Automobile parts laser automatic welding method

Abstract

The application provides a kind of automobile parts laser automatic welding method, it is related to laser welding process control and automatic welding field, including, the welding initial state characteristic parameter set is associated with the preset standard welding track, preset reference heat input parameter set Analysis, this kind of automobile parts laser automatic welding method based on, by obtaining the assembly detection data of the automobile parts assembly body to be welded, determine the welding initial state characteristic parameter set, and the welding initial state characteristic parameter set is associated with the preset standard welding track, preset reference heat input parameter set Analysis, generate welding correction control parameter set, form target welding execution track and target welding heat input parameter set, realize adaptive welding control for workpiece assembly position deviation, weld position deviation and joint gap distribution difference, improve the pertinence of welding track planning and the matching of heat input adjustment.

Description

Technical Field

[0001] This invention relates to the field of laser welding process control and automated welding technology, specifically to an automated laser welding method for automotive parts. Background Technology

[0002] As automobile manufacturing moves towards lightweight, precision, and continuous production, the connection methods for automotive parts are gradually shifting from traditional arc welding and resistance welding to welding processes with higher precision and efficiency. Laser welding, due to its concentrated energy, high welding speed, small heat-affected zone, low deformation, and ease of automation integration, has been applied to the production of automotive parts. Existing production lines typically combine lasers, welding heads, industrial robots, tooling fixtures, positioning mechanisms, and conveying units to complete processes such as workpiece loading, positioning and clamping, trajectory execution, weld formation, and unloading and transfer. For brackets, reinforcements, connecting plates, shell-like components, and some irregularly shaped spliced ​​parts, existing technologies mostly implement automatic welding through preset welding paths and parameters, combined with fixture limits to ensure the stability of the assembly position. In some application scenarios, visual recognition, position detection, or post-weld inspection units are also configured to assist in improving welding accuracy and production cycle time, thereby meeting the connection and forming requirements in the mass production and continuous manufacturing of automotive parts.

[0003] The core defect of existing laser automatic welding technology is that the welding process lacks stable and effective adaptive control capabilities for workpiece assembly status, weld position deviation and joint gap changes. As a result, the system often still completes the operation with the pre-set welding trajectory and process parameters, making it difficult to synchronously correct the welding position and heat input status when actual production conditions fluctuate. This makes it difficult to continuously guarantee the consistency of weld formation, the stability of connection strength and the reliability of mass production. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides an automated laser welding method for automotive parts. The technical problem this invention aims to solve is: how to address the issues of inaccurate weld tracking, unstable molten pool, and poor weld quality consistency caused by assembly deviations and joint gap fluctuations through an automated laser welding process based on assembly inspection, correlation analysis, trajectory correction, heat input correction, and closed-loop adaptive adjustment of welding feedback.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a laser automatic welding method for automotive parts, comprising:

[0006] S1. Obtain assembly inspection data of the automotive component assembly to be welded, and determine the set of welding initial state characterization parameters corresponding to the automotive component assembly to be welded based on the assembly inspection data. The set of welding initial state characterization parameters includes workpiece assembly position deviation parameters, weld position deviation parameters, and joint gap distribution parameters.

[0007] S2. Perform correlation analysis between the set of welding initial state characterization parameters and the preset standard welding trajectory and the preset reference heat input parameter set to determine the welding correction control parameter set corresponding to the automotive component assembly to be welded. The welding correction control parameter set includes trajectory correction parameters and heat input correction parameters.

[0008] S3. Based on the trajectory correction parameters, the preset standard welding trajectory is corrected to generate a target welding execution trajectory for compensating for the weld position deviation parameters; based on the heat input correction parameters, a set of target welding heat input parameters matching the joint gap distribution parameters is generated.

[0009] S4. Perform laser welding on the automotive component assembly to be welded according to the target welding execution trajectory and the target welding heat input parameter set, and collect welding feedback data in real time during the laser welding process. Determine dynamic welding state characterization parameters based on the welding feedback data, and perform closed-loop adaptive correction on the target welding execution trajectory and the target welding heat input parameter set.

[0010] S5. Based on the target welding execution trajectory after closed-loop adaptive correction and the target welding thermal input parameter set after closed-loop adaptive correction, complete the laser welding of the automotive component assembly to be welded and output the corresponding welding result data.

[0011] Preferably, the assembly inspection data is obtained through a visual inspection device and a gap measurement device set at the welding station; the assembly inspection data includes workpiece assembly posture data of the automotive parts assembly to be welded, actual weld position data, and joint gap measurement data.

[0012] Preferably, the workpiece assembly position deviation parameter is obtained by comparing the workpiece assembly pose data with a preset assembly reference pose; the actual weld position data is obtained by comparing the actual weld position data with the preset standard welding trajectory; the joint gap distribution parameter is obtained by measuring the gap at multiple sampling positions along the welding path direction, and arranging the gap measurement values ​​corresponding to each sampling position in the order from the welding start end to the welding end end.

[0013] Preferably, the preset reference thermal input parameter set includes laser power, welding speed, and focal position; the target welding thermal input parameter set is a parameter set obtained by correcting the laser power, welding speed, and focal position respectively.

[0014] Preferably, the association analysis includes:

[0015] S21. Determine the trajectory correction relationship corresponding to the preset standard welding trajectory based on the workpiece assembly position deviation parameter and the weld position deviation parameter;

[0016] S22. Determine the heat input correction relationship corresponding to the preset reference heat input parameter set based on the joint gap distribution parameters;

[0017] S23. Determine the set of welding correction control parameters based on the trajectory correction relationship and the heat input correction relationship.

[0018] Preferably, the determination of the set of welding correction control parameters includes: dividing the preset standard welding trajectory into multiple control segments along the welding direction; determining the trajectory correction parameters corresponding to each control segment based on the weld position deviation parameters corresponding to each control segment; and determining the heat input correction parameters corresponding to each control segment based on the joint gap distribution parameters corresponding to each control segment.

[0019] Preferably, the target welding execution trajectory is obtained by correcting the trajectory node coordinates and node postures of each control segment; the correction includes overall posture compensation correction based on the workpiece assembly position deviation parameters and local trajectory compensation correction based on the weld position deviation parameters.

[0020] Preferably, the welding feedback data includes weld seam tracking image data, molten pool morphology data, and laser reflection intensity data; the dynamic welding state characterization parameters include real-time weld seam tracking deviation parameters, molten pool stability parameters, and penetration state parameters.

[0021] Preferably, the closed-loop adaptive correction includes: real-time position correction of the target welding execution trajectory based on the real-time weld tracking deviation parameter, and real-time parameter correction of the target welding thermal input parameter set based on the molten pool stability parameter and the penetration state parameter.

[0022] Preferably, the real-time position correction is performed during the laser welding process by adjusting the trajectory node coordinates and node postures of the currently executed target welding trajectory based on the continuously acquired and updated real-time weld tracking deviation parameters; the real-time parameter correction is performed during the laser welding process by adjusting the laser power, welding speed, and focal position in the currently executed target welding heat input parameter set based on the continuously acquired and updated molten pool stability parameters and fusion penetration state parameters.

[0023] This invention provides an automated laser welding method for automotive parts. It offers the following advantages:

[0024] This method for automatic laser welding of automotive parts acquires assembly inspection data of the automotive parts assembly to be welded, determines the set of initial welding state characterization parameters, and performs correlation analysis with the set of preset standard welding trajectories and preset reference thermal input parameter sets to generate a set of welding correction control parameters. This forms the target welding execution trajectory and the target welding thermal input parameter set, enabling adaptive welding control for workpiece assembly position deviations, weld position deviations, and joint gap distribution differences. This improves the targeting of welding trajectory planning and the matching of thermal input adjustment.

[0025] The control method combines pre-weld correction with closed-loop adaptive correction during the welding process. Based on weld tracking image data, molten pool morphology data and laser reflection intensity data, the target welding execution trajectory and target welding heat input parameter set are adjusted in real time. This helps to improve weld tracking accuracy, enhance molten pool stability, ensure consistent penetration state, and reduce the risk of welding defects caused by assembly errors and welding process fluctuations, thereby further improving welding quality stability. Attached Figure Description

[0026] Figure 1 This is an overall flowchart of the method of the present invention;

[0027] Figure 2 This is a schematic diagram illustrating the generation of welding initial state characterization parameters according to the present invention;

[0028] Figure 3 This is a flowchart of the correlation analysis of welding correction control parameters in this invention;

[0029] Figure 4 This is a schematic diagram illustrating the target welding execution trajectory and heat input generation of the present invention;

[0030] Figure 5 This is a flowchart of the closed-loop adaptive correction control of the present invention. Detailed Implementation

[0031] The technical solutions in the embodiments of the present invention have been clearly and completely described. 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.

[0032] Example 1

[0033] like Figure 1-5 As shown, this embodiment of the invention provides a laser automatic welding method for automotive parts, including:

[0034] S1. Acquire assembly inspection data of the automotive component assembly to be welded, and determine the set of initial welding state characterization parameters corresponding to the automotive component assembly to be welded based on the assembly inspection data. The set of initial welding state characterization parameters includes workpiece assembly position deviation parameters, weld position deviation parameters, and joint gap distribution parameters. Assembly inspection data is acquired through a vision inspection device and a gap measurement device set at the welding station. Assembly inspection data includes workpiece assembly pose data, actual weld position data, and joint gap measurement data of the automotive component assembly to be welded. Workpiece assembly position deviation parameters are obtained by comparing the workpiece assembly pose data with a preset assembly reference pose. The actual weld position data are obtained by comparing with a preset standard welding trajectory. Joint gap distribution parameters are obtained by measuring the gap at multiple sampling positions along the welding path direction, and arranging the gap measurement values ​​corresponding to each sampling position in order from the welding start end to the welding end end.

[0035] S2. Correlation analysis is performed between the initial welding state characterization parameter set and the preset standard welding trajectory and preset reference thermal input parameter set to determine the welding correction control parameter set corresponding to the automotive component assembly to be welded. The welding correction control parameter set includes trajectory correction parameters and thermal input correction parameters. The preset reference thermal input parameter set includes laser power, welding speed, and focal point position. The target welding thermal input parameter set is the parameter set obtained after correcting the laser power, welding speed, and focal point position respectively. The correlation analysis includes:

[0036] S21. Determine the trajectory correction relationship corresponding to the preset standard welding trajectory based on the workpiece assembly position deviation parameters and the weld position deviation parameters.

[0037] S22. Determine the heat input correction relationship corresponding to the preset reference heat input parameter set based on the joint gap distribution parameters.

[0038] S23. Determine the set of welding correction control parameters based on the trajectory correction relationship and the heat input correction relationship.

[0039] The determination of the set of welding correction control parameters includes: dividing the preset standard welding trajectory into multiple control segments along the welding direction; determining the trajectory correction parameters corresponding to each control segment based on the weld position deviation parameters corresponding to each control segment; and determining the heat input correction parameters corresponding to each control segment based on the joint gap distribution parameters corresponding to each control segment.

[0040] S3. Based on the trajectory correction parameters, the preset standard welding trajectory is corrected to generate a target welding execution trajectory for compensating for weld position deviation parameters. Based on the heat input correction parameters, a set of target welding heat input parameters matching the joint gap distribution parameters is generated. The target welding execution trajectory is obtained by correcting the trajectory node coordinates and node postures of each control segment. The correction includes overall posture compensation correction based on workpiece assembly position deviation parameters and local trajectory compensation correction based on weld position deviation parameters.

[0041] S4. Following the target welding trajectory and the target welding thermal input parameter set, laser welding is performed on the automotive component assembly to be welded. Welding feedback data is collected in real-time during the laser welding process. Dynamic welding state characterization parameters are determined based on the welding feedback data, and closed-loop adaptive correction is performed on the target welding trajectory and the target welding thermal input parameter set. Welding feedback data includes weld seam tracking image data, molten pool morphology data, and laser reflection intensity data. Dynamic welding state characterization parameters include real-time weld seam tracking deviation parameters, molten pool stability parameters, and penetration state parameters. Closed-loop adaptive correction includes: real-time position correction of the target welding trajectory based on the real-time weld seam tracking deviation parameters, and real-time parameter correction of the target welding thermal input parameter set based on the molten pool stability parameters and penetration state parameters. Real-time position correction adjusts the trajectory node coordinates and node attitudes of the currently executed target welding trajectory based on continuously collected and updated real-time weld seam tracking deviation parameters during the laser welding process. Real-time parameter correction adjusts the laser power, welding speed, and focal point position in the currently executed target welding thermal input parameter set based on continuously collected and updated molten pool stability parameters and penetration state parameters during the laser welding process.

[0042] S5. Based on the target welding execution trajectory after closed-loop adaptive correction and the target welding thermal input parameter set after closed-loop adaptive correction, the laser welding of the automotive component assembly to be welded is completed, and the corresponding welding result data is output.

[0043] This invention can comprehensively identify assembly deviations, weld position changes, and joint gap differences before welding, and form a targeted trajectory and heat input collaborative correction scheme accordingly. By segmented control, it improves the parameter matching accuracy of different welding sections, thereby enhancing weld formation consistency and joint connection reliability.

[0044] This invention introduces weld seam tracking, molten pool status and reflection signal feedback during the welding process to achieve closed-loop adaptive adjustment of trajectory and energy parameters, which helps to enhance the stability of the welding process and reduce risks such as weld deviation, lack of fusion, burn-through and forming fluctuations.

[0045] The present invention also has the ability to output welding results and retain process data, which can provide support for quality traceability, defect analysis and subsequent process optimization, and help improve the control accuracy, production consistency and implementation efficiency of automated welding production lines.

[0046] Example 2

[0047] This embodiment aims to verify the feasibility of generating a set of welding correction control parameters based on assembly inspection data to achieve adaptive correction of welding trajectory and thermal input parameters.

[0048] 1. Production tasks and sources of baseline process parameters

[0049] On April 12, 2025, a welding workshop of an automotive parts manufacturing company was welding the body beam and reinforcing plate assembly on the No. 2 laser welding production line.

[0050] The body crossbeam and reinforcing plate assembly is an lap joint structure with a weld seam design length of 420mm. The welding equipment is a fiber laser paired with an industrial robot.

[0051] The laser welding baseline process parameters for this model of part are recorded in the production line process database as follows:

[0052] The laser power was 3.2kW, the welding speed was 2.6m / min, and the focal point was -1.0mm. These baseline parameters are derived from the vehicle model welding process finalization test report and represent the standard process parameters used during stable assembly production.

[0053] To facilitate welding control analysis, the 420mm welding trajectory is divided into 6 control segments according to the welding direction. Each control segment is approximately 70mm long. The robot's offline programming trajectory has a total of 6 trajectory node segments on the weld, so it is divided into 6 control segments according to the node segments.

[0054] 2. Sources of assembly inspection data

[0055] After the assembly is positioned by the fixture, assembly inspection data is collected using the testing equipment configured at the welding station:

[0056] The weld location detection equipment is an industrial vision camera, and the joint gap detection equipment is a laser profile scanner. These detection devices are part of the production line's online quality inspection system, and the inspection data is automatically recorded and stored by the MES system.

[0057] 3. Workpiece assembly posture detection results

[0058] The actual pose data of the assembly is acquired through a vision inspection system and compared with the assembly reference pose set on the production line. The inspection data recorded in the MES system is as follows:

[0059] X-direction offset +0.41mm, Y-direction offset -0.34mm, Z-direction offset +0.16mm, rotation around X-axis 0.19°, rotation around Y-axis -0.15°, rotation around Z-axis 0.07°.

[0060] The above data comes from the online assembly inspection records of this batch of welding tasks, reflecting the actual assembly position deviation of the assembly in the fixture.

[0061] 4. Weld location detection results

[0062] The industrial vision system scans the weld along the robot's standard welding trajectory, sets up detection sampling points at the center of each control section, and extracts the coordinates of the weld center.

[0063] The industrial vision system extracts the two sides of the weld seam using an image edge detection algorithm, calculates the position of the weld seam centerline, and then compares the coordinates with the robot's offline programmed trajectory to obtain the lateral offset. The image edge detection is a weld seam boundary extraction algorithm based on grayscale gradients, which determines the position of the weld seam centerline by calculating the midpoint of the two sides of the boundary.

[0064] The detection results recorded by the MES system are as follows:

[0065] Section 1: 0.14mm, Section 2: 0.27mm, Section 3: 0.30mm, Section 4: 0.25mm, Section 5: 0.19mm, Section 6: 0.12mm.

[0066] The above data comes from the weld centerline measurement result file generated by the pre-welding visual inspection system.

[0067] 5. Joint gap test results

[0068] The MES system recorded the following detection values ​​after measuring the joint gap along the welding path using a laser profile scanner:

[0069] Section 1: 0.20mm, Section 2: 0.24mm, Section 3: 0.35mm, Section 4: 0.41mm, Section 5: 0.32mm, Section 6: 0.22mm.

[0070] The above data comes from the pre-welding gap inspection records of this batch, reflecting the actual distribution of joint gaps along the welding path.

[0071] 6. Basis for Calculating Trajectory Correction Parameters

[0072] The production line welding control software uses the compensation coefficient generated during the historical weld seam tracking and debugging process when calculating the trajectory compensation amount.

[0073] During the commissioning test, the weld center deviation was minimized and the weld tracking was stable when the compensation coefficient was 0.9. Therefore, the compensation coefficient was adopted in the production control software.

[0074] Calculate the trajectory correction amount for each segment based on the compensation coefficient:

[0075] Section 1: 0.14×0.9=0.126mm, Section 2: 0.27×0.9=0.243mm, Section 3: 0.30×0.9=0.270mm, Section 4: 0.25×0.9=0.225mm, Section 5: 0.19×0.9=0.171mm, Section 6: 0.12×0.9=0.108mm.

[0076] Simultaneously, based on the assembly pose detection results, overall pose compensation is performed on the robot's welding trajectory:

[0077] X-direction compensation -0.41mm, Y-direction compensation +0.34mm, Z-direction compensation -0.16mm.

[0078] The aforementioned compensation amount is automatically calculated by the robot control system based on the detection data.

[0079] 7. Basis for heat input correction parameters

[0080] The production line process database records welding process test results under different joint gap conditions. Based on welding window test data completed in 2024, when the joint gap increases, it is necessary to increase the heat input to ensure stable penetration depth.

[0081] According to the database records, within the inspection gap range, the welding control system matches the welding window data in the process database to obtain the corresponding welding parameters for each section. A parameter correspondence table between laser power and welding speed is established using the joint gap as the independent variable. This parameter correspondence table is derived from welding window test data completed during the welding process development phase for this vehicle model.

[0082] In the welding window data, when the joint gap increases to more than 0.30 mm, the process database recommends increasing the power by 5%-10% based on the baseline. When the joint gap increases, the process database also reduces the welding speed to maintain stable heat input per unit length.

[0083] Table 1: Matching results of welding parameters for each control section.

[0084] Section power speed 1 3.2kW 2.6 m / min 2 3.2kW 2.6 m / min 3 3.36kW 2.52 m / min 4 3.52kW 2.44 m / min 5 3.36kW 2.52 m / min 6 3.2kW 2.6 m / min

[0085] When the gap exceeds 0.35mm, the production line process database recommends adjusting the focal position downward by 0.3mm to improve the stability of the melt depth. Therefore, in section 4, the focal position is adjusted from -1.0mm to -1.3mm.

[0086] 8. Generation of welding correction control parameters

[0087] Based on the above trajectory correction amount and the matching results of thermal input parameters, the welding control system generates a set of welding correction control parameters for this batch of assemblies.

[0088] The set of welding correction control parameters includes the trajectory correction amount for each control segment, as well as the corresponding laser power, welding speed, and focal position parameters. These parameters are loaded and executed by the robot control system to generate the corresponding target welding execution trajectory and target welding thermal input parameters.

[0089] Based on the assembly posture data, weld position deviation data, and joint gap detection data obtained in this embodiment, the welding control system can perform overall posture compensation and segmented trajectory correction on the standard welding trajectory, and match the corresponding laser power, welding speed, and focal position parameters to generate a set of welding correction control parameters. This indicates that the method of the present invention can achieve adaptive adjustment of welding control parameters according to the actual assembly state.

[0090] Example 3

[0091] This embodiment selects an actual welding production task in the welding workshop of an automotive parts manufacturing company to illustrate the method of generating a target welding execution trajectory based on welding correction control parameters and implementing closed-loop adaptive correction during the welding process, so as to verify the feasibility of applying the closed-loop adaptive correction method under the laser welding production conditions of automotive parts.

[0092] 1. Production tasks and sources of basic data

[0093] At 9:18 AM on April 12, 2025, the No. 2 laser welding production line in the welding workshop of an automotive parts manufacturing company was performing the welding task of the body crossbeam and the reinforcing plate assembly.

[0094] The welding equipment is a laser welding workstation consisting of a fiber laser and a six-axis industrial robot. The welding station is equipped with a weld seam vision tracking system and a molten pool monitoring system.

[0095] The weld seam design length is 420mm, data sourced from the vehicle body welding process drawings. The standard welding parameters for this part model are recorded in the production line process database as follows:

[0096] The laser power is 3.2kW, the welding speed is 2.6m / min, and the focal point is -1.0mm.

[0097] The above data comes from the welding process type approval test report for this vehicle model, and represents the benchmark process parameters used for mass production at this welding station.

[0098] To facilitate trajectory control calculations, the robot's offline programming system divides the 420mm welding trajectory into six control segments, each 70mm in length, according to the welding direction. The division method is set based on the trajectory segmentation rules of the robot's offline programming system.

[0099] 2. Generation of target welding execution trajectory

[0100] The standard welding trajectory recorded in the robot's offline programming file contains 21 trajectory nodes, and the data comes from the robot program file.

[0101] Before welding begins, the vision inspection system checks the assembly status of the assembly. The inspection results are from the vision system inspection log at the welding station, and are as follows:

[0102] The assembly deviation in the X direction is +0.32mm, the assembly deviation in the Y direction is -0.18mm, and the assembly deviation in the Z direction is +0.25mm.

[0103] The system performs overall pose compensation on all trajectory nodes based on the above detection data. The coordinates of the starting node of the standard trajectory are (850.000, 420.000, 310.000), and the coordinates of the node after compensation are adjusted to (850.320, 419.820, 310.250).

[0104] The vision system then scans and measures the center position of the weld. Weld deviation data is derived from the scan results of the weld tracking camera; the main results are as follows:

[0105] Lateral deviation of weld in section 2: 0.42mm; lateral deviation of weld in section 3: 0.58mm; lateral deviation of weld in section 4: 0.36mm.

[0106] Based on the above detection results, the system performs local compensation on the corresponding trajectory nodes according to the weld seam normal direction. The weld seam normal direction is calculated by the weld seam tracking vision system based on the weld seam centerline trajectory.

[0107] The original coordinates of the 9th trajectory node in section 3 are (913.500, 418.200, 310.200). After correction based on the weld deviation of 0.58mm obtained by detection, the node coordinates are adjusted to (913.500, 418.780, 310.200).

[0108] After overall pose compensation and local weld deviation compensation, the target welding execution trajectory is generated and written into the robot control program.

[0109] 3. Welding heat input parameter settings

[0110] Based on the heat input correction parameters generated in the previous steps, the system determines the welding parameters for each control section. The data comes from the parameter calculation records of the welding control system. The welding control system calculates the heat input correction parameters for each control section based on the joint gap distribution parameters and the reference heat input parameters obtained in the previous steps.

[0111] The weld gap test results showed that the average gaps of sections 2, 3 and 4 were 0.32 mm, 0.41 mm and 0.28 mm, respectively.

[0112] Table 2: Welding Control System Parameter Calculation Record Table.

[0113] Section laser power Welding speed Focus position 1 3.25kW 2.55 m / min -1.0mm 2 3.30kW 2.50 m / min -1.0mm 3 3.35kW 2.45 m / min -1.1mm 4 3.30kW 2.50 m / min -1.0mm 5 3.25kW 2.55 m / min -1.0mm 6 3.20kW 2.60 m / min -1.0mm

[0114] The above parameters are automatically written into the robot welding program by the welding control system.

[0115] 4. Real-time data feedback during the welding process

[0116] At 9:21 AM on April 12, 2025, the robot began executing the welding procedure. The welding monitoring system collected welding process data at a frequency of 200 Hz, and the data came from the welding monitoring system's log files.

[0117] When the welding length reaches 170mm, the system records the following monitoring data: the real-time weld tracking deviation is 0.64mm, the molten pool stability parameter is 0.71, which is calculated by the molten pool monitoring system based on the area fluctuation and brightness change of the molten pool morphology image, and the laser reflected light intensity is 0.83, which is the intensity value of the laser reflected light signal after normalization processing by the monitoring system.

[0118] The above data is calculated in real time by the weld seam visual tracking system and the molten pool monitoring system.

[0119] 5. Closed-loop adaptive correction

[0120] Because the weld tracking deviation exceeded the system's set control threshold of 0.50mm, the welding control system triggered real-time trajectory correction. The system adjusted the currently executing trajectory node by 0.60mm along the weld normal direction.

[0121] The node coordinates before correction were (917.200, 418.620, 310.180). Based on the weld deviation of 0.58mm obtained by detection, the node coordinates were corrected along the normal direction of the weld. The node coordinates after correction were (917.200, 419.220, 310.180).

[0122] The robot controller completes trajectory updates within a 20ms control cycle.

[0123] At the same time, the system determines that the current heat input is insufficient based on the molten pool stability parameters and adjusts the welding parameters accordingly.

[0124] The laser power was increased from 3.35kW to 3.42kW, the welding speed was reduced from 2.45m / min to 2.38m / min, and the focal position was kept at -1.1mm.

[0125] After 1.5 seconds of parameter adjustment, the molten pool stability parameter returned to 0.82, and the system resumed the original welding speed.

[0126] 6. Welding Results

[0127] Welding was completed at 9:23 AM, and the welding quality inspection system conducted online inspection of the weld. The inspection data came from the welding quality inspection system report.

[0128] The test results are as follows:

[0129] The maximum tracking deviation of the weld was 0.64 mm, the average width of the weld was 1.28 mm, and the weld continuity test results showed that no defects such as incomplete penetration or lack of fusion were found.

[0130] Welding process data and test results are automatically stored in the welding process database by the production line control system for production quality traceability and process optimization analysis.

[0131] In actual production tasks, by acquiring assembly inspection data and performing overall posture compensation and local weld deviation correction on the standard welding trajectory, and based on feedback information such as weld tracking deviation, molten pool stability and laser reflection intensity collected by the welding monitoring system, the welding execution trajectory and welding heat input parameters are adjusted in real time. The welding process remains stable, the weld position matches the target trajectory, the weld formation is continuous and there are no defects such as incomplete penetration or incomplete fusion. This shows that the method of the present invention achieves coordinated control of welding trajectory and heat input parameters, and improves the stability of welding process and the consistency of welding quality.

[0132] 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 automatic laser welding of automotive parts, characterized in that, include: S1. Obtain assembly inspection data of the automotive component assembly to be welded, and determine the set of welding initial state characterization parameters corresponding to the automotive component assembly to be welded based on the assembly inspection data. The set of welding initial state characterization parameters includes workpiece assembly position deviation parameters, weld position deviation parameters, and joint gap distribution parameters. S2. Perform correlation analysis between the set of welding initial state characterization parameters and the preset standard welding trajectory and the preset reference heat input parameter set to determine the welding correction control parameter set corresponding to the automotive component assembly to be welded. The welding correction control parameter set includes trajectory correction parameters and heat input correction parameters. S3. Based on the trajectory correction parameters, the preset standard welding trajectory is corrected to generate a target welding execution trajectory for compensating for the weld position deviation parameters; Based on the heat input correction parameters, a target welding heat input parameter set that matches the joint gap distribution parameters is generated; S4. Perform laser welding on the automotive component assembly to be welded according to the target welding execution trajectory and the target welding heat input parameter set, and collect welding feedback data in real time during the laser welding process. Determine dynamic welding state characterization parameters based on the welding feedback data, and perform closed-loop adaptive correction on the target welding execution trajectory and the target welding heat input parameter set. S5. Based on the target welding execution trajectory after closed-loop adaptive correction and the target welding thermal input parameter set after closed-loop adaptive correction, complete the laser welding of the automotive component assembly to be welded and output the corresponding welding result data.

2. The automatic laser welding method for automotive parts according to claim 1, characterized in that: The assembly inspection data is obtained through a visual inspection device and a gap measurement device set at the welding station; the assembly inspection data includes the workpiece assembly posture data of the automotive parts to be welded, the actual position data of the weld, and the joint gap measurement data.

3. The automatic laser welding method for automotive parts according to claim 2, characterized in that: The workpiece assembly position deviation parameter is obtained by comparing the workpiece assembly pose data with the preset assembly reference pose; the actual weld position data is obtained by comparing the weld actual position data with the preset standard welding trajectory. The joint gap distribution parameters are obtained by measuring the gap at multiple sampling positions along the welding path, and the gap measurement values ​​corresponding to each sampling position are arranged in order from the welding start end to the welding end end.

4. The automatic laser welding method for automotive parts according to claim 1, characterized in that: The preset reference thermal input parameter set includes laser power, welding speed, and focal position; the target welding thermal input parameter set is a parameter set obtained by correcting the laser power, welding speed, and focal position respectively.

5. The automatic laser welding method for automotive parts according to claim 1, characterized in that: The association analysis includes: S21. Determine the trajectory correction relationship corresponding to the preset standard welding trajectory based on the workpiece assembly position deviation parameter and the weld position deviation parameter; S22. Determine the heat input correction relationship corresponding to the preset reference heat input parameter set based on the joint gap distribution parameters; S23. Determine the set of welding correction control parameters based on the trajectory correction relationship and the heat input correction relationship.

6. The automatic laser welding method for automotive parts according to claim 1, characterized in that: The determination of the set of welding correction control parameters includes: dividing the preset standard welding trajectory into multiple control segments along the welding direction; determining the trajectory correction parameters corresponding to each control segment based on the weld position deviation parameters corresponding to each control segment; and determining the heat input correction parameters corresponding to each control segment based on the joint gap distribution parameters corresponding to each control segment.

7. The automatic laser welding method for automotive parts according to claim 6, characterized in that: The target welding execution trajectory is obtained by correcting the trajectory node coordinates and node postures of each control segment; the correction includes overall posture compensation correction based on the workpiece assembly position deviation parameters and local trajectory compensation correction based on the weld position deviation parameters.

8. The automatic laser welding method for automotive parts according to claim 1, characterized in that: The welding feedback data includes weld tracking image data, molten pool morphology data, and laser reflection intensity data; the dynamic welding state characterization parameters include real-time weld tracking deviation parameters, molten pool stability parameters, and penetration state parameters.

9. The automatic laser welding method for automotive parts according to claim 8, characterized in that: The closed-loop adaptive correction includes: real-time position correction of the target welding execution trajectory based on the real-time weld tracking deviation parameter, and real-time parameter correction of the target welding thermal input parameter set based on the molten pool stability parameter and the penetration state parameter.

10. The automatic laser welding method for automotive parts according to claim 9, characterized in that: The real-time position correction is to adjust the trajectory node coordinates and node postures of the currently executed target welding trajectory based on the continuously acquired and updated real-time weld tracking deviation parameters during the laser welding process; the real-time parameter correction is to adjust the laser power, welding speed, and focal position in the currently executed target welding heat input parameter set based on the continuously acquired and updated molten pool stability parameters and fusion penetration state parameters during the laser welding process.

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