Intelligent control system suitable for automobile body welding manufacturing

Abstract

The invention discloses an intelligent control system suitable for automobile body welding manufacturing, and relates to the technical field of automobile welding. According to the technical scheme, the method is characterized by comprising the steps that welding part types, welding accuracy levels and welding environment interference sources of a welding target area of an automobile body are recognized and classified, and then a first type of welding procedure set and a second type of welding procedure set are obtained; performing process parameter suitability analysis on a welding part material of the automobile body welding target area to obtain a material adaptation parameter set, and performing process parameter suitability analysis on a welding joint form of the automobile body welding target area to obtain a joint form adaptation parameter set; judging the spatial relationship between the welding position and the interference source in the second welding process set to obtain a first interference level and a second interference level; the effect is that production welding quality can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of automobile welding, and more specifically, it relates to an intelligent control system applicable to the welding manufacturing of automobile bodies. Background Art

[0002] In the field of automobile manufacturing, the welding quality of the vehicle body is directly related to the safety, reliability and performance of the whole vehicle. However, in actual production, there are environmental interference sources (such as electromagnetic interference, vibration, thermal disturbance, etc.) in the welding target area of the vehicle body. The welding links spatially associated with the interference sources are easily affected, and the problems of welding stress concentration and deformation are prominent. Traditional welding process planning and quality control mostly rely on empirical parameters, and do not fully couple the interference environment, material characteristics and stress deformation laws, making it difficult to accurately predict and control the stress concentration coefficient and deformation amount during the welding process. After welding, defects such as deformation of vehicle body parts exceeding tolerances and fatigue cracking caused by stress concentration often occur, resulting in a high rework rate, low production efficiency, and even affecting the assembly and use safety of the whole vehicle. Summary of the Invention

[0003] Aiming at the deficiencies of the existing technology, the purpose of the present invention is to provide an intelligent control system applicable to the welding manufacturing of automobile bodies.

[0004] To achieve the above purpose, the present invention provides the following technical solutions: An intelligent control system applicable to the welding manufacturing of automobile bodies, comprising: A classification module: After identifying and classifying the welding part types, welding accuracy grades and welding environment interference sources in the welding target area of the automobile body, a first type of welding process set and a second type of welding process set are obtained; An analysis module: After performing process parameter adaptability analysis on the welding part materials in the welding target area of the automobile body, a material adaptability parameter set is obtained, and after performing process parameter adaptability analysis on the welding joint forms in the welding target area of the automobile body, a joint form adaptability parameter set is obtained; A judgment module: Judging the spatial relationship between the welding position and the interference source in the second type of welding process set to obtain a first interference level and a second interference level; A processing module: Processing and analyzing the material adaptability parameter set, the joint form adaptability parameter set, the first type of welding process set, the second type of welding process set, the first interference level and the second interference level to obtain a quality prediction status set; An evaluation module: Evaluating the welding quality of the welding target area of the automobile body according to the quality prediction status set to obtain a welding quality warning result.

[0005] Preferably, the material adaptation parameter set, the joint form adaptation parameter set, the first type of welding process set, the second type of welding process set, the first interference level, and the second interference level are processed and analyzed to obtain a quality prediction status set, which specifically includes the following steps: Based on the material adaptation parameter set and the joint form adaptation parameter set, predict the welding penetration status and the bead formation status of the first type of welding process set to obtain a first type of quality prediction result set; According to the first interference level, predict the welding spatter rate and the porosity status of the second type of welding process set to obtain a second type of quality prediction result set; According to the second interference level, predict the welding residual stress and the welding deformation amount of the second type of welding process set to obtain a third type of quality prediction result set; Among them, the first type of quality prediction result set, the second type of quality prediction result set, and the third type of quality prediction result set are combined to form a quality prediction status set.

[0006] Preferably, after identifying and classifying the welding component types, welding accuracy levels, and welding environment interference sources in the welding target area of the automobile body, a first type of welding process set and a second type of welding process set are obtained, which specifically includes the following steps: Divide the welding target area of the automobile body into several welding area blocks; Detect and identify the welding component types, welding accuracy levels, and welding environment interference sources of each welding area block to obtain a detection and identification result set; Extract a first vehicle body component area set, a second vehicle body component area set, and an interference source area set from the detection and identification result set; Perform process matching on the first vehicle body component area set and the second vehicle body component area set to obtain a first type of welding process set; Perform spatial correlation matching on the first vehicle body component area set and the interference source area set to obtain a second type of welding process set.

[0007] Preferably, extracting a first vehicle body component area set, a second vehicle body component area set, and an interference source area set from the detection and identification result set specifically includes the following steps: Extract a first vehicle body component area set after extracting a first component welding area from the detection and identification result set; extract a second vehicle body component area set after extracting a second component welding area from the detection and identification result set; among them, the welding accuracy level of the first vehicle body component area set is lower than the welding accuracy level of the second vehicle body component area set; Extract an interference source area set after extracting an environmental interference area from the detection and identification result set.

[0008] Preferably, after performing process parameter adaptability analysis on the welding component materials in the welding target area of the automobile body, a material adaptation parameter set is obtained, which specifically includes the following steps: After statistically analyzing the component material types, thicknesses, and heat treatment status information in the first set of vehicle body component regions and the second set of vehicle body component regions, a set of material characteristic information is obtained; Detect the geometric dimensions, assembly gaps, and groove angles of the welding joint forms to obtain a set of joint form characteristic information; Match the set of material characteristic information and the set of joint form characteristic information in the process knowledge base to obtain a set of material adaptation parameters; wherein, the set of material adaptation parameters includes welding current, voltage, and welding speed parameters.

[0009] Preferably, after performing process parameter adaptability analysis on the welding joint forms in the welding target regions of the vehicle body, a set of joint form adaptation parameters is obtained, specifically: Match the set of material characteristic information and the set of joint form characteristic information in the process knowledge base to obtain a set of joint form adaptation parameters; wherein, the set of joint form adaptation parameters includes the wire diameter and shielding gas flow rate parameters adapted to the joint form.

[0010] Preferably, based on the set of material adaptation parameters and the set of joint form adaptation parameters, predict and control the welding heat input and penetration conditions for the first set of welding processes, and output a set of first-class process control parameters and a set of first-class quality prediction results, specifically including the following steps: Monitor the real-time parameters of the welding equipment and the cooling rate in each region of the first set of welding processes to obtain a set of equipment status information; Judge the heat input stability of the first set of vehicle body component regions and the second set of vehicle body component regions according to the set of equipment status information and the set of material adaptation parameters to obtain a set of heat input fluctuation levels; Regulate the welding equipment parameters according to the set of heat input fluctuation levels and the set of joint form characteristic information to obtain a set of first-class process control parameters; Predict the welding penetration conditions and bead formation conditions based on the set of first-class process control parameters to obtain a set of first-class quality prediction results.

[0011] Preferably, judge the spatial relationship between the welding positions and interference sources in the second set of welding processes to obtain a first interference level and a second interference level, specifically including the following steps: Compare the distances between the welding positions and the interference source positions in the second set of welding processes, and compare the interference source intensity with a preset interference source intensity threshold; If the distance between the welding position and the interference source position in the second set of welding processes is greater than or equal to the preset distance threshold, and the interference source intensity is greater than or equal to the preset interference source intensity threshold, then it is determined as the first interference level; otherwise, it is determined as the second interference level.

[0012] Preferably, predicting and controlling the welding spatter amount and porosity incidence of the second type welding process set according to the first interference level, and outputting the second type process control parameter set and the second type quality prediction result set specifically includes the following steps: Detect the intensity, frequency and range of each interference source in the interference source area to obtain an interference source feature information set; According to the first interference level and the interference source characteristic information set, anti-interference parameters are introduced to obtain the second type of process control parameter set; The welding spatter rate and porosity condition are predicted based on the second type of process control parameter set to obtain the second type of quality prediction result set.

[0013] Preferably, predicting and controlling the welding stress concentration factor and deformation of the second type welding process set according to the second interference level, and outputting a third type process control parameter set and a third type quality prediction result set specifically includes the following steps: Predicting welding stress and welding deformation based on the second interference level and interference source feature information set to obtain a stress and deformation prediction set; The third type of process control parameter set is obtained by regulating the welding equipment parameters according to the stress deformation prediction set and the material characteristic information set; Based on the third type of process control parameter set, the welding residual stress and welding deformation are predicted to obtain the third type of quality prediction result set.

[0014] Compared with the prior art, the present invention has the following beneficial effects: The present invention focuses on the second type of process set affected by the second interference level in automobile body welding, and has achieved remarkable results in stress and deformation control. It first conducts thermal-mechanical simulation based on the second interference level and the material properties coupled with the interference source characteristics to accurately predict welding stress concentration and deformation trends, and identify potential risks such as stress concentration at the root of the joint and out-of-tolerance deformation of body parts in advance; then it links the material characteristics to intelligently control the welding parameters, and optimizes all aspects from heat input gradient, cooling strategy to welding sequence, actively suppressing the generation of stress and deformation, so that welding of different materials such as high-strength steel and aluminum alloy can adapt to process requirements and ensure joint performance and body size accuracy. Therefore, this application can improve production welding quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 The present invention proposes a module schematic diagram of an intelligent control system suitable for automobile body welding manufacturing; Figure 2 A schematic diagram of the steps for obtaining a third-category quality prediction result set in an intelligent control system suitable for automobile body welding manufacturing proposed by the present invention; Figure 3Schematic diagram of steps for obtaining a material adaptation parameter set in an intelligent control system applicable to automotive body welding manufacturing proposed by the present invention. Detailed implementation manners

[0016] To make the above objects, features and advantages of the present invention more obvious and understandable, the following detailed description of the specific implementation manners of the present invention will be given in conjunction with the accompanying drawings of the specification.

[0017] In the following description, many specific details are set forth to fully understand the present invention. However, the present invention can also be implemented in other ways different from those described herein. Those skilled in the art can make similar generalizations without departing from the connotation of the present invention. Therefore, the present invention is not limited by the specific embodiments disclosed below.

[0018] Secondly, the so-called "one embodiment" or "embodiment" herein refers to a specific feature, structure or characteristic that can be included in at least one implementation manner of the present invention. The "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor is it an embodiment that is separate or selectively mutually exclusive with other embodiments.

[0019] Refer to Figures 1 - 3 as shown.

[0020] The embodiments will further illustrate an intelligent control system applicable to automotive body welding manufacturing proposed by the present invention.

[0021] An intelligent control system applicable to automotive body welding manufacturing includes: Classification module: After identifying and classifying the types of welding parts, welding accuracy levels, and welding environment interference sources in the welding target area of the automotive body, a first set of welding processes and a second set of welding processes are obtained; Analysis module: After performing process parameter adaptability analysis on the materials of the welding parts in the welding target area of the automotive body, a material adaptation parameter set is obtained. After performing process parameter adaptability analysis on the welding joint forms in the welding target area of the automotive body, a joint form adaptation parameter set is obtained; Judgment module: Judge the spatial relationship between the welding position and the interference source in the second set of welding processes to obtain a first interference level and a second interference level; Processing module: Process and analyze the material adaptation parameter set, joint form adaptation parameter set, first set of welding processes, second set of welding processes, first interference level, and second interference level to obtain a quality prediction status set; Evaluation module: Evaluate the welding quality of the welding target area of the automotive body according to the quality prediction status set to obtain a welding quality warning result.

[0022] This application disassembles the welding target area of the car body into several welding area blocks, identifies the types of welded parts in each welding area block (such as different parts like the frame and doors), the welding accuracy levels (requirements such as high precision and ordinary precision), and the welding environment interference sources (such as electromagnetic interference equipment and vibration sources), thereby forming a detection and recognition result set. According to the welding accuracy, the areas in the result set are divided into the first car body part area set (low accuracy level) and the second car body part area set (high accuracy level), and then the interference source area set is extracted. Through process matching, the first and second car body part area sets are integrated into the first type of welding process set (mainly the conventional welding processes between different car body parts); through spatial correlation matching, the first car body part area set and the interference source area set are combined into the second type of welding process set (involving welding processes in an interfered environment).

[0023] Statistical material type (such as steel, aluminum, etc.), thickness, and heat treatment status of the parts in the first and second car body part area sets are formed into a material characteristic information set; at the same time, geometric dimensions of the welding joint form (such as weld length and width), assembly gap, and groove angle are detected to obtain the joint form characteristic information set. Relying on the process knowledge base, the material characteristic information set is matched with the joint form characteristic information set to respectively obtain a material adaptation parameter set (covering welding current, voltage, welding speed, etc., for ensuring the welding quality of the material) and a joint form adaptation parameter set (including wire diameter, shielding gas flow rate, etc., adapting to the welding requirements of the joint).

[0024] The judgment module measures the spatial relationship between the welding position and the interference source for the second type of welding process set. By comparing the distance between the welding position and the interference source position, as well as the interference source intensity and the preset threshold, the interference level is defined: if the distance ≥ the preset distance threshold and the interference source intensity ≥ the preset intensity threshold, it is determined as the first interference level (strong interference environment); otherwise, it is the second interference level (weak interference or no interference environment), thereby distinguishing welding scenarios with different interference degrees.

[0025] The processing module integrates multi-dimensional information for quality prediction. Based on the material adaptation parameter set and the joint form adaptation parameter set, the real-time parameters (such as actual current and voltage) and cooling rate of the welding equipment in the first type of welding process set are monitored, thereby predicting the welding penetration depth and weld bead formation status to form the first type of quality prediction result set; according to the first interference level, combined with the characteristics of the interference source such as intensity, frequency, and action range, anti-interference parameters are introduced to regulate and predict the welding spatter rate and porosity condition to generate the second type of quality prediction result set; based on the second interference level and the interference source characteristics, the welding residual stress and deformation amount are predicted, and after regulating the equipment parameters, the third type of quality prediction result set is formed.

[0026] Welding quality is assessed against pre-set quality standards (e.g., acceptable ranges for penetration deviation, spatter rate, and deformation). If the predicted result exceeds the standard, quality risk points (e.g., insufficient penetration, excessive porosity, etc.) are identified and a welding quality warning is generated.

[0027] The material adaptation parameter set, the joint form adaptation parameter set, the first type welding process set, the second type welding process set, the first interference level and the second interference level are processed and analyzed to obtain a quality prediction status set, specifically including the following steps: Based on the material adaptation parameter set and the joint form adaptation parameter set, the welding penetration condition and weld bead shape condition of the first type of welding process set are predicted to obtain the first type of quality prediction result set; The second type of quality prediction result set is obtained by predicting the welding spatter rate and porosity condition of the second type of welding process set according to the first interference level; According to the second interference level, the welding residual stress and welding deformation of the second type of welding process set are predicted to obtain the third type of quality prediction result set; Among them, the first type of quality prediction result set, the second type of quality prediction result set and the third type of quality prediction result set are combined to form a quality prediction status set.

[0028] The material adaptation parameter set mainly reflects the key parameters of welding current and voltage corresponding to the material of the welding parts, and the joint form adaptation parameter set mainly covers the welding wire diameter and shielding gas flow rate adapted to the joint.

[0029] The first type of welding process set (mostly areas with relatively basic welding accuracy requirements and minimal interference) relies on material adaptation parameter sets (such as welding heat input parameters for different steel and aluminum materials) and joint adaptation parameter sets (such as process parameters for butt and fillet joints) to simulate the energy input and weld pool formation patterns during welding. This process determines the matching relationship between welding current, voltage, and speed and joint geometry (groove angle, assembly gap), and predicts weld penetration (whether strength requirements are met and whether penetration is uniform) and weld bead shape (bead width, height, surface flatness, etc.), forming the first type of quality prediction result set.

[0030] Based on the prediction of the first interference level, when the welding position is subject to strong interference (such as electromagnetic interference and airflow interference, which corresponds to the first interference level), the focus is on welding spatter rate (interference can easily cause arc instability, resulting in abnormal droplet transfer and spatter) and porosity (interference can draw in air, destroying the protective atmosphere, and causing porosity). Using data such as the interference intensity and impact range associated with the first interference level, combined with welding parameters (such as current and voltage) from the second type of welding process set, the physical changes in the welding process under interference are simulated. The spatter rate is predicted to exceed the standard, and the number and distribution of porosity are predicted to generate the second type of quality prediction result set.

[0031] For the second interference level, focus on welding residual stress (local stress concentration is likely to be caused by the welding thermal cycle) and welding deformation (deformation is caused by uneven thermal expansion and contraction). Based on the interference characteristics corresponding to the second interference level (such as low-intensity vibration, weak magnetic field), combined with the thermophysical properties of the material (obtained from the material adaptation parameter set), judge the influence of welding heat input and cooling rate on residual stress and deformation, predict whether the stress distribution causes cracking risk, and judge whether the deformation exceeds the dimensional tolerance, so as to form the third type of quality prediction result set.

[0032] Integrate the first type of quality prediction result set (basic welding quality), the second type of quality prediction result set (spatter and porosity under strong interference), and the third type of quality prediction result set (stress and deformation under weak interference) to form a quality prediction status set. This set comprehensively covers the quality risk points in each process and each interference scenario of automobile body welding. Subsequently, it can support the evaluation module to carry out welding quality early warning, and also provide a basis for the generation of process adjustment data sets (such as adjusting welding parameters, increasing anti-interference measures), realizing the closed-loop logic from process parameter analysis to quality prediction, and ensuring the reliability and stability of welding manufacturing.

[0033] After identifying and classifying the welding component types, welding accuracy levels, and welding environment interference sources in the welding target area of the automobile body, the first type of welding process set and the second type of welding process set are obtained. The specific steps are as follows: Divide the welding target area of the automobile body into several welding area blocks; Detect and identify the welding component types, welding accuracy levels, and welding environment interference sources of each welding area block to obtain a detection and identification result set; Extract the first body component area set, the second body component area set, and the interference source area set from the detection and identification result set; Perform process matching on the first body component area set and the second body component area set to obtain the first type of welding process set; Perform spatial correlation matching on the first body component area set and the interference source area set to obtain the second type of welding process set.

[0034] This application first performs spatial discretization processing on the welding target area of the automobile body. According to the geometric characteristics of the body structure and the operability of the welding process, the continuous welding target area is divided into several relatively independent welding area blocks.

[0035] Perform multi-dimensional feature detection and recognition for each divided welding area block. Use detection technologies (such as visual recognition, sensor monitoring, etc.) to detect the type of welding components (specify specific body components such as the frame, door, seat bracket, etc.), the welding accuracy level (distinguish different levels such as high-precision and ordinary precision according to process requirements), and the welding environment interference sources (identify environmental factors that may affect welding quality such as electromagnetic interference equipment, vibration sources, air flow disturbances, etc.). Summarize these characteristic information of each area block to form a detection and recognition result set.

[0036] Extract area sets from the detection and recognition result set according to characteristic attributes. Distinguish the first body component area set and the second body component area set based on differences such as the welding accuracy level (since the welding accuracy requirements are different for the two, different processes need to be matched subsequently); at the same time, extract the interference source area set (focus on the areas with environmental interference factors). This step sorts out the complex detection results into area sets with clear attributes through screening and classification of characteristic information, preparing for the construction of the process set in terms of characteristic classification.

[0037] Match the processes of the first body component area set and the second body component area set. According to the adaptation rules of welding methods and process parameters (such as welding current, voltage, speed, etc.) corresponding to different body component types and welding accuracy levels in the welding process knowledge base, integrate and associate the welding areas in the two area sets according to the process logic that can be executed collaboratively and meet quality requirements to form the first type of welding process set.

[0038] Perform spatial correlation matching for the first body component area set and the interference source area set. Analyze the spatial position relationship, distance, interference transmission path, etc. between the first body component area (the location of the body component to be welded) and the interference source area (the location of the environmental interference factor). Match and construct the second type of welding process set for the areas with spatial correlation (i.e., the welding area may be affected by the interference source) according to the influence law of interference on welding quality (such as the attenuation characteristics of electromagnetic interference with distance, the effect of vibration transmission on welding stability).

[0039] Extract the first body component area set, the second body component area set, and the interference source area set from the detection and recognition result set, which specifically includes the following steps: After extracting the first component welding area from the detection and recognition result set, obtain the first body component area set; after extracting the second component welding area from the detection and recognition result set, obtain the second body component area set; among them, the welding accuracy level of the first body component area set is lower than that of the second body component area set; After extracting the environmental interference area from the detection and recognition result set, obtain the interference source area set.

[0040] This application uses the detection and recognition result set as the basic data source for area set extraction. Based on the analysis of the result set, the first component welding area and the second component welding area are distinguished and extracted according to the difference in welding accuracy levels, forming the first body component area set and the second body component area set respectively. Among them, the first body component area set has a lower welding accuracy level; the second body component area set has a higher accuracy level. At the same time, the environmental interference area is extracted from the result set to construct the interference source area set, clarifying the areas that may be affected by environmental factors during the welding process.

[0041] After performing a process parameter adaptability analysis on the welding component materials in the welding target area of the automotive body, a material adaptability parameter set is obtained, which specifically includes the following steps: After counting the component material types, thicknesses, and heat treatment status information in the first body component area set and the second body component area set, a material characteristic information set is obtained; Detect the geometric dimensions, assembly gaps, and groove angles of the welding joint forms to obtain the joint form characteristic information set; Match the material characteristic information set and the joint form characteristic information set in the process knowledge base to obtain the material adaptability parameter set; among them, the material adaptability parameter set includes welding current, voltage, and welding speed parameters.

[0042] This application conducts a systematic statistics of material information with the first body component area set and the second body component area set divided within the welding target area of the automotive body as the analysis objects.

[0043] Material type: Identify specific categories such as high-strength steel, low-carbon steel, and aluminum alloy. Due to significant differences in atomic structure and physical properties (such as melting point, thermal conductivity, and thermal expansion coefficient) among different materials, it directly determines the heat input requirements, molten pool solidification characteristics, and joint mechanical properties during welding. For example, aluminum alloy has a low melting point and high thermal conductivity, and precise control of heat input is required during welding to avoid burn-through or lack of fusion; high-strength steel is more sensitive to phase transformation and hardening caused by the welding thermal cycle.

[0044] Thickness parameter: Measure and record the thickness dimensions of the components. Thickness, as a key variable, directly affects the heat conduction and energy distribution during welding. Welding thick plates requires higher heat input to ensure the penetration meets the standard, and it is prone to large residual stresses due to heat accumulation; welding thin plates requires strict restriction of heat input to prevent burn-through and excessive deformation, and the thickness difference requires targeted adjustment of process parameters.

[0045] Heat treatment status: Carefully distinguish the treatment conditions such as annealing, quenching, and tempering. Heat treatment significantly affects its welding response by changing the internal metallographic structure of the material (such as grain size and precipitate phase distribution). For example, quenched steel has high hardness and brittleness and is prone to cracking during welding, and auxiliary processes such as preheating and post-heating need to be adapted; annealed materials have good plasticity and a relatively wide welding process window.

[0046] Construct a material feature information set through comprehensive statistics and integration of the above attributes.

[0047] Synchronize the welding joint form and capture three key structural parameters using detection techniques (such as industrial CT scanning and 3D laser measurement): Geometric dimensions: Measure the joint length, width, height, and the designed contour dimensions of the weld seam to clarify the spatial form of the joint. For example, the geometric differences between lap joints and butt joints directly determine the accessibility of the welding arc and the flow path of the molten pool, thereby affecting the weld formation quality.

[0048] Assembly gap: Quantify the assembly gap between the welded components. An excessive gap is likely to cause burn-through and lack of fusion defects, requiring an increase in the welding current or the supply of filler material; a too-small gap may lead to stress concentration and unstable arc, demanding precise regulation of voltage and speed, and its value provides a direct constraint for the adaptation of process parameters.

[0049] Groove angle: Measure the inclination angles of V-shaped, X-shaped, etc. grooves. The groove angle affects the distribution of arc heat input, the shape of the molten pool, and the weld fusion ratio. A large-angle groove requires more filler metal and higher heat input to ensure the penetration depth; a small-angle groove is more sensitive to the arc stiffness and welding speed.

[0050] Construct a joint form feature information set based on the detection data to fully restore the geometric constraint conditions of the joint structure on the welding process, enabling the in-depth fit between parameter adaptation and the physical form of the joint.

[0051] Use the material feature information set and the joint form feature information set as inputs to a pre-constructed process knowledge base. This knowledge base aggregates a large amount of process data verified through experiments and accumulated from production practices, establishing an associated mapping of material properties - joint structure - process parameters: Store the parameter ranges of welding current (determining the arc thermal power and affecting the penetration depth / fusion width), voltage (relating to the arc length and stability and affecting the weld formation appearance), welding speed (controlling the heat input rate and balancing the welding efficiency and joint quality), etc. that are suitable for combinations of different material types (such as steel / aluminum), thicknesses (1 mm / 5 mm), heat treatment states (annealed / quenched), joint forms (butt / joint), geometric dimensions (gap 0.5 mm / 1 mm), and groove angles (30° / 60°).

[0052] Screen the parameter combination in the process knowledge base that best fits the current "material - joint" characteristics and output the material-adapted parameter set. This parameter set covers core variables such as welding current, voltage, and welding speed to ensure a high degree of matching between the welding heat input, molten pool formation, weld quality, and the material properties of the components and the requirements of the joint structure.

[0053] After performing a process parameter adaptability analysis on the welding joint forms in the target area of the automobile body, a joint form adaptability parameter set is obtained, specifically: The joint form adaptability parameter set is obtained by matching the material characteristic information set and the joint form characteristic information set in the process knowledge base; among them, the joint form adaptability parameter set includes the wire diameter and shielding gas flow rate parameters adapted to the joint form.

[0054] This application focuses on matching adaptability parameters for the welding joint forms of the automobile body. First, two types of core information that can reflect the material of the welded part (from the material characteristic information set, including material type, thickness, heat treatment status, etc.) and the structure of the joint itself (from the joint form characteristic information set, covering geometric dimensions, assembly gap, groove angle, etc.) are obtained. Then, relying on the process knowledge base, which has pre-stored a large number of mature welding parameter cases corresponding to different materials and joint forms. The above two types of information are input into the process knowledge base for intelligent matching, and the wire diameter (the wire diameter affects the droplet transfer and weld formation, and needs to be adapted to the melting characteristics of the material and the joint gap) and shielding gas flow rate (the shielding gas flow rate is related to the arc stability and the molten pool protection effect, and needs to match the antioxidant requirements of the material and the welding environment of the joint) and other parameters adapted to the current material and joint form are selected from the library to form the joint form adaptability parameter set, providing a basis for selecting appropriate wire and shielding gas supply parameters during the welding process and ensuring the welding quality and joint performance.

[0055] Based on the material adaptability parameter set and the joint form adaptability parameter set, the welding heat input and penetration conditions of the first type of welding process set are predicted and regulated, and the first type of process regulation parameter set and the first type of quality prediction result set are output, specifically including the following steps: Monitor the real-time parameters and cooling rate of the welding equipment in each area of the first type of welding process set to obtain the equipment status information set; Judge the heat input stability of the first body part area set and the second body part area set according to the equipment status information set and the material adaptability parameter set to obtain the heat input fluctuation level set; Regulate the welding equipment parameters according to the heat input fluctuation level set and the joint form characteristic information set to obtain the first type of process regulation parameter set; Predict the welding penetration condition and bead formation condition based on the first type of process regulation parameter set to obtain the first type of quality prediction result set.

[0056] This application monitors the real-time operating parameters (such as welding current, voltage, wire feeding speed, etc.) of the welding equipment in each welding area and the cooling rate during the welding process for the first type of welding process set by means of sensors and other monitoring means.

[0057] Based on the equipment status information set and combined with the material adaptation parameter set obtained in previous analysis (including key parameters such as welding current and voltage for different materials), the thermal input stability of the first and second body component regions is determined. The stability of the thermal input is determined by comparing the compatibility of the real-time monitored welding parameters with the material adaptation parameters, as well as the impact of the cooling rate on the thermal input. A set of thermal input fluctuation levels is then determined. For example, if the real-time welding current deviates significantly from the material adaptation current range, or if an abnormal cooling rate causes heat accumulation or excessive heat dissipation, the risk of high thermal input fluctuation is determined. This allows the thermal input status of different welding regions to be clearly defined.

[0058] Based on the heat input fluctuation level set and combined with the joint form characteristic information set (covering structural parameters that influence weld formation, such as joint geometry, assembly gap, and groove angle), welding equipment parameters are specifically controlled. If the heat input fluctuation level is high and the joint groove angle is small, which is detrimental to arc stability, parameters such as welding voltage and wire feed speed are adjusted to optimize arc shape and heat input distribution. If the joint assembly gap is large, the welding current can be appropriately increased to avoid lack of fusion defects. This dynamic adjustment generates a first-class process control parameter set, adapting welding equipment parameters to the material characteristics and joint structure, ensuring a stable welding process.

[0059] Based on the first set of process control parameters, the system uses welding process simulation and empirical models to predict weld penetration and weld bead formation. Based on controlled parameters such as welding current, voltage, and speed, combined with the material's thermophysical properties (such as melting point and thermal conductivity) and joint form constraints, the system simulates the formation, expansion, and solidification of the molten pool to determine whether penetration meets strength requirements and whether the weld distribution is uniform. Furthermore, the system analyzes weld bead width, height, and surface flatness to determine the presence of defects such as undercuts and humps. These predictions are combined into the first set of quality prediction results, providing early insights into welding quality trends and providing a basis for subsequent quality control and process optimization.

[0060] The spatial relationship between the concentrated welding position and the interference source in the second type of welding process is judged to obtain a first interference level and a second interference level, which specifically includes the following steps: comparing the distance between the concentrated welding position of the second type of welding process and the position of the interference source, and comparing the intensity of the interference source with a preset interference source intensity threshold; If the distance between the welding position in the second type of welding process and the interference source position is greater than or equal to the preset distance threshold, and the interference source intensity is greater than or equal to the preset interference source intensity threshold, it is determined to be the first interference level; otherwise it is determined to be the second interference level.

[0061] This application targets the second type of welding process set (welding processes involving the spatial association between body components and interference sources). First, it obtains the welding position and interference source position information and calculates and compares the distances between them. At the same time, it detects the interference source intensity and compares it with a preset intensity threshold. When the distance between the welding position and the interference source is ≥ the preset distance threshold and the interference source intensity is ≥ the preset intensity threshold, it is determined as the first interference level (strong interference scenario, which has a great impact on welding quality); if these two conditions are not met, it is determined as the second interference level (weak interference or no interference scenario, which has a relatively small impact on welding quality). Through this dual-dimensional judgment based on distance and intensity, different interference degrees are distinguished, providing a basis for formulating targeted anti-interference welding processes and ensuring welding quality subsequently.

[0062] According to the first interference level, predict and regulate the welding spatter amount and porosity incidence rate for the second type of welding process set, and output the second type of process regulation parameter set and the second type of quality prediction result set. The specific steps are as follows: Detect the intensity, frequency, and action range of each interference source in the interference source area set to obtain the interference source characteristic information set; Introduce anti-interference parameters according to the first interference level and the interference source characteristic information set to obtain the second type of process regulation parameter set; Predict the welding spatter rate and porosity condition according to the second type of process regulation parameter set to obtain the second type of quality prediction result set.

[0063] This application constructs a multi-dimensional interference characteristic cognition for the interference source area set associated with the second type of welding process set by using detection technologies. With the help of equipment such as electromagnetic spectrum analyzers and vibration acceleration sensors, capture the interference source intensity and interference frequency. For example, electromagnetic interference is quantified by electric field intensity (V / m) and magnetic field intensity (A / m), and vibration interference is measured by acceleration (m / s²) to clarify the force threshold of the interference on the welding process; identify the pulse frequency of electromagnetic interference and the natural frequency of vibration interference, etc. Different frequencies will resonate or couple with the welding arc and molten pool, thus affecting welding stability; delimit the three-dimensional space range effectively affected by the interference through spatial field strength distribution tests and vibration propagation attenuation experiments, and determine the boundary conditions for the welding area to be affected by the interference.

[0064] Based on the first interference level (strong interference scenario), the system uses the interference source feature information set as a guide to match anti-interference parameters with the welding process knowledge base. The knowledge base contains a large number of experimentally validated "interference type-control strategy" association rules. For example, when encountering high-frequency electromagnetic interference (e.g., 10kHz-100kHz), which can easily cause the arc to oscillate, dynamic arc control parameters are introduced. For example, adjusting the welding power supply output waveform to a pulsed square wave optimizes arc stiffness and stability to offset electromagnetic interference on arc shape. Mechanical vibration interference (e.g., frequencies of 20Hz-200Hz) can cause liquid metal splashing and gas entrainment in the molten pool. Therefore, molten pool shape stabilization parameters are introduced. These include adjusting the wire feed speed fluctuation compensation coefficient (e.g., setting a real-time compensation of ±5%) and optimizing the shielding gas turbulence suppression device (e.g., adding a gas rectifier). These parameters can be used to modify the stress state and gas environment of the molten pool to reduce the impact of vibration. By screening, combining, and fine-tuning various anti-interference parameters, a second set of process control parameters tailored to the current strong interference environment is generated, enabling the welding process system to proactively resist interference.

[0065] A multi-physics coupled welding quality prediction model was constructed based on the second-category process control parameter set. Computational fluid dynamics (CFD) was used to simulate the flow behavior of the molten pool. The arc heat input and the molten pool temperature field distribution were calculated based on the controlled welding current and voltage parameters. The probability of spatter generation during droplet transfer was deduced by combining information on interference source characteristics (such as the frequency of molten pool disturbance caused by vibration frequency). When electromagnetic interference causes the arc blow angle to exceed 5°, the droplet spatter rate increases from the normal 2%-3% to 8%-10%. Anti-interference parameters stabilize the arc and control the blow angle to within 3°, bringing the spatter rate back to an acceptable range. A machine learning model (such as a random forest or BP neural network) was also constructed. Using historical welding data (porosity counts under different interference intensities and control parameters) as training samples, the model inputs the current interference characteristics and control parameters to predict the number, size, and distribution of pores. Increasing the shielding gas flow rate from 15 L / min to 20 L / min due to interference control can reduce the porosity rate from 5% to below 1%. The spatter rate prediction results (such as spatter particle size and density distribution) and the porosity prediction results (such as porosity type and location probability) are integrated to form a second type of quality prediction result set, which intuitively presents the welding quality trend after anti-interference control and provides a decision-making basis for adjusting the process in advance and avoiding quality risks in actual production.

[0066] The second type of welding process set is subjected to prediction and control of the welding stress concentration factor and deformation according to the second interference level, and a third type of process control parameter set and a third type of quality prediction result set are output, specifically comprising the following steps: Predicting welding stress and welding deformation based on the second interference level and interference source feature information set to obtain a stress and deformation prediction set; Adjust the welding equipment parameters according to the stress and deformation prediction set and the material characteristic information set to obtain the third type of process adjustment parameter set; Predict the welding residual stress and the welding deformation amount based on the third type of process adjustment parameter set to obtain the third type of quality prediction result set.

[0067] Based on the second interference level (weak interference or specific interference scenario determination result), this application combines the interference source characteristic information set (including interference intensity, frequency, action range) and uses multi-physics field simulation technology (such as thermal-mechanical coupling simulation) to predict the welding stress and the welding deformation amount.

[0068] Judge the synergistic effect between the interference source (such as weak vibration, low-intensity electromagnetic interference) and the welding heat input. The interference may cause uneven heat transfer during the welding process (such as vibration causing disordered molten pool flow and changing local heat accumulation). Combine the thermal expansion coefficient and elastic modulus of the material (from the material characteristic information set) to simulate the generation, transfer, and concentration process of thermal stress. For example, during aluminum alloy welding, weak vibration interference may increase the stress concentration coefficient in the heat-affected zone from 1.2 to 1.5. Identify the stress concentration area (such as the root of the joint, the edge of the groove) through simulation calculation of the stress distribution contour map.

[0069] Predict the welding deformation trend based on the spatial action range of the interference source and the stiffness characteristics of the material. The interference may change the solidification sequence of the molten pool (such as electromagnetic interference causing the arc to shift, resulting in differences in local cooling rates). Combine the geometric constraints of the joint form (such as butt joint, fillet joint) to simulate the deformation amount (such as the angle of angular deformation, the longitudinal shrinkage amount). For example, during the welding of low-carbon steel fillet joints, weak electromagnetic interference may increase the angular deformation amount from 0.5° to 1.2°. Output the deformation amount value and distribution through simulation. Integrate the stress and deformation prediction results to form the stress and deformation prediction set.

[0070] Under the guidance of the stress and deformation prediction set, link the material characteristic information set (material type, thickness, heat treatment state) and adjust the welding equipment parameters: Stress concentration control: If the prediction result shows that the stress concentration coefficient at the root of the joint is too high (such as exceeding the allowable value of 1.5), adjust the welding parameters in combination with the fracture toughness of the material (such as high-strength steel needs to control stress concentration to avoid cracking). For example, reduce the welding current gradient (such as from 20A / s to 10A / s), and extend the residence time of the arc in the stress concentration area to make the heat input more uniform; or optimize the welding sequence (such as changing from continuous welding to segmental backstep welding) to disperse the stress distribution.

[0071] Deformation control: If the predicted deformation exceeds the tolerance requirements (for example, the welding deformation of body components needs to be controlled within ±0.5 mm), adjust the welding heat input and cooling strategy according to the thermophysical properties of the material. For example, for thick aluminum alloy components, increase the interlayer cooling time (such as extending from 30 s to 60 s) to inhibit the accumulation of thermal deformation; or introduce pre-deformation compensation (such as presetting the reverse deformation angle) to offset the welding deformation.

[0072] Generate the third set of process control parameters through such targeted control, enabling the welding process to actively adapt to the stress and deformation control requirements and suppressing the generation of excessive stress and deformation from the source.

[0073] Based on the third set of process control parameters, use the thermal-mechanical coupling simulation and quality prediction model again to conduct verification predictions on the welding residual stress and welding deformation: Residual stress prediction: Simulate the welding thermal cycle and stress release process after control, and combine the stress relaxation characteristics of the material (such as the stress relaxation rate of annealed material is faster than that of quenched material) to predict the final distribution of residual stress (such as the reduction amplitude of stress peak and the improvement effect of stress uniformity). For example, after control, the stress concentration coefficient at the root of the joint is reduced from 1.5 to 1.2, meeting the allowable stress requirements.

[0074] Deformation prediction: Analyze the influence of control parameters on the solidification and thermal shrinkage of the molten pool, and combine the continuous action of interference sources to predict whether the deformation converges within the tolerance range (such as the angular deformation drops from 1.2° to 0.6°, meeting the body assembly requirements), and integrate the prediction results of residual stress and deformation to form the third set of quality prediction results.

[0075] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art can understand and implement it without creative labor.

[0076] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus the necessary general hardware platform, and of course, also 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, which can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., including several instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0077] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements 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. An intelligent control system applicable to automobile body welding manufacturing, characterized in that, Including: Classification module: After identifying and classifying the types of welding components, welding accuracy levels, and welding environment interference sources in the welding target area of the automotive body, a first type of welding process set and a second type of welding process set are obtained; Analysis module: After performing process parameter adaptability analysis on the materials of the welding components in the welding target area of the automotive body, a material adaptability parameter set is obtained. After performing process parameter adaptability analysis on the welding joint forms in the welding target area of the automotive body, a joint form adaptability parameter set is obtained; Judgment module: Judging the spatial relationship between the welding positions and interference sources in the second type of welding process set to obtain a first interference level and a second interference level; Processing module: Processing and analyzing the material adaptability parameter set, joint form adaptability parameter set, first type of welding process set, second type of welding process set, first interference level, and second interference level to obtain a quality prediction status set; Evaluation module: Evaluating the welding quality of the welding target area of the automotive body according to the quality prediction status set to obtain a welding quality warning result.

2. The intelligent control system applicable to the welding manufacturing of an automobile body according to claim 1, wherein, Processing and analyzing the material adaptability parameter set, joint form adaptability parameter set, first type of welding process set, second type of welding process set, first interference level, and second interference level to obtain a quality prediction status set, which specifically includes the following steps: Based on the material adaptability parameter set and the joint form adaptability parameter set, predicting the welding penetration status and bead formation status of the first type of welding process set to obtain a first type of quality prediction result set; Predicting the welding spatter rate and porosity status of the second type of welding process set according to the first interference level to obtain a second type of quality prediction result set; Predicting the welding residual stress and welding deformation amount of the second type of welding process set according to the second interference level to obtain a third type of quality prediction result set; Among them, the first type of quality prediction result set, the second type of quality prediction result set, and the third type of quality prediction result set are combined to form a quality prediction status set.

3. An intelligent control system applicable to automobile body welding manufacturing according to claim 2, characterized in that, After identifying and classifying the types of welding components, welding accuracy levels, and welding environment interference sources in the welding target area of the automotive body, a first type of welding process set and a second type of welding process set are obtained, which specifically includes the following steps: Dividing the welding target area of the automotive body into several welding area blocks; Detecting and identifying the types of welding components, welding accuracy levels, and welding environment interference sources of each welding area block to obtain a detection and identification result set; Extracting a first body component area set, a second body component area set, and an interference source area set from the detection and identification result set; Performing process matching on the first body component area set and the second body component area set to obtain a first type of welding process set; Performing spatial correlation matching on the first body component area set and the interference source area set to obtain a second type of welding process set.

4. An intelligent control system applicable to automobile body welding manufacturing according to claim 3, characterized in that, Extracting a first body component area set, a second body component area set, and an interference source area set from the detection and identification result set, which specifically includes the following steps: After extracting the first component welding area from the detection and recognition result set, a first vehicle body component area set is obtained; after extracting the second component welding area from the detection and recognition result set, a second vehicle body component area set is obtained; wherein, the welding accuracy level of the first vehicle body component area set is lower than that of the second vehicle body component area set. After extracting the environmental interference area from the detection and recognition result set, an interference source area set is obtained.

5. An intelligent control system applicable to automobile body welding manufacturing according to claim 4, characterized in that, After performing process parameter adaptability analysis on the welding component materials of the vehicle body welding target area, a material adaptability parameter set is obtained, which specifically includes the following steps: After counting the component material types, thicknesses, and heat treatment status information in the first vehicle body component area set and the second vehicle body component area set, a material characteristic information set is obtained. Detect the geometric dimensions, assembly gaps, and groove angles of the welding joint form to obtain a joint form characteristic information set. Match according to the material characteristic information set and the joint form characteristic information set in the process knowledge base to obtain a material adaptability parameter set; wherein, the material adaptability parameter set includes welding current, voltage, and welding speed parameters.

6. The intelligent control system applicable to automobile body welding manufacturing according to claim 5, characterized in that, After performing process parameter adaptability analysis on the welding joint form of the vehicle body welding target area, a joint form adaptability parameter set is obtained, specifically: Match according to the material characteristic information set and the joint form characteristic information set in the process knowledge base to obtain a joint form adaptability parameter set; wherein, the joint form adaptability parameter set includes the wire diameter and shielding gas flow rate parameters adapted to the joint form.

7. An intelligent control system applicable to automotive body welding manufacturing according to claim 6, characterized in that, Based on the material adaptability parameter set and the joint form adaptability parameter set, predict and control the welding heat input and penetration conditions of the first type of welding process set, and output a first type of process control parameter set and a first type of quality prediction result set, which specifically includes the following steps: Monitor the real-time parameters and cooling rate of the welding equipment in each area of the first type of welding process set to obtain an equipment status information set. Judge the heat input stability of the first vehicle body component area set and the second vehicle body component area set according to the equipment status information set and the material adaptability parameter set to obtain a heat input fluctuation level set. Regulate the welding equipment parameters according to the heat input fluctuation level set and the joint form characteristic information set to obtain a first type of process control parameter set. Predict the welding penetration condition and bead formation condition based on the first type of process control parameter set to obtain a first type of quality prediction result set.

8. An intelligent control system applicable to automobile body welding manufacturing according to claim 7, characterized in that, Judge the spatial relationship between the welding position and the interference source in the second type of welding process set to obtain a first interference level and a second interference level, which specifically includes the following steps: Compare the distance between the welding position and the interference source position in the second type of welding process set, and compare the interference source intensity with the preset interference source intensity threshold. If the distance between the welding position and the interference source position in the second type of welding process set is greater than or equal to the preset distance threshold, and the interference source intensity is greater than or equal to the preset interference source intensity threshold, it is determined as the first interference level; otherwise, it is determined as the second interference level.

9. An intelligent control system applicable to automotive body welding manufacturing according to claim 8, characterized in that, Predict and control the welding spatter amount and porosity rate of the second type of welding process set according to the first interference level, and output a second type of process control parameter set and a second type of quality prediction result set, which specifically includes the following steps: Detect the intensity, frequency, and action range of each interference source in the interference source area to obtain an interference source feature information set; Introduce anti-interference parameters according to the first interference level and the interference source feature information set to obtain a second type of process control parameter set; Predict the welding spatter rate and porosity condition according to the second type of process control parameter set to obtain a second type of quality prediction result set.

10. An intelligent control system applicable to automobile body welding manufacturing according to claim 9, characterized in that, Predict and control the welding stress concentration coefficient and deformation amount of the second type of welding process set according to the second interference level, and output a third type of process control parameter set and a third type of quality prediction result set, which specifically include the following steps: Predict the welding stress and welding deformation amount based on the second interference level and the interference source feature information set to obtain a stress and deformation prediction set; Regulate the welding equipment parameters according to the stress and deformation prediction set and the material feature information set to obtain a third type of process control parameter set; Predict the welding residual stress and welding deformation amount based on the third type of process control parameter set to obtain a third type of quality prediction result set.

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