quality data after adjustment is compared with the actual welding quality data of the preset welding process parameters in the target welding process card information to obtain the comparison result;

[0034] Based on the comparison results, the target welding process card information is updated using the adjusted welding process parameters.

[0035] On the other hand, this application provides a welding control system, which includes:

[0036] The first acquisition unit is used to acquire the welding conditions of the workpiece to be welded, wherein the welding conditions include at least one of the following: base material type, welding material type, plate thickness, butt joint type, and bevel type.

[0037] The second acquisition unit is used to acquire a preset welding quality prediction model;

[0038] The quality prediction unit is used to input the welding conditions and multiple initial welding process parameters into the welding quality prediction model, and to receive the welding quality prediction data of each of the initial welding process parameters output by the welding quality prediction model.

[0039] A process screening unit is used to determine a target welding process parameter from multiple initial welding process parameters based on the welding quality prediction data.

[0040] The welding processing unit is used to perform welding processing on the workpiece to be welded using the target welding process parameters.

[0041] On the other hand, this application also provides an electronic device, the electronic device comprising:

[0042] One or more processors;

[0043] Memory; and

[0044] One or more applications, wherein the one or more applications are stored in the memory and configured to be executed by the processor to implement the welding control method described above.

[0045] On the other hand, this application also provides a computer-readable storage medium having a computer program stored thereon, the computer program being loaded by a processor to perform the steps in the above-described welding control method.

[0046] The welding control method, welding control system, electronic equipment, and medium provided in this application include: acquiring welding conditions for the workpiece to be welded, wherein the welding conditions include at least one of base material type, welding material type, plate thickness, butt joint type, and bevel type; acquiring a preset welding quality prediction model; inputting the welding conditions and multiple initial welding process parameters into the welding quality prediction model, and receiving welding quality prediction data of each initial welding process parameter output by the welding quality prediction model; determining a target welding process parameter from the multiple initial welding process parameters based on the welding quality prediction data; and performing welding processing on the workpiece to be welded using the target welding process parameter. This application uses a welding quality prediction model to predict the welding quality under specified welding conditions and initial welding process parameters, and then uses the welding quality prediction data to select a target welding process parameter, making the target welding process parameter applicable to the specified welding conditions, thereby improving welding efficiency. Attached Figure Description

[0047] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0048] Figure 1 This is a schematic flowchart of an embodiment of the welding control method provided in this application.

[0049] Figure 2 This is a schematic flowchart of another embodiment of the welding control method provided in this application;

[0050] Figure 3 This is a schematic flowchart of another embodiment of the welding control method provided in this application.

[0051] Figure 4 This is a schematic diagram of an embodiment of the welding control system provided in this application.

[0052] Figure 5 This is a schematic diagram of an embodiment of the electronic device provided in this application. Detailed Implementation

[0053] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0054] In the description of this application, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the stated features. In the description of this application, "multiple" means two or more, unless otherwise explicitly specified.

[0055] In this application, the phrase "in some embodiments of this application" is used to mean "used as an example, illustration, or description." Any embodiment described in this application as "in some embodiments of this application" is not necessarily to be construed as being more preferred or advantageous than other embodiments. The following description is provided to enable any person skilled in the art to make and use this application. Details are set forth in the following description for purposes of explanation. It should be understood that those skilled in the art will recognize that this application can be made without using these specific details. Therefore, this application is not intended to be limited to the embodiments shown, but is consistent with the broadest scope of the principles and features disclosed herein.

[0056] This application provides a welding control method, a welding control system, an electronic device, and a medium, which will be described in detail below.

[0057] First, the welding control method provided in the embodiments of this application will be introduced.

[0058] In the embodiments of the welding control method of this application, the welding control system is used as the execution subject. For the sake of simplicity and ease of description, the execution subject will be omitted in the subsequent method embodiments.

[0059] Please see Figure 1 , Figure 1This is a schematic flowchart of an embodiment of the welding control method provided in this application. The welding control method includes:

[0060] 101. Obtain the welding conditions of the workpiece to be welded. The welding conditions include at least one of the following: base material type, welding material type, plate thickness, butt joint type, and bevel type.

[0061] In this embodiment, the workpiece to be welded refers to a workpiece that requires welding treatment, such as a workpiece assembled in a ship, or a workpiece of other equipment, which is not limited here. The workpiece to be welded may include base material. The welding method can be laser welding, arc welding, or other methods. Welding conditions refer to the scenario information when performing welding treatment, such as at least one of the following: base material type, welding material type, plate thickness, butt joint type, and bevel type. Among them, plate thickness may include the thickness of the base material, the thickness of the welding material, etc. The butt joint type is the welding joint type, which may include butt joints, T-joints, corner joints, and lap joints, etc. The bevel type may include various bevel types such as I-shaped, V-shaped, Y-shaped, double Y-shaped, U-shaped, double U-shaped, single-sided V-shaped, double single-sided Y-shaped, J-shaped, etc. It is understood that under different welding conditions, the welding process parameters used to achieve better welding results are often different.

[0062] In some embodiments of this application, the welding conditions of the workpiece to be welded can be set manually or determined based on the incoming material information of the workpiece to be welded, and there is no limitation here.

[0063] 102. Obtain the preset welding quality prediction model.

[0064] In this embodiment, the welding quality prediction model can be used to predict the welding quality of a workpiece and output corresponding welding quality prediction data. The welding quality prediction model can be obtained in advance through model training. For example, the structure of the welding quality prediction model can adopt a BP (back propagation) neural network structure. BP neural network technology can perform complex pattern learning and recognition, and arrive at intelligent judgments or decisions.

[0065] The following describes the generation process of the welding quality prediction model. Specifically, before obtaining the welding conditions of the workpiece to be welded, the process may include: obtaining historical welding conditions, historical welding process parameters, and historical welding quality data of historical welded workpieces; generating a model training dataset based on the historical welding conditions, historical welding process parameters, and historical welding quality data, for example, the model training dataset may include historical welding conditions, historical welding process parameters, and historical welding quality data of multiple different historical welded workpieces; and training the model using the model training dataset based on a supervised learning model to obtain the welding quality prediction model.

[0066] Historical welding conditions may include at least one of the following: historical base material type, historical welding material type, historical plate thickness, historical butt joint type, and historical bevel type. Historical welding process parameters may include at least one of the following: welding current, welding voltage, and welding speed. Historical welding quality data may include actual measured values ​​of at least one of the following: hardness, tensile strength, and bending strength of the historically welded workpiece after welding. Historical welding quality data can be obtained through testing with appropriate measuring instruments, or by sending the historically welded workpiece to a third-party testing institution for testing.

[0067] When training the model using the training dataset, historical welding conditions and process parameters are used as input to the initial model, while the corresponding actual historical welding quality data is used as the expected output of the initial model. In other words, the actual historical welding quality data serves as the label for the corresponding historical welding conditions and process parameters. Based on the actual output and expected output of the initial model, the model parameters are fine-tuned to obtain the welding quality prediction model. The specific process of model training will not be elaborated here.

[0068] It should be noted that, due to the diverse and complex types of welding process data, it is difficult to predict welding quality in low-dimensional space. Therefore, by using deep learning and leveraging the powerful learning capabilities of neural networks, the connections between neurons can be adjusted to fit complex high-dimensional mapping relationships. This results in a welding quality prediction model with a high-dimensional mapping between welding conditions, welding process parameters, and welding quality prediction data, which can make welding quality prediction more accurate.

[0069] 103. Input the welding conditions and multiple initial welding process parameters into the welding quality prediction model, and receive the welding quality prediction data of each initial welding process parameter output by the welding quality prediction model.

[0070] In this embodiment, the initial welding process parameters may include at least one of welding current, welding voltage, and welding speed. Welding quality prediction data may include predicted values ​​of at least one of the hardness, tensile strength, and bending strength of the workpiece after welding. The initial welding process parameters can be manually preset or randomly generated within a preset numerical range. After inputting the welding conditions and initial welding process parameters into the welding quality prediction model, the model can output the predicted welding quality data under those conditions and initial welding process parameters. The predicted welding quality data under different welding conditions and different initial welding process parameters are generally different. The welding conditions, initial welding process parameters, and historical welding conditions and parameters input into the welding quality prediction model are all normalized data to reduce the impact of numerical differences.

[0071] 104. Based on welding quality prediction data, the target welding process parameters are determined from multiple initial welding process parameters.

[0072] In this embodiment of the application, since the welding quality prediction data can reflect the prediction result of the welding quality, the target welding process parameter with better welding quality can be determined from multiple initial welding process parameters based on the welding quality prediction data.

[0073] In some embodiments of this application, determining a target welding process parameter from multiple initial welding process parameters based on welding quality prediction data may include: converting the welding quality prediction data of each initial welding process parameter into a corresponding welding quality prediction score; and selecting the initial welding process parameter with the highest welding quality prediction score from among the multiple initial welding process parameters as the target welding process parameter.

[0074] 105. Use the target welding process parameters to perform welding treatment on the workpiece to be welded.

[0075] In this embodiment, the workpiece is welded according to the target welding process parameters. For example, welding control commands can be generated based on the target welding process parameters and sent to the corresponding welding robot to control the robot to weld the workpiece. In this way, the welded workpiece can theoretically achieve the welding quality corresponding to the predicted welding quality data, making the target welding process parameters applicable to the welding conditions of the workpiece and resulting in better welding quality. Especially when the welding conditions of the workpiece are new, this method can be used to determine a suitable set of target welding process parameters.

[0076] In some embodiments of this application, the welding process of the workpiece to be welded using target welding process parameters may further include: acquiring actual welding quality data of the workpiece to be welded, wherein the method of acquiring actual welding quality data is similar to that of acquiring historical welding quality data as described above, and will not be elaborated here; adjusting the welding quality prediction model based on welding conditions, target welding process parameters, and actual welding quality data. For example, welding conditions, target welding process parameters, and actual welding quality data can be used as a set of training samples in the model training dataset. The welding conditions and target welding process parameters are input into the welding quality prediction model, and the actual welding quality data is used as the expected output of the welding quality prediction model to further train the model and adjust it. In this way, the welding quality prediction model can be continuously optimized based on actual welding quality data, thereby continuously improving the prediction accuracy and stability of the welding quality prediction model.

[0077] The welding control method provided in this application uses a welding quality prediction model to predict the welding quality under specified welding conditions and initial welding process parameters. Based on the welding quality prediction data, target welding process parameters are then selected to ensure applicability to the specified welding conditions, thereby improving welding efficiency. Furthermore, since arc welds are commonly used for sub-assemblies in shipbuilding, and arc welds are often more difficult to perform, using target welding process parameters for workpieces requiring arc welds yields better welding results.

[0078] In some embodiments of this application, such as Figure 2 As shown, after obtaining the welding conditions for the workpiece to be welded, it may also include:

[0079] 201. In the preset welding process database, check whether welding conditions are stored. The welding process database stores multiple welding process card information, and each welding process card information includes the associated preset welding conditions and preset welding process parameters.

[0080] In this embodiment, the welding process database is used to store welding process-related information, such as welding process card information. The welding process card information includes associated preset welding conditions and preset welding process parameters. The association between the preset welding conditions and preset welding process parameters indicates that under the preset welding conditions, welding according to the preset welding process parameters yields better welding results.

[0081] In some embodiments of this application, the preset welding conditions in the welding process card information can be the historical welding conditions of historical welded workpieces, and the preset welding process parameters in the welding process card information can be the historical welding process parameters of historical welded workpieces. The actual welding quality data under these preset welding conditions and preset welding process parameters can be the actual historical welding quality data of historical welded workpieces. The historical welding conditions, historical welding process parameters, and actual historical welding quality data of historical welded workpieces can be obtained through big data technology. Big data technology includes steps such as data cleaning, data integration, data selection, data transformation, data mining, pattern evaluation, and knowledge representation, thereby collecting and purifying the complex welding process-related data to obtain welding process data in the required format. The processed welding process data will be evaluated to initially screen out qualified historical welded workpieces, thereby obtaining the historical welding conditions, historical welding process parameters, and actual historical welding quality data of the qualified historical welded workpieces. Data mining refers to the process of identifying and extracting practical and valuable knowledge from a large amount of data. Data cleaning refers to removing noisy or non-standard data (e.g., incorrect format or range). Data integration refers to combining large amounts of data from various databases or web pages to form a set. Data selection refers to choosing and analyzing data from the database according to task requirements, selecting data that meets those requirements. Data transformation refers to transforming data into a form suitable for data mining through specific operations, such as summarization or focusing. Pattern evaluation refers to measuring the patterns obtained in the previous step (data mining) according to relevant defined metrics, ultimately identifying useful patterns that represent true knowledge. Knowledge representation refers to using visualization and knowledge representation to provide and display the knowledge obtained from data mining to users, facilitating faster understanding.

[0082] In some embodiments of this application, the welding process database can be constructed based on blockchain technology. Blockchain is a distributed database technology that connects data blocks in an orderly manner and uses cryptography to ensure its immutability and unforgeability. Blockchain technology can achieve the functions of openness, transparency, immutability, unforgeability, and traceability of all data information in the system without the need for third-party endorsement. Specifically, the welding process card information in the welding process database is stored in a chain structure, that is, each welding process card information is a data chain. Each data chain includes multiple blocks, and each block includes a block header and a block body. The block header stores the hash value of the previous block, the hash value of the current block body, and a timestamp, etc. The block body stores a set of preset welding conditions and a set of preset welding process parameters and their hash values. The preset welding conditions stored in each block of the same data chain are the same. Based on blockchain technology, the storage and encryption functions of a huge amount of welding process data can be realized, increasing the difficulty of data tampering and greatly enhancing the security of welding process data. Based on this, all node computers connected to this blockchain can share completely consistent data, thereby enabling timely and accurate sharing of welding process data among all connected nodes and reducing the cost of sharing welding process data between sister units.

[0083] 202. If the welding process database does not store welding conditions, execute the step of obtaining the preset welding quality prediction model.

[0084] In this embodiment of the application, if the welding process database does not store the welding conditions of the workpiece to be welded, it indicates that the welding conditions are new welding conditions. If the welding process database does not store the welding process parameters that have a better welding effect under the welding conditions, then the preset welding quality prediction model and its subsequent steps can be executed to automatically predict the target welding process parameters that have a better welding effect under the welding conditions through the welding quality prediction model.

[0085] In some embodiments of this application, for cases where the welding conditions are new, after welding the workpiece using target welding process parameters, the process may further include: acquiring actual welding quality data of the workpiece; determining whether the workpiece is welded successfully based on the actual welding quality data; the judgment rule for whether the workpiece is welded successfully can be preset based on actual needs. For example, if the actual measured value of at least one of the hardness, tensile strength, and bending strength after welding is greater than the corresponding qualified threshold in the actual welding quality data, the workpiece can be determined to be welded successfully; when the workpiece is welded successfully, it indicates that the welding effect of welding under the welding conditions according to the target welding process parameters is good. Therefore, a welding process card information can be generated based on the welding conditions and target welding process parameters and stored in the welding process database for subsequent use. In the welding process card information generated based on the welding conditions and target welding process parameters, the welding conditions are a set of preset welding conditions, and the target welding process parameters are preset welding process parameters associated with the preset welding conditions. If the workpiece is welded unsuccessfully, the welding conditions and target welding process parameters are not stored in the welding process database.

[0086] The technical solution disclosed in this application detects whether the welding conditions of the workpiece to be welded are new welding conditions in a preset welding process database. When the welding conditions of the workpiece to be welded are new welding conditions, a welding quality prediction model is used to automatically predict the target welding process parameters with better welding effect, so that the welding quality prediction model has the ability to learn autonomously in the face of new welding conditions.

[0087] In some embodiments of this application, such as Figure 3 As shown, after checking whether welding conditions are stored in the preset welding process database, it may also include:

[0088] 301. If welding conditions are stored in the welding process database, determine the target welding process card information that includes the welding conditions from among the multiple welding process card information in the welding process database.

[0089] In this embodiment of the application, if the welding process database stores the welding conditions of the workpiece to be welded, it indicates that the welding conditions are existing welding conditions. Therefore, the target welding process card information including the welding conditions can be directly called from the multiple welding process card information in the welding process database.

[0090] 302. Based on the preset welding process parameters in the target welding process card information, perform welding processing on the workpiece to be welded.

[0091] In this embodiment of the application, preset welding process parameters associated with the welding conditions of the workpiece to be welded are determined in the target welding process card information, and the workpiece to be welded is welded based on the preset welding process parameters, thereby achieving a better welding effect.

[0092] In some embodiments of this application, welding the workpiece to be welded based on the preset welding process parameters in the target welding process card information may include: adjusting the preset welding process parameters in the target welding process card information that are associated with the welding conditions of the workpiece to be welded, to obtain adjusted welding process parameters. The adjustment may be performed manually by triggering the corresponding adjustment command based on past experience, i.e., the preset welding process parameters are manually fine-tuned. The adjusted welding process parameters are then used to weld the workpiece, thereby making the welding control of the workpiece to be welded more flexible.

[0093] In a further embodiment, after welding the workpiece using adjusted welding process parameters, the process may further include: obtaining actual adjusted welding quality data of the workpiece, the method for obtaining the actual adjusted welding quality data being similar to that for the actual welding quality data of the workpiece described above, and will not be repeated here; comparing the actual adjusted welding quality data with the actual welding quality data of the preset welding process parameters in the target welding process card information to obtain a comparison result, wherein the comparison result indicates whether the actual welding effect of the adjusted welding process parameters is better than the preset welding process parameters in the target welding process card information; based on the comparison result, updating the target welding process card information using the adjusted welding process parameters, for example, if the comparison result indicates that the actual welding effect of the adjusted welding process parameters is better than the preset welding process parameters in the target welding process card information, replacing the preset welding process parameters in the target welding process card information with the adjusted welding process parameters, so that in the next welding process, welding can be performed according to the welding process parameters with better actual welding effect, thereby improving the welding effect of subsequent welding processes.

[0094] In some embodiments of this application, comparing the adjusted actual welding quality data with the actual welding quality data of the preset welding process parameters in the target welding process card information to obtain a comparison result may include: converting the adjusted actual welding quality data into a first welding quality prediction score, and converting the actual welding quality data of the preset welding process parameters in the target welding process card information into a second welding quality prediction score; if the first welding quality prediction score is greater than the second welding quality prediction score, the comparison result is that the actual welding effect of the adjusted welding process parameters is better than the preset welding process parameters in the target welding process card information; if the first welding quality prediction score is less than or equal to the second welding quality prediction score, the comparison result is that the actual welding effect of the adjusted welding process parameters is not better than the preset welding process parameters in the target welding process card information.

[0095] It should be noted that when the welding process database is constructed based on blockchain technology, the preset welding process parameters in the target welding process card information are replaced with adjusted welding process parameters. Specifically, a new block is generated based on the adjusted welding process parameters and the welding conditions of the workpiece to be welded. This new block is then connected to the end of the data chain of the target welding process card information and broadcast to all nodes connected to the blockchain. Furthermore, in the descriptions of the above embodiments, the preset welding process parameters in the target welding process card information and the preset welding process parameters associated with the preset welding conditions in the welding process card information both refer to the preset welding process parameters in the block at the end of the data chain of the target welding process card information. Thus, the preset welding process parameters obtained from the welding process database are currently the optimal welding process parameters.

[0096] The technical solution disclosed in this application detects whether the welding conditions of the workpiece to be welded are new welding conditions in a preset welding process database. When the welding conditions of the workpiece to be welded are existing welding conditions, welding is performed directly based on the preset welding process parameters in the corresponding target welding process card information, making the welding effect more likely to achieve the expected welding effect.

[0097] To better implement the welding control method in the embodiments of this application, a welding control system is also provided in the embodiments of this application, such as... Figure 4 As shown, the welding control system 400 includes:

[0098] The first acquisition unit 401 is used to acquire the welding conditions of the workpiece to be welded, including at least one of the following: base material type, welding material type, plate thickness, butt joint type, and bevel type.

[0099] The second acquisition unit 402 is used to acquire a preset welding quality prediction model;

[0100] The quality prediction unit 403 is used to input welding conditions and multiple initial welding process parameters into the welding quality prediction model, and to receive welding quality prediction data of each initial welding process parameter output by the welding quality prediction model.

[0101] The process screening unit 404 is used to determine the target welding process parameters from multiple initial welding process parameters based on welding quality prediction data.

[0102] The welding processing unit 405 is used to perform welding processing on the workpiece to be welded using the target welding process parameters.

[0103] The welding control system provided in this application uses a welding quality prediction model to predict the welding quality under specified welding conditions and initial welding process parameters. Then, based on the welding quality prediction data, it selects target welding process parameters so that the target welding process parameters are applicable to the specified welding conditions, thereby improving welding efficiency.

[0104] In some embodiments of this application, the second acquisition unit 402 is further configured to:

[0105] Obtain actual welding quality data for the workpiece to be welded;

[0106] The welding quality prediction model is adjusted based on welding conditions, target welding process parameters, and actual welding quality data.

[0107] In some embodiments of this application, the second acquisition unit 402 is further configured to:

[0108] Obtain historical welding conditions, historical welding process parameters, and actual historical welding quality data for historical welded workpieces;

[0109] A model training dataset is generated based on historical welding conditions, historical welding process parameters, and actual historical welding quality data.

[0110] The welding quality prediction model is obtained by training the model using the model training dataset.

[0111] In some embodiments of this application, the second acquisition unit 402 is further configured to:

[0112] In the preset welding process database, it is checked whether welding conditions are stored. The welding process database stores multiple welding process card information, and each welding process card information includes the associated preset welding conditions and preset welding process parameters.

[0113] If the welding process database does not store welding conditions, proceed with the step of obtaining a preset welding quality prediction model.

[0114] In some embodiments of this application, the second acquisition unit 402 is further configured to:

[0115] Obtain actual welding quality data for the workpiece to be welded;

[0116] Based on actual welding quality data, determine whether the workpiece to be welded is qualified;

[0117] When the workpiece to be welded passes the welding test, a welding process card is generated based on the welding conditions and target welding process parameters, and stored in the welding process database.

[0118] In some embodiments of this application, the welding processing unit 405 is further configured to:

[0119] If welding conditions are stored in the welding process database, the target welding process card information, which includes the welding conditions, is determined from among the multiple welding process card information in the welding process database.

[0120] Based on the preset welding process parameters in the target welding process card information, the workpiece to be welded is processed.

[0121] In some embodiments of this application, the welding processing unit 405 is further configured to:

[0122] The preset welding process parameters in the target welding process card information are adjusted to obtain the adjusted welding process parameters;

[0123] The workpiece to be welded is then processed using the adjusted welding process parameters.

[0124] Obtain actual welding quality data of the workpiece after adjustment;

[0125] The actual welding quality data after adjustment is compared with the actual welding quality data of the preset welding process parameters in the target welding process card information to obtain the comparison results.

[0126] Based on the comparison results, the target welding process card information is updated using the adjusted welding process parameters.

[0127] In addition to the welding control methods and welding control systems described above, this application also provides an electronic device that integrates any of the welding control systems provided in this application. The electronic device includes:

[0128] One or more processors;

[0129] Memory; and

[0130] One or more applications, wherein the applications are stored in memory and configured to be executed by a processor, any step of any embodiment of the welding control method described above.

[0131] This application also provides an electronic device that integrates any of the welding control systems provided in this application. For example... Figure 5 As shown, it illustrates a structural schematic diagram of the electronic device involved in the embodiments of this application, specifically:

[0132] The electronic device may include components such as a processor 501 with one or more processing cores, a storage unit 502 with one or more computer-readable storage media, a power supply 503, and an input unit 504. Those skilled in the art will understand that... Figure 5 The electronic device structure shown does not constitute a limitation on the electronic device and may include more or fewer components than shown, or combine certain components, or have different component arrangements. Wherein:

[0133] The processor 501 is the control center of the electronic device. It connects various parts of the electronic device via various interfaces and lines. By running or executing software programs and / or modules stored in the storage unit 502, and by calling data stored in the storage unit 502, it performs various functions and processes data, thereby providing overall monitoring of the electronic device. Optionally, the processor 501 may include one or more processing cores; preferably, the processor 501 may integrate an application processor and a modem processor, wherein the application processor mainly handles the operating system, user interface, and applications, while the modem processor mainly handles wireless communication. It is understood that the modem processor may not be integrated into the processor 501.

[0134] Storage unit 502 can be used to store software programs and modules. Processor 501 executes various functional applications and data processing by running the software programs and modules stored in storage unit 502. Storage unit 502 may mainly include a program storage area and a data storage area. The program storage area may store the operating system, at least one application program required for a function (such as sound playback function, image playback function, etc.), etc.; the data storage area may store data created according to the use of the electronic device, etc. In addition, storage unit 502 may include high-speed random access memory, and may also include non-volatile memory, such as at least one disk storage device, flash memory device, or other volatile solid-state storage device. Accordingly, storage unit 502 may also include a memory controller to provide processor 501 with access to storage unit 502.

[0135] The electronic device also includes a power supply 503 that supplies power to various components. Preferably, the power supply 503 can be logically connected to the processor 501 through a power management system, thereby enabling functions such as charging, discharging, and power consumption management through the power management system. The power supply 503 may also include one or more DC or AC power supplies, recharging systems, power fault detection circuits, power converters or inverters, power status indicators, and other arbitrary components.

[0136] The electronic device may also include an input unit 504, which can be used to receive input digital or character information and generate keyboard, mouse, joystick, optical or trackball signal inputs related to user settings and function control.

[0137] Although not shown, the electronic device may also include a display unit, etc., which will not be described in detail here. Specifically, in the embodiments of this application, the processor 501 in the electronic device loads the executable files corresponding to the processes of one or more applications into the storage unit 502 according to the following instructions, and the processor 501 runs the applications stored in the storage unit 502 to realize various functions, such as:

[0138] Obtain the welding conditions of the workpiece to be welded; obtain the preset welding quality prediction model; input the welding conditions and multiple initial welding process parameters into the welding quality prediction model, and receive the welding quality prediction data of each initial welding process parameter output by the welding quality prediction model; based on the welding quality prediction data, determine the target welding process parameter from multiple initial welding process parameters; use the target welding process parameter to perform welding processing on the workpiece to be welded.

[0139] Therefore, embodiments of this application provide a computer-readable storage medium, which may include: read-only memory (ROM), random access memory (RAM), a magnetic disk, or an optical disk, etc. The computer-readable storage medium stores multiple instructions, which can be loaded by a processor to execute steps in any of the welding control methods provided in embodiments of this application. For example, the instructions can execute the following steps:

[0140] Obtain the welding conditions of the workpiece to be welded; obtain the preset welding quality prediction model; input the welding conditions and multiple initial welding process parameters into the welding quality prediction model, and receive the welding quality prediction data of each initial welding process parameter output by the welding quality prediction model; based on the welding quality prediction data, determine the target welding process parameter from multiple initial welding process parameters; use the target welding process parameter to perform welding processing on the workpiece to be welded.

[0141] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0142] The welding control method, welding control system, electronic equipment, and medium provided in the embodiments of this application have been described in detail above. Specific examples have been used to illustrate the principles and implementation methods of this application. The description of the above embodiments is only for the purpose of helping to understand the method and core ideas of this application. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this application. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A welding control method, characterized in that, The welding control method includes: Obtain the welding conditions of the workpiece to be welded, wherein the welding conditions include at least one of the following: base material type, welding material type, plate thickness, butt joint type, and bevel type; check whether the welding conditions are stored in the preset welding process database; if the welding conditions are not stored in the welding process database, obtain the preset welding quality prediction model. The welding conditions and multiple initial welding process parameters are input into the welding quality prediction model, and the welding quality prediction data of each initial welding process parameter output by the welding quality prediction model are received. The welding quality prediction data includes the predicted value of at least one of the hardness, tensile strength and bending strength of the workpiece to be welded after welding. Based on the welding quality prediction data, the target welding process parameters are determined from among the multiple initial welding process parameters; The workpiece to be welded is then subjected to welding using the target welding process parameters. If the welding conditions are stored in the welding process database, a target welding process card information including the welding conditions is determined from multiple welding process card information in the welding process database. The preset welding process parameters in the target welding process card information are adjusted to obtain the adjusted welding process parameters. The adjusted welding process parameters are then used to weld the workpiece to be welded. Obtain the adjusted welding quality actual data of the workpiece to be welded, compare the adjusted welding quality actual data with the welding quality actual data of the preset welding process parameters in the target welding process card information, obtain the comparison result, and update the target welding process card information using the adjusted welding process parameters based on the comparison result.

2. The welding control method as described in claim 1, characterized in that, After performing welding on the workpiece using the target welding process parameters, the process further includes: Obtain the actual welding quality data of the workpiece to be welded; The welding quality prediction model is adjusted based on the welding conditions, the target welding process parameters, and the actual welding quality data.

3. The welding control method as described in claim 1, characterized in that, Before obtaining the welding conditions of the workpiece to be welded, the method further includes: Obtain historical welding conditions, historical welding process parameters, and actual historical welding quality data for historical welded workpieces; Based on the historical welding conditions, the historical welding process parameters, and the actual historical welding quality data, a model training dataset is generated. The welding quality prediction model is obtained by training the model using the model training dataset.

4. The welding control method as described in claim 1, characterized in that, The welding process database stores multiple welding process card information, and each welding process card information includes associated preset welding conditions and preset welding process parameters.

5. The welding control method as described in claim 1, characterized in that, After performing welding on the workpiece using the target welding process parameters, the process further includes: Obtain the actual welding quality data of the workpiece to be welded; Based on the actual welding quality data, determine whether the workpiece to be welded is qualified. When the workpiece to be welded passes the welding test, a welding process card is generated based on the welding conditions and the target welding process parameters, and stored in the welding process database.

6. A welding control system, characterized in that, The welding control system includes: The first acquisition unit is used to acquire the welding conditions of the workpiece to be welded, wherein the welding conditions include at least one of the following: base material type, welding material type, plate thickness, butt joint type, and bevel type. The second acquisition unit is used to detect whether the welding conditions are stored in a preset welding process database. If the welding conditions are not stored in the welding process database, a preset welding quality prediction model is acquired. The quality prediction unit is used to input the welding conditions and multiple initial welding process parameters into the welding quality prediction model, and to receive the welding quality prediction data of each of the initial welding process parameters output by the welding quality prediction model. The welding quality prediction data includes the predicted value of at least one of the hardness, tensile strength and bending strength of the workpiece to be welded after welding. A process screening unit is used to determine a target welding process parameter from multiple initial welding process parameters based on the welding quality prediction data. A welding processing unit is used to perform welding processing on the workpiece to be welded using the target welding process parameters; If the welding conditions are stored in the welding process database, a target welding process card information including the welding conditions is determined from multiple welding process card information in the welding process database. The preset welding process parameters in the target welding process card information are adjusted to obtain the adjusted welding process parameters. The adjusted welding process parameters are then used to weld the workpiece to be welded. Obtain the adjusted welding quality actual data of the workpiece to be welded, compare the adjusted welding quality actual data with the welding quality actual data of the preset welding process parameters in the target welding process card information, obtain the comparison result, and update the target welding process card information using the adjusted welding process parameters based on the comparison result.

7. An electronic device, characterized in that, The electronic device includes: One or more processors; Memory; and One or more applications, wherein the one or more applications are stored in the memory and configured to be executed by the processor to implement the welding control method of any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that, It stores a computer program, which is loaded by a processor to execute the steps of the welding control method according to any one of claims 1 to 5.

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