An intelligent welding control method for automobile parts

Abstract

This invention provides an intelligent welding control method for automotive parts, relating to the field of automotive parts welding control technology. The method includes acquiring welding task information and welding reference process parameters for the automotive parts to be welded. By acquiring the welding task information and welding reference process parameters, this intelligent welding control method constructs an initial set of welding control parameters and combines this with pre-welding inspection data to characterize the state of the welding object, achieving targeted adaptation of welding control parameters before welding. During the welding process, it further combines multi-dimensional monitoring data such as current, voltage, molten pool image, temperature, spatter state, and welding torch posture to perform real-time analysis and forward prediction of the welding formation state, thereby achieving advance compensation and dynamic updating of welding parameters for the next control cycle. This improves the adaptive control capability, welding formation consistency, and welding quality stability of the automotive parts welding process.

Description

Technical Field

[0001] This invention relates to the field of automotive component welding control technology, specifically to an intelligent welding control method for automotive components. Background Technology

[0002] In the manufacturing process of automotive parts, welding is a crucial step in achieving structural connections and assembly, widely used in the production of body structural components, chassis components, bracket components, and other metal connectors. Current automotive parts welding production typically operates based on preset process parameters. These parameters include welding current, voltage, welding speed, wire feed speed, and shielding gas flow rate, all predetermined according to the material type, plate thickness, weld type, and assembly requirements of the object being welded. The corresponding welding process is then completed by program-controlled equipment. Some existing technologies also incorporate sensor detection methods to monitor local conditions during the welding process, assisting welding equipment in automating the welding operation.

[0003] However, existing technologies still have significant shortcomings in practical applications. When the assembly gap, bevel condition, surface cleanliness, material batch, positioning accuracy, or on-site working conditions of the welded object fluctuate, the preset parameter control method is difficult to adapt to changes in the welding state in a timely manner, which can easily lead to problems such as unstable weld formation, incomplete penetration, burn-through, porosity, slag inclusion, increased spatter, and welding deformation. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides an intelligent welding control method for automotive parts. The technical problem this invention aims to solve is: how to address the issue of unstable weld formation and high defect risk caused by the inability of preset parameter control to adapt to changes in welding conditions in a timely manner by integrating intelligent welding control methods that combine pre-welding information adaptation, real-time process monitoring, forming prediction, and dynamic parameter compensation.

[0005] To achieve the above objectives, the present invention provides the following technical solution: an intelligent welding control method for automotive parts, comprising:

[0006] S1. Obtain welding task information and welding reference process parameters for the automotive parts to be welded. The welding task information includes welding joint type, base material type, plate thickness parameters, weld trajectory information, and target weld formation requirements. Based on the welding task information and the welding reference process parameters, determine the initial welding control parameter set corresponding to the automotive parts to be welded. The initial welding control parameter set includes welding current parameters, welding voltage parameters, welding speed parameters, wire feed speed parameters, shielding gas flow rate parameters, and welding torch movement parameters.

[0007] S2. Obtain the pre-welding inspection data corresponding to the automotive parts to be welded. The pre-welding inspection data includes assembly gap data, bevel status data, surface cleanliness data, material batch data, positioning accuracy data, and welding environment status data. Based on the pre-welding inspection data, determine the set of welding object status characterization parameters, and perform pre-welding adaptation processing on the initial welding control parameter set based on the set of welding object status characterization parameters to obtain the target welding control parameter set.

[0008] S3. Perform welding operations according to the target welding control parameter set, and simultaneously acquire welding process monitoring data during the welding process. The welding process monitoring data includes welding current feedback data, welding voltage feedback data, molten pool image data, welding zone temperature data, spatter status data, and welding torch current position data; determine the welding process status feedback parameter set based on the welding process monitoring data;

[0009] S4. Based on the set of welding object state characterization parameters, the set of welding process state feedback parameters, and the current position data of the welding torch, determine a preset look-ahead interval along the direction of the welding torch's movement along the weld trajectory, and generate a set of welding formation prediction parameters corresponding to the preset look-ahead interval. The set of welding formation prediction parameters is used to characterize the trends of weld penetration, weld width, heat input, and welding defect occurrence within the preset look-ahead interval. According to the set of welding formation prediction parameters, determine a set of welding control corrections for the next control cycle. The set of welding control corrections is used to compensate for at least one parameter in the target welding control parameter set in advance.

[0010] S5. Update the target welding control parameter set according to the welding control correction set, and execute the welding operation of the next control cycle based on the updated target welding control parameter set. Repeat S3-S5 until the welding process of the automotive parts to be welded is completed.

[0011] Preferably, the determination of the initial welding control parameter set includes: calling the corresponding reference parameter group from a pre-established welding reference process library according to the welding joint type, the base material category and the plate thickness parameter; and initializing the welding current parameter, welding voltage parameter, welding speed parameter, wire feed speed parameter, shielding gas flow rate parameter and welding torch motion parameter in the reference parameter group according to the weld trajectory information and the target weld formation requirements, to obtain the initial welding control parameter set. The welding torch motion parameters include welding torch height parameter, welding torch oscillation parameter and welding torch travel posture parameter.

[0012] Preferably, the assembly gap data and the bevel status data are obtained by a laser displacement detection device, the surface cleanliness data are obtained by an industrial vision inspection device, the material batch data are obtained by a batch traceability identification reading device, the positioning accuracy data are obtained by a fixture positioning detection device, and the welding environment status data are obtained by a temperature and humidity detection device and an airflow detection device.

[0013] Preferably, the set of welding object state characterization parameters includes joint gap characteristic values, groove geometric characteristic values, surface contamination level parameters, material batch correction coefficient, positioning deviation, and environmental disturbance coefficient. The joint gap characteristic values ​​include the gap mean and gap fluctuation. The groove geometric characteristic values ​​include the groove angle, groove depth, and blunt edge dimension. The positioning deviation includes the weld centerline offset and workpiece posture deviation.

[0014] Preferably, the pre-welding adaptation process includes: comparing the set of welding object state characterization parameters with the set of reference state parameters corresponding to the welding reference process parameters, determining the pre-welding adaptation amount of welding current parameter, welding voltage parameter, welding speed parameter, wire feed speed parameter, shielding gas flow rate parameter, and welding torch movement parameter, and loading each pre-welding adaptation amount into the initial welding control parameter set to obtain the target welding control parameter set.

[0015] Preferably, the determination of the welding process state feedback parameter set includes: actual heat input parameters calculated based on the welding current feedback data, the welding voltage feedback data, and the welding speed parameters; molten pool length parameters, molten pool width parameters, and molten pool area parameters extracted based on the molten pool image data; temperature gradient parameters extracted based on the welding zone temperature data; spatter frequency parameters extracted based on the spatter state data; and welding torch offset parameters determined based on the welding torch current pose data.

[0016] Preferably, the preset look-ahead interval is determined based on the current pose data of the welding torch, the weld trajectory information, the current welding speed parameter, and the preset look-ahead time length; the starting point of the preset look-ahead interval is the weld trajectory position corresponding to the welding torch at the end of the current control cycle, and the ending point of the preset look-ahead interval is the weld trajectory position corresponding to the product of the current welding speed parameter and the preset look-ahead time length along the weld trajectory forward direction from the starting point.

[0017] Preferably, the generation of the welding forming prediction parameter set includes: performing time-series correlation analysis on the welding process state feedback parameter set of the current control cycle and the previous several control cycles, and combining the welding object state characterization parameter set and the current position data of the welding torch to calculate the predicted penetration depth parameter, predicted weld width parameter, predicted heat input parameter and predicted defect risk parameter within the preset look-ahead interval. The predicted defect risk parameter includes at least one of the following: incomplete penetration risk parameter, burn-through risk parameter, porosity risk parameter, slag inclusion risk parameter, spatter abnormality risk parameter and welding deformation risk parameter.

[0018] Preferably, the welding control correction set includes at least one of current correction, voltage correction, welding speed correction, wire feed speed correction, shielding gas flow correction, welding torch height correction, and welding torch lateral offset correction; the determination of the welding control correction set includes: comparing the predicted penetration depth parameter, the predicted penetration width parameter, the predicted heat input parameter, and the predicted defect risk parameter with the corresponding target thresholds, and determining the corresponding welding control correction according to a preset parameter-risk mapping rule.

[0019] Preferably, the update of the target welding control parameter set includes: applying amplitude and rate of change constraints to each welding control correction, so that the updated welding current parameter, welding voltage parameter, welding speed parameter, wire feed speed parameter, shielding gas flow rate parameter, and welding torch motion parameter are all within the corresponding process allowable range; when the predicted defect risk parameter is higher than the preset risk threshold, the welding speed parameter and welding current parameter are updated first, and when the predicted defect risk parameter is higher than the preset risk threshold for two consecutive control cycles, a speed reduction process or a welding suspension process is performed.

[0020] This invention provides an intelligent welding control method for automotive parts. It has the following beneficial effects:

[0021] This intelligent welding control method for automotive parts acquires welding task information and welding reference process parameters to construct an initial set of welding control parameters. It then combines pre-welding inspection data to characterize the state of the welding object, achieving targeted adaptation of welding control parameters before welding. During the welding process, it further integrates multi-dimensional monitoring data such as current, voltage, molten pool image, temperature, spatter status, and welding torch posture to perform real-time analysis and forward prediction of the welding formation state. This enables advance compensation and dynamic updating of welding parameters for the next control cycle, improving the adaptive control capability, welding formation consistency, and welding quality stability of the automotive parts welding process.

[0022] The technical solution, which combines pre-welding condition detection, process feedback monitoring, look-ahead interval prediction, and closed-loop correction control, can adjust welding current, voltage, welding speed, wire feed speed, shielding gas flow rate, and welding torch motion parameters in a timely manner based on assembly gap, bevel condition, surface cleanliness, environmental disturbances, and changes in heat input, molten pool, and defect risk during the welding process. This reduces the risk of defects such as incomplete penetration, burn-through, porosity, slag inclusion, abnormal spatter, and welding deformation. At the same time, it helps to improve the process adaptability, process stability, and intelligent control level of welding operations for complex automotive parts. Attached Figure Description

[0023] Figure 1 This is an overall flowchart of the method of the present invention;

[0024] Figure 2 This is a flowchart illustrating the determination of initial welding control parameters for this invention.

[0025] Figure 3 This is a flowchart of the pre-welding adaptation process of the present invention;

[0026] Figure 4 This is a flowchart of the welding process monitoring and feedback for the present invention;

[0027] Figure 5 This is a flowchart of the forward-looking interval prediction and correction quantity generation process of the present invention;

[0028] Figure 6 This is a flowchart of the control parameter update and loop process of the present invention. Detailed Implementation

[0029] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0030] Example 1

[0031] like Figure 1-6 As shown, this embodiment of the invention provides an intelligent welding control method for automotive parts, including:

[0032] S1. Obtain the welding task information and welding reference process parameters for the automotive parts to be welded. The welding task information includes the welding joint type, base material category, plate thickness parameters, weld trajectory information, and target weld formation requirements. Based on the welding task information and welding reference process parameters, determine the initial welding control parameter set corresponding to the automotive parts to be welded. The initial welding control parameter set includes welding current parameters, welding voltage parameters, welding speed parameters, wire feed speed parameters, shielding gas flow rate parameters, and welding torch movement parameters. Determining the initial welding control parameter set includes: according to the welding joint type, base material category, and plate thickness parameters, calling the corresponding reference parameter group from the pre-established welding reference process library, and initializing the welding current parameters, welding voltage parameters, welding speed parameters, wire feed speed parameters, shielding gas flow rate parameters, and welding torch movement parameters in the reference parameter group according to the weld trajectory information and target weld formation requirements, to obtain the initial welding control parameter set. The welding torch movement parameters include welding torch height parameters, welding torch oscillation parameters, and welding torch travel posture parameters.

[0033] S2. Obtain pre-welding inspection data for the automotive parts to be welded. This data includes assembly gap data, bevel condition data, surface cleanliness data, material batch data, positioning accuracy data, and welding environment condition data. Based on the pre-welding inspection data, determine the set of welding object state characterization parameters. Then, adapt the initial welding control parameter set to this set to obtain the target welding control parameter set. Assembly gap data and bevel condition data are obtained using a laser displacement detection device; surface cleanliness data is obtained using an industrial vision inspection device; material batch data is obtained using a batch traceability identification reading device; positioning accuracy data is obtained using a fixture positioning detection device; and welding environment condition data is obtained using a temperature and humidity detection device and an airflow detection device. The welding object state characterization parameter set includes joint gap characteristic values, bevel geometric characteristic values, surface contamination level parameters, material batch correction coefficients, positioning deviation, and environmental disturbance coefficients. Joint gap characteristic values ​​include the average gap and gap fluctuation; bevel geometric characteristic values ​​include the bevel angle, bevel depth, and blunt edge dimension; and positioning deviation includes the weld centerline offset and workpiece posture deviation. The pre-welding adaptation process includes: comparing the set of parameters representing the state of the welding object with the set of reference state parameters corresponding to the welding reference process parameters, determining the pre-welding adaptation values ​​for welding current, welding voltage, welding speed, wire feed speed, shielding gas flow rate, and welding torch movement, and loading each pre-welding adaptation value into the initial welding control parameter set to obtain the target welding control parameter set.

[0034] S3. Execute the welding operation according to the target welding control parameter set, and simultaneously acquire welding process monitoring data during the welding process. The welding process monitoring data includes welding current feedback data, welding voltage feedback data, molten pool image data, welding zone temperature data, spatter status data, and welding torch current position data. Determine the welding process status feedback parameter set based on the welding process monitoring data. The determination of the welding process status feedback parameter set includes: actual heat input parameters calculated from welding current feedback data, welding voltage feedback data, and welding speed parameters; molten pool length, molten pool width, and molten pool area parameters extracted from molten pool image data; temperature gradient parameters extracted from welding zone temperature data; spatter frequency parameters extracted from spatter status data; and welding torch offset parameters determined from the welding torch current position data.

[0035] S4. Based on the set of welding object state characterization parameters, the set of welding process state feedback parameters, and the current position data of the welding torch, a preset look-ahead interval is determined along the weld trajectory, and a set of welding formation prediction parameters corresponding to the preset look-ahead interval is generated. This set of welding formation prediction parameters characterizes the trends in weld penetration, weld width, heat input, and the risk of welding defects within the preset look-ahead interval. Based on the set of welding formation prediction parameters, a set of welding control corrections for the next control cycle is determined. This set of welding control corrections is used to compensate for at least one parameter in the target welding control parameter set in advance. The preset look-ahead interval is determined based on the current position data of the welding torch, weld trajectory information, the current welding speed parameter, and the preset look-ahead time length. The starting point of the preset look-ahead interval is the weld trajectory position corresponding to the welding torch at the end of the current control cycle, and the ending point is the weld trajectory position corresponding to the product of the current welding speed parameter and the preset look-ahead time length, extending along the weld trajectory direction from the starting point. The generation of the welding formation prediction parameter set includes: performing time-series correlation analysis on the welding process state feedback parameter set of the current control cycle and several previous control cycles, and combining the welding object state characterization parameter set and the current welding torch pose data to calculate the predicted penetration depth parameter, predicted weld width parameter, predicted heat input parameter, and predicted defect risk parameter within a preset look-ahead interval. The predicted defect risk parameter includes at least one of the following: incomplete penetration risk parameter, burn-through risk parameter, porosity risk parameter, slag inclusion risk parameter, spatter abnormality risk parameter, and welding deformation risk parameter. The welding control correction quantity set includes at least one of the following: current correction quantity, voltage correction quantity, welding speed correction quantity, wire feed speed correction quantity, shielding gas flow rate correction quantity, welding torch height correction quantity, and welding torch lateral offset correction quantity. The determination of the welding control correction quantity set includes: comparing the predicted penetration depth parameter, predicted weld width parameter, predicted heat input parameter, and predicted defect risk parameter with the corresponding target threshold, and determining the corresponding welding control correction quantity according to the preset parameter-risk mapping rule.

[0036] S5. Update the target welding control parameter set based on the welding control correction set, and execute the welding operation for the next control cycle based on the updated target welding control parameter set. Repeat steps S3-S5 until the welding process of the automotive parts to be welded is completed. The update of the target welding control parameter set includes: applying amplitude and rate-of-change constraints to each welding control correction value to ensure that the updated welding current, welding voltage, welding speed, wire feed speed, shielding gas flow rate, and welding torch movement parameters are all within their respective process allowable ranges. When the predicted defect risk parameter is higher than the preset risk threshold, the welding speed and welding current parameters are updated first. If the predicted defect risk parameter is higher than the preset risk threshold for two consecutive control cycles, a speed reduction or welding suspension process is implemented.

[0037] By combining pre-welding inspection data with real-time monitoring information during the welding process, welding control parameters can be dynamically adapted and continuously corrected. This improves the matching degree between parameters and actual working conditions, enhances the adaptability to assembly deviations, material fluctuations, and environmental disturbances, and also improves the accuracy of welding status identification and the timeliness of control. This helps to improve the consistency of weld formation, reduce the risk of defects such as incomplete penetration, burn-through, porosity, spatter, and welding deformation, and further improve the stability, quality traceability, and engineering applicability of automated welding processes in complex automotive component welding scenarios.

[0038] Example 2

[0039] This embodiment illustrates how, in the actual welding process of automotive parts, an initial set of welding control parameters is determined based on welding task information, and how the initial set of welding control parameters is adapted and corrected in conjunction with pre-welding inspection data to obtain a target set of welding control parameters corresponding to the actual state of the workpiece.

[0040] 1. Target audience and data sources

[0041] This embodiment selects a front longitudinal beam reinforcement plate and mounting bracket assembly from a certain automotive parts welding production line as the implementation object. The base material of the front longitudinal beam reinforcement plate and the mounting bracket is DP590 high-strength steel. The thickness of the front longitudinal beam reinforcement plate is 2.0mm, the thickness of the mounting bracket is 2.5mm, the joint type is lap fillet weld, and the total length of the weld is 356mm.

[0042] The welding task information for the product is taken from the current production work order; the welding reference process parameters are taken from the corresponding welding process card; the assembly gap data and bevel status data are taken from the pre-welding laser inspection record; the surface cleanliness data is taken from the visual inspection record; the material batch data is taken from the traceability system reading record; the positioning accuracy data is taken from the fixture inspection record; and the environmental status data is taken from the workstation temperature, humidity, and airflow detection record.

[0043] The above data all correspond to the actual records of the same workpiece in the same welding task.

[0044] 2. Obtain welding task information and determine the initial set of welding control parameters.

[0045] The welding task information includes: the welding joint type is lap fillet weld, the base material is DP590 high-strength steel, the plate thickness parameters are 2.0mm / 2.5mm, the weld trajectory is a straight section + a circular arc transition section + a short straight end section, the target weld formation requirements are a weld width of 5.5mm-6.5mm, an excess height of 0.8mm-1.2mm, and no obvious undercut, lack of fusion, or burn-through.

[0046] Based on the above-mentioned weld joint type, base material category, and plate thickness parameters, the corresponding reference parameter set is retrieved from the welding reference process library. The reference parameter set is derived from the existing welding process card of the workpiece, specifically: welding current 170A, welding voltage 19.0V, welding speed 58cm / min, wire feed speed 6.2m / min, shielding gas flow rate 18L / min, welding torch height 12.5mm, welding torch oscillation width 0.8mm, and welding torch forward tilt angle 9°.

[0047] Based on the workpiece's weld trajectory information and the target weld formation requirements, the control system initializes and corrects the reference parameter group. The weld trajectory record shows that the length of the arc transition section is 96mm, accounting for 26.97% of the total weld length, and the minimum arc radius is 82mm. The corresponding trajectory correction rules in the process card are: when the arc transition section accounts for more than 25% and the minimum arc radius is less than 90mm, the welding current increases by 4A, the welding voltage increases by 0.2V, the wire feed speed increases by 0.3m / min, and the remaining parameters remain unchanged from the reference values.

[0048] Since the target weld formation requirements are consistent with the default formation requirements in the called welding process card, no additional correction was triggered separately, and an initial set of welding control parameters was generated accordingly.

[0049] According to the initial parameter distribution record, the initial welding control parameter set is as follows: welding current 174A, welding voltage 19.2V, welding speed 58cm / min, wire feed speed 6.5m / min, shielding gas flow rate 18L / min, welding torch height 12.5mm, welding torch oscillation width 0.8mm, and welding torch forward tilt angle 9°.

[0050] 3. Obtain pre-welding inspection data

[0051] Before the actual welding, the workpiece is inspected.

[0052] Assembly gap data and bevel condition data were obtained using a laser displacement detection device. A total of 10 detection points were set along the weld direction, and the measured assembly gap values ​​at each detection point were 0.41mm, 0.44mm, 0.46mm, 0.49mm, 0.52mm, 0.55mm, 0.53mm, 0.50mm, 0.47mm, and 0.45mm, respectively.

[0053] Based on the above data, the average joint gap is 0.482 mm, and the gap fluctuation is 0.14 mm.

[0054] When synchronously detecting bevel status data, the overlapping edge contours corresponding to 10 detection points are used as the measurement objects. The bevel angle is taken as the angle between the fitted lines of the contours on both sides of the edge to be welded, the bevel depth is taken as the edge indentation depth, and the blunt edge dimension is taken as the width of the unopened section of the welded edge.

[0055] The measured bevel angles corresponding to the 10 testing points were 58.4°, 58.6°, 58.7°, 58.9°, 59.0°, 59.1°, 58.8°, 58.7°, 58.9° and 58.9° respectively. The average bevel angle was 58.8°.

[0056] The measured bevel depths at the 10 testing points were 1.20mm, 1.22mm, 1.24mm, 1.26mm, 1.28mm, 1.27mm, 1.25mm, 1.23mm, 1.22mm, and 1.23mm, respectively. The average bevel depth was 1.24mm.

[0057] The measured values ​​of the blunt edge dimensions corresponding to the 10 detection points were 0.43mm, 0.45mm, 0.46mm, 0.47mm, 0.49mm, 0.48mm, 0.46mm, 0.45mm, 0.44mm and 0.47mm, respectively. The average blunt edge dimension was 0.46mm.

[0058] The above data comes from the laser scanning record of this workpiece before welding.

[0059] Surface cleanliness data was obtained using an industrial vision inspection device. The inspection results showed that the oil stain area accounted for 1.6% and the oxide spot area accounted for 0.4% within 10mm on both sides of the weld. According to the production line surface condition grading rules, the state where the oil stain area accounted for no more than 2.0% and the oxide spot area accounted for no more than 0.5% was determined to be Level 1 pollution.

[0060] Material batch data is obtained through a batch traceability identifier reader. The reading result shows that the base material batch used for this workpiece is DP590-251118-A06. In the welding process library, DP590-250901-B01 is set as the baseline batch, with a process matching coefficient of 1.00. In the pre-stored process matching coefficient table, the material batch correction coefficient corresponding to DP590-251118-A06 is 1.02. Therefore, the read material batch correction coefficient is 1.02.

[0061] Positioning accuracy data was obtained through a fixture positioning detection device. The detection results showed that the weld centerline offset was 0.22 mm and the workpiece posture deviation was 0.6°.

[0062] Welding environment data were obtained using temperature and humidity detection devices and airflow detection devices. During the detection, the ambient temperature at the workstation was 27.1℃, the relative humidity was 61%, and the transverse airflow velocity was 0.26m / s.

[0063] The preset temperature, humidity, and airflow conversion rules in the welding process library are as follows:

[0064] When the ambient temperature is between 25℃ and 30℃, the relative humidity is between 55% and 65%, and the lateral airflow velocity is between 0.20m / s and 0.30m / s, the environmental disturbance coefficient is taken as 1.03.

[0065] When the ambient temperature is between 20℃ and 25℃, the relative humidity is between 45% and 55%, and the lateral airflow velocity is not higher than 0.20m / s, the environmental disturbance coefficient is taken as 1.00.

[0066] Since the measured values ​​fall within the aforementioned ranges of 25℃-30℃, 55%-65%, and 0.20m / s-0.30m / s, the calculated environmental disturbance coefficient is 1.03.

[0067] Based on the above pre-welding inspection data, the set of parameters characterizing the welding state of this workpiece is determined as follows: joint gap characteristic values ​​are 0.482mm and 0.14mm, bevel geometric characteristic values ​​are 58.8°, 1.24mm and 0.46mm, surface contamination level parameter is level 1, material batch correction coefficient is 1.02, positioning deviation is 0.22mm and 0.6°, and environmental disturbance coefficient is 1.03.

[0068] 4. Pre-welding adaptation treatment and set of target welding control parameters

[0069] The above set of parameters characterizing the state of the welding object is compared with the set of reference state parameters corresponding to the welding reference process parameters. The set of reference state parameters is derived from the welding process card corresponding to this workpiece, and specifically includes: average reference gap 0.35mm, reference gap fluctuation 0.08mm, reference bevel angle 60.0°, reference bevel depth 1.30mm, reference blunt edge dimension 0.40mm, reference surface contamination level 0, reference material batch correction factor 1.00, reference weld centerline offset not greater than 0.15mm, reference workpiece posture deviation not greater than 0.5°, and reference environmental disturbance factor 1.00.

[0070] The comparison results show that the actual assembly gap of this workpiece is higher than that of the reference condition, the gap fluctuation is larger, there is slight contamination on the surface, the center line of the weld is offset to a certain extent, and the on-site environmental disturbance is slightly higher than that of the reference condition.

[0071] Based on the above comparison results, the control system determines the pre-welding adaptation amount corresponding to each welding control parameter according to the pre-welding adaptation rule table. The pre-welding adaptation rule table includes at least the following rule entries: when the average joint gap is greater than the average reference gap and the gap fluctuation is greater than the reference gap fluctuation, the welding current increases by 4A, the welding voltage increases by 0.2V, the welding speed decreases by 3cm / min, and the wire feed speed increases by 0.3m / min; when the surface contamination level is level 1, the shielding gas flow rate increases by 1L / min; when the weld centerline offset is greater than 0.20mm, the welding torch height decreases by 0.2mm, and the welding torch oscillation width increases by 0.2mm; when the workpiece posture deviation is greater than 0.5°, the welding torch tilt angle decreases by 1°; when the environmental disturbance coefficient is greater than 1.00, the shielding gas flow rate increases by another 1L / min. If the same welding control parameter is affected by multiple state deviation factors simultaneously, the corresponding correction amounts are superimposed, and the result of the superposition is taken as the final pre-welding adaptation amount of the welding control parameter.

[0072] According to the above rules, in this pre-welding adaptation process, the average joint gap and gap fluctuation are too large, resulting in corrections of +4A, +0.2V, -3cm / min, and +0.3m / min for welding current, welding voltage, welding speed, and wire feed speed, respectively. The surface contamination level is level 1, resulting in a correction of +1L / min for shielding gas flow rate. The weld centerline offset is 0.22mm, resulting in corrections of -0.2mm and +0.2mm for welding torch height and welding torch swing width, respectively. The workpiece posture deviation is 0.6°, resulting in a correction of -1° for welding torch tilt angle. The environmental disturbance coefficient is 1.03, resulting in a further correction of +1L / min for shielding gas flow rate. The final pre-welding adaptation values ​​for each parameter are as follows: welding current parameter pre-welding adaptation value is +4A, welding voltage parameter pre-welding adaptation value is +0.2V, welding speed parameter pre-welding adaptation value is -3cm / min, wire feed speed parameter pre-welding adaptation value is +0.3m / min, shielding gas flow rate parameter pre-welding adaptation value is +2L / min, welding torch height parameter pre-welding adaptation value is -0.2mm, welding torch oscillation width adaptation value is +0.2mm, and welding torch tilt angle adaptation value is -1°.

[0073] After loading the above pre-welding adaptation parameters into the initial welding control parameter set, the target welding control parameter set for this workpiece is obtained, which is as follows: welding current 178A, welding voltage 19.4V, welding speed 55cm / min, wire feed speed 6.8m / min, shielding gas flow rate 20L / min, welding torch height 12.3mm, welding torch oscillation width 1.0mm, and welding torch forward tilt angle 8°.

[0074] In this embodiment, the control system combines welding task information and pre-welding inspection data to perform a two-stage correction on the benchmark parameter set, ultimately obtaining a target welding control parameter set that matches the actual assembly state of the workpiece and the on-site working conditions. This demonstrates that the method of the present invention can achieve the coordinated application of welding task information and pre-welding measured conditions, providing a more targeted and adaptable parameter basis for subsequent welding execution.

[0075] Example 3

[0076] This embodiment aims to illustrate how, based on the completion of pre-welding adaptation, real-time monitoring of the welding process status, forward prediction of welding forming trends, and dynamic correction of welding control parameters can be used to achieve advance compensation for weld forming deviations and stable control of welding quality.

[0077] 1. Target objects and pre-welding conditions

[0078] In this embodiment, a certain automobile chassis reinforcement bracket and side plate assembly are selected as the welding objects.

[0079] Both welded components are made of DP590 high-strength steel. The thickness of the reinforcing bracket plate is 2.0mm, and the thickness of the side plate is 2.5mm. The joint type is lap fillet weld, and the length of a single weld is 318mm.

[0080] Before welding, welding task information acquisition and pre-welding adaptation processing were completed according to the previous steps. Pre-welding inspection results showed: the average joint gap was 0.46mm, the gap fluctuation was 0.18mm, the bevel angle was 58°, the bevel depth was 0.72mm, the blunt edge dimension was 0.41mm, the surface contamination level was level 1, the material batch correction factor was 1.03, the weld centerline offset was 0.21mm, the workpiece posture deviation was 0.8°, and the environmental disturbance factor was 0.12.

[0081] The surface contamination level is determined by classifying the area of ​​oil and oxide adhesion on the surface of the weld seam to be welded. The material batch correction coefficient is obtained by matching the incoming inspection data of the corresponding material batch with the benchmark process parameter library. The environmental disturbance coefficient is obtained by normalizing and converting the results of the lateral airflow intensity test at the welding station.

[0082] Based on the above test results, after pre-welding adaptation of the initial welding control parameters, the target welding control parameter set is as follows: welding current 236A, welding voltage 23.7V, welding speed 9.7mm / s, wire feed speed 6.3m / min, shielding gas flow rate 18L / min, welding torch height 14.0mm, and welding torch lateral offset initially set to 0mm. The system control cycle is set to 80ms, and the preset look-ahead time is set to 0.45s.

[0083] 2. Determination of the set of welding process status feedback parameters

[0084] After welding begins, the robot performs the welding operation according to the aforementioned set of target welding control parameters.

[0085] When the welding progressed to approximately 142.6 mm in weld length, the system collected a set of typical welding process monitoring data: the welding current feedback value fluctuated between 232A and 234A, the welding voltage feedback value was 23.4V-23.6V, the weld pool length obtained from the weld pool image recognition was 8.5 mm, the weld pool width was 5.8 mm, the weld pool area was 36.7 mm², the temperature gradient in the welding zone was 84℃ / mm, the spatter frequency was 3 times / s, and the current lateral offset of the welding torch was +0.29 mm. Based on the current welding speed parameters, the actual heat input was calculated to be approximately 0.57 kJ / mm.

[0086] Therefore, the system determines the set of welding process state feedback parameters corresponding to the control cycle, and associates the set of welding process state feedback parameters with the set of welding object state characterization parameters, providing an input basis for subsequent forward prediction.

[0087] 3. Determination of the preset look-ahead interval and generation of the welding forming prediction parameter set

[0088] Based on this, the system combines the set of parameters representing the state of the welding object, the set of parameters for feedback on the current welding process state, and the current position data of the welding torch to determine the preset look-ahead interval in the direction of the welding torch's advance.

[0089] Since the current welding speed is 9.7 mm / s and the preset look-ahead time is 0.45 s, the look-ahead interval length is approximately 4.37 mm. The system uses the weld trajectory point corresponding to the end position of the current control cycle as the starting point of the look-ahead interval, determining the look-ahead interval to be the section from 143.4 mm to 147.8 mm of the weld.

[0090] After performing time-series correlation analysis on the welding process status feedback parameters of the current control cycle and the three control cycles prior to it, the set of welding formation prediction parameters for the look-ahead interval is obtained: predicted penetration depth is 1.49 mm, predicted weld width is 5.4 mm, predicted heat input is 0.55 kJ / mm, incomplete penetration risk parameter is 0.78, abnormal spatter risk parameter is 0.36, and burn-through risk parameter is 0.07.

[0091] The predicted results were compared with preset target thresholds, where the lower limit of the target penetration depth was set at 1.65 mm, the target range of the penetration width was set at 5.7 mm-6.3 mm, and the incomplete penetration risk threshold was set at 0.60. Since the predicted penetration depth was lower than the target lower limit and the incomplete penetration risk parameter was higher than the risk threshold, the system determined that there was a tendency for incomplete penetration in the next control cycle, and parameter compensation needed to be performed in advance.

[0092] 4. Generation of welding control correction set and updating of target welding control parameter set

[0093] According to the preset parameter-risk mapping rule, when the predicted penetration depth is lower than the target lower limit and the incomplete penetration risk parameter is greater than 0.60, the welding current is increased and the welding speed is reduced. When the porosity risk parameter is greater than 0.60, the shielding gas flow rate is increased and the welding speed and welding torch height are appropriately reduced.

[0094] The system generates a set of welding control corrections for the next control cycle: current correction is +11A, voltage correction is +0.3V, welding speed correction is -0.7mm / s, wire feed speed correction is +0.25m / min, welding torch height correction is -0.3mm, and welding torch lateral offset correction is -0.20mm.

[0095] Subsequently, the system applies amplitude and rate of change constraints to the aforementioned correction values. In this embodiment, the upper limit of the single-cycle current correction amplitude is set to 15A, the upper limit of the welding speed correction amplitude is set to 1.0mm / s, the upper limit of the welding torch height correction amplitude is set to 0.5mm, and the upper limit of the welding torch lateral offset correction amplitude is set to 0.3mm.

[0096] After verification, the above corrections were all within the allowable range of the process, and therefore were directly applied to the original target welding control parameter set.

[0097] The updated target welding control parameters are: welding current 247A, welding voltage 24.0V, welding speed 9.0mm / s, wire feed speed 6.55m / min, shielding gas flow rate 18L / min, welding torch height 13.7mm, and welding torch lateral offset correction to -0.20mm.

[0098] 5. Verification of welding effect after parameter update

[0099] The system enters the next control cycle according to the updated target welding control parameters. The monitoring data collected again at the end of the next control cycle shows that: the welding current feedback is stable between 244A and 246A, the welding voltage feedback is between 23.9V and 24.1V, the weld pool length increases to 9.1mm, the weld pool width increases to 6.1mm, the weld pool area increases to 41.5mm², the temperature gradient in the welding zone decreases to 75℃ / mm, the spatter frequency decreases to 1 time / s, and the welding torch lateral offset decreases to +0.07mm.

[0100] After the system re-performed forward predictions, the predicted weld penetration increased to 1.79 mm, the predicted weld width was 6.0 mm, and the incomplete penetration risk parameter decreased to 0.31. This indicates that the welding control corrections generated in the previous control cycle have already compensated for subsequent welding formation deviations in advance.

[0101] 6. Control correction process under another abnormal operating condition

[0102] After the aforementioned risk of incomplete penetration is suppressed, the system will make slight adjustments to the welding control parameters based on real-time monitoring results in the subsequent control cycles, and maintain the overall stability of the welding process.

[0103] As welding continued to a point approximately 228.3 mm in length, the system detected another set of abnormal changes. A small amount of residual oil was present on the workpiece surface near this location, and the lateral airflow at the welding station briefly intensified.

[0104] The data collected during the current control cycle are as follows: welding current feedback is 236A-238A, welding voltage feedback is 23.8V-24.0V, weld pool width fluctuation range is 5.7mm-6.5mm, weld pool area fluctuation range is 37.9mm²-42.6mm², welding zone temperature gradient increases to 95℃ / mm, and spatter frequency increases to 5 times / s.

[0105] The system combines the pre-welding environmental disturbance coefficient and the current state feedback parameters to predict the look-ahead interval, obtaining a porosity risk parameter of 0.69 and a welding deformation risk parameter of 0.44.

[0106] According to the parameter-risk mapping rule, the system preferentially generates the following corrections: shielding gas flow rate correction +2L / min, welding speed correction -0.4mm / s, voltage correction +0.1V, and welding torch height correction -0.1mm.

[0107] After constraint processing, the updated target welding control parameters are: shielding gas flow rate 20L / min, welding speed 8.6mm / s, welding voltage 24.1V, welding torch height 13.6mm, and other parameters remain unchanged.

[0108] After adopting the updated target welding control parameters and entering the next control cycle, the spatter frequency decreased to 2 times / s, the molten pool width fluctuation converged to 5.9mm-6.2mm, and the predicted porosity risk parameter decreased to 0.27, indicating that the problem of insufficient local protection at this location was effectively suppressed.

[0109] 7. Parameter update strategy under continuous control cycle

[0110] In this embodiment, when the system determines that the risk parameter of incomplete penetration is higher than 0.60 in two consecutive control cycles in the 143.4mm-144.2mm and 144.2mm-145.0mm sections of the weld, the welding speed parameter and welding current parameter are updated first according to the control strategy. However, after the second update, the risk parameter has dropped from 0.78 to 0.31, so the welding pause process is not triggered and the welding can continue.

[0111] After the entire 318mm weld was completed, the weld quality was inspected. The inspection results showed that the weld surface was continuous and uniform, with no obvious undercut, burn-through, or incomplete penetration. The weld width was stable between 5.9mm and 6.3mm, the penetration depth was stable between 1.76mm and 1.91mm, and the weld reinforcement was 1.4mm to 1.8mm.

[0112] Appearance and cross-section inspections were conducted on 12 of the samples, and no defects such as porosity or slag inclusions exceeding the allowable range of the process were found.

[0113] In summary, compared with conventional welded samples in the same batch that did not employ look-ahead correction control, the amount of weld spatter in this embodiment decreased by approximately 35%, and the range of weld width fluctuation decreased by approximately 28%. This indicates that by cyclically executing the above steps, this embodiment can compensate for the parameters of the next control cycle in advance based on the real-time status during the welding process, thereby improving the stability of weld formation and the consistency of welding quality.

[0114] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for intelligent welding control of automotive parts, characterized in that, include: S1. Obtain welding task information and welding reference process parameters for the automotive parts to be welded. The welding task information includes welding joint type, base material type, plate thickness parameters, weld trajectory information, and target weld formation requirements. Based on the welding task information and the welding reference process parameters, determine the initial welding control parameter set corresponding to the automotive parts to be welded. The initial welding control parameter set includes welding current parameters, welding voltage parameters, welding speed parameters, wire feed speed parameters, shielding gas flow rate parameters, and welding torch movement parameters. S2. Obtain the pre-welding inspection data corresponding to the automotive parts to be welded. The pre-welding inspection data includes assembly gap data, bevel status data, surface cleanliness data, material batch data, positioning accuracy data, and welding environment status data. Based on the pre-welding inspection data, determine the set of welding object status characterization parameters, and perform pre-welding adaptation processing on the initial welding control parameter set based on the set of welding object status characterization parameters to obtain the target welding control parameter set. S3. Perform welding operations according to the target welding control parameter set, and simultaneously acquire welding process monitoring data during the welding process. The welding process monitoring data includes welding current feedback data, welding voltage feedback data, molten pool image data, welding zone temperature data, spatter status data, and welding torch current position data; determine the welding process status feedback parameter set based on the welding process monitoring data; S4. Based on the set of welding object state characterization parameters, the set of welding process state feedback parameters, and the current position data of the welding torch, determine the preset look-ahead interval of the welding torch along the direction of the weld trajectory, and generate the set of welding forming prediction parameters corresponding to the preset look-ahead interval. The set of welding forming prediction parameters is used to characterize the trends of melt depth change, melt width change, heat input change, and welding defect occurrence risk within the preset look-ahead interval. Based on the set of predicted welding forming parameters, a set of welding control corrections for the next control cycle is determined. The set of welding control corrections is used to compensate for at least one parameter in the set of target welding control parameters in advance. S5. Update the target welding control parameter set according to the welding control correction set, and execute the welding operation of the next control cycle based on the updated target welding control parameter set. Repeat S3-S5 until the welding process of the automotive parts to be welded is completed.

2. The intelligent welding control method for automotive parts according to claim 1, characterized in that: The determination of the initial welding control parameter set includes: calling the corresponding reference parameter group from the pre-established welding reference process library according to the welding joint type, the base material category and the plate thickness parameter; and initializing the welding current parameter, welding voltage parameter, welding speed parameter, wire feed speed parameter, shielding gas flow rate parameter and welding torch motion parameter in the reference parameter group according to the weld trajectory information and the target weld formation requirements, to obtain the initial welding control parameter set. The welding torch motion parameters include welding torch height parameter, welding torch oscillation parameter and welding torch travel posture parameter.

3. The intelligent welding control method for automotive parts according to claim 1, characterized in that: The assembly gap data and the bevel status data are obtained through a laser displacement detection device, the surface cleanliness data are obtained through an industrial vision inspection device, the material batch data are obtained through a batch traceability identification reading device, the positioning accuracy data are obtained through a fixture positioning detection device, and the welding environment status data are obtained through a temperature and humidity detection device and an airflow detection device.

4. The intelligent welding control method for automotive parts according to claim 1, characterized in that: The set of parameters characterizing the state of the welded object includes joint gap characteristic value, groove geometric characteristic value, surface contamination level parameter, material batch correction coefficient, positioning deviation, and environmental disturbance coefficient. The joint gap characteristic value includes the gap mean and gap fluctuation. The groove geometric characteristic value includes the groove angle, groove depth, and blunt edge dimension. The positioning deviation includes the weld centerline offset and workpiece posture deviation.

5. The intelligent welding control method for automotive parts according to claim 1, characterized in that: The pre-welding adaptation process includes: comparing the set of welding object state characterization parameters with the set of reference state parameters corresponding to the welding reference process parameters, determining the pre-welding adaptation amount of welding current parameter, welding voltage parameter, welding speed parameter, wire feed speed parameter, shielding gas flow rate parameter, and welding torch movement parameter, and loading each pre-welding adaptation amount into the initial welding control parameter set to obtain the target welding control parameter set.

6. The intelligent welding control method for automotive parts according to claim 1, characterized in that: The determination of the welding process state feedback parameter set includes: the actual heat input parameters calculated based on the welding current feedback data, the welding voltage feedback data, and the welding speed parameters; the molten pool length parameters, molten pool width parameters, and molten pool area parameters extracted based on the molten pool image data; the temperature gradient parameters extracted based on the welding zone temperature data; the spatter frequency parameters extracted based on the spatter state data; and the welding torch offset parameters determined based on the welding torch current pose data.

7. The intelligent welding control method for automotive parts according to claim 1, characterized in that: The preset look-ahead interval is determined based on the current pose data of the welding torch, the weld trajectory information, the current welding speed parameters, and the preset look-ahead time length. The starting point of the preset look-ahead interval is the weld trajectory position corresponding to the welding torch at the end of the current control cycle, and the ending point of the preset look-ahead interval is the weld trajectory position corresponding to the product of the current welding speed parameters and the preset look-ahead time length along the direction of the weld trajectory from the starting point.

8. The intelligent welding control method for automotive parts according to claim 1, characterized in that: The generation of the welding forming prediction parameter set includes: performing time-series correlation analysis on the welding process state feedback parameter set of the current control cycle and the previous several control cycles, and combining the welding object state characterization parameter set and the current position data of the welding torch to calculate the predicted penetration depth parameter, predicted weld width parameter, predicted heat input parameter and predicted defect risk parameter within the preset look-ahead interval. The predicted defect risk parameter includes at least one of the following: incomplete penetration risk parameter, burn-through risk parameter, porosity risk parameter, slag inclusion risk parameter, spatter abnormality risk parameter and welding deformation risk parameter.

9. The intelligent welding control method for automotive parts according to claim 1, characterized in that: The set of welding control corrections includes at least one of the following: current correction, voltage correction, welding speed correction, wire feed speed correction, shielding gas flow rate correction, welding torch height correction, and welding torch lateral offset correction. The determination of the welding control correction set includes: comparing the predicted penetration depth parameter, the predicted penetration width parameter, the predicted thermal input parameter, and the predicted defect risk parameter with the corresponding target thresholds, and determining the corresponding welding control correction amount according to the preset parameter-risk mapping rule.

10. The intelligent welding control method for automotive parts according to claim 1, characterized in that: The update of the target welding control parameter set includes: applying amplitude and rate of change constraints to each welding control correction, so that the updated welding current, welding voltage, welding speed, wire feed speed, shielding gas flow rate, and welding torch motion parameters are all within the corresponding process allowable range; when the predicted defect risk parameter is higher than the preset risk threshold, the welding speed parameter and welding current parameter are updated first, and when the predicted defect risk parameter is higher than the preset risk threshold for two consecutive control cycles, a speed reduction or welding suspension is performed.

Images