Electric tricycle rear axle welding quality control system and method

Abstract

The present application provides a welding quality control system and method for the rear axle of an electric tricycle, which relates to the field of welding technology. The weld seam preprocessing module performs weld seam analysis on the rear axle of the electric tricycle to determine the initial welding parameter combination; the simulated welding analysis module simulates welding according to the initial welding parameter combination and draws a welding change trend diagram; the welding path planning and evaluation module traverses the welding change trend diagram to plan multiple welding paths and perform welding evaluation to generate welding status values; the welding defect analysis module performs welding detection and defect analysis based on the welding status value and the welding change trend diagram to generate a welding quality score; the welding parameter optimization module adjusts the initial welding parameter combination according to the welding quality score to generate a welding optimization plan. The present application solves the technical problem that the existing technology is difficult to adapt to the dynamic changes of the welding environment and workpiece conditions, resulting in the inability to effectively control the welding quality, and improves the control of the rear axle welding quality.

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 for short-distance urban transportation and rural freight transport due to their energy-saving, environmentally friendly, and economical practicality. As a key component for load-bearing and transmission, the rear axle's structural strength and weld 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 impact of worker status on welding quality. However, 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 electric tricycle rear axle. For example, factors such as the different structures and material thicknesses of the rear axle are not fully considered, which may lead to loose welds or uneven welds during the welding process. On the other hand, 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 prone to welding defects. The application achieves the technical effect of enhancing the stability and consistency of the welding process of the rear axle of an 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 chart to plan the welding path, determine multiple welding paths, perform welding evaluation according to the multiple welding paths, and generate a welding status value; the welding defect analysis module is used to perform welding detection on the rear axle of the electric tricycle according to the welding status value and the welding change trend chart, 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 based on 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 based on 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, determine multiple welding paths, perform welding evaluation according to the multiple welding paths, and generate a welding status value; performing welding inspection on the rear axle of the electric tricycle according to the welding status value in combination with the welding change trend chart, performing defect analysis based on the inspection results, and generating a welding quality score; dynamically adjusting the initial welding parameter combination according to the welding quality score to generate 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:

[0008] The weld parameter preprocessing module performs weld analysis on the rear axle of the electric tricycle to obtain a set of rear axle weld parameters. It then performs preprocessing based on the set of rear axle weld parameters to determine an initial welding parameter combination, providing an accurate parameter basis for the welding process. The simulated welding analysis module simulates 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 plots a welding change trend graph, providing an important reference for subsequent welding path planning and evaluation and welding defect analysis. The welding path planning and evaluation module traverses the welding change trend graph to plan welding paths, determine multiple welding paths, perform welding evaluations based on the multiple welding paths, and generate welding status values ​​to reflect the quality of each welding path. By evaluating the welding status under different paths, data support is provided for ultimately finding the optimal welding method, ensuring that the welding process can proceed along a better path and reducing potential welding problems. The welding defect analysis module performs welding inspection on the rear axle of the electric tricycle based on the welding status value and the welding change trend chart, performs defect analysis based on 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 based on 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 meets the requirements.

[0009] 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.

[0010] 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

[0011] 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.

[0012] Figure 2 A schematic diagram of the process of obtaining a rear axle weld parameter set in an electric tricycle rear axle welding quality control system provided in an embodiment of the present application.

[0013] Figure 3A schematic diagram of a flow chart for generating a welding prediction parameter set in a welding quality control system for an electric tricycle rear axle provided in an embodiment of the present application.

[0014] Figure 4 A flow chart of a method for controlling welding quality of an electric tricycle rear axle provided in an embodiment of the present application.

[0015] Description of the reference numerals: weld parameter preprocessing module 10 , simulated welding analysis module 20 , welding path planning and evaluation module 30 , welding defect analysis module 40 , welding parameter optimization module 50 . DETAILED DESCRIPTION

[0016] The embodiments of the present application provide a welding quality control system and method for the rear axle of an electric tricycle, thereby solving 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 prone to welding defects. The embodiment of the present application achieves the technical effect of enhancing the stability and consistency of the welding process of the rear axle of an electric tricycle, thereby improving the control of the welding quality of the rear axle.

[0017] Example 1, as Figure 1 As shown, an embodiment of the present application provides a rear axle welding quality control system for an electric tricycle, the system comprising:

[0018] 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.

[0019] Specifically, the rear axle weld parameter set is a collection of data related to the rear axle welds of electric tricycles. This data covers 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 location of the weld on the bridge (such as the coordinate position relative to a specific reference point), and the material properties used in the weld (such as the type and strength of the material). 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 welding current, welding speed, welding voltage, etc. Different weld conditions require different initial welding parameter combinations to ensure the quality of the weld.

[0020] Weld seam analysis on the rear axle of an electric tricycle is performed using tools such as weld seam detectors and material composition analyzers to obtain various weld characteristics. The weld seam detector can accurately measure the width and height of the weld seam, obtaining its geometric characteristics. A coordinate measurement system can also be used to determine the weld seam's position relative to the overall rear axle structure, obtaining weld position characteristics. The material composition analyzer can determine the composition of the weld material, obtaining weld material characteristics. These different types of weld characteristic information are aggregated to form a rear axle weld parameter set.

[0021] The rear axle weld parameter set is then cleaned and preprocessed to remove inaccurate or duplicate data. For example, if equipment errors occur during weld position feature detection, some data points with significant deviations need to be removed. Based on this preprocessed rear axle weld parameter set, the welding equipment's welding process database is used to match the corresponding initial welding parameter combination. These parameters provide input data for subsequent welding simulations, ensuring that subsequent welding simulations and parameter optimization are based on relatively accurate data.

[0022] 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.

[0023] Specifically, a welding prediction parameter set is a set of data generated after simulating welding using the initial welding parameter combination. It is used to predict various changes that may occur during the actual welding process, such as changes in geometry, position, and material properties after welding. A welding trend chart graphically displays the changing trends of various welding parameters over time or throughout the welding process, such as weld strength, deformation, and material properties.

[0024] Using welding simulation software, such as SYSWELD, simulate welding according to the initial welding parameter combination obtained from the weld parameter preprocessing module 10, predict various possible welding process conditions, and obtain a welding prediction parameter set. Environmental information surrounding the actual welding process, such as temperature, humidity, and air flow conditions, is collected; these factors can affect welding quality. Welding environmental conditions are superimposed on the welding prediction parameters, and the simulation results are corrected to more closely resemble actual conditions. Comprehensively analyze the welding prediction parameters at different time points or stages of the welding process, and create a welding change trend chart to intuitively display the changing trends of the welding process under specific welding parameters and conditions. This provides data reference for subsequent path planning and defect analysis.

[0025] 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.

[0026] Specifically, the welding path refers to the path of the welding gun or welding heat source on the rear axle of the electric tricycle during welding. Different welding paths may have different effects on welding quality and efficiency. The welding condition value is a comprehensive assessment value used to reflect the overall condition of welding when following a specific welding path, including comprehensive conditions such as weld strength, heat input distribution, and weld uniformity.

[0027] Traverse the welding change trend chart and identify key information in the welding change 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 change 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. Combined with simulation tools, 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.

[0028] 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 results, and generate a welding quality score.

[0029] Specifically, the welding defect analysis module 40 analyzes and evaluates the simulated welding results of the electric tricycle's rear axle, combining welding status values ​​and welding change trends. It detects possible welding defects and collects detailed information about the defects, such as their location, type, and severity. Based on this detailed information, it conducts a risk assessment, calculates their impact on the overall weld quality, and generates a welding quality score to indicate the overall weld quality. This welding inspection and defect analysis provides a basis for subsequent optimization of welding parameters, thereby ensuring continuous improvement in welding quality.

[0030] 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.

[0031] Specifically, a welding optimization plan is a set of welding parameters generated by optimizing an initial welding parameter combination based on the welding quality score. The welding parameter optimization module 50 analyzes the initial welding parameter combination based on the welding quality score to identify key parameters that may cause defects, such as cracks caused by low current or undercutting caused by excessive speed. Using optimization algorithms such as genetic algorithms and particle swarm optimization, the module optimizes and adjusts the initial welding parameter combination to generate a new parameter combination. The validity of this welding parameter combination is then verified in simulation software, and further parameter adjustments are made based on the verification results. Through continuous optimization iterations, the module finds a welding parameter combination that meets the expected welding quality score and determines the final welding optimization plan. The welding optimization plan is then loaded into the welding equipment via an intelligent control system, enabling real-time adjustments to welding operations, significantly improving welding quality and reliability.

[0032] Further, such as Figure 2 As shown, the weld parameter preprocessing module 10 of the embodiment of the present application is further configured to perform the following steps:

[0033] Step P1-11: Perform weld inspection on the rear axle of the electric tricycle to obtain multiple weld features, where the multiple weld features include weld geometry features, weld position features, and weld material features.

[0034] 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.

[0035] 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.

[0036] Step P1-14: Perform weld material inspection on the rear axle of the electric tricycle according to the weld material characteristics, and generate rear axle weld material parameters.

[0037] Step P1-15: Correlate and 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.

[0038] Specifically, the welds of the rear axle of the electric tricycle are inspected using detection tools such as sensors, image acquisition equipment or material analyzers, and multiple weld features are extracted. 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.

[0039] These weld features are further inspected and analyzed, and these features are quantified into specific weld parameters. First, weld edge detection is performed based on the weld geometric features to generate the rear axle weld geometric parameters. These parameters can more accurately quantify the weld geometry and provide a basis for subsequent welding process analysis and parameter determination. For example, the acquired weld image (e.g., an image obtained by a laser scanning device) is preprocessed, such as grayscale and filtering, to improve image quality. Then, edge detection algorithms, such as the Canny edge detection algorithm, are used to accurately detect the weld edge. The weld width is calculated by calculating the distance between the two edge points, and the weld length is calculated by calculating the length of the edge curve. These geometry-related parameters are summarized to generate the rear axle weld geometric parameters.

[0040] Weld angle detection is performed on the rear axle based on the weld location feature, generating rear axle weld location parameters. The weld angle is related to the weld orientation and force distribution. The coordinate data in the weld location feature is used to calculate the weld vector relationship in three-dimensional space, deriving the weld angle and generating rear axle weld location parameters.

[0041] The rear axle weld material is inspected based on the weld material characteristics, generating rear axle weld material parameters. Different materials require different welding conditions, and obtaining these rear axle weld material parameters helps determine appropriate welding process parameters, such as welding current and voltage. Rear axle weld material parameters, such as hardness and thermal conductivity, are determined based on the material type in the weld material characteristics.

[0042] The previously generated rear axle weld geometry, location, and material parameters are then linked and integrated. For example, the corresponding geometry, location, and material parameters are linked using the weld number as an index. Alternatively, these parameters can be stored in a data structure, where each structure represents a weld and its members are the geometry, location, and material parameters, respectively. This creates a rear axle weld parameter set. This parameter set encompasses all aspects of the weld, providing comprehensive data support for subsequent welding parameter preprocessing and other tasks.

[0043] Furthermore, the weld parameter preprocessing module 10 of the embodiment of the present application is further configured to perform the following steps:

[0044] Step P1-21: Clean the rear axle weld parameter set, 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, and determine multiple weld data classes.

[0045] Step P1-22: Based on the multiple weld data classes, regression analysis is performed on the rear axle weld geometric parameters, the rear axle weld position parameters, and the rear axle weld material parameters to generate multiple weld vectors.

[0046] 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.

[0047] Specifically, the weld parameter set is cleaned to remove any erroneous, noisy, or duplicate data that may exist in the rear axle weld parameter set to improve data quality. The data is then categorized and organized according to weld geometry, location, and material parameters. For example, a cluster analysis algorithm is used to analyze the weld parameter set, grouping welds with similar geometry, location, and material characteristics into a single category, resulting in multiple weld data clusters.

[0048] For each weld data class, a multivariate linear regression or nonlinear regression model is used to perform regression analysis on the geometric, location, and material parameters of the rear axle welds within that class, generating a weld vector that represents the comprehensive characteristics of that weld. Regression analysis is performed on multiple weld data classes one by one to determine multiple weld vectors. These weld vectors are a form of data representation that reflects the relationship between different parameters.

[0049] Finally, these multiple weld vectors are matched with weld geometry, weld location, and weld material characteristics, 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 with a specific geometry (such as a narrow and long weld), at a specific location (such as near 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. 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 in subsequent simulated welding operations.

[0050] 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, narrows the adjustment range of subsequent parameter optimization adjustments, and quickly determines the control parameters that are most suitable for the current welding environment and workpiece specifications.

[0051] Further, such as Figure 3 As shown, the simulated welding analysis module 20 of the embodiment of the present application is further configured to perform the following steps:

[0052] Step P2-11: performing simulated welding based on the initial welding parameter combination and the weld geometric features to generate simulated welding geometric data.

[0053] Step P2-12: performing simulated welding based on the initial welding parameter combination and the weld position characteristics to generate simulated welding structure coordinate data.

[0054] Step P2-13: performing simulated welding based on the initial welding parameter combination and the weld material characteristics to generate a simulated welding heat affected zone.

[0055] 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.

[0056] Step P2-15: Perform welding prediction based on the simulated welding structure coordinate data in combination with the welding deformation amount to determine welding position change prediction parameters.

[0057] Step P2-16: Perform welding prediction based on the welding heat affected zone and the heat affected zone size data to determine welding material change prediction parameters.

[0058] 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.

[0059] 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 geometry (such as the initial weld width, length, and shape, etc.) are input into the welding simulation software. Based on preset physical models, such as the heat conduction equation and metal melting and solidification models, the software calculates the time-dependent changes in the weld geometry during the welding process and generates simulated welding geometry data. This data can reflect geometric information related to the welding process under specific welding parameters and geometry, such as weld penetration depth and width.

[0060] Similarly, the initial welding parameter combination and weld position characteristics (such as the coordinates of the weld relative to the weldment reference point and the weld angle) are input into the welding simulation software. The effects of the welding heat input on the weldment structure deformation during the welding process are simulated, and the coordinate changes of each point on the weldment in three-dimensional space are calculated to obtain the simulated weld structure coordinate data. For example, when welding the rear axle of a complex electric tricycle, the weld position characteristics and welding parameters will affect the distortion, bending, and other deformations of the rear axle during welding. By calculating this deformation, the welding simulation software obtains the coordinate data of each key position of the rear axle at different times during the welding process, i.e., the simulated weld structure coordinate data.

[0061] The initial welding parameter combination and weld material characteristics (such as thermal conductivity and specific heat capacity) are input into the welding simulation software. The heat conduction model within the welding simulation software is then used to calculate the heat transfer within the weldment during the welding process. Based on the material's melting point, phase transition temperature, and other characteristics, the software determines which areas are affected by the welding heat and undergo changes in their structure and properties, thereby generating a simulated weld heat-affected zone.

[0062] The welding strength corresponding to welds of different geometric shapes is calculated through welding simulation software. Based on the correspondence between geometric data and welding strength, the welding strength corresponding to each weld is determined, and the welding geometry change prediction parameters are generated.

[0063] Similarly, welding predictions are performed based on simulated welded structure coordinate data combined with welding deformation. This predicts the positional shift of the weldment due to deformation during welding and generates welding positional shift prediction parameters. For example, if the simulated coordinate data indicates a weld position offset, the theoretical model of welding deformation can be used to predict the extent to which this offset affects the overall positional shift of the structure, thus generating the welding positional shift prediction parameters.

[0064] Weld predictions are performed based on the heat-affected zone (HAZ) and HAZ size data. Based on the material's phase diagram and thermal cycle curve, the material's property changes within the HAZ are determined, generating weld material change prediction parameters. For example, an excessively large HAZ may cause changes in the material's microstructure, affecting properties such as hardness.

[0065] 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.

[0066] Furthermore, the simulated welding analysis module 20 of the embodiment of the present application is further configured to perform the following steps:

[0067] Step P2-21: Perform welding dynamic analysis according to the welding sequence based on the welding geometry change prediction parameters and the welding environment information to generate dynamic welding strength parameters.

[0068] 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.

[0069] 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.

[0070] Step P2-24: superimpose the dynamic welding strength parameters, the welding structure coordinate change parameters, and the welding temperature field time change parameters in multiple layers according to the welding sequence to draw a welding change trend diagram.

[0071] Specifically, welding environment information (such as ambient temperature, humidity, and wind speed) affects heat transfer and cooling rates during welding. This welding environment information is imported into welding simulation software to calculate welding variation data under actual welding conditions. This involves combining weld geometry change prediction parameters with welding environment information to simulate dynamic changes during the welding process. This analysis is performed according to the welding time sequence, calculating the weld strength at each moment and generating dynamic weld strength parameters. These parameters reflect the dynamic changes in weld strength over time during the actual welding process, caused by changes in weld geometry and environmental factors.

[0072] Welding environment information also affects weld deformation. For example, changes in ambient temperature can affect the thermal expansion and contraction of welds. In welding simulation software, a weld deformation model is established based on the principles of material mechanics. This model combines welding position change prediction parameters with welding environment information to perform welding dynamic analysis according to the welding sequence. The weld structure coordinates at each moment are calculated, generating a weld structure coordinate change parameter. This parameter describes the dynamic changes in the weld structure's spatial coordinates during the welding process due to changes in welding position and environmental influences.

[0073] The welding environment affects heat absorption and release during welding, thereby influencing the phase change of the material. In welding simulation software, a temperature field model is established based on heat conduction theory. Dynamic welding analysis is performed according to the welding sequence based on predicted parameters of welding material changes and welding environment information. This generates a welding temperature field time-varying parameter, which displays the dynamic changes in the welding temperature field over time due to changes in welding materials and environmental factors.

[0074] Using data analysis software (such as Matlab), the predicted parameters for welding geometry changes, welding position changes, and welding material changes at different time points or stages of the welding process are comprehensively analyzed along with the welding environment information. A welding trend chart is plotted with time as the horizontal axis and weld strength, weld deformation, and material properties as the vertical axis, visually demonstrating the changing trends of the welding process under specific welding parameters and conditions. This welding trend chart consists of three layers, each with corresponding trend data based on a time series. For example, the change in weld strength over time is plotted as a curve, the change in weld structure coordinates is plotted as a spatial trajectory, and the change in weld temperature field is plotted as a contour map or color map. By stacking multiple layers, a complete welding trend chart is formed, providing a basis for evaluating and optimizing the welding process.

[0075] Furthermore, the welding defect analysis module 40 of the embodiment of the present application is further configured to perform the following steps:

[0076] Step P41: constructing a support vector machine model, synchronizing the welding change trend diagram to the support vector machine model according to the welding state value to perform anomaly detection, and determining multiple abnormal welding labels.

[0077] Step P42: Marking the welding change trend diagram based on the multiple abnormal welding labels to generate multiple welding abnormality areas.

[0078] Step P43: performing defect analysis on the rear axle of the electric tricycle based on the multiple welding abnormality areas to determine multiple welding defect types, wherein the multiple welding defect types include defect distribution data.

[0079] 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.

[0080] Step P45: Perform quality evaluation based on the multiple defect risk values ​​according to the defect distribution data to generate the welding quality score.

[0081] Specifically, a support vector machine model is trained by collecting a large amount of historical welding data. This historical welding data includes normal and abnormal welding trend graphs and corresponding welding status values. The trained support vector machine model detects anomalies in the welding trend graphs, identifying abnormalities that deviate from the normal welding pattern and labeling the identified abnormal locations with abnormal weld labels. These abnormal weld labels represent conditions that deviate from the normal welding state, such as pores and cracks.

[0082] Abnormal welding labels correspond to data points in the welding change trend chart. Based on these labels, abnormal areas can be marked on the welding change trend chart, thereby generating multiple welding abnormality areas. These areas will be the focus of subsequent defect analysis. For example, if in the curve of welding strength changes over time, the points within a certain time period are marked as abnormal, 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.

[0083] Different weld anomaly areas may correspond to different weld defect types. For example, if the weld strength is abnormal in an area, insufficient weld strength may be the cause; if the weld structure coordinates are abnormal in an area, weld deformation may be the cause; if the weld temperature field is abnormal in an area, heat-affected zone abnormalities (such as structural changes caused by overheating or overcooling) may be the cause. By analyzing the characteristics of each abnormal area (such as the degree and location of the abnormality), the specific weld defect type can be determined. At the same time, the distribution of each defect type in the rear axle of the electric tricycle can be statistically analyzed. For example, if a certain area has multiple defect points with insufficient weld strength, the location information and defect size of these points can be recorded to obtain defect distribution data.

[0084] For each weld defect type, factors influencing its overall performance are analyzed. For example, for defects involving insufficient weld strength, the risk value can be determined based on factors such as the degree of insufficient strength and the loads borne by the rear axle during use. If the degree of insufficient strength is significant and the rear axle bears heavy loads, the defect risk value will be higher. For defects involving weld deformation, the risk value can take into account factors such as the magnitude of the deformation and its impact on rear axle assembly accuracy. Through impact assessment, a corresponding defect risk value is generated for each weld defect type. These risk values ​​quantify the degree of impact of each defect type on rear axle weld quality.

[0085] Using a weighted average algorithm, quality evaluation is performed based on multiple defect risk values ​​and defect distribution data to generate a welding quality score. This score comprehensively reflects the welding quality of the rear axle of the electric tricycle.

[0086] The above steps can efficiently identify and classify welding defects and accurately evaluate welding quality, which not only improves the accuracy of welding detection but also provides a scientific basis for the optimization of welding process.

[0087] Furthermore, step P45 further includes:

[0088] Step P451: Perform regional division based on the defect distribution data to determine multiple areas to be evaluated.

[0089] Step P452: Perform weighted calculation on the multiple areas to be evaluated according to the multiple defect risk values ​​to generate multiple weight coefficients.

[0090] 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.

[0091] Step P454: performing welding quality scoring on the plurality of areas to be evaluated according to the sequence to be evaluated, and generating a plurality of quality evaluation results.

[0092] 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.

[0093] Specifically, the rear axle of the electric tricycle is divided into multiple evaluation areas based on the actual distribution of defects. This allows for more targeted evaluation of the welding quality in different areas. For example, if the defects are mainly concentrated in different locations on the rear axle, such as the weld joints, the middle, and the edges, the rear axle can be divided into different evaluation areas based on these locations.

[0094] A weighted calculation is performed based on the defect risk values ​​corresponding to the defects in different areas to be evaluated, determining a weight coefficient for each area. This allows the importance of each area to the overall weld quality to be reflected in the subsequent evaluation. For example, for each area, the defect risk value for each defect type is used as a weight, and the defect size in the defect distribution data is weighted to obtain a weight system for that area. The areas to be evaluated are sorted in descending order according to the weight coefficients to generate a sequence to be evaluated, allowing the areas to be evaluated in order of importance.

[0095] Based on the evaluation sequence, each area to be evaluated is scored for welding quality, resulting in multiple quality evaluation results, allowing for a specific quantitative assessment of the welding quality of each area. Scoring criteria can be developed for each area based on its specific welding defect characteristics (such as defect type, quantity, and severity). For example, areas with a high number of defects, high severity, and a high score would be assigned a lower score, while areas with fewer defects and less severe severity would be assigned a higher score. Using this criterion, each area to be evaluated is scored, resulting in multiple quality evaluation results.

[0096] The quality evaluation results are graded, with different score ranges corresponding to different quality levels. For example, 90 to 100 is excellent, 80 to 89 is good, 70 to 79 is acceptable, and 0 to 69 is unacceptable. 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, which quantitatively evaluates welding quality and provides guidance for subsequent optimization of welding parameter combinations.

[0097] In summary, the electric tricycle rear axle welding quality control system provided by the embodiments of the present application has the following technical effects:

[0098] The weld parameter preprocessing module 10 obtains the weld parameter set for the electric tricycle's rear axle through weld analysis, including weld geometry, positional characteristics, and material characteristics. By analyzing and detecting these characteristics, accurate data support can be provided for the subsequent welding process, ensuring that the welding process can be customized for specific welding conditions. The simulated welding analysis module 20 simulates welding based on the initial welding parameter combination and weld feature information, generating a welding prediction parameter set to evaluate factors such as geometric changes, positional changes, and material changes during the welding process. Further analysis is performed based on welding environment information, creating a welding trend graph. This provides visualization and predictive analysis of the welding process, helping to optimize welding paths and parameter settings. The welding path planning and evaluation module 30 plans multiple welding paths based on the welding trend graph, evaluates these paths, and generates welding status values. This allows for a prediction of the welding path quality, selects the optimal path for the actual welding operation, and avoids defects caused by improper path selection. The welding defect analysis module 40 examines welding results based on welding status values ​​and welding trend graphs. By constructing a support vector machine model, it detects anomalies and labels defects, accurately identifying potential welding issues and classifying them. This generates a welding quality score, providing 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 based on the welding quality score, generates an optimized solution, and executes it. This intelligent adjustment of welding parameters enables real-time optimization of welding quality, ensuring the efficiency and stability of the entire welding process.

[0099] Overall, the embodiments of this application achieve comprehensive control of the welding quality of the rear axle of an electric tricycle by integrating multiple functional modules. Through precise parameter control and dynamic quality assessment, the stability and consistency of welding quality are significantly improved, and 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 its service life.

[0100] Example 2, as Figure 4As shown, an embodiment of the present application provides a method for controlling welding quality of a rear axle of an electric tricycle, the method comprising:

[0101] Step S1: performing weld analysis on the rear axle of the electric tricycle to obtain a rear axle weld parameter set, performing preprocessing based on the rear axle weld parameter set, and determining an initial welding parameter combination.

[0102] Step S2: 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 combined with welding environment information, and drawing a welding change trend diagram.

[0103] Step S3: traversing the welding change trend diagram to plan a welding path, determining multiple welding paths, performing welding evaluation according to the multiple welding paths, and generating a welding state value.

[0104] Step S4: performing welding inspection on the rear axle of the electric tricycle according to the welding state value in combination with the welding change trend diagram, performing defect analysis according to the inspection results, and generating a welding quality score.

[0105] 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.

[0106] Furthermore, in step S1 of the embodiment of the present application, the weld seam of the rear axle of the electric tricycle is analyzed to obtain a rear axle weld seam parameter set, which also includes:

[0107] The rear axle of the electric tricycle is subjected to weld inspection to obtain a plurality of weld features, wherein the plurality of weld features include weld geometric features, weld position features, and weld material features; the rear axle of the electric tricycle is subjected to weld edge inspection according to the weld geometric features to generate rear axle weld geometric parameters; the rear axle of the electric tricycle is subjected to weld angle inspection according to the weld position features to generate rear axle weld position parameters; the rear axle of the electric tricycle is subjected to weld material inspection according to the weld material features to 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 correlated and integrated to obtain the rear axle weld parameter set.

[0108] 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 the following steps are further included:

[0109] The rear axle weld parameter set is cleaned and classified 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 subjected to regression analysis 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.

[0110] Furthermore, in step S2 of the embodiment of the present application, performing simulated welding according to the initial welding parameter combination to generate a welding prediction parameter set also includes:

[0111] Based on the initial welding parameter combination and the weld geometric characteristics, simulated welding is performed to generate simulated welding geometric data; based on the initial welding parameter combination and the weld position characteristics, simulated welding is performed to generate simulated welding structure coordinate data; based on the initial welding parameter combination and the weld material characteristics, simulated welding is performed to generate a simulated welding heat affected zone; welding prediction is performed based on the simulated welding geometric data and the welding strength to determine welding geometry change prediction parameters; welding prediction is performed based on the simulated welding structure coordinate data and the welding deformation to determine welding position change prediction parameters; welding prediction is performed based on the welding heat affected zone and the 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.

[0112] Furthermore, in step S2 of the embodiment of the present application, welding analysis is performed according to the welding prediction parameter set in combination with welding environment information, and a welding change trend graph is drawn, which also includes:

[0113] 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.

[0114] Furthermore, step S4 of the embodiment of the present application further includes:

[0115] 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 anomaly detection, and multiple abnormal welding labels are determined; the welding change trend diagram is marked based on the multiple abnormal welding labels to generate multiple welding abnormality areas; a defect analysis is performed on the rear axle of the electric tricycle based on the multiple welding abnormality areas to determine multiple welding defect types, and the multiple welding defect types include defect distribution data; a welding impact analysis is performed on the rear axle of the electric tricycle based on the multiple welding defect types to generate multiple defect risk values; a quality evaluation is performed based on the multiple defect risk values ​​and the defect distribution data to generate the welding quality score.

[0116] Furthermore, performing quality evaluation according to the defect distribution data based on the multiple defect risk values ​​to generate the welding quality score further includes:

[0117] 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.

[0118] Through the above detailed description of an electric tricycle rear axle welding quality control system in this specification, those skilled in the art can clearly understand the electric tricycle rear axle welding quality control method in this embodiment. For the method disclosed in Example 2, since it corresponds to the system disclosed in Example 1 and has corresponding execution steps and beneficial effects, the relevant parts can be referred to the system part description.

[0119] The above description of the disclosed embodiments is intended to enable one skilled in the art to implement or use the present application. Various modifications to these embodiments will be readily apparent to one 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 is not limited to the embodiments shown herein, but is intended to 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, the weld parameter preprocessing module being 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; 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 and evaluation module, the welding path planning and evaluation module being configured to traverse the welding change trend graph to plan a welding path, determine a plurality of welding paths, perform welding evaluation according to the plurality of welding paths, and generate a welding state value; a welding defect analysis module, the welding defect analysis module being used to 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 based on the inspection results, and generate a welding quality score; a welding parameter optimization module, the welding parameter optimization module being 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; 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 and the weld geometric characteristics to generate simulated welding geometric data; Perform simulated welding based on the initial welding parameter combination and the weld position characteristics to generate simulated welding structure coordinate data; Performing simulated welding based on the initial welding parameter combination and the weld material characteristics to generate a simulated welding heat affected zone; Perform welding prediction based on the simulated welding geometry data in combination 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 zone and the heat affected zone size data to determine welding material change prediction parameters; adding 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; The simulated welding analysis module performs welding analysis according to the welding prediction parameter set in combination with welding environment information and draws a welding change trend diagram, and the execution steps include: Based on the welding geometry change prediction parameters and the welding environment information, a welding dynamic analysis is performed according to the welding time sequence to generate a dynamic welding strength parameter; Based on the welding position change prediction parameters and the welding environment information, a welding dynamic analysis is performed according to the welding time sequence to generate welding structure coordinate change parameters; Based on the welding material change prediction parameters and the welding environment information, a welding dynamic analysis is performed according to the welding time 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.

2. The electric tricycle rear axle welding quality control system according to 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. 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; Performing weld material testing on the rear axle of the electric tricycle according to the weld material characteristics, and generating 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 according to claim 2, characterized in that: The weld parameter preprocessing module performs preprocessing based on 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; performing regression analysis on the rear axle weld geometric parameters, the rear axle weld position parameters, and the rear axle weld material parameters based on the multiple weld data classes 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 based on the matching results, and the initial welding parameter combination is constructed.

4. The electric tricycle rear axle welding quality control system according to claim 1, characterized in that: The welding defect analysis module is used to perform welding inspection on the rear axle of the electric tricycle according to the welding state value combined with the welding change trend diagram, perform defect analysis based on the inspection results, and generate a welding quality score. The execution steps include: Constructing a support vector machine model, synchronizing the welding change trend graph to the support vector machine model according to the welding state value to perform anomaly detection, and determining multiple abnormal welding labels; Marking the welding change trend graph based on the multiple abnormal welding tags to generate multiple welding abnormality areas; Performing 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 a 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 evaluation is performed based on the multiple defect risk values ​​according to the defect distribution data to generate the welding quality score.

5. The electric tricycle rear axle welding quality control system according to claim 4, characterized in that: Performing a quality evaluation based on the multiple defect risk values ​​and the defect distribution data 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 areas to be evaluated in descending order based on the multiple weight coefficients to generate a sequence to be evaluated; 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.

6. A method for controlling welding quality of rear axle of 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 5, comprising: By analyzing the weld seam of the rear axle of the electric tricycle, a rear axle weld seam parameter set is obtained, and preprocessing is performed according to the rear axle weld seam parameter set to determine 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 combined with welding environment information, and drawing a welding change trend diagram; Traversing the welding change trend graph to plan a welding path, determining multiple welding paths, performing welding evaluation according to the multiple welding paths, and generating a welding state value; Performing welding inspection on the rear axle of the electric tricycle according to the welding state value and the welding change trend diagram, performing defect analysis according to the inspection results, and generating 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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