Welding control method for manufacturing electric tricycles

Abstract

The present application provides a welding control method for manufacturing electric tricycles, which relates to the field of intelligent welding technology and includes: determining a plurality of adapted welding parameters according to the matching of multiple part features; taking minimizing the thermal influence as the expected condition, optimizing a plurality of initial welding sequences according to a thermal evaluation function and a plurality of adapted welding parameters, and outputting an optimal welding sequence; sorting the plurality of adapted welding parameters according to the optimal welding sequence to generate an adapted welding parameter sequence, and performing welding control according to the optimal welding sequence and the adapted welding parameter sequence. Through the present application, the technical problem in the existing method that due to the inability to set a matching part welding sequence according to the structural features of the electric tricycle, local thermal stress concentration is likely to occur, resulting in poor welding quality of the body frame can be solved. It can effectively reduce the concentration of thermal stress, reduce the occurrence of deformation and cracks during the welding process, and significantly improve the welding quality and structural stability of the body frame.

Description

Technical Field

[0001] The present application relates to the field of intelligent welding technology, and in particular to a welding control method for manufacturing electric tricycles. Background Art

[0002] As an important means of transportation, electric tricycles are widely used in urban travel and cargo transportation. Their body frames are usually welded from multiple metal parts. These welded parts not only require high welding quality, but also must have good structural strength and stability to ensure that they can withstand different loads and stresses during long-term use.

[0003] However, as the manufacturing process of electric tricycles becomes increasingly complex, traditional welding methods have gradually exposed many problems. Existing welding technologies mostly rely on operating experience to determine the welding sequence, and do not fully consider the complex structural design of electric tricycles and the thermal influence characteristics of each component. Local thermal stress concentration is prone to occur, resulting in the welding quality and structural stability of the body frame failing to meet expected requirements. Summary of the invention

[0004] The purpose of this application is to provide a welding control method for the manufacture of electric tricycles, so as to solve the technical problem that the existing welding method cannot set a matching welding sequence according to the structural characteristics of the electric tricycle, resulting in the phenomenon of local thermal stress concentration and poor welding quality of the body frame.

[0005] In view of the above problems, the present application provides a welding control method for manufacturing electric tricycles, including: obtaining multiple welding parts and multiple part features of the electric tricycle, and determining multiple adaptive welding parameters according to the matching of the multiple part features; enumerating welding sequences according to the multiple welding parts to generate multiple initial welding sequences; based on the welding twin space, with minimizing thermal influence as the expected condition, optimizing the multiple initial welding sequences according to the thermal evaluation function and the multiple adaptive welding parameters, and outputting the optimal welding sequence; mapping and sorting the multiple adaptive welding parameters according to the optimal welding sequence to generate an adaptive welding parameter sequence, and performing welding control on the electric tricycle according to the optimal welding sequence and the adaptive welding parameter sequence.

[0006] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0007] By obtaining multiple welding parts and multiple part features of an electric tricycle, multiple adapted welding parameters are determined by matching according to the multiple part features; then, based on the multiple welding parts, welding sequence enumeration is performed to generate multiple initial welding sequences; further, based on the welding twin space, with the minimization of thermal influence as the expected condition, the multiple initial welding sequences are optimized according to the thermal evaluation function and the multiple adapted welding parameters, and the optimal welding sequence is output; then, the multiple adapted welding parameters are mapped and sorted according to the optimal welding sequence to generate an adapted welding parameter sequence; finally, the electric tricycle is welded and controlled according to the optimal welding sequence and the adapted welding parameter sequence; through the above method, the matching degree between the part welding sequence and the structure of the electric tricycle can be improved, the concentration of thermal stress can be effectively reduced, and the occurrence of deformation and cracks during the welding process can be reduced, thereby significantly improving the welding quality and structural stability of the body frame.

[0008] The above description is only an overview of the technical solution of this application. In order to be able to understand the technical means of this application more clearly, it can be implemented according to the content of the specification. And in order to make the above and other purposes, features and advantages of this application more obvious and understandable, the following specifically illustrates the specific implementation manners of this application. It should be understood that the content described in this part is not intended to identify the key or important features of the embodiments of this application, nor is it used to limit the scope of this application. Other features of this application will become easily understandable through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] In order to more clearly illustrate the technical solutions in this application or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only exemplary, and for those of ordinary skill in the art, other drawings can be obtained according to the provided drawings without creative efforts.

[0010] Figure 1 It is a schematic flowchart of a welding control method for manufacturing an electric tricycle according to this application.

[0011] Figure 2 It is a schematic flowchart of determining multiple adapted welding parameters in a welding control method for manufacturing an electric tricycle according to this application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0012] The present application provides a welding control method for manufacturing electric tricycles, which solves the technical problem in the existing welding methods that due to the inability to set the welding sequence of corresponding parts according to the structural characteristics of the electric tricycle, local thermal stress concentration is likely to occur, resulting in poor welding quality of the vehicle body frame. It can improve the matching degree between the welding sequence of parts and the structure of the electric tricycle, effectively reduce the concentration of thermal stress, reduce the occurrence of deformation and cracks during welding, and thus significantly improve the welding quality and structural stability of the vehicle body frame.

[0013] Next, the technical solutions in the present application will be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. It should be understood that the present application is not limited by the exemplary embodiments described herein. Based on the embodiments of the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the scope of protection of the present application. Additionally, it should be noted that for the sake of description, only the parts related to the present application are shown in the drawings rather than all of them.

[0014] Embodiment, please refer to the attached Figure 1 , the present application provides a welding control method for manufacturing electric tricycles, which specifically includes the following steps:

[0015] Step 1: Obtain multiple welding parts and multiple part features of the electric tricycle, and determine multiple adapted welding parameters according to the matching of the multiple part features.

[0016] Further, as Figure 2 shown, Step 1 of the present application further includes:

[0017] Obtain multiple welding parts and multiple part features of the electric tricycle, where the part features and the welding parts correspond one by one, and the part features at least include material type, joint type, and component stress; randomly select a first part feature, where the first part feature includes a first material type, a first joint type, and a first component stress; input the first material type, the first joint type, and the first component stress into a welding parameter analyzer for analysis, output a first adapted welding parameter, and add it to the multiple adapted welding parameters.

[0018] Specifically, according to the design drawings and structural requirements of the electric tricycle body frame, all the parts that need to be welded are identified. These parts usually include frame connection points, support structures, battery trays, battery racks, and the connection points between wheels and the frame, etc., obtaining multiple welding parts; further obtaining multiple part characteristics of the multiple welding parts, where each welding part corresponds to a part characteristic, and the part characteristic at least includes material type, joint type, and component stress. The material type refers to different types of metal materials that may be used in different parts of the body frame, such as carbon steel, aluminum alloy, stainless steel, etc. The thermal conductivity, melting point, and expansion coefficient of different materials vary greatly; the joint types include butt joints, corner joints, T-joints, etc.; the component stress refers to the stress situation of each part. Some parts need to bear greater loads. For example, the support parts of the body frame or the connection parts of the suspension system are under greater stress. Parts with greater stress need to adopt higher welding quality and greater welding strength to ensure the stability and safety of the body.

[0019] Next, randomly select any one part characteristic from the multiple part characteristics and set it as the first part characteristic. Among them, the first part characteristic includes the first material type, the first joint type, and the first component stress. Then input the first material type, the first joint type, and the first component stress into the welding parameter analyzer constructed based on the BP neural network for analysis, output the first adapted welding parameters, and add them to the multiple adapted welding parameters.

[0020] Furthermore, to construct the welding parameter analyzer, the present application also includes:

[0021] Taking the welding equipment attribute parameters as constraints and the expected welding quality as conditional constraints, retrieve the operation logs of similar equipment, obtain the sample material type set, sample joint type set, sample component stress set, and sample welding parameter set, where the welding parameters include welding current, welding voltage, and welding speed; use the sample material type set, sample joint type set, sample component stress set, and sample welding parameter set to conduct supervised training on the BP neural network until convergence, and obtain the welding parameter analyzer.

[0022] Specifically, obtain the property parameters of the welding equipment and the expected welding quality, where the expected welding quality can be set according to the welding requirements of the part, including the weld strength, the smoothness of the welded joint (there are no obvious defects on the surface of the welded joint, such as pores, cracks, etc.); the property parameters of the welding equipment include characteristics such as equipment type and operating parameters. Then, using the property parameters of the welding equipment as equipment constraints and the expected welding quality as conditional constraints (that is, the retrieved sample data needs to meet the expected welding quality), retrieve the operating logs of existing similar welding equipment to obtain sample data in different welding scenarios, where the sample data includes a set of sample material types, a set of sample joint types, a set of sample component forces, and a set of sample welding parameters. The welding parameters include welding current, welding voltage, and welding speed. Among them, the welding current affects the welding heat input and determines the depth and width of the weld; the welding voltage affects the stability of the arc and the shape of the molten pool, further affecting the appearance and strength of the weld; the welding speed determines the duration of the welding heat input, thereby affecting the quality of the welded joint and the heat affected zone.

[0023] Then, construct a welding parameter analyzer based on the BP neural network. The welding parameter analyzer is a BP neural network model in machine learning that can be iteratively optimized and is obtained through supervised training with the sample data set. The welding parameter analyzer includes an input layer, a hidden layer, and an output layer. The input data of the input layer are the material type, joint type, and component force, and the output data of the output layer are the welding parameters (welding current, welding voltage, and welding speed). Then, use the set of sample material types, the set of sample joint types, the set of sample component forces, and the set of sample welding parameters as training data to perform supervised training on the welding parameter analyzer. First, the data of the input layer (material type, joint type, component force) undergoes forward propagation through the neural network, and after non-linear transformation by the hidden layer, the predicted values of the welding parameters of the output layer are finally obtained; then, according to the difference between the predicted values of the welding parameters of the output layer and the actual welding parameters, calculate the error. A commonly used error calculation method is the mean square error; then, use the backpropagation algorithm to adjust the weights and biases in the neural network according to the error, calculate the gradient, and update the parameters of the neural network to make the output of the network closer to the true welding parameters; repeat the iterative training, and each time optimize the parameters through backpropagation. When the training error is less than the set threshold or after the set number of iterations, stop the training process. At this time, the prediction accuracy of the model has reached the expectation, and the trained welding parameter analyzer is obtained.

[0024] By constructing a welding parameter analyzer, the welding parameters can be automatically adjusted and optimized through a machine learning model, thereby providing accurate welding parameters. Through this automatic control, the uncertainty brought by manual adjustment can be avoided, and further, welding defects caused by improper selection of welding parameters, such as pores, cracks, uneven welds, etc., can be effectively avoided, and the strength and stability of the welded joint can be improved.

[0025] Step 2: Enumerate the welding sequences based on the multiple welding positions to generate multiple initial welding sequences.

[0026] Specifically, when enumerating the welding sequences based on the multiple welding positions, the core purpose of the welding sequence enumeration is to explore the sequential combinations of welding different positions. For example, assuming there are N welding positions, all possible permutation ways can be generated through enumeration. For N positions, the number of enumerated welding sequences is N! (the factorial of N), that is, all possible permutation combinations, to obtain multiple initial welding sequences. Among them, each welding sequence represents a different position permutation scheme, and these initial welding sequences will form the basis for subsequent optimization of the welding sequence.

[0027] Step 3: Based on the welding twin space, with the expectation of minimizing the thermal influence, optimize the multiple initial welding sequences according to the thermal evaluation function and the multiple adapted welding parameters, and output the optimal welding sequence.

[0028] Furthermore, Step 3 of this application further includes:

[0029] Taking the welding equipment attribute parameters and the electric tricycle attribute characteristics as the basis, simulate and construct the welding twin space; randomly select a first welding sequence from the multiple initial welding sequences, and map and arrange the multiple adapted welding parameters according to the first welding sequence to generate a first parameter sequence.

[0030] Specifically, the goal of constructing the welding twin space is to reflect the actual welding process through a virtual model, simulate various physical phenomena during the welding process to predict factors such as welding quality and thermal influence; then, taking the welding equipment attribute parameters (equipment type, operating parameters, etc.) and the electric tricycle attribute characteristics (structural characteristics, geometric shape, material type, etc.) as the basis, based on the digital twin technology, through welding simulation software (such as ANSYS, Abaqus, COMSOL, etc.) combined with heat conduction, deformation, and stress analysis models, conduct a complete welding process simulation to construct the welding twin space. Among them, the welding twin space is a virtual digital environment that can reflect the state of the actual welding process in real time, including material state, temperature distribution, deformation, etc.

[0031] Then, randomly select any one of the multiple initial welding sequences as the first welding sequence, and map and arrange the multiple adapted welding parameters according to the first welding sequence, that is, according to the first welding sequence, map and arrange the multiple adapted welding parameters corresponding to the multiple welding positions to obtain a first parameter sequence.

[0032] Within the welding twin space, welding simulation is performed according to the first welding sequence and the first parameter sequence to obtain regional temperature distribution diagrams under several simulated welding nodes, and a first temperature distribution diagram set is constructed.

[0033] Further, this application also includes:

[0034] Obtain several welding parts in the first welding sequence except for the first welding part, and obtain several heat-affected coverage areas of the several welding parts, where the first welding part is the initial welding part in the first welding sequence; perform welding simulation according to the first welding sequence and the first parameter sequence, and based on the several heat-affected coverage areas, record the regional temperature distribution diagrams of the several welding start nodes of the several welding parts to obtain several regional temperature distribution diagrams, and construct the first temperature distribution diagram set, where the regional temperature distribution diagram is the temperature distribution characteristic of the heat-affected coverage area of the welding part.

[0035] Specifically, first, select the first welding part in the first welding sequence, where the first welding part is the initial welding part in the first welding sequence, that is, the part to be welded first. Then obtain several welding parts in the first welding sequence except for the first welding part, that is, after the first welding sequence is determined, the remaining welding parts need to be determined next, and these parts are usually affected by the heat of the first welding part; further obtain several heat-affected coverage areas of the several welding parts, where the heat-affected coverage area corresponds to the welding part one by one, and each welding part will receive heat transfer and diffusion during welding, and the temperature within this range will affect the welding quality of the welding part, then this area is the heat-affected coverage area of the welding part.

[0036] Then, within the welding twin space, perform welding simulation according to the first welding sequence and the first parameter sequence, and according to the several heat-affected coverage areas, record the regional temperature distribution diagrams of the several welding start time nodes of the several welding parts to obtain several regional temperature distribution diagrams, where the regional temperature distribution diagram corresponds to the welding part one by one, and the regional temperature distribution diagram is the temperature distribution characteristic of the heat-affected coverage area of the welding part, that is, the temperature distribution diagram shows the temperature conditions of the area where the welding part is located and its surrounding area at this time node, and can reflect the distribution of the heat source, the diffusion of heat, and the generation of thermal stress; construct the first temperature distribution diagram set according to the several regional temperature distribution diagrams. Through the analysis of the temperature distribution diagram, it is possible to identify which welding parts may be affected by greater thermal influence, and which parts are prone to thermal stress concentration or deformation problems during welding. Based on these analysis results, the welding sequence can be optimized to avoid excessive thermal stress concentration and reduce deformation and cracks.

[0037] Perform thermal influence calculation on the first temperature distribution atlas according to the thermal evaluation function to obtain a first thermal influence coefficient.

[0038] Furthermore, this application also includes:

[0039] Respectively perform regional screening on several regional temperature distribution maps in the first temperature distribution atlas according to a predetermined temperature threshold value, set the regions with temperatures higher than the predetermined temperature threshold value as thermal influence regions to obtain several thermal influence region sets; perform calculation of the proportion of the regional area on the several thermal influence region sets to obtain several influence region ratios, where the influence region ratio is the ratio of the sum of the areas of multiple thermal influence regions in the thermal influence region set to the area of the corresponding thermally influenced coverage region; respectively perform analysis of the regional distribution dispersion on the several thermal influence region sets to obtain several regional distribution dispersions.

[0040] Specifically, obtain a predetermined temperature threshold value, which can be set according to the thermal properties of the material, the requirements of the welding process, and characteristics such as the melting point and yield point of the material. Among them, different welding parts correspond to different predetermined temperature threshold values; then respectively perform regional screening on several regional temperature distribution maps in the first temperature distribution atlas according to the predetermined temperature threshold value, and set the regions with temperatures higher than the predetermined temperature threshold value as thermal influence regions, that is, the high-temperature regions that have an adverse effect on the welding quality, to obtain several thermal influence region sets.

[0041] Next, for the several thermal influence region sets, computer-aided design (CAD) software or a finite element simulation platform can be used to extract the actual area of the thermal influence regions. For example, during the welding simulation process, the regions with temperatures higher than the set threshold value will be identified and marked as thermal influence regions. The area of each region can be obtained through grid calculation or through the integration calculation of two-dimensional / three-dimensional geometric shapes, and the areas of multiple thermal influence regions are summed to obtain several total thermal influence region areas; further perform calculation of the proportion of the regional area based on the several total thermal influence region areas and several thermally influenced coverage regions to obtain several influence region ratios, where the influence region ratio is the ratio of the total thermal influence region area to the area of the corresponding thermally influenced coverage region. The influence region ratio reflects the degree of influence of heat transfer and thermal diffusion during the welding process at a certain welding part. The larger the influence region ratio, the greater the degree of thermal influence on this welding part during the welding process, which may lead to problems such as thermal stress concentration, deformation, or cracks at this part.

[0042] Then, perform regional distribution dispersion analysis on the several sets of thermal influence regions respectively. For example, the spatial positions of the thermal influence regions can be used as data points, and the dispersion can be measured by calculating the spatial distribution variance of these regions. The regional distribution dispersion is an important index to measure the uniformity of the spatial distribution of the thermal influence regions during the welding process. A low dispersion means that the distribution of the thermal influence regions is relatively uneven, which may lead to an increased risk of local thermal stress concentration, deformation, and cracks. A high dispersion indicates that the heat is more evenly distributed, which is beneficial to reducing thermal stress concentration, thereby improving the welding quality and stability, and obtaining several regional distribution dispersions.

[0043] Finally, based on the thermal evaluation function, perform thermal influence calculation according to the several influence region ratios and several regional distribution dispersions to obtain a first thermal influence coefficient. Among them, the thermal influence coefficient reflects the overall thermal influence degree of the welding sequence, is positively correlated with the influence region ratio, and is negatively correlated with the regional distribution dispersion. That is, the larger the influence region ratio, the greater the degree of thermal influence on this region during the welding process, and the higher the risk of thermal stress concentration; the lower the regional distribution dispersion, it may lead to an increased risk of local thermal stress concentration, deformation, and cracks, then the risk of thermal stress concentration is higher.

[0044] Based on the thermal evaluation function, calculate the first thermal influence coefficient according to the several influence region ratios and several regional distribution dispersions.

[0045] Furthermore, this application also includes:

[0046] The expression of the thermal evaluation function is: ; where is the thermal influence coefficient, is the region ratio weight, is the distribution dispersion weight, N is the number of several welding parts, is the importance of the nth welding part, is the influence region ratio of the nth welding part, is the regional distribution dispersion of the nth welding part.

[0047] Specifically, in the thermal evaluation function, is the thermal influence coefficient, is the region ratio weight, is the distribution dispersion weight, where the sum of the region ratio weight and the distribution dispersion weight is 1, and the weight configuration can be carried out according to the index influence degree. The greater the index influence degree, the greater the corresponding weight; N is the number of several welding parts, is the importance of the nth welding part, where the greater the part importance, the greater the influence degree; is the influence area ratio of the nth welding part, is the regional distribution dispersion of the nth welding part.

[0048] Continue to optimize other welding sequences among the multiple initial welding sequences until a predetermined convergence condition is reached, obtain multiple thermal influence coefficients, and output the initial welding sequence corresponding to the minimum thermal influence coefficient as the optimal welding sequence.

[0049] Specifically, using the same method, continue to optimize other welding sequences among the multiple initial welding sequences until all the multiple initial welding sequences are analyzed, and obtain multiple thermal influence coefficients of the multiple initial welding sequences; finally, select the initial welding sequence corresponding to the minimum thermal influence coefficient as the optimal welding sequence, where the optimal welding sequence can effectively reduce local thermal stress concentration and reduce the risk of deformation and cracks.

[0050] Step four: Map and sort the multiple adapted welding parameters according to the optimal welding sequence to generate an adapted welding parameter sequence, and perform welding control on the electric tricycle according to the optimal welding sequence and the adapted welding parameter sequence.

[0051] Specifically, map and sort the multiple adapted welding parameters according to the optimal welding sequence, that is, map and sort the corresponding adapted welding parameters according to the order of multiple welding parts in the optimal welding sequence to obtain an adapted welding parameter sequence; finally, during the welding process, perform welding control on the electric tricycle according to the optimal welding sequence and the adapted welding parameter sequence.

[0052] Furthermore, after performing welding control on the electric tricycle according to the optimal welding sequence and the adapted welding parameter sequence, the present application further includes:

[0053] During the welding control process, fix-point monitor the cylinder pressure and gas temperature of the shielding gas, calculate the pressure deviation and gas temperature deviation; obtain the adapted welding parameters under the current monitoring node, perform compensation analysis on the adapted welding parameters according to the pressure deviation and gas temperature deviation, generate compensated welding parameters, and update the adapted welding parameters, where a parameter compensation model is constructed through machine learning to perform welding parameter compensation analysis.

[0054] Specifically, during the welding control process, at a predetermined monitoring time node (such as monitoring every 3 minutes), the cylinder pressure and gas temperature of the shielding gas are monitored at fixed points. Among them, the cylinder pressure directly affects the welding atmosphere and protection effect. If the pressure is too low, it may cause unstable gas flow and affect the welding quality; during the flow of the shielding gas, it is heated, and the increase in its temperature may lead to a decrease in the protection effect and affect the welding quality. Then, based on the monitored cylinder pressure and gas temperature, they are compared with the preset standard pressure and gas temperature, and the pressure deviation and gas temperature deviation are calculated.

[0055] Based on machine learning, a parameter compensation model is constructed. For example, through existing historical data or experimental data, a BP neural network is used to construct a welding parameter compensation model. The inputs of this model are the pressure deviation and gas temperature deviation, and the output is the adjustment amount of the welding parameters. The parameter compensation model is trained to convergence through sample data. The construction principle and training process of the parameter compensation model are the same as those of the above-mentioned welding parameter analyzer. Those skilled in the art can refer to the construction and training steps of the above-mentioned welding parameter analyzer. Finally, the pressure deviation and gas temperature deviation are input into the parameter compensation model, and the parameter compensation amount is output; further, the adapted welding parameters at the current monitoring node are obtained, and the above-mentioned adapted welding parameters are optimized and compensated according to the parameter compensation amount, that is, the two are added together to obtain the compensated welding parameters, and the adapted welding parameters are updated with the compensated welding parameters. By real-time monitoring the cylinder pressure and gas temperature of the shielding gas and using machine learning methods to compensate and optimize the welding parameters, a more accurate and stable welding process can be achieved. This intelligent compensation method can significantly improve the welding quality, reduce welding defects caused by changes in the shielding gas, and improve production efficiency at the same time.

[0056] In summary, the welding control method for manufacturing an electric tricycle provided by this application has the following technical effects:

[0057] By obtaining multiple welding parts and multiple part features of the electric tricycle, multiple adapted welding parameters are determined by matching according to the multiple part features; then, the welding sequence is enumerated according to the multiple welding parts to generate multiple initial welding sequences; further, based on the welding twin space, with the minimization of thermal influence as the expected condition, the multiple initial welding sequences are optimized according to the thermal evaluation function and the multiple adapted welding parameters, and the optimal welding sequence is output; then, the multiple adapted welding parameters are mapped and sorted according to the optimal welding sequence to generate an adapted welding parameter sequence; finally, the electric tricycle is welded and controlled according to the optimal welding sequence and the adapted welding parameter sequence; through the above method, the matching degree between the part welding sequence and the structure of the electric tricycle can be improved, the concentration of thermal stress can be effectively reduced, and the occurrence of deformation and cracks during the welding process can be reduced, thereby significantly improving the welding quality and structural stability of the body frame.

[0058] The foregoing description of the disclosed embodiments enables those skilled in the art to practice or use the present application. Various modifications to these embodiments will be readily 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. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0059] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalent technologies, the present application is also intended to cover these changes and modifications.

Claims

1. A welding control method for manufacturing electric tricycles, characterized in that, Including: Obtain multiple welding parts and multiple part features of an electric tricycle, and determine multiple adapted welding parameters according to the matching of the multiple part features; Enumerate the welding sequence based on the multiple welding parts to generate multiple initial welding sequences; Based on the welding twin space, with the minimization of thermal influence as the expected condition, optimize the multiple initial welding sequences according to the thermal evaluation function and the multiple adapted welding parameters, and output the optimal welding sequence; Map and sort the multiple adapted welding parameters according to the optimal welding sequence to generate an adapted welding parameter sequence, and perform welding control on the electric tricycle according to the optimal welding sequence and the adapted welding parameter sequence; Determine multiple adapted welding parameters according to the matching of the multiple part features, including: Obtain multiple welding parts and multiple part features of an electric tricycle, where the part features and the welding parts correspond one by one, and the part features at least include material type, joint type, and component stress; Randomly select a first part feature, where the first part feature includes a first material type, a first joint type, and a first component stress; Input the first material type, the first joint type, and the first component stress into a welding parameter analyzer for analysis, output the first adapted welding parameter, and add it to the multiple adapted welding parameters; Construct a welding parameter analyzer, including: Taking the welding equipment attribute parameters as constraints and the expected welding quality as conditional constraints, retrieve the operation logs of similar equipment, and obtain a sample material type set, a sample joint type set, a sample component stress set, and a sample welding parameter set, where the welding parameters include welding current, welding voltage, and welding speed; Use the sample material type set, the sample joint type set, the sample component stress set, and the sample welding parameter set to perform supervised training on a BP neural network until convergence, and obtain the welding parameter analyzer; Based on the welding twin space, with the minimization of thermal influence as the expected condition, optimize the multiple initial welding sequences according to the thermal evaluation function and the multiple adapted welding parameters, and output the optimal welding sequence, including: Taking the welding equipment attribute parameters and the electric tricycle attribute features as benchmarks, simulate and construct a welding twin space; Randomly select a first welding sequence from the multiple initial welding sequences, and map and arrange the multiple adapted welding parameters according to the first welding sequence to generate a first parameter sequence; In the welding twin space, perform welding simulation according to the first welding sequence and the first parameter sequence, obtain the regional temperature distribution maps under several simulated welding nodes, and construct a first temperature distribution map set; Calculate the thermal influence on the first temperature distribution map set according to the thermal evaluation function to obtain a first thermal influence coefficient; Continue to optimize other welding sequences in the multiple initial welding sequences until a predetermined convergence condition is reached, obtain multiple thermal influence coefficients, and output the initial welding sequence corresponding to the minimum thermal influence coefficient as the optimal welding sequence.

2. The welding control method for manufacturing an electric tricycle according to claim 1, characterized in that, Perform welding simulation according to the first welding sequence and the first parameter sequence, obtain the regional temperature distribution maps under several simulated welding nodes, and construct a first temperature distribution map set, including: Obtain a number of welding parts in the first welding sequence except the first welding part, and obtain a number of heat-affected coverage areas of the number of welding parts, where the first welding part is the initial welding part in the first welding sequence; Perform welding simulation according to the first welding sequence and the first parameter sequence. Based on the number of heat-affected coverage areas, record the area temperature distribution maps of the number of welding start nodes of the number of welding parts to obtain a number of area temperature distribution maps, and construct the first temperature distribution map set, where the area temperature distribution map is the temperature distribution characteristic of the heat-affected coverage area of the welding part.

3. The welding control method for manufacturing an electric tricycle according to claim 2, characterized in that, Perform heat-affected calculation on the first temperature distribution map set according to the heat evaluation function to obtain a first heat-affected coefficient, including: Perform area screening on the number of area temperature distribution maps in the first temperature distribution map set according to a predetermined temperature threshold value, and set the area with a temperature higher than the predetermined temperature threshold value as the heat-affected area to obtain a number of heat-affected area sets; Calculate the area ratio of the number of heat-affected area sets to obtain a number of influence area ratios, where the influence area ratio is the ratio of the sum of the areas of multiple heat-affected areas in the heat-affected area set to the area of the corresponding heat-affected coverage area; Perform area distribution dispersion analysis on the number of heat-affected area sets respectively to obtain a number of area distribution dispersions; Based on the heat evaluation function, calculate the first heat-affected coefficient according to the number of influence area ratios and the number of area distribution dispersions.

4. The welding control method for manufacturing an electric tricycle according to claim 3, characterized in that, The expression of the heat evaluation function is: ； Among them, is the thermal influence coefficient, is the regional proportion weight, is the distribution dispersion weight, N is the number of several welding parts, is the importance degree of the nth welding part, is the influence area proportion of the nth welding part, is the regional distribution dispersion degree of the nth welding part.

5. The welding control method for manufacturing an electric tricycle according to claim 1, characterized in that, Perform welding control on the electric tricycle according to the optimal welding sequence and the adapted welding parameter sequence. After that, it further includes: During the welding control process, regularly monitor the cylinder pressure and gas temperature of the shielding gas, and calculate the pressure deviation and gas temperature deviation; Obtain the adapted welding parameters at the current monitoring node, perform compensation analysis on the adapted welding parameters according to the pressure deviation and gas temperature deviation, generate compensation welding parameters, and update the adapted welding parameters, where a parameter compensation model is constructed through machine learning to perform welding parameter compensation analysis.

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