Aluminum alloy rivet welding control method and system for vehicle body door frame structure

Abstract

The invention discloses an aluminum alloy rivet welding control method and system for a vehicle body door frame structure, and relates to the technical field of rivet welding, and the technical scheme is characterized by comprising the following steps: collecting the characteristic information of an aluminum alloy part of the current vehicle body door frame structure to be subjected to rivet welding; according to the aluminum alloy part feature information, a similar rivet welding feature part set and a difference rivet welding feature part set are screened out, and rivet welding point distribution features of the similar rivet welding feature part set are counted to obtain a rivet point density distribution information set; under the condition of preset welding parameters, the incidence relation between the rivet welding strength and the deformation change trend value of the similar rivet welding feature component set is processed and analyzed, and a first rivet welding correlation feature coefficient is obtained; according to the difference rivet welding characteristic component group, the rivet welding piece in the historical difference area is extracted, and the effect is that the rivet welding defects of excessive deformation and insufficient strength caused by parameter errors are effectively reduced.

Description

Technical Field

[0001] This invention relates to the field of riveting and welding technology, and more specifically, to a method and system for controlling the riveting and welding of aluminum alloys for vehicle body door frame structures. Background Technology

[0002] In the automotive manufacturing industry, the body door frame, as a key load-bearing and protective component of the vehicle structure, shoulders the important mission of ensuring vehicle passive safety and maintaining the overall rigidity of the body. The quality control of its aluminum alloy riveting process has long been a focus of industry attention. From a practical production perspective, aluminum alloy has become the preferred material for lightweight vehicle bodies. However, its high thermal conductivity and large coefficient of linear expansion make the riveting process challenging. Inaccurate welding heat input can easily lead to component deformation. Traditional riveting control methods often rely on engineers' experience to set parameters. Without fully considering the differences in the characteristics of aluminum alloy components, using the same riveting parameters for different areas of the body door frame can easily lead to riveting defects, increasing rework costs and production cycles. In other words, the riveting parameters of traditional methods are difficult to adapt to the diverse needs of components. Summary of the Invention

[0003] To address the shortcomings of existing technologies, the present invention aims to provide a method and system for controlling the riveting and welding of aluminum alloy in vehicle body door frame structures.

[0004] To achieve the above objectives, the present invention provides the following technical solution: A method for controlling the riveting and welding of aluminum alloy in a vehicle body door frame structure, the method comprising the following steps: Collect feature information of aluminum alloy components in the current vehicle body door frame structure to be riveted and welded; Based on the characteristic information of the aluminum alloy components, similar riveting and welding characteristic component groups and different riveting and welding characteristic component groups are selected, and the distribution characteristics of riveting and welding points of similar riveting and welding characteristic component groups are statistically analyzed to obtain a set of riveting point density distribution information. The first riveting correlation characteristic coefficient is obtained by processing and analyzing the correlation between the riveting strength and deformation trend value of similar riveting characteristic component groups under preset welding parameters. Based on the characteristic component group of the difference in riveting and welding, the riveting and welding parts in the historical difference area are extracted. Under the preset welding parameters, the correlation between the riveting and welding strength and the trend value of the deformation of the riveting and welding parts in the historical difference area is processed and analyzed to obtain the second riveting and welding correlation characteristic coefficient. Based on the information set of rivet density distribution, the first riveting and welding correlation characteristic coefficient and the second riveting and welding correlation characteristic coefficient, a riveting and welding quality control prediction model is established, and an adaptive riveting and welding parameter set is predicted. The overlapping riveting and welding parameter part and the differential riveting and welding parameter part are extracted from the adaptive riveting and welding parameter set. The differential riveting parameters are verified based on the first riveting correlation characteristic coefficient and the adaptive riveting parameter set to obtain the verified riveting parameters. The verified riveting parameters are then integrated with the coincident riveting parameters to output the aluminum alloy riveting control parameters of the vehicle body door frame structure.

[0005] Preferably, the distribution characteristics of riveting and welding points in similar riveting and welding feature groups are statistically analyzed to obtain a set of information on the density distribution of riveting points, specifically including the following steps: Information on the density of riveting and welding points in groups of similar riveting and welding feature components; Based on the information on the density of the riveting and welding points, the distribution characteristics of sparse and dense areas of the riveting and welding points are statistically analyzed to obtain a set of information on the density distribution of riveting points.

[0006] Preferably, the first riveting-weld correlation characteristic coefficient is obtained by processing and analyzing the correlation between the riveting strength and deformation trend value of similar riveting-welded component groups under preset welding parameter conditions, specifically including the following steps: Under preset welding parameters, historical similar component welding strength information and historical similar component deformation information of similar riveted and welded component groups were collected. The statistical analysis of the changes in the welding strength of similar components in the past and the actual welding strength of similar components after undergoing a preset welding process; The deformation data of historically similar components changes after undergoing a pre-defined welding process; Calculate the first strength change difference trend value between the actual similar component's riveting strength information and the historical similar component's riveting strength information; Calculate the first deformation change difference trend value between the actual deformation information of similar components and the historical deformation information of similar components; Extract the first riveting-welding correlation characteristic coefficient between the first strength change difference trend value and the first deformation change difference trend value.

[0007] Preferably, extracting the riveted and welded parts from the historical difference area based on the difference in riveting and welding feature component group specifically includes the following steps: Collect riveted parts from different areas of the component group with different riveting characteristics; Select historically different riveted parts from the riveted parts with different riveting areas: wherein the riveting process feature information in the difference riveting feature component group and the similar riveting feature component group of the historically different riveted parts with different riveting areas is the same.

[0008] Preferably, the second riveting-weld correlation characteristic coefficient is obtained by processing and analyzing the correlation between the riveting strength and deformation trend value of the riveted parts in the historical difference area under preset welding parameter conditions. Specifically, this includes the following steps: Collect information on the welding strength and deformation of the historical difference areas to which the welded parts belong; The statistical analysis of the historical differences in weld strength and the actual changes in weld strength in the affected areas after undergoing a pre-defined welding process; The statistical analysis shows the actual deformation of the historical difference area after undergoing a pre-set welding process. Calculate the second strength change trend value between the actual weld strength information of the difference area and the historical weld strength information of the difference area; Calculate the second deformation change difference trend value between the actual deformation information of the difference area and the historical deformation information of the difference area; Extract the second riveting-welding correlation characteristic coefficient between the second strength change difference trend value and the second deformation change difference trend value.

[0009] Preferably, the adaptive riveting and welding parameter set is predicted, specifically including the following steps: Based on the characteristic information of the aluminum alloy component, at least two component regions for riveting operations are extracted from the component to be riveted, and a predetermined set of riveting operation regions is output. After obtaining the characteristic information of the corresponding riveting and welding process based on the characteristic information of each area in the predetermined riveting and welding operation area set, the predetermined process characteristic information set is output. After inputting the predetermined riveting and welding operation area set and the predetermined process characteristic information set into the riveting and welding quality control prediction model, an adaptive riveting and welding parameter set is obtained.

[0010] Preferably, the overlapping riveting parameters and the differential riveting parameters are extracted from the adaptive riveting parameter set, specifically as follows: After comparing the adaptive riveting parameter set with the predetermined riveting parameter set, the overlapping riveting parameter part and the differential riveting parameter part are extracted.

[0011] Preferably, the verified riveting parameters are obtained by verifying the differential riveting parameters based on the first riveting-weld correlation characteristic coefficient and the adaptive riveting-weld parameter set, specifically including the following steps: The first quality compliance dataset is obtained by judging the degree of compliance of the riveting quality of the differential riveting parameters based on the first riveting correlation feature coefficient and the predetermined riveting parameter set. The second quality compliance dataset is obtained by judging the degree of compliance of the riveting quality of the differential riveting parameter part based on the first riveting correlation feature coefficient and the adaptive riveting parameter set. The first and second quality compliance datasets are sorted in descending order of numerical values ​​to obtain the quality compliance ranking results. The riveting control parameters corresponding to the differential riveting parameters in the quality compliance ranking results are marked as the verification riveting parameter section.

[0012] An aluminum alloy riveting and welding control system for a vehicle door frame structure, characterized in that it includes: Data Acquisition Module: Acquires feature information of aluminum alloy components in the current vehicle body door frame structure to be riveted and welded; The filtering module filters out similar riveting and welding feature component groups and different riveting and welding feature component groups based on the feature information of the aluminum alloy components, and obtains a set of riveting point density distribution information by statistically analyzing the riveting and welding point distribution characteristics of the similar riveting and welding feature component groups. First processing module: Under preset welding parameters, the correlation between the welding strength and deformation trend value of similar riveted and welded feature component groups is processed and analyzed to obtain the first riveted and welded correlation feature coefficient; The second processing module extracts the riveted parts in the historical difference area based on the riveted parts feature group, and processes and analyzes the correlation between the riveting strength and deformation trend value of the riveted parts in the historical difference area under the preset welding parameters to obtain the second riveting correlation feature coefficient. Prediction module: Based on the information set of rivet density distribution, the first riveting and welding correlation characteristic coefficient and the second riveting and welding correlation characteristic coefficient, a riveting and welding quality control prediction model is established, and an adaptive riveting and welding parameter set is predicted. The overlapping riveting and welding parameter part and the differential riveting and welding parameter part are extracted from the adaptive riveting and welding parameter set. Control module: Based on the first riveting and welding correlation characteristic coefficient and the adaptive riveting and welding parameter set, the differential riveting and welding parameter part is verified to obtain the verified riveting and welding parameter part. After integrating the verified riveting and welding parameter part with the coincident riveting and welding parameter part, the control parameter results of aluminum alloy riveting and welding of the vehicle body door frame structure are output.

[0013] An electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement a method for controlling the riveting and welding of aluminum alloy in a vehicle body door frame structure.

[0014] Compared with the prior art, the present invention has the following beneficial effects: This invention filters similar but differently riveted component groups and differentially riveted component groups based on the characteristic information of aluminum alloy parts. Combining the information set of riveting point density distribution, it controls the structural characteristics of different component groups. The correlation between riveting strength and deformation is analyzed for both groups, resulting in the first and second riveting correlation characteristic coefficients, thus quantifying the intrinsic relationship between process parameters and quality indicators. An adaptive riveting parameter set is generated through a riveting quality control prediction model, achieving intelligent parameter adaptation. The adaptive parameter set distinguishes between overlapping and differential parameters, and the differential parts are then verified and integrated to form the final control parameters. This process ensures that the parameters retain both general and reliable components while optimizing for differentially riveted parts, significantly improving parameter adaptability and effectively reducing riveting defects such as excessive deformation and insufficient strength caused by parameter errors. Attached Figure Description

[0015] Figure 1 This is a schematic diagram illustrating the steps of an aluminum alloy riveting and welding control method for a vehicle body door frame structure proposed in this invention. Figure 2 This invention provides a schematic diagram of a module for an aluminum alloy riveting and welding control system for a vehicle body door frame structure. Figure 3 This is a schematic diagram of the structure of the electronic device provided in an embodiment of the present invention.

[0016] 610. Processor; 620. Communication interface; 630. Memory; 640. Communication bus. Detailed Implementation

[0017] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0018] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0019] Secondly, the term "an embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places throughout this specification does not necessarily refer to the same embodiment, nor is it a single embodiment or an embodiment selectively excluded from other embodiments.

[0020] Reference Figures 1-3 As shown.

[0021] Example 1 further illustrates the aluminum alloy riveting and welding control method for a vehicle body door frame structure proposed in this invention.

[0022] A method for controlling the riveting and welding of aluminum alloy in a vehicle body door frame structure, the method comprising the following steps: Collect feature information of aluminum alloy components in the current vehicle body door frame structure to be riveted and welded; Based on the characteristic information of aluminum alloy components, similar riveting and welding characteristic component groups and different riveting and welding characteristic component groups are selected, and the distribution characteristics of riveting and welding points in similar riveting and welding characteristic component groups are statistically analyzed to obtain a set of riveting point density distribution information. The first riveting correlation characteristic coefficient is obtained by processing and analyzing the correlation between the riveting strength and deformation trend value of similar riveting characteristic component groups under preset welding parameters. Based on the characteristic component group of the difference in riveting and welding, the riveting and welding parts in the historical difference area are extracted. Under the preset welding parameters, the correlation between the riveting and welding strength and the trend value of the deformation of the riveting and welding parts in the historical difference area is processed and analyzed to obtain the second riveting and welding correlation characteristic coefficient. Based on the information set of rivet density distribution, the first riveting and welding correlation characteristic coefficient and the second riveting and welding correlation characteristic coefficient, a riveting and welding quality control prediction model is established, and an adaptive riveting and welding parameter set is predicted. The overlapping riveting and welding parameter part and the differential riveting and welding parameter part are extracted from the adaptive riveting and welding parameter set. The differential riveting parameters are verified based on the first riveting correlation characteristic coefficient and the adaptive riveting parameter set to obtain the verified riveting parameters. The verified riveting parameters are then integrated with the coincident riveting parameters to output the aluminum alloy riveting control parameters of the vehicle body door frame structure.

[0023] This application obtains the characteristic information of aluminum alloy components of the current vehicle body door frame structure to be riveted and welded, including material properties, geometric parameters and surface condition. The material properties include the alloy composition ratio, hardness and ductility of the aluminum alloy, the geometric parameters include the external dimensions, thickness and curvature, etc., and the surface condition includes flatness, roughness and pretreatment status.

[0024] Based on the characteristic information of aluminum alloy components, two groups of components with similar riveting and welding characteristics were selected: a group of components with similar riveting and welding characteristics and a group of components with different riveting and welding characteristics. The group of components with similar riveting and welding characteristics consists of components that are similar in core riveting and welding-related features. For example, the alloy composition, hardness, and ductility of the aluminum alloy are similar; the differences in dimensions, thickness, and curvature are minimal; and the requirements for connection strength and allowable deformation in riveting and welding are also relatively consistent. After identification by a feature matching algorithm, these components are grouped together. The group of components with different riveting and welding characteristics differs significantly in key structural and process-related features. For example, there may be differences in local material properties, significant differences in geometric parameters, or differences in riveting and welding requirements.

[0025] After statistically analyzing the distribution characteristics of the riveting and welding points, a set of information on the density and sparseness of the riveting and welding points is constructed, thus presenting the spatial layout of the location, quantity density, spacing patterns, and arrangement patterns of the riveting and welding points. The first riveting and welding correlation characteristic coefficient is obtained by analyzing the correlation between the riveting and welding strength and the trend of deformation under preset welding parameters. This quantifies the correlation between the strength and deformation of similar components under preset parameters, helping to determine the trend of riveting and welding parameters applicable to such similar components. Based on the group of components with different riveting and welding characteristics, riveting and welding parts in historically different areas are extracted, and the correlation between the riveting and welding strength and the deformation is analyzed under preset parameters to obtain the second riveting and welding correlation characteristic coefficient.

[0026] The preset welding parameters include welding current, voltage, pressure, time, ambient temperature, and humidity. Initial data from historical riveting processes of similar component groups are collected, including historical riveting strength information (such as initial test values ​​of tensile strength and shear strength) and deformation information (such as deformation and warpage before and after welding). Actual riveting strength and deformation information of similar components after the preset welding process are also collected. By calculating the difference and trend between the two sets of data, the first trend value of the difference in strength and deformation between the actual and historical riveting strengths are derived. After exploring the intrinsic relationship between these two trend values, the first riveting correlation characteristic coefficient is extracted. This coefficient quantifies the correlation between the riveting strength and deformation changes of similar component groups under the preset parameters, such as the proportional relationship between the increase in strength and the increase in deformation.

[0027] For components with different riveting and welding characteristics, historically different riveting and welding areas are extracted. The riveting and welding process characteristics must be consistent with those of similar groups to ensure the effectiveness of data comparison. Under preset welding parameters, historically different riveting and welding strength information and historically different deformation information are collected, as well as the actual post-weld riveting and welding strength information and actual post-weld deformation information of the different areas. The second trend value of strength change difference and the second trend value of deformation change difference are calculated, and then the second riveting and welding correlation characteristic coefficient is extracted, reflecting the correlation between the strength and deformation changes of the different component group under the same preset parameter framework.

[0028] Using the information set of rivet density distribution, the first riveting-weld correlation feature coefficient, and the second riveting-weld correlation feature coefficient as input variables, a riveting-weld quality control prediction model is built using machine learning algorithms (such as neural networks and decision trees). This model integrates spatial distribution characteristics and mechanical deformation correlation laws to predict the adaptability of riveting-weld parameters, resulting in an adaptive riveting-weld parameter set. This parameter set provides welding parameter suggestions based on the characteristics of different regions.

[0029] The preset standard parameter set is a predetermined riveting and welding parameter set based on previous mature processes or theoretical calculations. The adaptive riveting and welding parameter set is compared with the preset standard parameter set to distinguish the overlapping riveting and welding parameter part and the differential riveting and welding parameter part. The overlapping riveting and welding parameter part is adapted to the standard parameters, while the differential riveting and welding parameter part is the parameters that need to be adjusted due to the differences in component characteristics.

[0030] Based on the first riveting-welding correlation characteristic coefficient and the adaptive riveting-welding parameter set, the differential riveting-welding parameter part is verified to obtain the verified riveting-welding parameter part. This verified riveting-welding parameter part is then integrated with the coincident riveting-welding parameter part to output the aluminum alloy riveting control parameter results for the body door frame structure. Finally, the verified differential parameters and coincident parameters are integrated to output complete and accurate aluminum alloy riveting control parameter results for the body door frame structure. These parameters are used to guide actual production, ensuring that the riveting-welding process conforms to the component characteristics and consistently meets quality standards.

[0031] The specific process of integrating the verification riveting and welding parameters with the overlapping riveting and welding parameters is as follows: First, identify the corresponding riveting and welding areas of the vehicle body door frame. For the same parameters in the same area, if the values ​​are close, they are directly used. If the values ​​are significantly different, the verification parameters are given higher weight based on the quality compliance ranking, and the final value is determined after weighted calculation. If parameter conflicts are to be handled, parameters with high quality compliance are selected first, and redundant items are eliminated to ensure a smooth transition of parameters at each stage, covering all areas and meeting quality standards.

[0032] The distribution characteristics of riveting and welding points in similar riveting and welding feature groups are statistically analyzed to obtain a set of information on the density distribution of riveting points. This includes the following steps: Information on the density of riveting and welding points in groups of similar riveting and welding feature components; Based on the information on the density of the riveting and welding points, the distribution characteristics of sparse and dense areas of the riveting and welding points are statistically analyzed to obtain a set of information on the density distribution of riveting points.

[0033] This application first statistically analyzes the density of riveting and welding points in similar riveting and welding feature component groups to identify which areas have many riveting and welding points and which areas have few. Based on the density information of riveting and welding point distribution, the distribution characteristics of sparse and dense areas of riveting and welding points are statistically analyzed, such as the size and location of sparse areas and the concentration and shape of dense areas. These characteristics are then integrated to form a set of riveting point density distribution information.

[0034] The first riveting correlation characteristic coefficient is obtained by processing and analyzing the correlation between the riveting strength and deformation trend value of similar riveting feature component groups under preset welding parameters. The specific steps include: Under preset welding parameters, historical similar component welding strength information and historical similar component deformation information of similar riveted and welded component groups were collected. The statistical analysis of the changes in the welding strength of similar components in the past and the actual welding strength of similar components after undergoing a preset welding process; The deformation data of historically similar components changes after undergoing a pre-defined welding process; Calculate the first strength change difference trend value between the actual similar component's riveting strength information and the historical similar component's riveting strength information; Calculate the first deformation change difference trend value between the actual deformation information of similar components and the historical deformation information of similar components; Extract the first riveting-welding correlation characteristic coefficient between the first strength change difference trend value and the first deformation change difference trend value.

[0035] This application collects historical similar component riveting strength information and historical similar component deformation information of similar riveting feature component groups under pre-set welding parameters. The historical similar component riveting strength information includes the tensile and shear mechanical properties of such components after riveting in the past; the historical similar component deformation information is the deformation and warping caused by thermal stress and mechanical force factors.

[0036] After the pre-set welding process, the welding strength and deformation information of similar actual components are monitored. Professional testing equipment is used to capture the true strength and deformation of the components after welding. This equipment can be a mechanical testing machine to test strength and a 3D scanner to measure deformation.

[0037] The first strength change difference trend value is obtained by calculating the difference between the actual welding strength information of similar components and the historical welding strength information of similar components. The first strength change difference trend value quantifies the direction and magnitude of the strength fluctuation during the welding process compared with historical data. The first deformation change difference trend value is obtained by calculating the difference between the actual deformation information of similar components and the historical deformation information of similar components. The first deformation change difference trend value shows the trend of deformation change.

[0038] The mathematical relationship and influence law between the first trend value of strength change difference and the first trend value of deformation change difference are extracted, and then the first riveting-weld correlation characteristic coefficient is obtained. This coefficient reflects the correlation between the riveting strength and deformation change trends of similar riveting-welded component groups under preset welding parameter conditions, providing key characteristic basis for subsequent riveting-weld quality control and parameter optimization, and facilitating the prediction of the comprehensive impact of welding process on the mechanical properties and deformation of components.

[0039] Based on the group of riveting and welding feature components with historical differences, the following steps are specifically included: Collect riveted parts from different areas of the component group with different riveting characteristics; Historically different riveted parts were selected from the riveted parts with different riveting features: the riveting process feature information in the difference riveting feature component group and the similar riveting feature component group of the historically different riveted parts with different riveting features is the same.

[0040] This application first collects riveting components from different areas within a group of components with different riveting characteristics. These components, due to their unique characteristics, constitute unique riveting operation objects. Next, historical data is filtered from the collected components. Specifically, historical components with different riveting characteristics are selected from these components, ensuring that their respective groups share consistency with similar groups in key elements of the riveting process. These key elements include welding equipment type, welding parameter range, and tooling specifications. This ensures that the selected historical components with different riveting characteristics not only reflect the characteristics of the different component group but also allow for correlation analysis with data from similar component groups due to process feature matching. This provides comparable historical samples for subsequent research on the differences in riveting quality control.

[0041] The second riveting-weld correlation characteristic coefficient is obtained by processing and analyzing the correlation between the riveting strength and deformation trend value of riveted parts in historical difference areas under preset welding parameters. The specific steps include: Collect information on the welding strength and deformation of the historical difference areas to which the welded parts belong; The statistical analysis of the historical differences in weld strength and the actual changes in weld strength in the affected areas after undergoing a pre-defined welding process; The statistical analysis shows the actual deformation of the historical difference area after undergoing a pre-set welding process. Calculate the second strength change trend value between the actual weld strength information of the difference area and the historical weld strength information of the difference area; Calculate the second deformation change difference trend value between the actual deformation information of the difference area and the historical deformation information of the difference area; Extract the second riveting-welding correlation characteristic coefficient between the second strength change difference trend value and the second deformation change difference trend value.

[0042] This application first collects historical difference area riveting strength information and historical difference area deformation information of the riveted and welded parts. The historical difference area riveting strength information includes tensile and shear strength properties after riveting and welding, and the historical difference area deformation information includes deformation and warping data caused by welding.

[0043] After undergoing a pre-defined welding process, the actual weld strength and deformation information of the difference areas are collected. The difference between the actual weld strength and historical weld strength information of the difference areas is calculated to obtain the second strength change trend value. This second strength change trend value quantifies the direction and magnitude of the fluctuation of the strength index during welding compared to historical data. The difference between the actual deformation and historical deformation information of the difference areas is calculated to obtain the second deformation change trend value, which shows the trend of deformation change.

[0044] The relationship and influence between the second strength variation difference trend value and the second deformation variation difference trend value are extracted, thus obtaining the second riveting-weld correlation characteristic coefficient. This coefficient reflects the correlation between the riveting strength and deformation variation trend of the riveted parts in the historical difference area under preset welding parameters.

[0045] And predict the adaptive riveting and welding parameter set, specifically including the following steps: After extracting at least two component regions for riveting operations from the current component to be riveted based on the feature information of the aluminum alloy component, the predetermined set of riveting operation regions is output. After obtaining the characteristic information of the corresponding riveting and welding process based on the characteristic information of each area in the predetermined riveting and welding operation area set, the predetermined process characteristic information set is output. After inputting the predetermined riveting and welding operation area set and the predetermined process characteristic information set into the riveting and welding quality control prediction model, an adaptive riveting and welding parameter set is obtained.

[0046] This application first identifies and extracts at least two component areas for riveting operations from the current component to be riveted based on the characteristic information of the aluminum alloy component. These areas have their own riveting requirements due to different component characteristics (such as structure and material distribution). After extraction, the predetermined set of riveting operation areas is integrated and output.

[0047] For each region in the predetermined riveting and welding operation area set, its characteristic information is determined. The characteristic information includes the region's geometry, thickness, and connection relationship with other components. Based on these characteristics, the corresponding riveting and welding process characteristic information is matched, such as the appropriate welding current range, pressure, and welding sequence. The process characteristic information of all regions is sorted out and the predetermined process characteristic information set is output.

[0048] After inputting the predetermined set of riveting and welding operation areas and the predetermined set of process feature information into the riveting and welding quality control prediction model, an adaptive set of riveting and welding parameters is output. This set of parameters can adapt to the characteristics and process requirements of each area and guide the actual riveting and welding operation to achieve high-quality control.

[0049] The overlapping riveting parameters and the differential riveting parameters are extracted from the adaptive riveting parameter set, specifically: After comparing the adaptive riveting parameter set with the predetermined riveting parameter set, the overlapping riveting parameter part and the differential riveting parameter part are extracted.

[0050] This application first obtains an adaptive riveting and welding parameter set and a predetermined riveting and welding parameter set. The adaptive riveting and welding parameter set is a set of parameters generated by a prediction model in combination with factors such as component characteristics and process requirements, adapted to the current riveting and welding operation; the predetermined riveting and welding parameter set is a set of reference parameters based on past experience, standard processes, or theoretical presets.

[0051] The adaptive riveting and welding parameter set is compared with the predetermined riveting and welding parameter set. During the comparison, each parameter item is screened one by one, including parameters for each stage of the riveting and welding operation and parameters corresponding to different areas. Each stage of the riveting and welding operation includes pre-pressing, welding, holding pressure and cooling stages. During the comparison, it is determined whether the parameter value of each parameter item in the two sets is consistent with the process requirements.

[0052] By comparing and filtering parameters, overlapping and differential riveting parameters are identified. Overlapping parameters are those whose values ​​and requirements match perfectly in both parameter sets. These parameters can be directly used in actual operations because they meet both the preset standards and the adaptive requirements of the current component. Differential riveting parameters are those whose values ​​and requirements differ between the two parameter sets. These differences stem from the adaptation and adjustment to the unique characteristics and processes of the current component. These parameters need further verification to determine the final applicable riveting parameters, thereby ensuring the quality and effect of riveting.

[0053] The verified riveting parameters are obtained by verifying the differential riveting parameters based on the first riveting correlation characteristic coefficient and the adaptive riveting parameter set. This process includes the following steps: The first quality compliance dataset is obtained by judging the degree of compliance of the riveting quality of the differential riveting parameters based on the first riveting correlation feature coefficient and the predetermined riveting parameter set. The second quality compliance dataset is obtained by judging the degree of compliance of the riveting quality of the differential riveting parameter part based on the first riveting correlation feature coefficient and the adaptive riveting parameter set. The first and second quality compliance datasets are sorted in descending order of numerical values ​​to obtain the quality compliance ranking results. The riveting control parameters corresponding to the differential riveting parameters in the quality compliance ranking results are marked as the verification riveting parameter section.

[0054] This application first uses a first riveting-weld correlation characteristic coefficient and a predetermined riveting-weld parameter set to determine the quality compliance of the differential riveting-weld parameter portion. After evaluating the degree to which the quality of the differential portion meets the standards after riveting and welding using the predetermined riveting-weld parameter set, a first quality compliance dataset is generated, such as whether the strength and deformation amount meet the standards, thereby recording the compliance values ​​corresponding to each differential parameter.

[0055] The quality compliance rate of the differential riveting parameters is judged based on the first riveting-welding correlation characteristic coefficient and the adaptive riveting-welding parameter set to obtain the second quality compliance rate dataset. Similarly, the compliance values ​​of each differential parameter are recorded.

[0056] The first and second quality compliance datasets are sorted in descending order of numerical values. The ranking, from highest to lowest compliance value, clearly presents the quality compliance performance of the different riveting and welding parameters under different parameter sets, thus yielding the quality compliance ranking results.

[0057] The first and second quality compliance datasets are sorted in descending order of numerical values ​​to generate a quality compliance ranking result. This ranking result includes the priority order of different riveting and welding control parameters under varying riveting and welding scenarios, based on their quality compliance values. Since the range of parameters requiring focused verification is clearly defined in the varying riveting and welding parameter section, the riveting and welding control parameters corresponding to this section are selected from the quality compliance ranking result and marked as the verification parameters. By prioritizing parameters with higher assurance of riveting and welding quality compliance based on the quality compliance ranking, precise guidance is provided for parameter verification and determination in subsequent actual riveting and welding operations.

[0058] By marking these riveting and welding parameters for verification, small-batch trial production can be conducted to validate them before large-scale riveting operations begin. This allows for early detection of quality issues that may arise from unreasonable parameters, such as insufficient weld strength and excessive deformation. Verifying these parameters helps optimize the riveting and welding process. By comparing overlapping parameters, it highlights options with high potential for meeting quality standards among the differing parameters, driving the entire aluminum alloy riveting and welding process of the vehicle body door frame structure towards high quality and stability. This ensures the performance and quality of the final product, making actual riveting and welding production both efficient and meeting quality standards.

[0059] Example 2 further illustrates the aluminum alloy riveting and welding control system for a vehicle body door frame structure proposed in this invention.

[0060] An aluminum alloy riveting and welding control system for a vehicle door frame structure includes: Data Acquisition Module: Acquires feature information of aluminum alloy components in the current vehicle body door frame structure to be riveted and welded; The filtering module filters out similar riveting and welding feature component groups and different riveting and welding feature component groups based on the feature information of aluminum alloy components, and obtains a set of riveting point density distribution information by statistically analyzing the riveting and welding point distribution characteristics of similar riveting and welding feature component groups. First processing module: Under preset welding parameters, the correlation between the welding strength and deformation trend value of similar riveted and welded feature component groups is processed and analyzed to obtain the first riveted and welded correlation feature coefficient; The second processing module extracts the riveted parts in the historical difference area based on the riveted parts feature group, and processes and analyzes the correlation between the riveting strength and deformation trend value of the riveted parts in the historical difference area under the preset welding parameters to obtain the second riveting correlation feature coefficient. Prediction module: Based on the information set of rivet density distribution, the first riveting and welding correlation characteristic coefficient and the second riveting and welding correlation characteristic coefficient, a riveting and welding quality control prediction model is established, and an adaptive riveting and welding parameter set is predicted. The overlapping riveting and welding parameter part and the differential riveting and welding parameter part are extracted from the adaptive riveting and welding parameter set. Control module: Based on the first riveting and welding correlation characteristic coefficient and the adaptive riveting and welding parameter set, the differential riveting and welding parameter part is verified to obtain the verified riveting and welding parameter part. After integrating the verified riveting and welding parameter part with the coincident riveting and welding parameter part, the control parameter results of aluminum alloy riveting and welding of the vehicle body door frame structure are output.

[0061] An electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor performs a method for controlling the riveting of aluminum alloy in a vehicle body door frame structure.

[0062] like Figure 3 As shown, the electronic device may include a processor 610, a communication interface 620, a memory 630, and a communication bus 640, wherein the processor 610, the communication interface 620, and the memory 630 communicate with each other through the communication bus 640. The processor 610 can call logical instructions in the memory 630 to execute an aluminum alloy riveting control method for a vehicle body door frame structure.

[0063] Furthermore, the logical instructions in the aforementioned memory 630 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory, random access memory, magnetic disks, or optical disks.

[0064] On the other hand, the present invention also provides a computer program product, the computer program product including a computer program that can be stored on a non-transitory computer-readable storage medium, and when the computer program is executed by a processor, the computer is able to execute a method for controlling the riveting and welding of aluminum alloy in a vehicle body door frame structure.

[0065] In another aspect, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, is implemented to perform a method for controlling the riveting of aluminum alloy in a vehicle body door frame structure.

[0066] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0067] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0068] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for controlling the riveting and welding of aluminum alloy in a vehicle body door frame structure, characterized in that, The method includes the following steps: Collect feature information of aluminum alloy components in the current vehicle body door frame structure to be riveted and welded; Based on the characteristic information of the aluminum alloy components, similar riveting and welding characteristic component groups and different riveting and welding characteristic component groups are selected, and the distribution characteristics of riveting and welding points of similar riveting and welding characteristic component groups are statistically analyzed to obtain a set of riveting point density distribution information. The first riveting correlation characteristic coefficient is obtained by processing and analyzing the correlation between the riveting strength and deformation trend value of similar riveting characteristic component groups under preset welding parameters. Based on the characteristic component group of the difference in riveting and welding, the riveting and welding parts in the historical difference area are extracted. Under the preset welding parameters, the correlation between the riveting and welding strength and the trend value of the deformation of the riveting and welding parts in the historical difference area is processed and analyzed to obtain the second riveting and welding correlation characteristic coefficient. Based on the information set of rivet density distribution, the first riveting and welding correlation characteristic coefficient and the second riveting and welding correlation characteristic coefficient, a riveting and welding quality control prediction model is established, and an adaptive riveting and welding parameter set is predicted. The overlapping riveting and welding parameter part and the differential riveting and welding parameter part are extracted from the adaptive riveting and welding parameter set. The differential riveting parameters are verified based on the first riveting correlation characteristic coefficient and the adaptive riveting parameter set to obtain the verified riveting parameters. The verified riveting parameters are then integrated with the coincident riveting parameters to output the aluminum alloy riveting control parameters of the vehicle body door frame structure.

2. The aluminum alloy riveting and welding control method for a vehicle body door frame structure according to claim 1, characterized in that, The distribution characteristics of riveting and welding points in similar riveting and welding feature groups are statistically analyzed to obtain a set of information on the density distribution of riveting points. This includes the following steps: Information on the density of riveting and welding points in groups of similar riveting and welding feature components; Based on the information on the density of the riveting and welding points, the distribution characteristics of sparse and dense areas of the riveting and welding points are statistically analyzed to obtain a set of information on the density distribution of riveting points.

3. The aluminum alloy riveting and welding control method for a vehicle body door frame structure according to claim 2, characterized in that, The first riveting correlation characteristic coefficient is obtained by processing and analyzing the correlation between the riveting strength and deformation trend value of similar riveting feature component groups under preset welding parameters. The specific steps include: Under preset welding parameters, historical similar component welding strength information and historical similar component deformation information of similar riveted and welded component groups were collected. The statistical analysis of the changes in the welding strength of similar components in the past and the actual welding strength of similar components after undergoing a preset welding process; The deformation data of historically similar components changes after undergoing a pre-defined welding process; Calculate the first strength change difference trend value between the actual similar component's riveting strength information and the historical similar component's riveting strength information; Calculate the first deformation change difference trend value between the actual deformation information of similar components and the historical deformation information of similar components; Extract the first riveting-welding correlation characteristic coefficient between the first strength change difference trend value and the first deformation change difference trend value.

4. The aluminum alloy riveting and welding control method for a vehicle body door frame structure according to claim 3, characterized in that, Based on the group of riveting and welding feature components with historical differences, the following steps are specifically included: Collect riveted parts from different areas of the component group with different riveting characteristics; Select historically different riveted parts from the riveted parts with different riveting areas: wherein the riveting process feature information in the difference riveting feature component group and the similar riveting feature component group of the historically different riveted parts with different riveting areas is the same.

5. The aluminum alloy riveting and welding control method for a vehicle body door frame structure according to claim 4, characterized in that, The second riveting-weld correlation characteristic coefficient is obtained by processing and analyzing the correlation between the riveting strength and deformation trend value of riveted parts in historical difference areas under preset welding parameters. The specific steps include: Collect information on the welding strength and deformation of the historical difference areas to which the welded parts belong; The statistical analysis of the historical differences in weld strength and the actual changes in weld strength in the affected areas after undergoing a pre-defined welding process; The statistical analysis shows the actual deformation of the historical difference area after undergoing a pre-set welding process. Calculate the second strength change trend value between the actual weld strength information of the difference area and the historical weld strength information of the difference area; Calculate the second deformation change difference trend value between the actual deformation information of the difference area and the historical deformation information of the difference area; Extract the second riveting-welding correlation characteristic coefficient between the second strength change difference trend value and the second deformation change difference trend value.

6. The aluminum alloy riveting and welding control method for a vehicle body door frame structure according to claim 5, characterized in that, And predict the adaptive riveting and welding parameter set, specifically including the following steps: Based on the characteristic information of the aluminum alloy component, at least two component regions for riveting operations are extracted from the component to be riveted, and a predetermined set of riveting operation regions is output. After obtaining the characteristic information of the corresponding riveting and welding process based on the characteristic information of each area in the predetermined riveting and welding operation area set, the predetermined process characteristic information set is output. After inputting the predetermined riveting and welding operation area set and the predetermined process characteristic information set into the riveting and welding quality control prediction model, an adaptive riveting and welding parameter set is obtained.

7. The aluminum alloy riveting and welding control method for a vehicle body door frame structure according to claim 6, characterized in that, The overlapping riveting parameters and the differential riveting parameters are extracted from the adaptive riveting parameter set, specifically: After comparing the adaptive riveting parameter set with the predetermined riveting parameter set, the overlapping riveting parameter part and the differential riveting parameter part are extracted.

8. The aluminum alloy riveting and welding control method for a vehicle body door frame structure according to claim 7, characterized in that, The verified riveting parameters are obtained by verifying the differential riveting parameters based on the first riveting correlation characteristic coefficient and the adaptive riveting parameter set. This process includes the following steps: The first quality compliance dataset is obtained by judging the degree of compliance of the riveting quality of the differential riveting parameters based on the first riveting correlation feature coefficient and the predetermined riveting parameter set. The second quality compliance dataset is obtained by judging the degree of compliance of the riveting quality of the differential riveting parameter part based on the first riveting correlation feature coefficient and the adaptive riveting parameter set. The first and second quality compliance datasets are sorted in descending order of numerical values ​​to obtain the quality compliance ranking results. The riveting control parameters corresponding to the differential riveting parameters in the quality compliance ranking results are marked as the verification riveting parameter section.

9. An aluminum alloy riveting and welding control system for a vehicle body door frame structure, applied to the aluminum alloy riveting and welding control method for a vehicle body door frame structure as described in any one of claims 1-8, characterized in that, include: Data Acquisition Module: Acquires feature information of aluminum alloy components in the current vehicle body door frame structure to be riveted and welded; The filtering module filters out similar riveting and welding feature component groups and different riveting and welding feature component groups based on the feature information of the aluminum alloy components, and obtains a set of riveting point density distribution information by statistically analyzing the riveting and welding point distribution characteristics of the similar riveting and welding feature component groups. First processing module: Under preset welding parameters, the correlation between the welding strength and deformation trend value of similar riveted and welded feature component groups is processed and analyzed to obtain the first riveted and welded correlation feature coefficient; The second processing module extracts the riveted parts in the historical difference area based on the riveted parts feature group, and processes and analyzes the correlation between the riveting strength and deformation trend value of the riveted parts in the historical difference area under the preset welding parameters to obtain the second riveting correlation feature coefficient. Prediction module: Based on the information set of rivet density distribution, the first riveting and welding correlation characteristic coefficient and the second riveting and welding correlation characteristic coefficient, a riveting and welding quality control prediction model is established, and an adaptive riveting and welding parameter set is predicted. The overlapping riveting and welding parameter part and the differential riveting and welding parameter part are extracted from the adaptive riveting and welding parameter set. Control module: Based on the first riveting and welding correlation characteristic coefficient and the adaptive riveting and welding parameter set, the differential riveting and welding parameter part is verified to obtain the verified riveting and welding parameter part. After integrating the verified riveting and welding parameter part with the coincident riveting and welding parameter part, the control parameter results of aluminum alloy riveting and welding of the vehicle body door frame structure are output.

10. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the aluminum alloy riveting control method for a vehicle body door frame structure as described in any one of claims 1 to 8.

Images