Laser-electric arc hybrid welding method for high-strength aluminum alloy cabin section

Abstract

The invention belongs to the technical field of aerospace structural part welding and manufacturing, and particularly relates to a laser-electric arc hybrid welding method for a high-strength aluminum alloy cabin section. According to the method, a high stress concentration area and a deformation risk area are identified through pre-welding modeling and heat-force coupling simulation, then the laser and electric arc energy ratio is dynamically matched based on a heat input cooperation coefficient eta, and the welding speed and the laser defocusing amount are regulated and controlled in real time in combination with a weld joint forming stability index sigma calculated through a pre-trained LSTM model. And performing fusion depth compensation on the special area through the dynamic arc compensation factor phi, and finally optimizing the performance of the joint through postweld heat treatment. The method can adaptively cope with complex working conditions such as plate thickness fluctuation and track curvature change, defects such as air holes and hot cracks are effectively restrained, the internal defect rate of a welding seam is lower than 0.5%, the tensile strength of a welding joint reaches 90% or above of that of a base material, the method is adaptive to 2-series and 7-series high-strength aluminum alloy and complex welding seam tracks, and the welding quality is improved. And the harsh requirements of high reliability and long service life of aerospace structural parts are met.

Description

Technical Field

[0001] This invention belongs to the field of welding and manufacturing technology for aerospace structural components, specifically relating to a laser-arc hybrid welding method for high-strength aluminum alloy compartments, and particularly a collaborative welding control method based on heat input synergy coefficient, weld formation stability index and dynamic arc compensation factor. Background Technology

[0002] High-strength aluminum alloys (such as the 2-series and 7-series) have become key materials for large, thin-walled load-bearing structures such as cabins and fuel tanks in the aerospace field due to their excellent specific strength, corrosion resistance, and good machinability. As spacecraft become increasingly lightweight and large-scale, traditional integral forging or riveting processes face bottlenecks such as long cycles, high costs, and low connection efficiency when manufacturing complex cabin sections. Laser-arc hybrid welding technology, with its advantages of deep penetration, high speed, and low heat input, is considered a powerful solution for achieving integrated forming of large high-strength aluminum alloy cabin sections. However, this technology still faces many challenges in practical applications: high-strength aluminum alloys are extremely sensitive to heat input; improper control of the two heat sources, laser and arc, can easily lead to unstable weld formation, porosity, hot cracking, and other defects. Furthermore, during the welding of longitudinal seams in cabin sections several meters long, factors such as assembly gap fluctuations, slight changes in plate thickness, and heat accumulation effects make it difficult to maintain consistency in the penetration depth and formation throughout the weld area, severely affecting the mechanical properties and structural reliability of the welded joint.

[0003] Existing laser-arc hybrid welding processes mostly employ fixed parameters or are adjusted based on simple linear rules, lacking in-depth perception and intelligent decision-making regarding the multi-physics coupling behavior throughout the welding process. On the one hand, the energy ratio of the laser and arc fails to establish a quantitative correlation with the geometric characteristics and thermodynamic state of the joint, leading to inaccurate energy distribution in complex paths or variable cross-section regions. On the other hand, welding process monitoring is primarily based on post-process inspection, lacking real-time prediction and forward-looking control capabilities for weld formation quality (such as weld width, reinforcement height, and undercut), and failing to effectively intervene in the early stages of defect initiation. These shortcomings in control levels result in high-strength aluminum alloy segment welded joints often exhibiting problems such as low strength coefficients, poor fatigue performance, and excessive deformation, making it difficult to meet the stringent requirements of high reliability and long service life for aerospace structural components. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a laser-arc hybrid welding method for high-strength aluminum alloy sections.

[0005] The purpose of this invention is to address the problems of unstable weld formation, high defect rate, uneven joint mechanical properties, and insufficient reliability in laser-arc hybrid welding of high-strength aluminum alloy sections. By constructing a three-level intelligent welding control system based on heat input synergy coefficient, weld formation stability index, and dynamic arc compensation factor, this invention achieves integrated welding formation of such sections with high reliability, long life, and high performance.

[0006] This invention provides a laser-arc hybrid welding method for high-strength aluminum alloy sections, comprising the following steps: (1) Pre-welding modeling and parameter pre-planning: Obtain the geometric feature parameters of the weld trajectory of the section to be welded, and identify potential high stress concentration areas and deformation risk areas based on thermo-mechanical coupling simulation; (2) Energy matching based on heat input coordination coefficient (η): In the area identified in step (1), the heat input coordination coefficient η is calculated and adjusted in real time according to the geometric feature parameters on the welding path, η = laser power / (arc current × arc voltage) to dynamically adjust the energy ratio of laser and arc; (3) Real-time prediction and control based on weld formation stability index (σ): During the welding process, the molten pool image and temperature field data are collected in real time, input into the pre-trained long short-term memory network (LSTM) model, the weld formation stability index σ is calculated, and the welding speed and / or laser defocusing amount are dynamically adjusted according to the real-time value of σ. (4) Control of weld penetration consistency based on dynamic arc compensation factor (φ): For the weld start, end section, thick plate transition zone and curvature change zone, calculate the dynamic arc compensation factor φ, and dynamically adjust the arc current according to φ to compensate for the weld penetration.

[0007] As a further optimization of the laser-arc hybrid welding method for high-strength aluminum alloy sections, in step (2), the heat input synergy coefficient η is calculated and controlled by the following formula: η = η0 × (t / t) ref ) × (R ref / R) Where η0 is the basic coordination coefficient, t is the plate thickness at the current weld position, and t ref R is the reference plate thickness, and R is the radius of curvature of the weld at its current position. ref R is the reference radius of curvature; when the weld is a straight segment, R ref / R takes the value 1; Laser power P laser According to formula P laser = η × P arc Dynamic adjustments are made, where P arc This represents the arc power.

[0008] As a further optimization of the laser-arc hybrid welding method for high-strength aluminum alloy sections, in step (2), t ref Take the average plate thickness of the weld, R ref Take the minimum radius of curvature of the weld.

[0009] As a further optimization scheme for the laser-arc composite welding method of high-strength aluminum alloy compartments, in step (3), the pre-trained long short-term memory network (LSTM) model is obtained in the following way: welding experiments are carried out under various typical working conditions, molten pool images, temperature field data and process parameters are collected simultaneously, and the stability labels of the samples are marked according to the post-weld forming quality to construct a training dataset, and the LSTM network is trained in a supervised manner using the training dataset.

[0010] As a further optimization of the laser-arc hybrid welding method for high-strength aluminum alloy sections, in step (3), when the weld formation stability index σ calculated in real time is lower than the preset threshold σ th Adjust the welding speed according to the following formula: ΔV=K V ×(σ th -σ)×V0 Where ΔV is the welding speed adjustment amount, K V V0 is the speed adjustment coefficient, where V0 is the currently set welding speed and V is the adjusted welding speed. new =V0+ΔV, and the adjustment range is limited to ±5%.

[0011] As a further optimization of the laser-arc hybrid welding method for high-strength aluminum alloy sections, the speed adjustment coefficient K... V Set to 0.1, preset threshold σ th Take 0.8.

[0012] As a further optimization of the laser-arc hybrid welding method for high-strength aluminum alloy sections, in step (4), the dynamic arc compensation factor φ is calculated using the following formula: φ = 1 + α (rate of change of plate thickness) + β (rate of change of curvature) Where α and β are constants related to the thermophysical properties of aluminum alloy materials; The arc current is dynamically multiplied based on the calculated φ factor, and the adjustment range is limited to ±10%.

[0013] As a further optimization of the laser-arc hybrid welding method for high-strength aluminum alloy sections, When the high-strength aluminum alloy is the 7A04 series, α is 0.15~0.25 and β is 0.05~0.15; When the high-strength aluminum alloy is from the 2A12 series, α is 0.10~0.20 and β is 0.03~0.10; When the high-strength aluminum alloy is the 5A06 series, α is 0.12~0.22 and β is 0.04~0.12.

[0014] As a further optimization of the laser-arc composite welding method for high-strength aluminum alloy sections, the method also includes step (5): after welding, the welded joint is subjected to solution treatment and aging treatment; wherein, the solution treatment temperature is 470~500℃, and after holding for 1~2h, it is water-cooled; the aging treatment temperature is 120~150℃, and after holding for 12~24h, it is air-cooled.

[0015] As a further optimization of the laser-arc composite welding method for high-strength aluminum alloy sections, the plate thickness of the high-strength aluminum alloy sections is 5~15mm; the welded joint obtained by the method has a tensile strength of more than 90% of the tensile strength of the base material, and the internal defect rate of the weld is less than 0.5%.

[0016] Beneficial effects The laser-arc hybrid welding method for high-strength aluminum alloy sections provided by this invention effectively solves the technical bottlenecks of existing laser-arc hybrid welding technology in the welding of large high-strength aluminum alloy sections, such as unstable weld formation, high defect rate, uneven joint mechanical properties, and insufficient reliability, by constructing a three-level intelligent welding control system that integrates heat source coordination, weld tracking, and real-time feedback. It has many significant beneficial effects. This method achieves precise matching of laser and arc energy by introducing a heat input synergy coefficient. Combined with real-time prediction and control of the weld formation stability index, and targeted compensation for special areas by a dynamic arc compensation factor, it can adaptively cope with complex working conditions such as plate thickness fluctuations, trajectory curvature changes, assembly gap fluctuations, and heat accumulation effects during the welding process. It effectively suppresses common welding defects such as porosity, lack of fusion, and hot cracking, ensuring uniform penetration and regular formation throughout the weld area, and significantly reducing the proportion of internal weld defects. Simultaneously, precise heat input control and dynamic regulation create excellent thermodynamic conditions for the weld solidification process, promoting the formation of fine and uniform microstructures in the weld zone, reducing the precipitation of coarse columnar crystals and continuous network brittle phases, effectively alleviating joint softening, and significantly improving weld quality. The system enhances key mechanical properties such as strength and fatigue performance of welded joints, ensuring a higher degree of matching between joint performance and base material, thus meeting the stringent requirements of aerospace structural components for high reliability and long service life. Furthermore, its control system can be customized to the thermophysical properties of different grades of high-strength aluminum alloys through benchmark parameter calibration. It can also adapt to complex weld trajectories such as straight lines, curves, and variable cross-sections, addressing the welding needs of complex structures such as compartments. Compared to traditional welding processes with fixed parameters or simple linear adjustments, it represents a shift from post-inspection to real-time prediction and proactive control, improving the stability and controllability of the welding process, reducing reliance on operator experience, and helping to shorten production cycles and reduce manufacturing costs. This provides reliable technical support for the welding and forming of high-strength aluminum alloy compartments. Attached Figure Description

[0017] Figure 1 These are macroscopic and OM morphological images of the welded joint in Embodiment 1 of the present invention, wherein (a) is a macroscopic morphological image of the welded joint, (b) is a microscopic morphological image of the weld cross section, and (c) and (d) are both magnified views of (b).

[0018] Figure 2 These are SEM images of the microstructure of the welded joint in Embodiment 2 of the present invention, wherein (a) is a morphological image of the cross-section of the welded joint, and (b), (c), and (d) are SEM images of the microstructure of different regions in (a), respectively.

[0019] Figure 3 These are comparison images of industrial CT inspection results of the weld area in Embodiment 1 and the comparative example of the present invention, wherein (a) is the CT inspection result image of the welded joint in Embodiment 1, and (b) is the CT inspection result image of the welded joint in the comparative example.

[0020] Figure 4 This is a bar chart showing the mechanical properties of Embodiments 1, 2, and the comparative example of the present invention. Detailed Implementation

[0021] The laser-arc hybrid welding method for high-strength aluminum alloy sections of the present invention introduces a series of quantitative parameters to construct a three-level intelligent welding control system that integrates heat source coordination, weld seam tracking, and real-time feedback, thereby achieving the welding of aerospace-grade aluminum alloy joints with high strength, small deformation, consistent performance, and excellent reliability.

[0022] The laser-arc hybrid welding method for high-strength aluminum alloy sections of the present invention includes the following steps: (1) Pre-welding modeling and parameter pre-planning: The three-dimensional digital model of the high-strength aluminum alloy section is analyzed for welding path, and the geometric feature parameters of the weld trajectory are extracted, including local curvature radius, plate thickness variation gradient and joint type; based on thermo-mechanical coupling finite element simulation, the transient temperature field and residual stress distribution cloud map during the welding process are pre-calculated to identify potential high stress concentration areas and deformation risk areas.

[0023] (2) Precise energy matching based on heat input synergy coefficient (η): According to the region identified in step (1), a heat input synergy coefficient η is set for each micro-region on the welding path within the region, where η = laser power / (arc current × arc voltage). By controlling η, the energy contribution ratio of laser and arc is dynamically adjusted to achieve energy matching optimization and synergy effect of the two heat sources at different weld positions.

[0024] The heat input synergy coefficient η is set according to the following calculation formula: η = η0 × (t / t) ref ) × (R ref / R) This formula incorporates key physical factors from actual welding conditions into quantitative calculations, such as the plate thickness ratio t / t. ref This ratio is directly related to the heat input required for penetration; as the plate thickness increases, this ratio increases, and the system accordingly increases the proportion of laser power to enhance penetration depth; the radius of curvature ratio R ref / R corresponds to the stability requirements of the welding process. When the radius of curvature decreases (the trajectory bending intensifies), this ratio increases, and the system correspondingly increases the proportion of laser energy. Utilizing its strong directivity and concentrated energy, it compensates for arc drift and heat loss caused by trajectory changes, thereby maintaining the stability of the molten pool. The basic synergy coefficient η0 is a benchmark value related to the material and joint type, calibrated through process experiments, reflecting the differences in energy absorption and conduction characteristics of different aluminum alloys. Based on this formula, the system uses P... laser = η × P arcThe energy ratio of laser and electric arc is adjusted in real time to achieve adaptive control of energy distribution under operating conditions. Wherein, t ref For reference plate thickness (average weld thickness), R ref As a reference radius of curvature (taking the minimum radius of curvature of the weld), if the weld is a straight segment (R approaches infinity), then take R. ref / R = 1. By optimizing the calculated η value through real-time control, the energy matching and synergy of the two heat sources at different weld locations are achieved.

[0025] (3) Real-time prediction and control based on weld formation stability index (σ): During the welding process, visual sensors and infrared thermal imagers are integrated to collect molten pool images and temperature field distribution in real time at a frequency of no less than 2000fps and 500Hz. The molten pool width, tail indentation depth, and front and rear temperature gradient features are input into a pre-trained Long Short-Term Memory (LSTM) network model to calculate the weld formation stability index σ. This pre-trained LSTM model is obtained through supervised learning: First, welding experiments are conducted under various typical working conditions, and molten pool images, temperature field data, and process parameters are collected simultaneously. Based on the stability labels of the weld formation quality annotation samples, a training dataset is constructed. Then, the LSTM network is trained using this dataset to learn the mapping relationship from the input feature sequence to the stability index σ. The trained model is finally deployed in the real-time processor of the welding system for online prediction and control.

[0026] When σ is lower than the preset threshold σ th During this process, the system fine-tunes the welding speed by ±5% or the laser defocusing amount by ±0.5mm to prevent the formation of defects.

[0027] In this embodiment of the invention, the welding speed is adjusted to stabilize the molten pool formation. Specifically, when σ is lower than a preset threshold σ th At that time, the system automatically adjusts the welding speed according to the following formula to achieve closed-loop control: ΔV=K V ×(σ th -σ)×V0 where ΔV is the welding speed adjustment amount (m / min), K V σ is the speed adjustment coefficient (an empirical constant, preferably 0.1). th σ is the stability threshold (an empirical constant, preferably 0.8), σ is the real-time calculated value, and V0 is the currently set welding speed. The adjusted welding speed V new =V0+∆V, and the adjustment range is limited to ±5%.

[0028] (4) Weld depth consistency control based on dynamic arc compensation factor (φ): For special areas prone to insufficient or excessive weld depth, such as the weld start / end section, thick plate transition zone, and curvature abrupt change zone, a dynamic arc compensation factor φ is introduced. Its calculation formula is: φ=1+α•(plate thickness change rate)+β•(curvature change rate), where α and β are constants related to the thermophysical properties of the material. By dynamically adjusting the arc current through the φ factor, and limiting the adjustment range to within ±10%, accurate compensation and consistency control of the weld depth throughout the entire weld section are achieved.

[0029] Based on the above calibration method, the following empirical parameter ranges are adopted for high-strength aluminum alloys commonly used in aerospace: 7A04 series aluminum alloy: α is 0.15~0.25, β is 0.05~0.15; 2A12 series aluminum alloys: α is 0.10~0.20, β is 0.03~0.10; 5A06 series aluminum alloy: α is 0.12~0.22, β is 0.04~0.12.

[0030] The specific values ​​can be fine-tuned based on the actual welding shielding gas used, joint type, and heat input level.

[0031] (5) Post-weld treatment and performance improvement: The welded section is subjected to solution treatment (water cooling after holding at 470~500℃ for 1~2h) and aging treatment (air cooling after holding at 120~150℃ for 12~24h) to restore and improve the mechanical properties of the joint.

[0032] The present invention is further illustrated below with specific embodiments. These embodiments are exemplary and intended to illustrate the problem and explain the present invention, and are not intended to be limiting.

[0033] Example 1 Taking a certain type of aerospace module using 7A04 high-strength aluminum alloy as an example, with a plate thickness of 8mm and a butt joint type, the method of this invention is implemented as follows: (1) Modeling before welding: Extract the radius of curvature of the weld trajectory R<50mm, the plate thickness is uniform, and the pre-simulation shows that the middle part of the weld is a stress concentration area.

[0034] (2) Heat input coordinated control: Set a heat input coordinated coefficient η for each micro-region on the welding path within the pre-simulation identified area, taking the reference plate thickness t. ref = 8 mm, reference radius of curvature R ref= 50 mm (minimum radius of curvature of the weld), basic coordination coefficient η0 = 1.0. For the straight segment (radius of curvature infinite), η is calculated to be 1.0 × (8 / 8) × 1 = 1.0. Based on this η value, the laser power is set to 4.5kW, the arc current to 200A, and the arc voltage to 22.5V (corresponding to P). arc ≈4.5kW), welding speed 1.8m / min, shielding gas is 70%Ar+30%He.

[0035] (3) Weld stability control: Real-time monitoring of the σ index; when σ is lower than the preset threshold σ th When the value is 0.8, the system automatically adjusts the welding speed according to the closed-loop control formula: ΔV=K V ×(σ th -σ)×V0 (where K V =0.1, V0 is the currently set welding speed of 1.8m / min), the adjusted welding speed V new =V0+ΔV; If the calculated adjustment range exceeds ±5%, then take the upper limit ±5%.

[0036] (4) Dynamic arc compensation: The arc compensation factor is calculated according to the formula φ=1+α・(plate thickness change rate)+β・(curvature change rate), where α is 0.20 and β is 0.10; if the calculated adjustment range exceeds ±10%, the upper limit value ±10% is taken.

[0037] (5) Post-weld treatment: 490℃ / 1.5h solution treatment (water cooling) + 130℃ / 16h aging (air cooling).

[0038] Example 2 Taking a certain type of fuel tank using 2A12 high-strength aluminum alloy as an example, with a plate thickness of 12mm and double-sided welding, the method of this invention is implemented as follows: (1) Modeling before welding: Extract the area with a plate thickness change rate >10% and pre-simulate to identify high stress areas.

[0039] (2) Heat input coordinated control: Set a heat input coordinated coefficient η for each micro-region on the welding path within the pre-simulation identified area, taking the reference plate thickness t. ref = 12 mm, reference radius of curvature R ref = 100 mm, basic coordination coefficient η0 = 1.0. A dual-laser-dual-arc symmetrical arrangement is adopted, and the symmetry of heat input on both sides is controlled within ±3%.

[0040] (3) Weld stability control: Real-time monitoring of the σ index. When σ is lower than the preset threshold σth (value 0.8), the system automatically adjusts the welding speed according to the closed-loop control formula: ΔV=K V ×(σth -σ)×V0 (where K V =0.1, V0 is the currently set welding speed of 1.8m / min), the adjusted welding speed V new =V0+ΔV; If the calculated adjustment range exceeds ±5%, then take the upper limit ±5%.

[0041] (4) Dynamic arc compensation: The arc compensation factor is calculated according to the formula φ=1+α・(plate thickness change rate)+β・(curvature change rate), where α is 0.15 and β is 0.06; if the calculated adjustment range exceeds ±10%, the upper limit value ±10% is taken.

[0042] (5) Post-weld treatment: 480℃ / 2h solution treatment (water quenching) + 140℃ / 20h aging (air cooling).

[0043] Comparative Example Based on Example 1, the welding method was adjusted to use a traditional laser-arc hybrid welding method with fixed welding parameters (laser power 4.5kW, arc current 200A, welding speed 1.8m / min). There was no guidance from η, σ, and φ parameters, and no real-time monitoring or dynamic control function. The remaining steps were the same as in Example 1.

[0044] Test Example 1: Microstructural Characterization To analyze the grain morphology and second-phase distribution in the weld zone, microstructure characterization tests were conducted. Optical microscopy (OM) and scanning electron microscopy (SEM) were used for observation during the tests. The results are as follows: Figure 1 , Figure 2 As shown, the weld zone grains in Examples 1 and 2 are fine and uniform, with a high proportion of equiaxed crystals and a dispersed second phase. In contrast, the weld zone grains in the comparative example are coarse, exhibiting a coarse columnar crystal morphology, and a continuous network of brittle phases can be observed. This result indicates that the present invention creates favorable conditions for weld solidification through stable heat input and dynamic control, thereby obtaining an excellent weld microstructure.

[0045] Test Example 2: Non-destructive Testing of Weld Quality To detect internal defects in the weld, non-destructive testing of the weld quality was conducted. The tests were performed using an industrial CT inspection system, and the results are as follows: Figure 3 As shown in the figure, the welds of Examples 1 and 2, after inspection, showed a defect rate of less than 0.5% for porosity, lack of fusion, etc., while the weld of the comparative example showed obvious porosity, with a defect rate of approximately 2.5%. This difference fully demonstrates that the real-time control of the weld formation stability index (σ) and the compensation effect of the dynamic arc compensation factor (φ) can effectively suppress the generation of welding defects.

[0046] Test Example 3: Welded Joint Strength Test To quantify the room temperature static mechanical properties of the welded joint, a strength test was conducted. The test was performed according to GB / T2651-2008 standard, and the test results are as follows: Figure 4 As shown. The tensile strength of Example 1 (7A04 high-strength aluminum alloy) is 525 MPa, reaching 92.1% of its base material strength (570 MPa). The tensile strength of Example 2 (2A12 high-strength aluminum alloy) is 505 MPa, reaching 91.8% of its base material strength (550 MPa). However, the tensile strength of the comparative example (7A04 high-strength aluminum alloy) is only 420 MPa, which is only 73.7% of its base material strength. This test result shows that the present invention significantly reduces the joint softening phenomenon through precise energy coordination and dynamic compensation, and keeps the welded joint strength coefficient stable at over 90%.

[0047] Based on the verification results of the three test cases mentioned above, the laser-arc hybrid welding method for high-strength aluminum alloy sections provided by this invention constructs a parameter-driven three-level synergistic control system by introducing a series of new quantitative parameters, including the heat input synergy coefficient (η), weld formation stability index (σ), and dynamic arc compensation factor (φ). Through thorough verification using examples and comparative models, the solution of this invention achieves significant beneficial effects, specifically: the tensile strength of the welded joint can reach more than 90% of the base material, the weld quality is high, the internal defect rate is controlled below 0.5%, and the weld microstructure is fine and uniform, avoiding the precipitation of coarse columnar crystals and harmful phases. This invention is applicable to laser-arc hybrid welding of high-strength aluminum alloy sections, achieving synergistic improvements in strength, non-destructive testing pass rate, and microstructure, possessing extremely high engineering application value and promising prospects for promotion.

[0048] The above embodiments are exemplary and are intended to illustrate the technical concept and features of the present invention, so that those skilled in the art can understand the content of the present invention and implement it accordingly. They should not be construed as limiting the scope of protection of the present invention. All equivalent changes or modifications made according to the spirit and essence of the present invention should be covered within the scope of protection of the present invention.

Claims

1. A laser-arc hybrid welding method for high-strength aluminum alloy compartments, characterized in that, Includes the following steps: (1) Pre-welding modeling and parameter pre-planning: Obtain the geometric feature parameters of the weld trajectory of the section to be welded, and identify potential high stress concentration areas and deformation risk areas based on thermo-mechanical coupling simulation; (2) Energy matching based on heat input coordination coefficient (η): In the area identified in step (1), on the welding path, the heat input coordination coefficient η is calculated and adjusted in real time according to the geometric feature parameters, η = laser power / (arc current × arc voltage) to dynamically adjust the energy ratio of laser and arc; (3) Real-time prediction and control based on weld formation stability index (σ): During the welding process, the molten pool image and temperature field data are collected in real time, input into the pre-trained long short-term memory network (LSTM) model, the weld formation stability index σ is calculated, and the welding speed and / or laser defocusing amount are dynamically adjusted according to the real-time value of σ. (4) Control of weld penetration consistency based on dynamic arc compensation factor (φ): For the weld start, end section, thick plate transition zone and curvature change zone, calculate the dynamic arc compensation factor φ, and dynamically adjust the arc current according to φ to compensate for the weld penetration.

2. The laser-arc hybrid welding method for high-strength aluminum alloy sections according to claim 1, characterized in that, In step (2), the heat input synergy coefficient η is calculated and controlled using the following formula: η = η0 × (t / t ref ) × (R ref / R) Where η0 is the basic coordination coefficient, t is the plate thickness at the current weld position, and t ref R is the reference plate thickness, and R is the radius of curvature of the weld at its current position. ref R is the reference radius of curvature; when the weld is a straight segment, R ref / R takes the value 1; The laser power P laser According to formula P laser = η × P arc Dynamic adjustments are made, where P arc This represents the arc power.

3. The laser-arc hybrid welding method for high-strength aluminum alloy sections according to claim 2, characterized in that, In step (2), the t ref Take the average plate thickness of the weld, the R ref Take the minimum radius of curvature of the weld.

4. The laser-arc hybrid welding method for high-strength aluminum alloy sections according to claim 1, characterized in that, In step (3), the pre-trained Long Short-Term Memory (LSTM) network model is obtained in the following way: welding experiments are carried out under various typical working conditions, molten pool images, temperature field data and process parameters are collected simultaneously, and a training dataset is constructed based on the stability labels of the samples labeled with the post-weld forming quality. The LSTM network is then trained in a supervised manner using this training dataset.

5. The laser-arc hybrid welding method for high-strength aluminum alloy sections according to claim 1, characterized in that, In step (3), when the weld formation stability index σ calculated in real time is lower than the preset threshold σ th Adjust the welding speed according to the following formula: ΔV=K V ×(σ th -σ)×V0 Where ΔV is the welding speed adjustment amount, K V V0 is the speed adjustment coefficient, where V0 is the currently set welding speed and V is the adjusted welding speed. new =V0+ΔV, and the adjustment range is limited to ±5%.

6. The laser-arc hybrid welding method for high-strength aluminum alloy sections according to claim 5, characterized in that, The speed adjustment coefficient K V The preset threshold σ is set to 0.

1. th Take 0.

8.

7. The laser-arc hybrid welding method for high-strength aluminum alloy sections according to claim 1, characterized in that, In step (4), the dynamic arc compensation factor φ is calculated using the following formula: φ = 1 + α (rate of change of plate thickness) + β (rate of change of curvature) Where α and β are constants related to the thermophysical properties of aluminum alloy materials; The arc current is dynamically multiplied based on the calculated φ factor, and the adjustment range is limited to ±10%.

8. The laser-arc hybrid welding method for high-strength aluminum alloy sections according to claim 7, characterized in that, When the high-strength aluminum alloy is from the 7A04 series, α is 0.15~0.25 and β is 0.05~0.15; When the high-strength aluminum alloy is from the 2A12 series, α is 0.10~0.20 and β is 0.03~0.10; When the high-strength aluminum alloy is the 5A06 series, α is 0.12~0.22 and β is 0.04~0.

12.

9. The laser-arc hybrid welding method for high-strength aluminum alloy sections according to claim 1, characterized in that, The method further includes step (5): after welding, the welded joint is subjected to solution treatment and aging treatment; wherein, the temperature of solution treatment is 470~500℃, and after holding for 1~2 hours, it is water-cooled; the temperature of aging treatment is 120~150℃, and after holding for 12~24 hours, it is air-cooled.

10. The laser-arc hybrid welding method for high-strength aluminum alloy sections according to any one of claims 1-9, characterized in that, The thickness of the high-strength aluminum alloy section is 5~15mm; the welded joint obtained by the method has a tensile strength of more than 90% of the tensile strength of the base material, and the internal defect rate of the weld is less than 0.5%.

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