Welding quality control system and method for rear axle of electro-tricycle

Abstract

The invention provides an electric tricycle rear axle welding quality control system and method, and relates to the technical field of welding, a welding seam parameter preprocessing module is used for conducting welding seam analysis on an electric tricycle rear axle, and an initial welding parameter combination is determined; the simulation welding analysis module carries out simulation welding according to the initial welding parameter combination and draws a welding change trend chart; the welding path planning and evaluating module traverses the welding change trend chart to plan a plurality of welding paths and performs welding evaluation to generate a welding state value; the welding defect analysis module performs welding detection and defect analysis according to the welding state value and the welding change trend chart to generate a welding quality score; and the welding parameter optimization module adjusts the initial welding parameter combination according to the welding quality score to generate a welding optimization scheme. The technical problem that the welding quality cannot be effectively controlled due to the fact that the prior art is difficult to adapt to dynamic changes of the welding environment and workpiece conditions is solved, and control over the welding quality of the rear axle is improved.

Description

Technical Field

[0001] The present application relates to the field of welding technology, and in particular to a welding quality control system and method for a rear axle of an electric tricycle. Background Art

[0002] Electric tricycles are widely used in short-distance urban transportation and rural freight transportation due to their energy-saving, environmental protection, economical and practical features. The rear axle is a key component of the electric tricycle's load-bearing and transmission function. Its structural strength and welding quality are directly related to the vehicle's operational safety and service life.

[0003] With the development of automation technology, the welding of electric tricycles has also transitioned from traditional manual welding to automated welding. Automated welding equipment has high welding accuracy and stability, avoiding the influence of workers' status on welding quality, but the welding parameters of current automated welding equipment are often set based on some preset values ​​or limited sample data, lacking personalized optimization for the specific component of the rear axle of the electric tricycle. For example, factors such as the different structures and material thicknesses of the rear axle have not been fully considered, resulting in loose welding or uneven welds during the welding process. On the other hand, the existing automated welding equipment has poor adaptability to the welding environment. When environmental factors such as temperature and humidity change, the welding parameters cannot be adjusted in time, thus affecting the welding quality. Summary of the invention

[0004] The present application provides a welding quality control system and method for the rear axle of an electric tricycle, which solves the technical problem that the welding parameters in the welding process of the rear axle of an electric tricycle are relatively fixed and difficult to adapt to the dynamic changes of the welding environment and workpiece conditions, resulting in poor welding quality stability and easy occurrence of welding defects, and achieves the technical effect of enhancing the stability and consistency of the welding process of the rear axle of the electric tricycle, thereby improving the control of the welding quality of the rear axle.

[0005] In view of the above problems, on the one hand, the present application provides a welding quality control system for the rear axle of an electric tricycle, the system comprising: a weld parameter preprocessing module, the weld parameter preprocessing module is used to perform weld analysis on the rear axle of the electric tricycle, obtain a rear axle weld parameter set, perform preprocessing according to the rear axle weld parameter set, and determine an initial welding parameter combination; a simulated welding analysis module, the simulated welding analysis module is used to perform simulated welding according to the initial welding parameter combination, generate a welding prediction parameter set, perform welding analysis according to the welding prediction parameter set combined with welding environment information, and draw a welding change trend diagram; a welding path planning evaluation module, the welding path planning evaluation module The evaluation module is used to traverse the welding change trend diagram to plan the welding path, determine multiple welding paths, perform welding evaluation according to the multiple welding paths, and generate a welding state value; the welding defect analysis module is used to perform welding detection on the rear axle of the electric tricycle according to the welding state value combined with the welding change trend diagram, perform defect analysis according to the detection results, and generate a welding quality score; the welding parameter optimization module is used to dynamically adjust the initial welding parameter combination according to the welding quality score, generate a welding optimization plan, and execute the welding optimization plan to intelligently control the welding quality of the rear axle of the electric tricycle.

[0006] On the other hand, the present application also provides a method for controlling the welding quality of the rear axle of an electric tricycle, the method comprising: performing weld analysis on the rear axle of the electric tricycle to obtain a rear axle weld parameter set, performing preprocessing according to the rear axle weld parameter set, and determining an initial welding parameter combination; performing simulated welding according to the initial welding parameter combination to generate a welding prediction parameter set, performing welding analysis according to the welding prediction parameter set in combination with welding environment information, and drawing a welding change trend chart; traversing the welding change trend chart to plan a welding path, determining multiple welding paths, performing welding evaluation according to the multiple welding paths, and generating a welding status value; performing welding inspection on the rear axle of the electric tricycle according to the welding status value and the welding change trend chart, performing defect analysis according to the inspection results, and generating a welding quality score; dynamically adjusting the initial welding parameter combination according to the welding quality score, generating a welding optimization plan, and executing the welding optimization plan to intelligently control the welding quality of the rear axle of the electric tricycle.

[0007] One or more technical solutions provided in this application have at least the following technical effects or advantages: The weld parameter preprocessing module performs weld analysis on the rear axle of the electric tricycle, obtains a weld parameter set for the rear axle, performs preprocessing based on the weld parameter set for the rear axle, determines the initial welding parameter combination, and provides an accurate parameter basis for the welding process. The simulated welding analysis module performs simulated welding according to the initial welding parameter combination, predicts various situations that may occur during the welding process, generates a welding prediction parameter set, performs welding analysis based on the welding prediction parameter set combined with welding environment information, and draws a welding change trend chart, which provides an important reference for subsequent welding path planning evaluation and welding defect analysis. The welding path planning and evaluation module traverses the welding change trend chart to plan the welding path, determines multiple welding paths, performs welding evaluation based on the multiple welding paths, and generates a welding state value to reflect the quality of each welding path. By evaluating the welding state under different paths, data support is provided for ultimately finding the optimal welding method, ensuring that the welding process can be carried out in a better path and reducing possible welding problems. The welding defect analysis module performs welding inspection on the rear axle of the electric tricycle according to the welding state value combined with the welding change trend diagram, performs defect analysis according to the inspection results, generates a welding quality score, determines whether the welding quality meets the standard, and provides a basis for subsequent parameter optimization. The welding parameter optimization module dynamically adjusts the initial welding parameter combination according to the welding quality score, generates a welding optimization plan, and executes the welding optimization plan to intelligently control the welding quality of the rear axle of the electric tricycle to ensure that the welding quality can meet the requirements.

[0008] To sum up, the coordinated work of the above-mentioned multiple modules in this application can effectively control the welding quality of the rear axle of the electric tricycle, improve the accuracy, stability and reliability of welding, reduce welding defects, thereby strengthening the overall control of the welding quality of the rear axle of the electric tricycle and ensuring the safety and service life of the electric tricycle.

[0009] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Figure 1 A schematic structural diagram of a rear axle welding quality control system for an electric tricycle provided in an embodiment of the present application.

[0011] Figure 2 A schematic diagram of a flow chart for obtaining a rear axle weld parameter set in a rear axle welding quality control system for an electric tricycle provided in an embodiment of the present application.

[0012] Figure 3A schematic diagram of a flow chart for generating a welding prediction parameter set in a welding quality control system for a rear axle of an electric tricycle provided in an embodiment of the present application.

[0013] Figure 4 A schematic flow chart of a method for controlling welding quality of a rear axle of an electric tricycle provided in an embodiment of the present application.

[0014] Explanation of reference numerals: weld parameter preprocessing module 10 , simulation welding analysis module 20 , welding path planning and evaluation module 30 , welding defect analysis module 40 , welding parameter optimization module 50 . DETAILED DESCRIPTION

[0015] The embodiment of the present application provides a welding quality control system and method for the rear axle of an electric tricycle, which solves the technical problem that the welding parameters in the welding process of the rear axle of an electric tricycle are relatively fixed and difficult to adapt to the dynamic changes of the welding environment and workpiece conditions, resulting in poor welding quality stability and easy occurrence of welding defects. The technical effect of enhancing the stability and consistency of the welding process of the rear axle of the electric tricycle and thus improving the control of the welding quality of the rear axle is achieved.

[0016] Embodiment 1, as Figure 1 As shown, the embodiment of the present application provides a rear axle welding quality control system for an electric tricycle, the system comprising: The weld parameter preprocessing module 10 is used to perform weld analysis on the rear axle of the electric tricycle, obtain a rear axle weld parameter set, perform preprocessing based on the rear axle weld parameter set, and determine an initial welding parameter combination.

[0017] Specifically, the rear axle weld parameter set is a set of data related to the rear axle weld of the electric tricycle. These data cover various parameters that can describe the characteristics of the weld, such as the geometry of the weld (such as the length, width, thickness, etc. of the weld), the position of the weld on the bridge body (such as the coordinate position from a specific reference point), and the material properties used in the weld (such as the type and strength of the material, etc.). The initial welding parameter combination is a set of parameters used to start the welding operation. These parameters determine some key factors in the welding process, such as the size of the welding current, the welding speed, the welding voltage, etc. Different weld conditions require different initial welding parameter combinations to ensure the quality of welding.

[0018] Weld seam analysis of the rear axle of the electric tricycle is performed using testing tools such as weld seam detectors and material composition analyzers to obtain various characteristic information of the weld seam. The weld seam detector can accurately measure the width and height of the weld seam to obtain the geometric characteristics of the weld seam; the position of the weld seam relative to the overall structure of the rear axle can also be determined through the coordinate measurement system to obtain the position characteristics of the weld seam. The material composition analyzer can determine the composition of the weld seam material and obtain the characteristics of the weld seam material. These different types of weld seam characteristic information are summarized to form a rear axle weld parameter set.

[0019] The rear axle weld parameter set is then cleaned and preprocessed based on this parameter set to remove some inaccurate or duplicate data. For example, if some data points with large deviations appear due to equipment errors in the weld position feature detection, they need to be removed. According to the preprocessed rear axle weld parameter set, the corresponding initial welding parameter combination is matched in the welding process database of the welding equipment. These parameters provide input data for subsequent welding simulations, ensuring that subsequent welding simulations and parameter optimizations are based on relatively accurate data.

[0020] The simulated welding analysis module 20 is used to perform simulated welding according to the initial welding parameter combination, generate a welding prediction parameter set, perform welding analysis according to the welding prediction parameter set combined with welding environment information, and draw a welding change trend diagram.

[0021] Specifically, the welding prediction parameter set is a set of data obtained after simulating welding according to the initial welding parameter combination, which is used to predict various changes that may occur in the actual welding process, such as changes in geometry, position, material properties and other related parameters after welding. The welding change trend chart is a chart that graphically presents the trend of various parameters changing over time or welding progress during the welding process, such as the trend of various parameters changing over time, such as welding strength, deformation, material properties and so on.

[0022] Use welding simulation software, such as SYSWELD, to simulate welding according to the initial welding parameter combination obtained from the weld parameter preprocessing module 10, predict various possible situations of the welding process, and obtain a welding prediction parameter set. Collect environmental information around the actual welding, such as temperature, humidity, air flow, etc. These factors will affect the welding quality. Superimpose the welding environmental conditions on the welding prediction parameters and correct the simulation results to make them closer to the actual situation. Comprehensively analyze the welding prediction parameters at different time points or welding process stages, draw a welding change trend chart, and intuitively show the changing trend of the welding process under specific welding parameters and environment. Provide data reference for subsequent path planning and defect analysis.

[0023] The welding path planning and evaluation module 30 is used to traverse the welding change trend diagram to plan a welding path, determine multiple welding paths, perform welding evaluation according to the multiple welding paths, and generate a welding state value.

[0024] Specifically, the welding path refers to the trajectory of the welding gun or welding heat source moving on the rear axle of the electric tricycle during the welding process. Different welding paths may have different effects on welding quality, efficiency, etc. The welding state value is a comprehensive evaluation value used to reflect the overall state of welding when following a specific welding path, including comprehensive conditions such as weld strength, heat input distribution, weld uniformity, etc.

[0025] Traverse the welding trend chart and identify key information in the welding trend chart, such as the change curve of welding strength at different stages, the peak value of welding deformation, etc. Start planning the welding path based on this information. Use path optimization algorithms, such as A* algorithm or genetic algorithm, to consider factors such as welding sequence, trajectory shape and welding speed, and determine multiple welding paths according to the welding trend chart. For example, for the welding area from the center axis of the rear axle to the side beam, plan a straight path or a curved path to ensure the shortest weld length and uniform heat input. Combine simulation tools to evaluate the welding quality of each path, analyze key indicators such as heat input distribution, stress concentration and weld strength, and generate welding status values ​​for each path to reflect the welding effect of each path.

[0026] The welding defect analysis module 40 is used to perform welding detection on the rear axle of the electric tricycle according to the welding state value combined with the welding change trend diagram, perform defect analysis according to the detection result, and generate a welding quality score.

[0027] Specifically, the welding defect analysis module 40 analyzes and evaluates the simulated welding results of the rear axle of the electric tricycle in combination with the welding state value and the welding change trend, detects possible welding defects, collects specific information of welding defects, such as defect location, defect type, and defect degree, performs risk assessment based on the specific information of these welding defects, calculates their impact on the overall welding quality, and generates a welding quality score to indicate the overall welding quality. Through welding detection and defect analysis, a basis is provided for subsequent optimization of welding parameters, thereby ensuring continuous improvement of welding quality.

[0028] The welding parameter optimization module 50 is used to dynamically adjust the initial welding parameter combination according to the welding quality score, generate a welding optimization plan, and execute the welding optimization plan to intelligently control the welding quality of the rear axle of the electric tricycle.

[0029] Specifically, the welding optimization scheme is a set of welding parameters generated after optimizing the initial welding parameter combination according to the welding quality score. The welding parameter optimization module 50 will analyze the initial welding parameter combination according to the welding quality score to find out the key parameters that cause defects, such as cracks caused by too low current or undercut caused by too fast speed. Using optimization algorithms, such as genetic algorithms, particle swarm optimization algorithms, etc., optimization adjustments are made based on the initial welding parameter combination to generate new parameter combinations, and the effectiveness of the welding parameter combination is verified in the simulation software, and the parameters are adjusted according to the verification results. Through continuous optimization iterations, the welding parameter composition that meets the expected welding quality score is found and determined as the final welding optimization scheme. The welding optimization scheme is loaded into the welding equipment through the intelligent control system, and the welding operation is adjusted in real time, thereby significantly improving the welding quality and reliability.

[0030] Further, such as Figure 2 As shown, the weld parameter preprocessing module 10 of the embodiment of the present application is also used to perform the following steps: Step P1-11: Perform weld inspection on the rear axle of the electric tricycle to obtain multiple weld features, which include weld geometry features, weld position features, and weld material features.

[0031] Step P1-12: Perform weld edge detection on the rear axle of the electric tricycle according to the weld geometric features, and generate rear axle weld geometric parameters.

[0032] Step P1-13: Perform weld angle detection on the rear axle of the electric tricycle according to the weld position characteristics, and generate rear axle weld position parameters.

[0033] Step P1-14: Perform weld material detection on the rear axle of the electric tricycle according to the weld material characteristics, and generate rear axle weld material parameters.

[0034] Step P1-15: Associatively integrate the rear axle weld geometric parameters, the rear axle weld position parameters, and the rear axle weld material parameters to obtain the rear axle weld parameter set.

[0035] Specifically, the weld of the rear axle of the electric tricycle is inspected using detection tools such as sensors, image acquisition equipment or material analyzers to extract multiple weld features. These weld features include weld geometric features that describe information such as the shape and size of the weld, weld position features that describe the specific position information of the weld in the rear axle structure, and weld material features that describe information related to the weld material.

[0036] These weld features are further detected and analyzed, and these features are quantified into specific weld parameters. First, the weld edge is detected based on the weld geometry features to generate the rear axle weld geometry parameters. These parameters can more accurately quantify the weld geometry and provide a basis for subsequent welding process analysis and parameter determination. Exemplarily, the acquired weld image (such as an image obtained by a laser scanning device) is preprocessed, such as graying, filtering, etc., to improve the image quality. Then, the edge of the weld is accurately detected using an edge detection algorithm, such as the Canny edge detection algorithm. The width of the weld is calculated by the distance between the two edge points, and the length of the weld is calculated by the length of the edge curve. These geometry-related parameters are summarized to generate the rear axle weld geometry parameters.

[0037] The weld angle of the rear axle is detected based on the weld position feature, and then the weld position parameters of the rear axle are generated. The weld angle is related to the direction of welding and the force distribution. The vector relationship of the weld in three-dimensional space can be calculated based on the coordinate data in the weld position feature to obtain the weld angle, thereby generating the weld position parameters of the rear axle.

[0038] According to the weld material characteristics, the weld material of the rear axle is inspected and the weld material parameters of the rear axle are generated. Different materials require different welding conditions. Obtaining these rear axle weld material parameters helps determine the appropriate welding process parameters during the welding process, such as welding current, voltage, etc. The hardness, thermal conductivity and other rear axle weld material parameters of the weld are determined according to the material type in the weld material characteristics.

[0039] The previously generated rear axle weld geometry parameters, rear axle weld position parameters, and rear axle weld material parameters are associated and integrated. For example, the corresponding geometry parameters, position parameters, and material parameters are associated with the weld number as the index. Alternatively, the parameters can be stored using structures in the data structure. Each structure represents a weld, and the members of the structure are geometry parameters, position parameters, and material parameters, respectively, so as to obtain the rear axle weld parameter set. This parameter set covers all aspects of the weld information and provides comprehensive data support for subsequent welding parameter preprocessing and other work.

[0040] Furthermore, the weld parameter preprocessing module 10 of the embodiment of the present application is also used to perform the following steps: Step P1-21: clean the rear axle weld parameter set, and classify and sort it according to the rear axle weld geometric parameters, the rear axle weld position parameters, and the rear axle weld material parameters to determine multiple weld data classes.

[0041] Step P1-22: Based on the multiple weld data classes, the rear axle weld geometric parameters, the rear axle weld position parameters, and the rear axle weld material parameters are subjected to regression analysis to generate multiple weld vectors.

[0042] Step P1-23: Match the multiple weld vectors with the weld geometric features, the weld position features, and the weld material features, perform welding verification based on the matching results, and construct the initial welding parameter combination.

[0043] Specifically, the weld parameter set is cleaned to remove possible erroneous data, noise data or duplicate data in the rear axle weld parameter set to improve the data quality. Afterwards, the data is classified and sorted according to the weld geometry parameters, position parameters and material parameters. For example, the weld parameter set is analyzed using a cluster analysis algorithm, and welds with similar weld geometry features, weld position features and weld material features are classified into one category to obtain multiple weld data classes.

[0044] For each weld data class, a multivariate linear regression or nonlinear regression model is used to perform regression analysis on the rear axle weld geometry parameters, rear axle weld position parameters, and rear axle weld material parameters in the class to generate a weld vector that can represent the comprehensive characteristics of the weld. Multiple weld data classes are regressed one by one to determine multiple weld vectors. These weld vectors are a form of data representation that can reflect the relationship between different parameters.

[0045] Finally, these multiple weld vectors are matched with weld geometry features, weld position features, and weld material features, and welding verification is performed based on the matching results. For example, if a weld vector represents a welding parameter relationship suitable for a weld of a certain geometry (such as a narrow and long weld), at a specific location (such as a location close to the rear axle support point), and using a specific material (such as high-strength steel), then it is matched and verified with the actual detected weld situation. If the match is successful, the corresponding welding parameter combination is matched from the welding process database of the automatic welding equipment and determined as the initial welding parameter combination, which will be used for subsequent simulated welding operations.

[0046] The weld parameter preprocessing module 10 matches a relatively consistent initial welding parameter according to the configuration of the automated welding equipment through a detailed analysis of the weld characteristics, provides a benchmark for subsequent parameter adjustments, and narrows the adjustment range of subsequent parameter optimization adjustments, thereby quickly determining the control parameters that best suit the current welding environment and workpiece specifications.

[0047] Further, such as Figure 3 As shown, the simulated welding analysis module 20 of the embodiment of the present application is also used to perform the following steps: Step P2-11: Perform simulated welding based on the initial welding parameter combination and the weld geometric features to generate simulated welding geometric data.

[0048] Step P2-12: Perform simulated welding based on the initial welding parameter combination and the weld position characteristics to generate simulated welding structure coordinate data.

[0049] Step P2-13: Perform simulated welding based on the initial welding parameter combination and the weld material characteristics to generate a simulated welding heat affected zone.

[0050] Step P2-14: Perform welding prediction based on the simulated welding geometry data in combination with the welding strength to determine welding geometry change prediction parameters.

[0051] Step P2-15: Perform welding prediction based on the simulated welding structure coordinate data in combination with the welding deformation to determine welding position change prediction parameters.

[0052] Step P2-16: Perform welding prediction based on the welding heat affected zone and the heat affected zone size data to determine the welding material change prediction parameters.

[0053] Step P2-17: Add the welding geometry change prediction parameter, the welding position change prediction parameter, and the welding material change prediction parameter to the welding prediction parameter set.

[0054] Specifically, simulated welding is performed according to the initial welding parameter combination obtained from the weld parameter preprocessing module 10. The initial welding parameter combination (such as welding current, voltage, welding speed, etc.) and weld geometric characteristics (such as initial width, length, shape, etc. of the weld) are input into the welding simulation software. The software calculates the change of the weld geometry over time during the welding process according to the preset physical model, such as the heat conduction equation, metal melting and solidification model, etc., and generates simulated welding geometry data, which can reflect the geometry-related information in the welding process under specific welding parameters and geometric characteristics, such as the weld penetration depth, weld width, etc.

[0055] Similarly, the initial welding parameter combination and weld position characteristics (such as the coordinates of the weld relative to the weld reference point, the angle of the weld, etc.) are input into the welding simulation software to simulate the deformation of the weld structure caused by the input of welding heat during the welding process, calculate the coordinate changes of each point on the weld in three-dimensional space, and obtain the simulated welding structure coordinate data. For example, when welding a complex-shaped electric tricycle rear axle, the weld position characteristics and welding parameters will affect the distortion, bending and other deformations of the rear axle during the welding process. The welding simulation software calculates this deformation and obtains the coordinate data of each key position of the rear axle at different times during the welding process, that is, the simulated welding structure coordinate data.

[0056] The initial welding parameter combination and weld material characteristics (such as thermal conductivity and specific heat capacity of the material) are input into the welding simulation software, and the heat conduction model in the welding simulation software is used to calculate the heat propagation in the weldment during the welding process. According to the melting point, phase change temperature and other characteristics of the material, it is determined which areas are affected by the welding heat and have undergone changes in organization and performance, thereby generating a simulated welding heat-affected zone.

[0057] The welding strength corresponding to welds of different geometric shapes is calculated through welding simulation software. According to the corresponding relationship between geometric data and welding strength, the welding strength corresponding to each weld is determined, and the welding geometry change prediction parameters are generated.

[0058] Similarly, welding prediction is performed based on the simulated welding structure coordinate data combined with the welding deformation, predicting the position change of the weldment due to deformation during the welding process, and generating welding position change prediction parameters. For example, the simulated coordinate data shows that the weld position is offset. Based on the theoretical model of welding deformation, the degree of influence of this offset on the position change of the entire structure can be predicted, that is, the welding position change prediction parameter.

[0059] Welding prediction is performed based on the heat affected zone and the size data of the heat affected zone. According to the phase diagram and thermal cycle curve of the material, the performance change of the material in the heat affected zone is determined, and the welding material change prediction parameters are generated. For example, if the heat affected zone is too large, the microstructure of the material may change, thereby affecting the hardness and other properties of the material.

[0060] The welding geometry change prediction parameters, welding position change prediction parameters, and welding material change prediction parameters are added to the welding prediction parameter set to form a complete parameter set, providing comprehensive data support for subsequent welding process optimization and quality control.

[0061] Furthermore, the simulated welding analysis module 20 of the embodiment of the present application is also used to perform the following steps: Step P2-21: Based on the welding geometry change prediction parameters and the welding environment information, a welding dynamic analysis is performed according to the welding sequence to generate dynamic welding strength parameters.

[0062] Step P2-22: Based on the welding position change prediction parameters and the welding environment information, a welding dynamic analysis is performed according to the welding sequence to generate welding structure coordinate change parameters.

[0063] Step P2-23: Based on the welding material change prediction parameters and the welding environment information, a welding dynamic analysis is performed according to the welding sequence to generate welding temperature field time change parameters.

[0064] Step P2-24: According to the welding sequence, the dynamic welding strength parameters, the welding structure coordinate change parameters, and the welding temperature field time change parameters are superimposed in multiple layers to draw a welding change trend diagram.

[0065] Specifically, welding environment information (such as ambient temperature, humidity, wind speed, etc.) will affect heat transfer and cooling speed during welding. Import welding environment information into welding simulation software to calculate welding change data under actual welding environment. This includes: combining welding geometry change prediction parameters and welding environment information, simulating dynamic changes during welding, analyzing according to welding sequence, calculating welding strength at each moment, and generating dynamic welding strength parameters. These parameters reflect the dynamic changes of welding strength over time due to changes in welding geometry and environmental factors during actual welding.

[0066] Welding environment information will also affect the deformation of weldments. For example, changes in ambient temperature will affect the thermal expansion and contraction of weldments. In the welding simulation software, a weldment deformation model is established based on the principle of material mechanics. The welding position change prediction parameters and welding environment information are combined to perform welding dynamic analysis according to the welding sequence. The welding structure coordinates at each moment are calculated to generate welding structure coordinate change parameters. This parameter can describe the dynamic changes of the weldment structure in the spatial coordinates due to changes in the welding position and environmental influences during the welding process.

[0067] Welding environment information affects the heat absorption and release during welding, thus affecting the phase change of the material. In the welding simulation software, a temperature field model is established based on the heat conduction theory, and a welding dynamic analysis is performed according to the welding sequence based on the welding material change prediction parameters and welding environment information to obtain the welding temperature field at different times and positions, thereby generating the welding temperature field time change parameter. This parameter can show the dynamic changes of the welding temperature field over time due to changes in welding materials and environmental factors during the welding process.

[0068] Through data analysis software (such as Matlab), the welding geometry change prediction parameters, welding position change prediction parameters, welding material change prediction parameters and welding environment information at different time points or welding process stages are comprehensively analyzed, and a welding change trend chart is drawn with time as the horizontal axis and welding strength, welding deformation, material properties, etc. as the vertical axis, which intuitively shows the change trend of the welding process under specific welding parameters and environment. The welding change trend chart contains three layers, and each layer has a corresponding trend data according to the time series. For example, the change of welding strength over time is drawn as a curve, the change of welding structure coordinates is drawn as a spatial trajectory, and the change of welding temperature field is drawn as a contour map or color map. A complete welding change trend chart is formed by multi-layer superposition, which provides a basis for the evaluation and optimization of welding technology.

[0069] Furthermore, the welding defect analysis module 40 of the embodiment of the present application is also used to perform the following steps: Step P41: construct a support vector machine model, synchronize the welding change trend diagram to the support vector machine model according to the welding state value to perform anomaly detection, and determine multiple abnormal welding labels.

[0070] Step P42: Marking the welding change trend diagram based on the multiple abnormal welding labels to generate multiple welding abnormality areas.

[0071] Step P43: Perform defect analysis on the rear axle of the electric tricycle according to the multiple welding abnormality areas to determine multiple welding defect types, wherein the multiple welding defect types include defect distribution data.

[0072] Step P44: Perform welding impact analysis on the rear axle of the electric tricycle according to the multiple welding defect types to generate multiple defect risk values.

[0073] Step P45: Perform quality evaluation based on the multiple defect risk values ​​according to the defect distribution data to generate the welding quality score.

[0074] Specifically, a large amount of historical welding data is collected to train the support vector machine model. These historical welding data include normal and abnormal welding trend graphs and corresponding welding state values. The trained support vector machine model can identify abnormal conditions that are different from the normal welding mode by performing anomaly detection on the welding trend graph, and mark the identified abnormal positions with abnormal welding labels. These abnormal welding labels represent the presence of conditions that do not conform to the normal welding state at the corresponding positions, such as pores, cracks, etc.

[0075] Abnormal welding labels correspond to data points in the welding trend chart. Abnormal areas can be marked on the welding trend chart based on these labels, thereby generating multiple welding abnormal areas. These areas will be the focus of subsequent defect analysis. For example, if a point in a certain time period is marked as abnormal in the curve of welding strength changes over time, then the curve portion corresponding to this time period is a welding abnormality area. For the spatial trajectory of the change of welding structure coordinates, if certain points are marked as abnormal, then the local area where these points are located is the abnormal area. For the contour map or color map of the welding temperature field change, if the labels corresponding to the temperature field data in certain areas are abnormal, then these areas are welding abnormality areas.

[0076] Different welding abnormality areas may correspond to different welding defect types. For example, if there is an abnormal welding strength area, there may be a defect of insufficient welding strength; if there is an abnormal welding structure coordinate area, there may be a defect of welding deformation; if there is an abnormal welding temperature field area, there may be a defect of abnormal heat-affected zone (such as organizational changes caused by overheating or overcooling). By analyzing the characteristics of each abnormal area (such as the degree and location of the abnormality), the specific type of welding defect can be determined. At the same time, the distribution of each defect type in the rear axle of the electric tricycle can be counted. For example, if there are multiple defect points with insufficient welding strength in a certain area, the location information of these points and the defect size are recorded to obtain the defect distribution data.

[0077] For each type of welding defect, analyze the factors affecting the overall performance of the rear axle. For example, for defects of insufficient welding strength, the risk value can be determined by considering factors such as the degree of insufficient strength and the load borne by the rear axle during use. If the degree of insufficient strength is large and the rear axle is subjected to a large load, the defect risk value will be higher. For defects of welding deformation, the risk value can consider factors such as the size of the deformation and the impact of the deformation on the assembly accuracy of the rear axle. Generate corresponding defect risk values ​​for each type of welding defect through impact assessment. These risk values ​​can quantify the degree of impact of each defect type on the welding quality of the rear axle.

[0078] The weighted average algorithm is used to evaluate the quality according to the defect distribution data based on multiple defect risk values ​​to generate a welding quality score. This score can comprehensively reflect the welding quality of the rear axle of the electric tricycle.

[0079] Through the above steps, welding defects can be efficiently identified and classified, and welding quality can be accurately evaluated, which not only improves the accuracy of welding detection, but also provides a scientific basis for the optimization of welding process.

[0080] Furthermore, step P45 also includes: Step P451: Perform regional division based on the defect distribution data to determine multiple areas to be evaluated.

[0081] Step P452: Perform weighted calculation on the multiple areas to be evaluated according to the multiple defect risk values ​​to generate multiple weight coefficients.

[0082] Step P453: Sort the multiple areas to be evaluated in descending order based on the multiple weight coefficients to generate a sequence to be evaluated.

[0083] Step P454: Perform welding quality scoring on the multiple areas to be evaluated according to the sequence to be evaluated, and generate multiple quality evaluation results.

[0084] Step P455: Classify the multiple quality evaluation results into grades to generate multiple quality grades, and integrate the multiple quality evaluation results based on the multiple quality grades to obtain the welding quality score.

[0085] Specifically, the defects are divided according to their actual distribution positions on the rear axle of the electric tricycle, and multiple areas to be evaluated are determined so that the welding quality of different areas can be evaluated more specifically. For example, if the defects are mainly concentrated in different positions such as the weld joints, the middle part and the edge of the rear axle, the rear axle can be divided into different areas to be evaluated according to these positions.

[0086] According to the defect risk values ​​corresponding to the defects in different areas to be evaluated, weighted calculation is performed to determine the weight coefficient of each area, so as to reflect the difference in importance of different areas to the overall welding quality in subsequent evaluation. For example, for each area, the defect risk value of each type of defect is used as the weight, and the defect size in the defect distribution data is weighted to obtain the weight system of the area. The areas to be evaluated are sorted in descending order according to the weight coefficient to generate a sequence to be evaluated, so that the welding quality of the areas can be evaluated in order of importance.

[0087] According to the sequence to be evaluated, the welding quality of each area to be evaluated is scored, and multiple quality evaluation results are obtained, so as to make a specific quantitative evaluation of the welding quality of each area. For each area to be evaluated, a scoring standard can be formulated according to its own welding defect conditions (such as the type, quantity, severity, etc. of the defects). For example, a lower score is given to an area with many types of defects, a large number of defects and a high severity; a higher score is given to an area with fewer defects and a lighter degree. According to this standard, each area to be evaluated is scored to obtain multiple quality evaluation results.

[0088] The quality evaluation results are divided into grades, and different score ranges can be set to correspond to different quality levels, such as 90 to 100 for excellent, 80 to 89 for good, 70 to 79 for qualified, and 0 to 69 for unqualified. Based on these grades, the quality evaluation results are integrated and the proportion of each grade is calculated to obtain the final welding quality score, quantitatively evaluate the welding quality, and provide guidance for the subsequent optimization of welding parameter combinations.

[0089] In summary, the electric tricycle rear axle welding quality control system provided by the embodiment of the present application has the following technical effects: The weld parameter preprocessing module 10 obtains the weld parameter set of the rear axle of the electric tricycle through weld analysis, including weld geometry, position and material features. By analyzing and detecting these features, accurate data support can be provided for the subsequent welding process, ensuring that the welding process can be personalized for specific welding conditions. The simulated welding analysis module 20 simulates welding according to the initial welding parameter combination and the weld feature information, generates a welding prediction parameter set to evaluate factors such as geometric changes, position changes and material changes during the welding process, and further analyzes according to the welding environment information, draws a welding change trend diagram, provides visualization and predictive analysis of the welding process, and helps optimize the welding path and parameter settings. The welding path planning and evaluation module 30 plans multiple welding paths according to the welding change trend diagram, evaluates these paths, generates welding state values, and thus predicts the quality of the welding path, selects the optimal path for actual welding operations, and avoids defects caused by improper path selection. The welding defect analysis module 40 detects the welding results in combination with the welding state value and the welding change trend diagram, and constructs a support vector machine model to perform anomaly detection and defect marking, accurately identify potential problems in welding and classify defects, generate welding quality scores, and provide a basis for subsequent optimization and adjustment, ensuring effective control of welding quality. The welding parameter optimization module 50 dynamically adjusts the initial welding parameter combination according to the welding quality score, generates an optimization plan and executes it. Through the intelligent adjustment of welding parameters, real-time optimization of welding quality can be achieved, thereby ensuring the efficiency and stability of the entire welding process.

[0090] In general, the embodiment of the present application realizes comprehensive control of the welding quality of the rear axle of the electric tricycle by integrating multiple functional modules. Through precise parameter control and dynamic quality evaluation, the stability and consistency of the welding quality are significantly improved, the defect rate and rework rate are reduced, thereby ensuring the overall safety and durability of the rear axle of the electric tricycle and extending the service life of the rear axle of the electric tricycle.

[0091] Embodiment 2, as Figure 4 As shown, the embodiment of the present application provides a method for controlling the welding quality of the rear axle of an electric tricycle, the method comprising: Step S1: By performing weld analysis on the rear axle of the electric tricycle, a rear axle weld parameter set is obtained, and preprocessing is performed according to the rear axle weld parameter set to determine an initial welding parameter combination.

[0092] Step S2: simulate welding according to the initial welding parameter combination to generate a welding prediction parameter set, perform welding analysis according to the welding prediction parameter set combined with welding environment information, and draw a welding change trend diagram.

[0093] Step S3: traverse the welding change trend diagram to plan a welding path, determine multiple welding paths, perform welding evaluation according to the multiple welding paths, and generate a welding state value.

[0094] Step S4: Perform welding inspection on the rear axle of the electric tricycle according to the welding state value in combination with the welding change trend diagram, perform defect analysis according to the inspection result, and generate a welding quality score.

[0095] Step S5: dynamically adjusting the initial welding parameter combination according to the welding quality score, generating a welding optimization plan, and executing the welding optimization plan to intelligently control the welding quality of the rear axle of the electric tricycle.

[0096] Furthermore, in step S1 of the embodiment of the present application, the weld seam parameter set of the rear axle is obtained by performing weld seam analysis on the rear axle of the electric tricycle, and the method further includes: Perform weld inspection on the rear axle of the electric tricycle to obtain multiple weld features, which include weld geometry features, weld position features, and weld material features; perform weld edge inspection on the rear axle of the electric tricycle according to the weld geometry features to generate rear axle weld geometry parameters; perform weld angle inspection on the rear axle of the electric tricycle according to the weld position features to generate rear axle weld position parameters; perform weld material inspection on the rear axle of the electric tricycle according to the weld material features to generate rear axle weld material parameters; associate and integrate the rear axle weld geometry parameters, the rear axle weld position parameters, and the rear axle weld material parameters to obtain the rear axle weld parameter set.

[0097] Furthermore, in step S1 of the embodiment of the present application, preprocessing is performed according to the rear axle weld parameter set to determine an initial welding parameter combination, and further includes: The rear axle weld parameter set is cleaned, and is classified and sorted according to the rear axle weld geometric parameters, the rear axle weld position parameters, and the rear axle weld material parameters to determine a plurality of weld data classes; the rear axle weld geometric parameters, the rear axle weld position parameters, and the rear axle weld material parameters are regressed based on the plurality of weld data classes to generate a plurality of weld vectors; the plurality of weld vectors are matched with the weld geometric features, the weld position features, and the weld material features, welding verification is performed based on the matching results, and the initial welding parameter combination is constructed.

[0098] Furthermore, in step S2 of the embodiment of the present application, simulating welding according to the initial welding parameter combination to generate a welding prediction parameter set also includes: Based on the initial welding parameter combination combined with the weld geometry features, simulated welding is performed to generate simulated welding geometry data; based on the initial welding parameter combination combined with the weld position features, simulated welding is performed to generate simulated welding structure coordinate data; based on the initial welding parameter combination combined with the weld material features, simulated welding is performed to generate a simulated welding heat affected zone; welding prediction is performed based on the simulated welding geometry data combined with welding strength to determine welding geometry change prediction parameters; welding prediction is performed based on the simulated welding structure coordinate data combined with welding deformation to determine welding position change prediction parameters; welding prediction is performed based on the welding heat affected zone combined with heat affected zone size data to determine welding material change prediction parameters; the welding geometry change prediction parameters, the welding position change prediction parameters, and the welding material change prediction parameters are added to the welding prediction parameter set.

[0099] Furthermore, in step S2 of the embodiment of the present application, welding analysis is performed according to the welding prediction parameter set combined with welding environment information to draw a welding change trend diagram, and the following further includes: Based on the welding geometry change prediction parameters combined with the welding environment information, welding dynamic analysis is performed according to the welding sequence to generate dynamic welding strength parameters; based on the welding position change prediction parameters combined with the welding environment information, welding dynamic analysis is performed according to the welding sequence to generate welding structure coordinate change parameters; based on the welding material change prediction parameters combined with the welding environment information, welding dynamic analysis is performed according to the welding sequence to generate welding temperature field time change parameters; according to the welding sequence, the dynamic welding strength parameters, the welding structure coordinate change parameters, and the welding temperature field time change parameters are superimposed in multiple layers to draw a welding change trend diagram.

[0100] Furthermore, step S4 of the embodiment of the present application also includes: A support vector machine model is constructed, and the welding change trend diagram is synchronized to the support vector machine model according to the welding state value for abnormality detection, and a plurality of abnormal welding labels are determined; the welding change trend diagram is marked based on the plurality of abnormal welding labels, and a plurality of welding abnormality areas are generated; a defect analysis is performed on the rear axle of the electric tricycle according to the plurality of welding abnormality areas, and a plurality of welding defect types are determined, and the plurality of welding defect types include defect distribution data; a welding impact analysis is performed on the rear axle of the electric tricycle according to the plurality of welding defect types, and a plurality of defect risk values ​​are generated; a quality evaluation is performed according to the defect distribution data based on the plurality of defect risk values, and the welding quality score is generated.

[0101] Further, performing quality evaluation according to the defect distribution data based on the multiple defect risk values ​​to generate the welding quality score also includes: Based on the defect distribution data, regional division is performed to determine multiple areas to be evaluated; the multiple areas to be evaluated are weighted according to the multiple defect risk values ​​to generate multiple weight coefficients; the multiple areas to be evaluated are sorted in descending order based on the multiple weight coefficients to generate a sequence to be evaluated; welding quality scores are performed on the multiple areas to be evaluated according to the sequence to be evaluated to generate multiple quality evaluation results; the multiple quality evaluation results are graded to generate multiple quality grades, and the multiple quality evaluation results are integrated based on the multiple quality grades to obtain the welding quality score.

[0102] Through the above-mentioned detailed description of a welding quality control system for the rear axle of an electric tricycle, those skilled in the art can clearly understand a welding quality control method for the rear axle of an electric tricycle in this embodiment. As for the method disclosed in the second embodiment, since it corresponds to the system disclosed in the first embodiment and has corresponding execution steps and beneficial effects, the relevant parts can be referred to the description of the system part.

[0103] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. An electric tricycle rear axle welding quality control system, characterized in that: The system comprises: A weld parameter preprocessing module, wherein the weld parameter preprocessing module is used to perform weld analysis on the rear axle of the electric tricycle, obtain a rear axle weld parameter set, perform preprocessing according to the rear axle weld parameter set, and determine an initial welding parameter combination; A simulated welding analysis module, wherein the simulated welding analysis module is used to perform simulated welding according to the initial welding parameter combination, generate a welding prediction parameter set, perform welding analysis according to the welding prediction parameter set combined with welding environment information, and draw a welding change trend diagram; A welding path planning and evaluation module, the welding path planning and evaluation module is used to traverse the welding change trend diagram to plan a welding path, determine multiple welding paths, perform welding evaluation according to the multiple welding paths, and generate a welding state value; A welding defect analysis module, the welding defect analysis module is used to perform welding detection on the rear axle of the electric tricycle according to the welding state value combined with the welding change trend diagram, perform defect analysis according to the detection result, and generate a welding quality score; A welding parameter optimization module is used to dynamically adjust the initial welding parameter combination according to the welding quality score, generate a welding optimization plan, and execute the welding optimization plan to intelligently control the welding quality of the rear axle of the electric tricycle.

2. The electric tricycle rear axle welding quality control system as claimed in claim 1, characterized in that: The weld parameter preprocessing module is used to perform weld analysis on the rear axle of the electric tricycle to obtain a rear axle weld parameter set, and the execution steps include: Performing weld inspection on the rear axle of the electric tricycle to obtain a plurality of weld features, wherein the plurality of weld features include weld geometry features, weld position features, and weld material features; Perform weld edge detection on the rear axle of the electric tricycle according to the weld geometric features, and generate rear axle weld geometric parameters; Perform weld angle detection on the rear axle of the electric tricycle according to the weld position characteristics, and generate rear axle weld position parameters; Perform weld material detection on the rear axle of the electric tricycle according to the weld material characteristics, and generate rear axle weld material parameters; The rear axle weld geometric parameters, the rear axle weld position parameters, and the rear axle weld material parameters are associated and integrated to obtain the rear axle weld parameter set.

3. The electric tricycle rear axle welding quality control system as claimed in claim 2, characterized in that: The weld parameter preprocessing module performs preprocessing according to the rear axle weld parameter set to determine an initial welding parameter combination, and the execution steps include: Cleaning the rear axle weld parameter set, and classifying and arranging the rear axle weld parameter set according to the rear axle weld geometry parameter, the rear axle weld position parameter, and the rear axle weld material parameter, to determine a plurality of weld data classes; Based on the multiple weld data classes, the geometric parameters of the rear axle weld, the position parameters of the rear axle weld, and the material parameters of the rear axle weld are subjected to regression analysis to generate multiple weld vectors; The multiple weld vectors are matched with the weld geometric features, the weld position features, and the weld material features, welding verification is performed according to the matching results, and the initial welding parameter combination is constructed.

4. The electric tricycle rear axle welding quality control system as claimed in claim 2, characterized in that: The simulated welding analysis module is used to perform simulated welding according to the initial welding parameter combination to generate a welding prediction parameter set, and the execution steps include: Perform simulated welding based on the initial welding parameter combination combined with the weld geometric features to generate simulated welding geometric data; Perform simulated welding based on the initial welding parameter combination combined with the weld position characteristics to generate simulated welding structure coordinate data; Perform simulated welding based on the initial welding parameter combination combined with the weld material characteristics to generate a simulated welding heat affected zone; Perform welding prediction based on the simulated welding geometry data combined with welding strength to determine welding geometry change prediction parameters; Perform welding prediction based on the simulated welding structure coordinate data combined with welding deformation to determine welding position change prediction parameters; Perform welding prediction based on the welding heat affected area and the heat affected zone size data to determine welding material change prediction parameters; The welding geometry change prediction parameter, the welding position change prediction parameter, and the welding material change prediction parameter are added to the welding prediction parameter set.

5. The electric tricycle rear axle welding quality control system as claimed in claim 4, characterized in that: The simulated welding analysis module performs welding analysis according to the welding prediction parameter set combined with welding environment information and draws a welding change trend diagram, and the execution steps include: Based on the welding geometry change prediction parameters combined with the welding environment information, a welding dynamic analysis is performed according to the welding sequence to generate a dynamic welding strength parameter; Based on the welding position change prediction parameters combined with the welding environment information, a welding dynamic analysis is performed according to the welding sequence to generate welding structure coordinate change parameters; Based on the welding material change prediction parameters combined with the welding environment information, a welding dynamic analysis is performed according to the welding sequence to generate welding temperature field time change parameters; The dynamic welding strength parameters, the welding structure coordinate change parameters, and the welding temperature field time change parameters are superimposed in multiple layers according to the welding sequence to draw a welding change trend diagram.

6. The electric tricycle rear axle welding quality control system as claimed in claim 1, characterized in that: The welding defect analysis module is used to perform welding detection on the rear axle of the electric tricycle according to the welding state value combined with the welding change trend diagram, perform defect analysis according to the detection result, and generate a welding quality score. The execution steps include: Constructing a support vector machine model, synchronizing the welding change trend diagram to the support vector machine model according to the welding state value for abnormality detection, and determining a plurality of abnormal welding labels; Marking the welding change trend diagram based on the multiple abnormal welding tags to generate multiple welding abnormality areas; Perform defect analysis on the rear axle of the electric tricycle according to the multiple welding abnormality areas to determine multiple welding defect types, wherein the multiple welding defect types include defect distribution data; Performing welding impact analysis on the rear axle of the electric tricycle according to the multiple welding defect types to generate multiple defect risk values; A quality assessment is performed based on the multiple defect risk values ​​according to the defect distribution data to generate the welding quality score.

7. The electric tricycle rear axle welding quality control system as claimed in claim 6, characterized in that: Performing quality evaluation according to the defect distribution data based on the multiple defect risk values ​​to generate the welding quality score includes: Performing regional division based on the defect distribution data to determine a plurality of areas to be evaluated; Performing weighted calculation on the multiple areas to be evaluated according to the multiple defect risk values ​​to generate multiple weight coefficients; Sort the multiple to-be-evaluated regions in descending order based on the multiple weight coefficients to generate a to-be-evaluated sequence; Perform welding quality scoring on the plurality of areas to be evaluated according to the sequence to be evaluated, and generate a plurality of quality evaluation results; The multiple quality evaluation results are graded to generate multiple quality grades, and the multiple quality evaluation results are integrated based on the multiple quality grades to obtain the welding quality score.

8. A method for controlling the welding quality of the rear axle of an electric tricycle, characterized in that: The method is performed by an electric tricycle rear axle welding quality control system according to any one of claims 1 to 7, comprising: By performing weld analysis on the rear axle of the electric tricycle, a rear axle weld parameter set is obtained, and preprocessing is performed according to the rear axle weld parameter set to determine an initial welding parameter combination; Perform simulated welding according to the initial welding parameter combination to generate a welding prediction parameter set, perform welding analysis according to the welding prediction parameter set combined with welding environment information, and draw a welding change trend diagram; Traversing the welding change trend diagram to plan a welding path, determining a plurality of welding paths, performing welding evaluation according to the plurality of welding paths, and generating a welding state value; Perform welding inspection on the rear axle of the electric tricycle according to the welding state value and the welding change trend diagram, perform defect analysis according to the inspection result, and generate a welding quality score; The initial welding parameter combination is dynamically adjusted according to the welding quality score to generate a welding optimization plan, and the welding optimization plan is executed to intelligently control the welding quality of the rear axle of the electric tricycle.

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