Laser soldering tin processing method, device and equipment, storage medium and product
By acquiring and analyzing historical welding data, a digital twin model is constructed to optimize welding strategy, which solves the problem of low welding accuracy in laser welding processing, and realizes accurate welding setting and optimization, which improves welding accuracy.
Patent Information
- Application Number
- CN202411343396.X
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-09-25
- Publication Date
- 2024-12-17
AI Technical Summary
During laser soldering processing, due to the differences in material properties, the complexity of the solder environment and the experience level of the operator, the precise setting and optimization of solder parameters becomes difficult, resulting in low solder accuracy and excessively large solder heat-affected zone. , irregular solder joint shape and insufficient solder strength.
By obtaining the historical welding data of the sample processing materials, generating initial processing strategies, collecting real-time welding data, building a digital twin model for analysis, generating an optimized processing strategy, and laser welding processing of the materials to be processed based on this strategy to ensure that the materials to be processed are processed to ensure that the materials to be processed are processed to ensure that the materials to be processed are processed to ensure that the materials to be processed are processed to ensure that Same as sample material.
The precise setting and optimization of the welding machine processing process is achieved, the accuracy of laser welding machine processing is improved, and the quality of welding machine is improved.
Smart Images
Figure CN119140939A_ABST
Abstract
Description
Technical Field
[0001] The present application relates to the field of laser processing technology, and in particular to a laser soldering processing method, device, equipment, storage medium and product. Background Art
[0002] Laser soldering technology is widely used in the processing and manufacturing of precision parts due to its significant advantages such as high precision, non-contact processing, rapid response and control. In the process of laser soldering, soldering parameters have a crucial impact on soldering quality.
[0003] However, in actual processing, due to factors such as differences in material properties, the complexity of the soldering environment, and the operator's experience level, it is particularly difficult to accurately set and optimize soldering parameters. Improper parameter settings often lead to problems such as excessively large solder heat-affected zones, irregular solder joint shapes, and insufficient solder strength, thereby reducing soldering accuracy. Therefore, the industry is in urgent need of a laser soldering processing method that can improve soldering accuracy. Summary of the invention
[0004] The main purpose of this application is to provide a laser soldering processing method, device, equipment, storage medium and product, aiming to solve the technical problem of low soldering precision in existing laser soldering processing methods.
[0005] To achieve the above object, the present application provides a laser soldering method, which comprises the following steps:
[0006] Acquire historical solder data of sample processing materials, and generate an initial processing strategy based on the historical solder data;
[0007] Performing laser soldering processing on the sample processing material according to the initial processing strategy, and collecting real-time soldering data generated by the sample processing material during the laser soldering processing;
[0008] Constructing a digital twin model of the sample processing material, and inputting the real-time solder data into the digital twin model for analysis to obtain an optimized processing strategy;
[0009] Laser soldering is performed on the material to be processed based on the optimized processing strategy, and the material to be processed and the sample processing material are the same material.
[0010] In one embodiment, the step of obtaining historical solder data of the sample processing material and generating an initial processing strategy according to the historical solder data includes:
[0011] Acquire historical solder data of sample processing materials, wherein the historical solder data includes solder parameters, solder environment and solder results;
[0012] The historical solder data is screened according to the processing requirements of the sample processing material, and an initial processing strategy is generated based on the screened historical solder data, wherein the initial processing strategy includes solder parameters of the sample processing material under different solder environments.
[0013] In one embodiment, the step of performing laser soldering processing on the sample processing material according to the initial processing strategy and collecting real-time soldering data generated by the sample processing material during the laser soldering processing includes:
[0014] Determining current soldering parameters of the sample processing material in a current soldering environment according to the initial processing strategy, and performing laser soldering processing on the sample processing material according to the current soldering parameters;
[0015] If it is detected that the current solder environment changes, the current solder parameters are switched based on the initial processing strategy, and laser soldering is performed on the sample processing material based on the switched solder parameters;
[0016] Real-time soldering data generated by the sample processing material during the laser soldering process is collected through a preset sensor.
[0017] In one embodiment, the step of constructing a digital twin model of the sample processing material includes:
[0018] constructing an initial three-dimensional model corresponding to the sample processing material according to the characteristic data of the sample processing material, and performing lightweight processing on the three-dimensional model;
[0019] The solder prediction model is embedded into the three-dimensional model after lightweight processing to obtain a digital twin model of the sample processing material, and the solder prediction model is constructed based on the historical solder data and is used to predict the soldering results of the processing material.
[0020] In one embodiment, the step of inputting the real-time solder data into the digital twin model for analysis to obtain an optimized processing strategy comprises:
[0021] Inputting the real-time solder data into the digital twin model, and determining a predicted solder result of the sample processing material based on the model output result;
[0022] If the predicted soldering result does not meet the preset requirements, an optimized processing strategy is generated based on the predicted soldering result, wherein the optimized processing strategy includes solder parameters corresponding to the sample processing material under the soldering target;
[0023] If the predicted soldering result meets the preset requirements, the initial processing strategy is determined as the optimized processing strategy.
[0024] In one embodiment, if the predicted soldering result does not meet the preset requirements, the step of generating an optimized processing strategy based on the predicted soldering result includes:
[0025] If the predicted soldering result does not meet the preset requirements, the predicted soldering result is converted into a visual defect, and the visual defect is displayed to the user through the digital twin model;
[0026] The user is guided to select a solder target corresponding to the sample processing material based on the visualized defect, and an optimized processing strategy is generated according to the solder target.
[0027] In addition, to achieve the above-mentioned purpose, the present application also proposes a laser soldering processing device, which includes:
[0028] A strategy generation module, used to obtain historical solder data of sample processing materials and generate an initial processing strategy based on the historical solder data;
[0029] A data acquisition module, used for performing laser soldering processing on the sample processing material according to the initial processing strategy, and collecting real-time soldering data generated by the sample processing material during the laser soldering processing;
[0030] A model building module, used to build a digital twin model of the sample processing material, and input the real-time solder data into the digital twin model for analysis to obtain an optimized processing strategy;
[0031] The solder processing module is used to perform laser solder processing on the material to be processed based on the optimized processing strategy, and the material to be processed and the sample processing material are the same material.
[0032] In addition, to achieve the above-mentioned purpose, the present application also proposes a laser soldering processing device, which includes: a memory, a processor, and a laser soldering processing program stored in the memory and executable on the processor, wherein the laser soldering processing program is configured to implement the steps of the laser soldering processing method described above.
[0033] In addition, to achieve the above-mentioned purpose, the present application also proposes a storage medium, which is a computer-readable storage medium, and a laser soldering processing program is stored on the storage medium. When the laser soldering processing program is executed by the processor, the steps of the laser soldering processing method described above are implemented.
[0034] In addition, to achieve the above-mentioned purpose, the present invention also provides a computer program product, which includes a laser soldering processing program, and when the laser soldering processing program is executed by a processor, the steps of the laser soldering processing method described above are implemented.
[0035] The present application obtains historical solder data of sample processing materials, and generates an initial processing strategy based on the historical solder data; performs laser soldering on the sample processing materials according to the initial processing strategy, and collects real-time solder data generated by the sample processing materials during the laser soldering process; constructs a digital twin model of the sample processing materials, and inputs the real-time solder data into the digital twin model for analysis to obtain an optimized processing strategy; performs laser soldering on the material to be processed based on the optimized processing strategy, and the material to be processed and the sample processing material are the same material. Compared with the traditional laser soldering processing method, the above method of the present application first collects the real-time solder data generated by the sample processing material, which is the same material as the material to be processed, during the laser soldering process, and then analyzes the real-time solder data through the digital twin model of the sample processing material to obtain an optimized processing strategy, and finally performs laser soldering on the material to be processed based on the optimized processing strategy, thereby realizing the precise setting and optimization of the soldering process, thereby improving the accuracy of laser soldering processing. BRIEF DESCRIPTION OF THE DRAWINGS
[0036] Figure 1 A schematic diagram of the structure of a laser soldering processing device in the hardware operating environment involved in the embodiment of the present application;
[0037] Figure 2 This is a schematic diagram of the process of the first embodiment of the laser soldering method of the present application;
[0038] Figure 3 This is a schematic diagram of the process of the second embodiment of the laser soldering method of the present application;
[0039] Figure 4 This is a schematic diagram of the process of the third embodiment of the laser soldering method of the present application;
[0040] Figure 5 This is a structural block diagram of the first embodiment of the laser soldering processing device of the present application.
[0041] The realization of the purpose, functional features and advantages of this application will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION
[0042] It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.
[0043] Reference Figure 1 , Figure 1 This is a schematic diagram of the structure of the laser soldering processing equipment in the hardware operating environment involved in the embodiment of the present application.
[0044] like Figure 1 As shown, the laser soldering processing equipment may include: a processor 1001, such as a central processing unit (CPU), a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. Among them, the communication bus 1002 is used to realize the connection and communication between these components. The user interface 1003 may include a display screen (Display), an input unit such as a keyboard (Keyboard), and the optional user interface 1003 may also include a standard wired interface and a wireless interface. The network interface 1004 may optionally include a standard wired interface and a wireless interface (such as a wireless fidelity (Wireless-Fidelity, Wi-Fi) interface). The memory 1005 may be a high-speed random access memory (Random Access Memory, RAM), or a stable non-volatile memory (Non-Volatile Memory, NVM), such as a disk storage. The memory 1005 may also be a storage device independent of the aforementioned processor 1001.
[0045] Those skilled in the art will understand that Figure 1 The structure shown in the figure does not constitute a limitation on the laser soldering processing equipment, and may include more or less components than those shown in the figure, or combine certain components, or arrange the components differently.
[0046] like Figure 1 As shown, the memory 1005 as a storage medium may include an operating system, a network communication module, a user interface module, and a laser soldering processing program.
[0047] exist Figure 1 In the laser soldering processing equipment shown, the network interface 1004 is mainly used for data communication with the network server; the user interface 1003 is mainly used for data interaction with the user; the processor 1001 and the memory 1005 in the laser soldering processing equipment of the present application can be set in the laser soldering processing equipment, and the laser soldering processing equipment calls the laser soldering processing program stored in the memory 1005 through the processor 1001, and executes the laser soldering processing method provided in the embodiment of the present application.
[0048] The present application embodiment provides a laser soldering method, referring to Figure 2 , Figure 2 This is a schematic diagram of the process of the first embodiment of the laser soldering method of the present application.
[0049] In this embodiment, the laser soldering method comprises the following steps:
[0050] Step S10: Acquire historical solder data of sample processing materials, and generate an initial processing strategy according to the historical solder data.
[0051] It should be noted that the execution subject of the method of this embodiment can be a terminal device with laser soldering, data processing and program running functions, or an electronic device with the same or similar functions, such as the above-mentioned laser soldering processing equipment. The following takes the laser soldering processing equipment (hereinafter referred to as the processing equipment) as an example to illustrate this embodiment and the following embodiments.
[0052] It is understandable that the above historical soldering data may include historical soldering parameters, historical soldering environment, historical soldering results, etc., which is not limited in this embodiment.
[0053] It should be understood that the above initial processing strategy may refer to a strategy of switching different solder parameters under different solder environments according to sample processing materials.
[0054] In a specific implementation, before generating an initial processing strategy based on historical solder data, the historical solder data may be cleaned and preprocessed, for example, using statistical analysis tools or visualization methods (such as box plots, scatter plots) to identify and remove outliers and noise, standardizing the historical solder data to convert parameters of different dimensions into comparable standards, and interpolating or eliminating missing data to ensure the integrity of the historical solder data.
[0055] Step S20: performing laser soldering processing on the sample processing material according to the initial processing strategy, and collecting real-time soldering data generated by the sample processing material during the laser soldering processing.
[0056] It should be noted that the laser soldering process described in this embodiment is a processing method for connecting and fixing electronic components, metal parts or other conductive materials. It combines the high energy density of the laser with the good fluidity of the solder material to achieve accurate and uniform soldering effect. Laser soldering uses a high-energy laser beam as a heat source, and focuses the laser beam on the contact area between the solder and the welded parts. The laser beam irradiates the contact point of the solder (usually a tin alloy) and the material to be soldered, generating a large amount of heat, causing the solder to melt rapidly and penetrate between the metal parts to be soldered. After the laser beam is removed, the solder cools and solidifies rapidly to form a firm electrical and mechanical connection. For the soldering process, the main thing is to use the melted tin wire to solder the component pins to the pads together, so the main concern is whether the solder has defects such as cold soldering, exposed copper, insufficient soldering, and pull tips.
[0057] It should be understood that the above-mentioned real-time solder data may include real-time solder parameters, real-time solder environment, real-time solder results, etc., which are not limited in this embodiment.
[0058] Step S30: constructing a digital twin model of the sample processing material, and inputting the real-time solder data into the digital twin model for analysis to obtain an optimized processing strategy.
[0059] It should be noted that the digital twin model of sample processing materials is a virtual model based on physical entities (i.e., sample processing materials). It uses technologies such as big data, cloud computing, the Internet of Things (IoT), and artificial intelligence (AI) to accurately map and simulate the entire life cycle of physical entities in digital space, including design, manufacturing, operation, maintenance, and scrapping. By connecting sensors and other data acquisition devices on physical entities, the above-mentioned digital twin model can receive and process data from the physical world in real time to ensure that the state of the model is consistent with the physical entity.
[0060] In a specific implementation, the simulation analysis of the soldering process of the sample processing material can be performed in the above digital twin model to simulate the corresponding soldering effect under the real-time soldering data, and the soldering process of the sample processing material can be optimized by observing the key indicators in the simulation results (such as solder joint morphology, temperature distribution, stress state, etc.), thereby obtaining an optimized processing strategy. In particular, the above optimized processing strategy includes the corresponding soldering parameters of the sample processing material under the soldering target.
[0061] Step S40: performing laser soldering processing on the material to be processed based on the optimized processing strategy, wherein the material to be processed and the sample processing material are the same material.
[0062] It should be understood that in this embodiment, the sample processing material is equivalent to a test processing material before the material to be processed is formally processed. Therefore, the material to be processed and the sample processing material are the same material, thereby ensuring that the conclusions of the test processing can be equivalently applied to the material to be processed to improve the processing accuracy of the laser soldering processing method of this embodiment.
[0063] This embodiment obtains historical solder data of sample processing materials, and generates an initial processing strategy based on the historical solder data; performs laser soldering on the sample processing materials according to the initial processing strategy, and collects real-time solder data generated by the sample processing materials during the laser soldering process; constructs a digital twin model of the sample processing materials, and inputs the real-time solder data into the digital twin model for analysis to obtain an optimized processing strategy; performs laser soldering on the material to be processed based on the optimized processing strategy, and the material to be processed and the sample processing material are the same material. Compared with the traditional laser soldering processing method, the above method of this embodiment first collects the real-time solder data generated by the sample processing material, which is the same material as the material to be processed, during the laser soldering process, and then analyzes the real-time solder data through the digital twin model of the sample processing material to obtain an optimized processing strategy, and finally performs laser soldering on the material to be processed based on the optimized processing strategy, thereby realizing the precise setting and optimization of the soldering process, thereby improving the accuracy of laser soldering processing.
[0064] refer to Figure 3 , Figure 3 This is a schematic flow chart of the second embodiment of the laser soldering method of the present application.
[0065] In a feasible implementation manner, the step S10 may include:
[0066] Step S101: acquiring historical solder data of a sample processing material, wherein the historical solder data includes solder parameters, solder environment and solder results.
[0067] It should be noted that the above-mentioned soldering parameters may include laser power, spot diameter, soldering speed, wire feeding speed, defocus amount, laser pulse waveform, laser pulse width, etc.; the above-mentioned soldering environment may include temperature conditions, humidity conditions, wind speed conditions, cleanliness conditions, etc.; the above-mentioned soldering results may include soldering accuracy, solder joint appearance, solder joint strength, solder deformation, etc.
[0068] In a specific implementation, the historical solder data of the sample processing material may be obtained from the historical solder log of the sample processing material.
[0069] Step S102: screening the historical solder data according to the processing requirements of the sample processing material, and generating an initial processing strategy based on the screened historical solder data, wherein the initial processing strategy includes solder parameters of the sample processing material under different solder environments.
[0070] It should be understood that different users may have different processing requirements for the same processing material. For example, if the processing requirement of user A is 10 microns, then the solder accuracy in the solder result can be used as a screening condition to screen the above historical solder data, thereby obtaining solder parameters with a solder accuracy of 10 microns under several soldering environments, and then the above initial processing strategy can be generated based on these soldering environments and corresponding soldering parameters. Similarly, the processing requirements of user B can also be solder joint appearance, solder joint strength, solder deformation, etc. The steps for generating the initial processing strategy for user B are similar to those for user A above, and will not be repeated here.
[0071] In a feasible implementation manner, the step S20 may include:
[0072] Step S201: determining the current soldering parameters of the sample processing material in the current soldering environment according to the initial processing strategy, and performing laser soldering processing on the sample processing material according to the current soldering parameters.
[0073] Step S202: If it is detected that the current soldering environment has changed, the current soldering parameters are switched based on the initial processing strategy, and laser soldering processing is performed on the sample processing material based on the switched soldering parameters.
[0074] Step S203: collecting real-time solder data generated by the sample processing material during the laser soldering process through a preset sensor.
[0075] It should be noted that the above-mentioned preset sensors may include visual sensors (monitoring the solder joint position, solder joint width and molten pool state during the soldering process in real time through cameras), laser displacement sensors (scanning the surface of the solder part through laser beams to measure the solder joint position, solder joint depth and solder joint geometry), temperature sensors (monitoring the temperature of the molten pool and heat-affected zone during the soldering process to ensure that the solder temperature is within a reasonable range to prevent solder shortage caused by overheating or too rapid cooling), infrared sensors (detecting the infrared radiation intensity of the soldering area to monitor the solder temperature distribution and the heat input during the soldering process in real time), mechanical sensors (monitoring the mechanical stress and vibration during the soldering process to evaluate the solder quality and stability), etc., and this embodiment does not impose any restrictions on this.
[0076] This embodiment obtains historical solder data of a sample processing material, wherein the historical solder data includes solder parameters, solder environment, and solder results; screens the historical solder data according to processing requirements of the sample processing material, and generates an initial processing strategy based on the screened historical solder data, wherein the initial processing strategy includes solder parameters of the sample processing material under different solder environments; determines current solder parameters of the sample processing material under the current solder environment according to the initial processing strategy, and performs laser soldering processing on the sample processing material according to the current solder parameters; if a change in the current solder environment is detected, switches the current solder parameters based on the initial processing strategy, and performs laser soldering processing on the sample processing material based on the switched solder parameters; and collects real-time solder data generated by the sample processing material during the laser soldering process through a preset sensor. Compared with the traditional laser soldering processing method, the above method of this embodiment switches the current solder parameters based on the initial processing strategy when the current soldering environment changes, and performs laser soldering processing on the sample processing material based on the switched solder parameters, thereby avoiding the negative impact on the processing process of the sample processing material due to changes in the soldering environment.
[0077] refer to Figure 4 , Figure 4 This is a schematic flow chart of the third embodiment of the laser soldering method of the present application.
[0078] In a feasible implementation manner, the step S30 may include:
[0079] Step S301: constructing an initial three-dimensional model corresponding to the sample processing material according to the characteristic data of the sample processing material, and performing lightweight processing on the three-dimensional model.
[0080] In a specific implementation, the above-mentioned three-dimensional model can be lightweight based on the following methods. First, an algorithm can be used to reduce the number of polygons, such as using the Edge Collapse algorithm to merge adjacent vertices to reduce the number of faces, thereby reducing geometric complexity. Second, the normals of adjacent vertices can be merged to reduce the number of vertices while maintaining similar surface shading effects, identify and reuse the same vertex data, and reduce the number of vertices as much as possible. Third, textures can be used to replace geometric details, such as using normal maps instead of more complex geometric shapes to provide details without increasing the number of faces. Fourth, the three-dimensional model can be converted into a lightweight file format, such as converting the file to GLTF / GLB format, which is optimized for network transmission and real-time rendering, and supports compression and efficient transmission.
[0081] Step S302: embedding a solder prediction model into the lightweight three-dimensional model to obtain a digital twin model of the sample processing material, wherein the solder prediction model is constructed based on the historical solder data and is used to predict the soldering results of the processing material.
[0082] In the specific implementation, the above solder prediction model can be constructed based on the following steps. The first step is to clean the historical solder data, identify and delete outliers, duplicate records and data with a large number of missing values, and divide the historical solder data after data cleaning into training set, validation set and test set. The second step is to select a suitable machine learning or deep learning model, such as a linear regression model, a decision tree model, a random forest model, a gradient boosting machine model, a neural network model, etc. The third step is to use the training set to train the model. During the training process, the model performance is optimized by adjusting the model parameters (such as learning rate, number of iterations, regularization coefficient, etc.). The fourth step is to use the validation set data to verify the trained model, and adjust the model structure and parameters according to the verification results. The fifth step is to use the test set data to evaluate the final model. When the performance indicators of the final model (such as accuracy, recall rate, F1 score, etc.) reach the expected indicators, the final model is set as the above solder prediction model.
[0083] Step S303: inputting the real-time solder data into the digital twin model, and determining the predicted solder result of the sample processing material based on the model output result.
[0084] Step S304: if the predicted soldering result does not meet the preset requirement, an optimized processing strategy is generated based on the predicted soldering result, wherein the optimized processing strategy includes solder parameters corresponding to the sample processing material under the soldering target.
[0085] Step S305: If the predicted soldering result meets the preset requirement, the initial processing strategy is determined as the optimized processing strategy.
[0086] In a feasible implementation manner, the step S304 may include:
[0087] Step S3041: If the predicted soldering result does not meet the preset requirements, the predicted soldering result is converted into a visual defect, and the visual defect is displayed to the user through the digital twin model.
[0088] Step S3042: guiding the user to select a solder target corresponding to the sample processing material based on the visualized defect, and generating an optimized processing strategy according to the solder target.
[0089] This embodiment constructs an initial three-dimensional model corresponding to the sample processing material according to the characteristic data of the sample processing material, and performs lightweight processing on the three-dimensional model; embeds a solder prediction model into the three-dimensional model after lightweight processing to obtain a digital twin model of the sample processing material, wherein the solder prediction model is constructed based on the historical solder data and is used to predict the solder joint of the processing material; inputs the real-time solder data into the digital twin model, and determines the predicted solder result of the sample processing material based on the model output result; if the predicted solder result does not meet the preset requirements, the predicted solder result is converted into a visual defect, and the visual defect is displayed to the user through the digital twin model; based on the visual defect, the user is guided to select the solder target corresponding to the sample processing material, and an optimized processing strategy is generated according to the solder target, wherein the optimized processing strategy includes the solder parameters corresponding to the sample processing material under the solder target; if the predicted solder result meets the preset requirements, the initial processing strategy is determined as the optimized processing strategy. Compared with the traditional laser soldering processing method, the above method of this embodiment analyzes the real-time solder data through the digital twin model of the sample processing material cut into the solder prediction model, and generates an optimized processing strategy based on the analysis results to perform laser soldering processing on the material to be processed, thereby realizing the precise setting and optimization of the soldering process, and further improving the accuracy of laser soldering processing.
[0090] In addition, an embodiment of the present application further proposes a storage medium, on which a laser soldering processing program is stored. When the laser soldering processing program is executed by a processor, the steps of the laser soldering processing method described above are implemented.
[0091] Reference Figure 5 , Figure 5 This is a structural block diagram of the first embodiment of the laser soldering processing device of the present application.
[0092] like Figure 5 As shown, the laser soldering processing device proposed in the embodiment of the present application includes:
[0093] A strategy generation module 501 is used to obtain historical solder data of sample processing materials and generate an initial processing strategy according to the historical solder data;
[0094] A data acquisition module 502 is used to perform laser soldering processing on the sample processing material according to the initial processing strategy, and collect real-time soldering data generated by the sample processing material during the laser soldering processing;
[0095] A model building module 503 is used to build a digital twin model of the sample processing material, and input the real-time solder data into the digital twin model for analysis to obtain an optimized processing strategy;
[0096] The solder processing module 504 is used to perform laser solder processing on the material to be processed based on the optimized processing strategy, and the material to be processed and the sample processing material are the same material.
[0097] This embodiment obtains historical solder data of sample processing materials, and generates an initial processing strategy based on the historical solder data; performs laser soldering on the sample processing materials according to the initial processing strategy, and collects real-time solder data generated by the sample processing materials during the laser soldering process; constructs a digital twin model of the sample processing materials, and inputs the real-time solder data into the digital twin model for analysis to obtain an optimized processing strategy; performs laser soldering on the material to be processed based on the optimized processing strategy, and the material to be processed and the sample processing material are the same material. Compared with the traditional laser soldering processing method, the above method of this embodiment first collects the real-time solder data generated by the sample processing material, which is the same material as the material to be processed, during the laser soldering process, and then analyzes the real-time solder data through the digital twin model of the sample processing material to obtain an optimized processing strategy, and finally performs laser soldering on the material to be processed based on the optimized processing strategy, thereby realizing the precise setting and optimization of the soldering process, thereby improving the accuracy of laser soldering processing.
[0098] Based on the first embodiment of the laser soldering processing device of the present application, a second embodiment of the laser soldering processing device of the present application is proposed.
[0099] In this embodiment, the strategy generation module 501 is also used to obtain historical solder data of sample processing materials, and the historical solder data includes solder parameters, solder environment and solder results; the historical solder data is screened according to the processing requirements of the sample processing materials, and an initial processing strategy is generated based on the screened historical solder data, and the initial processing strategy includes the solder parameters of the sample processing materials under different solder environments.
[0100] Furthermore, the data acquisition module 502 is also used to determine the current solder parameters of the sample processing material in the current solder environment according to the initial processing strategy, and perform laser soldering processing on the sample processing material according to the current solder parameters; if it is detected that the current solder environment has changed, the current solder parameters are switched based on the initial processing strategy, and laser soldering processing is performed on the sample processing material based on the switched solder parameters; and real-time solder data generated by the sample processing material during the laser soldering process is collected through a preset sensor.
[0101] Furthermore, the model construction module 503 is also used to construct an initial three-dimensional model corresponding to the sample processing material according to the characteristic data of the sample processing material, and to perform lightweight processing on the three-dimensional model; embed the solder prediction model into the three-dimensional model after the lightweight processing to obtain a digital twin model of the sample processing material, and the solder prediction model is constructed based on the historical solder data and is used to predict the soldering results of the processing material.
[0102] Furthermore, the model building module 503 is also used to input the real-time solder data into the digital twin model, and determine the predicted soldering result of the sample processing material based on the model output result; if the predicted soldering result does not meet the preset requirements, an optimized processing strategy is generated based on the predicted soldering result, and the optimized processing strategy includes the solder parameters corresponding to the sample processing material under the solder target; if the predicted soldering result meets the preset requirements, the initial processing strategy is determined as the optimized processing strategy.
[0103] Furthermore, the model building module 503 is also used to convert the predicted solder result into a visual defect if the predicted solder result does not meet the preset requirements, and display the visual defect to the user through the digital twin model; based on the visual defect, guide the user to select the solder target corresponding to the sample processing material, and generate an optimized processing strategy according to the solder target.
[0104] Other embodiments or specific implementations of the laser soldering processing device of the present application can refer to the above-mentioned method embodiments and will not be described in detail here.
[0105] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or system including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or system. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the existence of other identical elements in the process, method, article or system including the element.
[0106] The serial numbers of the above-mentioned embodiments of the present application are for description only and do not represent the advantages or disadvantages of the embodiments.
[0107] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as a read-only memory / random access memory, a disk, or an optical disk), and includes a number of instructions for a terminal device (which can be a mobile phone, a computer, a server, or a network device, etc.) to execute the methods described in each embodiment of the present application.
[0108] The above are only preferred embodiments of the present application, and are not intended to limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the present application specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present application.
Claims
1. A laser soldering method, It is characterized in that The method comprises the following steps: Acquire historical solder data of sample processing materials, and generate an initial processing strategy based on the historical solder data; Performing laser soldering processing on the sample processing material according to the initial processing strategy, and collecting real-time soldering data generated by the sample processing material during the laser soldering processing; Constructing a digital twin model of the sample processing material, and inputting the real-time solder data into the digital twin model for analysis to obtain an optimized processing strategy; Laser soldering is performed on the material to be processed based on the optimized processing strategy, and the material to be processed and the sample processing material are the same material.
2. The laser soldering method according to claim 1, It is characterized in that The step of obtaining historical solder data of the sample processing material and generating an initial processing strategy according to the historical solder data includes: Acquire historical solder data of sample processing materials, wherein the historical solder data includes solder parameters, solder environment and solder results; The historical solder data is screened according to the processing requirements of the sample processing material, and an initial processing strategy is generated based on the screened historical solder data, wherein the initial processing strategy includes solder parameters of the sample processing material under different solder environments.
3. The laser soldering method according to claim 1, It is characterized in that The step of performing laser soldering processing on the sample processing material according to the initial processing strategy and collecting real-time soldering data generated by the sample processing material during the laser soldering processing includes: Determining current soldering parameters of the sample processing material in a current soldering environment according to the initial processing strategy, and performing laser soldering processing on the sample processing material according to the current soldering parameters; If it is detected that the current solder environment changes, the current solder parameters are switched based on the initial processing strategy, and laser soldering is performed on the sample processing material based on the switched solder parameters; Real-time soldering data generated by the sample processing material during the laser soldering process is collected through a preset sensor.
4. The laser soldering method according to claim 1, It is characterized in that The step of constructing a digital twin model of the sample processing material comprises: constructing an initial three-dimensional model corresponding to the sample processing material according to the characteristic data of the sample processing material, and performing lightweight processing on the three-dimensional model; The solder prediction model is embedded into the three-dimensional model after lightweight processing to obtain a digital twin model of the sample processing material, and the solder prediction model is constructed based on the historical solder data and is used to predict the soldering results of the processing material.
5. The laser soldering method according to claim 4, It is characterized in that The step of inputting the real-time solder data into the digital twin model for analysis to obtain an optimized processing strategy comprises: Inputting the real-time solder data into the digital twin model, and determining a predicted solder result of the sample processing material based on the model output result; If the predicted soldering result does not meet the preset requirements, an optimized processing strategy is generated based on the predicted soldering result, wherein the optimized processing strategy includes solder parameters corresponding to the sample processing material under the soldering target; If the predicted soldering result meets the preset requirements, the initial processing strategy is determined as the optimized processing strategy.
6. The laser soldering method according to claim 5, It is characterized in that If the predicted soldering result does not meet the preset requirements, the step of generating an optimized processing strategy based on the predicted soldering result includes: If the predicted soldering result does not meet the preset requirements, the predicted soldering result is converted into a visual defect, and the visual defect is displayed to the user through the digital twin model; The user is guided to select a solder target corresponding to the sample processing material based on the visualized defect, and an optimized processing strategy is generated according to the solder target.
7. A laser soldering processing device, It is characterized in that The laser soldering processing device comprises: A strategy generation module, used to obtain historical solder data of sample processing materials and generate an initial processing strategy based on the historical solder data; A data acquisition module, used for performing laser soldering processing on the sample processing material according to the initial processing strategy, and collecting real-time soldering data generated by the sample processing material during the laser soldering processing; A model building module, used to build a digital twin model of the sample processing material, and input the real-time solder data into the digital twin model for analysis to obtain an optimized processing strategy; The solder processing module is used to perform laser solder processing on the material to be processed based on the optimized processing strategy, and the material to be processed and the sample processing material are the same material.
8. A laser soldering processing equipment, It is characterized in that The device comprises: a memory, a processor, and a laser soldering processing program stored in the memory and executable on the processor, wherein the laser soldering processing program is configured to implement the steps of the laser soldering processing method according to any one of claims 1 to 6.
9. A storage medium, It is characterized in that The storage medium is a computer-readable storage medium, and a laser soldering processing program is stored on the storage medium. When the laser soldering processing program is executed by a processor, the steps of the laser soldering processing method according to any one of claims 1 to 6 are implemented.
10. A computer program product, It is characterized in that The computer program product includes a laser soldering processing program, and when the laser soldering processing program is executed by a processor, the steps of the laser soldering processing method according to any one of claims 1 to 6 are implemented.